Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism

PubMed Central

Uddin, Lucina Q.; Supekar, Kaustubh; Lynch, Charles J.; Khouzam, Amirah; Phillips, Jennifer; Feinstein, Carl; Ryali, Srikanth; Menon, Vinod

2014-01-01

IMPORTANCE Autism spectrum disorder (ASD) affects 1 in 88 children and is characterized by a complex phenotype, including social, communicative, and sensorimotor deficits. Autism spectrum disorder has been linked with atypical connectivity across multiple brain systems, yet the nature of these differences in young children with the disorder is not well understood. OBJECTIVES To examine connectivity of large-scale brain networks and determine whether specific networks can distinguish children with ASD from typically developing (TD) children and predict symptom severity in children with ASD. DESIGN, SETTING, AND PARTICIPANTS Case-control study performed at Stanford University School of Medicine of 20 children 7 to 12 years old with ASD and 20 age-, sex-, and IQ-matched TD children. MAIN OUTCOMES AND MEASURES Between-group differences in intrinsic functional connectivity of large-scale brain networks, performance of a classifier built to discriminate children with ASD from TD children based on specific brain networks, and correlations between brain networks and core symptoms of ASD. RESULTS We observed stronger functional connectivity within several large-scale brain networks in children with ASD compared with TD children. This hyperconnectivity in ASD encompassed salience, default mode, frontotemporal, motor, and visual networks. This hyperconnectivity result was replicated in an independent cohort obtained from publicly available databases. Using maps of each individual’s salience network, children with ASD could be discriminated from TD children with a classification accuracy of 78%, with 75% sensitivity and 80% specificity. The salience network showed the highest classification accuracy among all networks examined, and the blood oxygen–level dependent signal in this network predicted restricted and repetitive behavior scores. The classifier discriminated ASD from TD in the independent sample with 83% accuracy, 67% sensitivity, and 100% specificity. CONCLUSIONS AND RELEVANCE Salience network hyperconnectivity may be a distinguishing feature in children with ASD. Quantification of brain network connectivity is a step toward developing biomarkers for objectively identifying children with ASD. PMID:23803651

A generative model of whole-brain effective connectivity.

PubMed

Frässle, Stefan; Lomakina, Ekaterina I; Kasper, Lars; Manjaly, Zina M; Leff, Alex; Pruessmann, Klaas P; Buhmann, Joachim M; Stephan, Klaas E

2018-05-25

The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters. This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data - in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling - for example, for phenotyping individual patients in terms of whole-brain network structure. Copyright © 2018. Published by Elsevier Inc.

Correspondence Between Aberrant Intrinsic Network Connectivity and Gray-Matter Volume in the Ventral Brain of Preterm Born Adults.

PubMed

Bäuml, Josef G; Daamen, Marcel; Meng, Chun; Neitzel, Julia; Scheef, Lukas; Jaekel, Julia; Busch, Barbara; Baumann, Nicole; Bartmann, Peter; Wolke, Dieter; Boecker, Henning; Wohlschläger, Afra M; Sorg, Christian

2015-11-01

Widespread brain changes are present in preterm born infants, adolescents, and even adults. While neurobiological models of prematurity facilitate powerful explanations for the adverse effects of preterm birth on the developing brain at microscale, convincing linking principles at large-scale level to explain the widespread nature of brain changes are still missing. We investigated effects of preterm birth on the brain's large-scale intrinsic networks and their relation to brain structure in preterm born adults. In 95 preterm and 83 full-term born adults, structural and functional magnetic resonance imaging at-rest was used to analyze both voxel-based morphometry and spatial patterns of functional connectivity in ongoing blood oxygenation level-dependent activity. Differences in intrinsic functional connectivity (iFC) were found in cortical and subcortical networks. Structural differences were located in subcortical, temporal, and cingulate areas. Critically, for preterm born adults, iFC-network differences were overlapping and correlating with aberrant regional gray-matter (GM) volume specifically in subcortical and temporal areas. Overlapping changes were predicted by prematurity and in particular by neonatal medical complications. These results provide evidence that preterm birth has long-lasting effects on functional connectivity of intrinsic networks, and these changes are specifically related to structural alterations in ventral brain GM. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance.

PubMed

Keerativittayayut, Ruedeerat; Aoki, Ryuta; Sarabi, Mitra Taghizadeh; Jimura, Koji; Nakahara, Kiyoshi

2018-06-18

Although activation/deactivation of specific brain regions have been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here we investigated time-varying functional connectivity patterns across the human brain in periods of 30-40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding. © 2018, Keerativittayayut et al.

Multimodal Investigation of Network Level Effects Using Intrinsic Functional Connectivity, Anatomical Covariance, and Structure-to-Function Correlations in Unmedicated Major Depressive Disorder

PubMed Central

Scheinost, Dustin; Holmes, Sophie E; DellaGioia, Nicole; Schleifer, Charlie; Matuskey, David; Abdallah, Chadi G; Hampson, Michelle; Krystal, John H; Anticevic, Alan; Esterlis, Irina

2018-01-01

Converging evidence suggests that major depressive disorder (MDD) affects multiple large-scale brain networks. Analyses of the correlation or covariance of regional brain structure and function applied to structural and functional MRI data may provide insights into systems-level organization and structure-to-function correlations in the brain in MDD. This study applied tensor-based morphometry and intrinsic connectivity distribution to identify regions of altered volume and intrinsic functional connectivity in data from unmedicated individuals with MDD (n=17) and healthy comparison participants (HC, n=20). These regions were then used as seeds for exploratory anatomical covariance and connectivity analyses. Reduction in volume in the anterior cingulate cortex (ACC) and lower structural covariance between the ACC and the cerebellum were observed in the MDD group. Additionally, individuals with MDD had significantly lower whole-brain intrinsic functional connectivity in the medial prefrontal cortex (mPFC). This mPFC region showed altered connectivity to the ventral lateral PFC (vlPFC) and local circuitry in MDD. Global connectivity in the ACC was negatively correlated with reported depressive symptomatology. The mPFC–vlPFC connectivity was positively correlated with depressive symptoms. Finally, we observed increased structure-to-function correlation in the PFC/ACC in the MDD group. Although across all analysis methods and modalities alterations in the PFC/ACC were a common finding, each modality and method detected alterations in subregions belonging to distinct large-scale brain networks. These exploratory results support the hypothesis that MDD is a systems level disorder affecting multiple brain networks located in the PFC and provide new insights into the pathophysiology of this disorder. PMID:28944772

Multimodal Investigation of Network Level Effects Using Intrinsic Functional Connectivity, Anatomical Covariance, and Structure-to-Function Correlations in Unmedicated Major Depressive Disorder.

PubMed

Scheinost, Dustin; Holmes, Sophie E; DellaGioia, Nicole; Schleifer, Charlie; Matuskey, David; Abdallah, Chadi G; Hampson, Michelle; Krystal, John H; Anticevic, Alan; Esterlis, Irina

2018-04-01

Converging evidence suggests that major depressive disorder (MDD) affects multiple large-scale brain networks. Analyses of the correlation or covariance of regional brain structure and function applied to structural and functional MRI data may provide insights into systems-level organization and structure-to-function correlations in the brain in MDD. This study applied tensor-based morphometry and intrinsic connectivity distribution to identify regions of altered volume and intrinsic functional connectivity in data from unmedicated individuals with MDD (n=17) and healthy comparison participants (HC, n=20). These regions were then used as seeds for exploratory anatomical covariance and connectivity analyses. Reduction in volume in the anterior cingulate cortex (ACC) and lower structural covariance between the ACC and the cerebellum were observed in the MDD group. Additionally, individuals with MDD had significantly lower whole-brain intrinsic functional connectivity in the medial prefrontal cortex (mPFC). This mPFC region showed altered connectivity to the ventral lateral PFC (vlPFC) and local circuitry in MDD. Global connectivity in the ACC was negatively correlated with reported depressive symptomatology. The mPFC-vlPFC connectivity was positively correlated with depressive symptoms. Finally, we observed increased structure-to-function correlation in the PFC/ACC in the MDD group. Although across all analysis methods and modalities alterations in the PFC/ACC were a common finding, each modality and method detected alterations in subregions belonging to distinct large-scale brain networks. These exploratory results support the hypothesis that MDD is a systems level disorder affecting multiple brain networks located in the PFC and provide new insights into the pathophysiology of this disorder.

Large-scale coupling dynamics of instructed reversal learning.

PubMed

Mohr, Holger; Wolfensteller, Uta; Ruge, Hannes

2018-02-15

The ability to rapidly learn from others by instruction is an important characteristic of human cognition. A recent study found that the rapid transfer from initial instructions to fluid behavior is supported by changes of functional connectivity between and within several large-scale brain networks, and particularly by the coupling of the dorsal attention network (DAN) with the cingulo-opercular network (CON). In the present study, we extended this approach to investigate how these brain networks interact when stimulus-response mappings are altered by novel instructions. We hypothesized that residual stimulus-response associations from initial practice might negatively impact the ability to implement novel instructions. Using functional imaging and large-scale connectivity analysis, we found that functional coupling between the CON and DAN was generally at a higher level during initial than reversal learning. Examining the learning-related connectivity dynamics between the CON and DAN in more detail by means of multivariate patterns analyses, we identified a specific subset of connections which showed a particularly high increase in connectivity during initial learning compared to reversal learning. This finding suggests that the CON-DAN connections can be separated into two functionally dissociable yet spatially intertwined subsystems supporting different aspects of short-term task automatization. Copyright © 2017 Elsevier Inc. All rights reserved.

A Multiscale Parallel Computing Architecture for Automated Segmentation of the Brain Connectome

PubMed Central

Knobe, Kathleen; Newton, Ryan R.; Schlimbach, Frank; Blower, Melanie; Reid, R. Clay

2015-01-01

Several groups in neurobiology have embarked into deciphering the brain circuitry using large-scale imaging of a mouse brain and manual tracing of the connections between neurons. Creating a graph of the brain circuitry, also called a connectome, could have a huge impact on the understanding of neurodegenerative diseases such as Alzheimer’s disease. Although considerably smaller than a human brain, a mouse brain already exhibits one billion connections and manually tracing the connectome of a mouse brain can only be achieved partially. This paper proposes to scale up the tracing by using automated image segmentation and a parallel computing approach designed for domain experts. We explain the design decisions behind our parallel approach and we present our results for the segmentation of the vasculature and the cell nuclei, which have been obtained without any manual intervention. PMID:21926011

Altered intrinsic and extrinsic connectivity in schizophrenia.

PubMed

Zhou, Yuan; Zeidman, Peter; Wu, Shihao; Razi, Adeel; Chen, Cheng; Yang, Liuqing; Zou, Jilin; Wang, Gaohua; Wang, Huiling; Friston, Karl J

2018-01-01

Schizophrenia is a disorder characterized by functional dysconnectivity among distributed brain regions. However, it is unclear how causal influences among large-scale brain networks are disrupted in schizophrenia. In this study, we used dynamic causal modeling (DCM) to assess the hypothesis that there is aberrant directed (effective) connectivity within and between three key large-scale brain networks (the dorsal attention network, the salience network and the default mode network) in schizophrenia during a working memory task. Functional MRI data during an n-back task from 40 patients with schizophrenia and 62 healthy controls were analyzed. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes, we found that intrinsic (within-region) and extrinsic (between-region) effective connectivity involving prefrontal regions were abnormal in schizophrenia. Specifically, in patients (i) inhibitory self-connections in prefrontal regions of the dorsal attention network were decreased across task conditions; (ii) extrinsic connectivity between regions of the default mode network was increased; specifically, from posterior cingulate cortex to the medial prefrontal cortex; (iii) between-network extrinsic connections involving the prefrontal cortex were altered; (iv) connections within networks and between networks were correlated with the severity of clinical symptoms and impaired cognition beyond working memory. In short, this study revealed the predominance of reduced synaptic efficacy of prefrontal efferents and afferents in the pathophysiology of schizophrenia.

Control networks and hubs.

PubMed

Gratton, Caterina; Sun, Haoxin; Petersen, Steven E

2018-03-01

Executive control functions are associated with frontal, parietal, cingulate, and insular brain regions that interact through distributed large-scale networks. Here, we discuss how fMRI functional connectivity can shed light on the organization of control networks and how they interact with other parts of the brain. In the first section of our review, we present convergent evidence from fMRI functional connectivity, activation, and lesion studies that there are multiple dissociable control networks in the brain with distinct functional properties. In the second section, we discuss how graph theoretical concepts can help illuminate the mechanisms by which control networks interact with other brain regions to carry out goal-directed functions, focusing on the role of specialized hub regions for mediating cross-network interactions. Again, we use a combination of functional connectivity, lesion, and task activation studies to bolster this claim. We conclude that a large-scale network perspective provides important neurobiological constraints on the neural underpinnings of executive control, which will guide future basic and translational research into executive function and its disruption in disease. © 2017 Society for Psychophysiological Research.

Differential brain network activity across mood states in bipolar disorder.

PubMed

Brady, Roscoe O; Tandon, Neeraj; Masters, Grace A; Margolis, Allison; Cohen, Bruce M; Keshavan, Matcheri; Öngür, Dost

2017-01-01

This study aimed to identify how the activity of large-scale brain networks differs between mood states in bipolar disorder. The authors measured spontaneous brain activity in subjects with bipolar disorder in mania and euthymia and compared these states to a healthy comparison population. 23 subjects with bipolar disorder type I in a manic episode, 24 euthymic bipolar I subjects, and 23 matched healthy comparison (HC) subjects underwent resting state fMRI scans. Using an existing parcellation of the whole brain, we measured functional connectivity between brain regions and identified significant differences between groups. In unbiased whole-brain analyses, functional connectivity between parietal, occipital, and frontal nodes within the dorsal attention network (DAN) were significantly greater in mania than euthymia or HC subjects. In the default mode network (DMN), connectivity between dorsal frontal nodes and the rest of the DMN differentiated both mood state and diagnosis. The bipolar groups were separate cohorts rather than subjects imaged longitudinally across mood states. Bipolar mood states are associated with highly significant alterations in connectivity in two large-scale brain networks. These same networks also differentiate bipolar mania and euthymia from a HC population. State related changes in DAN and DMN connectivity suggest a circuit based pathology underlying cognitive dysfunction as well as activity/reactivity in bipolar mania. Altered activities in neural networks may be biomarkers of bipolar disorder diagnosis and mood state that are accessible to neuromodulation and are promising novel targets for scientific investigation and possible clinical intervention. Copyright © 2016 Elsevier B.V. All rights reserved.

Large-Scale Brain Systems in ADHD: Beyond the Prefrontal-Striatal Model

PubMed Central

Castellanos, F. Xavier; Proal, Erika

2012-01-01

Attention-deficit/hyperactivity disorder (ADHD) has long been thought to reflect dysfunction of prefrontal-striatal circuitry, with involvement of other circuits largely ignored. Recent advances in systems neuroscience-based approaches to brain dysfunction enable the development of models of ADHD pathophysiology that encompass a number of different large-scale “resting state” networks. Here we review progress in delineating large-scale neural systems and illustrate their relevance to ADHD. We relate frontoparietal, dorsal attentional, motor, visual, and default networks to the ADHD functional and structural literature. Insights emerging from mapping intrinsic brain connectivity networks provide a potentially mechanistic framework for understanding aspects of ADHD, such as neuropsychological and behavioral inconsistency, and the possible role of primary visual cortex in attentional dysfunction in the disorder. PMID:22169776

Disruption of posteromedial large-scale neural communication predicts recovery from coma.

PubMed

Silva, Stein; de Pasquale, Francesco; Vuillaume, Corine; Riu, Beatrice; Loubinoux, Isabelle; Geeraerts, Thomas; Seguin, Thierry; Bounes, Vincent; Fourcade, Olivier; Demonet, Jean-Francois; Péran, Patrice

2015-12-08

We hypothesize that the major consciousness deficit observed in coma is due to the breakdown of long-range neuronal communication supported by precuneus and posterior cingulate cortex (PCC), and that prognosis depends on a specific connectivity pattern in these networks. We compared 27 prospectively recruited comatose patients who had severe brain injury (Glasgow Coma Scale score <8; 14 traumatic and 13 anoxic cases) with 14 age-matched healthy participants. Standardized clinical assessment and fMRI were performed on average 4 ± 2 days after withdrawal of sedation. Analysis of resting-state fMRI connectivity involved a hypothesis-driven, region of interest-based strategy. We assessed patient outcome after 3 months using the Coma Recovery Scale-Revised (CRS-R). Patients who were comatose showed a significant disruption of functional connectivity of brain areas spontaneously synchronized with PCC, globally notwithstanding etiology. The functional connectivity strength between PCC and medial prefrontal cortex (mPFC) was significantly different between comatose patients who went on to recover and those who eventually scored an unfavorable outcome 3 months after brain injury (Kruskal-Wallis test, p < 0.001; linear regression between CRS-R and PCC-mPFC activity coupling at rest, Spearman ρ = 0.93, p < 0.003). In both etiology groups (traumatic and anoxic), changes in the connectivity of PCC-centered, spontaneously synchronized, large-scale networks account for the loss of external and internal self-centered awareness observed during coma. Sparing of functional connectivity between PCC and mPFC may predict patient outcome, and further studies are needed to substantiate this potential prognosis biomarker. © 2015 American Academy of Neurology.

Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs)

PubMed Central

Dimitriadis, Stavros I.; Salis, Christos; Tarnanas, Ioannis; Linden, David E.

2017-01-01

The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or “nodes,” and quantifying the strength of the connections between nodes, or “edges,” as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the “true” connectivity pattern can emerge. Here, we compared a large number of well-known topological thresholding techniques with the novel proposed data-driven scheme based on orthogonal minimal spanning trees (OMSTs). OMSTs filter brain connectivity networks based on the optimization between the global efficiency of the network and the cost preserving its wiring. We demonstrated the proposed method in a large EEG database (N = 101 subjects) with eyes-open (EO) and eyes-closed (EC) tasks by adopting a time-varying approach with the main goal to extract features that can totally distinguish each subject from the rest of the set. Additionally, the reliability of the proposed scheme was estimated in a second case study of fMRI resting-state activity with multiple scans. Our results demonstrated clearly that the proposed thresholding scheme outperformed a large list of thresholding schemes based on the recognition accuracy of each subject compared to the rest of the cohort (EEG). Additionally, the reliability of the network metrics based on the fMRI static networks was improved based on the proposed topological filtering scheme. Overall, the proposed algorithm could be used across neuroimaging and multimodal studies as a common computationally efficient standardized tool for a great number of neuroscientists and physicists working on numerous of projects. PMID:28491032

Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations

PubMed Central

Deco, Gustavo; Mantini, Dante; Romani, Gian Luca; Hagmann, Patric; Corbetta, Maurizio

2013-01-01

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure–function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure–function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications. PMID:23825427

Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations.

PubMed

Deco, Gustavo; Ponce-Alvarez, Adrián; Mantini, Dante; Romani, Gian Luca; Hagmann, Patric; Corbetta, Maurizio

2013-07-03

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

Foundational perspectives on causality in large-scale brain networks

NASA Astrophysics Data System (ADS)

Mannino, Michael; Bressler, Steven L.

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.

Foundational perspectives on causality in large-scale brain networks.

PubMed

Mannino, Michael; Bressler, Steven L

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain. Copyright © 2015 Elsevier B.V. All rights reserved.

Dynamics of Intersubject Brain Networks during Anxious Anticipation

PubMed Central

Najafi, Mahshid; Kinnison, Joshua; Pessoa, Luiz

2017-01-01

How do large-scale brain networks reorganize during the waxing and waning of anxious anticipation? Here, threat was dynamically modulated during human functional MRI as two circles slowly meandered on the screen; if they touched, an unpleasant shock was delivered. We employed intersubject correlation analysis, which allowed the investigation of network-level functional connectivity across brains, and sought to determine how network connectivity changed during periods of approach (circles moving closer) and periods of retreat (circles moving apart). Analysis of positive connection weights revealed that dynamic threat altered connectivity within and between the salience, executive, and task-negative networks. For example, dynamic functional connectivity increased within the salience network during approach and decreased during retreat. The opposite pattern was found for the functional connectivity between the salience and task-negative networks: decreases during approach and increases during approach. Functional connections between subcortical regions and the salience network also changed dynamically during approach and retreat periods. Subcortical regions exhibiting such changes included the putative periaqueductal gray, putative habenula, and putative bed nucleus of the stria terminalis. Additional analysis of negative functional connections revealed dynamic changes, too. For example, negative weights within the salience network decreased during approach and increased during retreat, opposite what was found for positive weights. Together, our findings unraveled dynamic features of functional connectivity of large-scale networks and subcortical regions across participants while threat levels varied continuously, and demonstrate the potential of characterizing emotional processing at the level of dynamic networks. PMID:29209184

Patterns of resting state connectivity in human primary visual cortical areas: a 7T fMRI study.

PubMed

Raemaekers, Mathijs; Schellekens, Wouter; van Wezel, Richard J A; Petridou, Natalia; Kristo, Gert; Ramsey, Nick F

2014-01-01

The nature and origin of fMRI resting state fluctuations and connectivity are still not fully known. More detailed knowledge on the relationship between resting state patterns and brain function may help to elucidate this matter. We therefore performed an in depth study of how resting state fluctuations map to the well known architecture of the visual system. We investigated resting state connectivity at both a fine and large scale within and across visual areas V1, V2 and V3 in ten human subjects using a 7Tesla scanner. We found evidence for several coexisting and overlapping connectivity structures at different spatial scales. At the fine-scale level we found enhanced connectivity between the same topographic locations in the fieldmaps of V1, V2 and V3, enhanced connectivity to the contralateral functional homologue, and to a lesser extent enhanced connectivity between iso-eccentric locations within the same visual area. However, by far the largest proportion of the resting state fluctuations occurred within large-scale bilateral networks. These large-scale networks mapped to some extent onto the architecture of the visual system and could thereby obscure fine-scale connectivity. In fact, most of the fine-scale connectivity only became apparent after the large-scale network fluctuations were filtered from the timeseries. We conclude that fMRI resting state fluctuations in the visual cortex may in fact be a composite signal of different overlapping sources. Isolating the different sources could enhance correlations between BOLD and electrophysiological correlates of resting state activity. © 2013 Elsevier Inc. All rights reserved.

Disrupted Topological Patterns of Large-Scale Network in Conduct Disorder

PubMed Central

Jiang, Yali; Liu, Weixiang; Ming, Qingsen; Gao, Yidian; Ma, Ren; Zhang, Xiaocui; Situ, Weijun; Wang, Xiang; Yao, Shuqiao; Huang, Bingsheng

2016-01-01

Regional abnormalities in brain structure and function, as well as disrupted connectivity, have been found repeatedly in adolescents with conduct disorder (CD). Yet, the large-scale brain topology associated with CD is not well characterized, and little is known about the systematic neural mechanisms of CD. We employed graphic theory to investigate systematically the structural connectivity derived from cortical thickness correlation in a group of patients with CD (N = 43) and healthy controls (HCs, N = 73). Nonparametric permutation tests were applied for between-group comparisons of graphical metrics. Compared with HCs, network measures including global/local efficiency and modularity all pointed to hypo-functioning in CD, despite of preserved small-world organization in both groups. The hubs distribution is only partially overlapped with each other. These results indicate that CD is accompanied by both impaired integration and segregation patterns of brain networks, and the distribution of highly connected neural network ‘hubs’ is also distinct between groups. Such misconfiguration extends our understanding regarding how structural neural network disruptions may underlie behavioral disturbances in adolescents with CD, and potentially, implicates an aberrant cytoarchitectonic profiles in the brain of CD patients. PMID:27841320

Large-scale changes in network interactions as a physiological signature of spatial neglect.

PubMed

Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L; Callejas, Alicia; Astafiev, Serguei V; Metcalf, Nicholas V; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z; Carter, Alex R; Shulman, Gordon L; Corbetta, Maurizio

2014-12-01

The relationship between spontaneous brain activity and behaviour following focal injury is not well understood. Here, we report a large-scale study of resting state functional connectivity MRI and spatial neglect following stroke in a large (n=84) heterogeneous sample of first-ever stroke patients (within 1-2 weeks). Spatial neglect, which is typically more severe after right than left hemisphere injury, includes deficits of spatial attention and motor actions contralateral to the lesion, and low general attention due to impaired vigilance/arousal. Patients underwent structural and resting state functional MRI scans, and spatial neglect was measured using the Posner spatial cueing task, and Mesulam and Behavioural Inattention Test cancellation tests. A principal component analysis of the behavioural tests revealed a main factor accounting for 34% of variance that captured three correlated behavioural deficits: visual neglect of the contralesional visual field, visuomotor neglect of the contralesional field, and low overall performance. In an independent sample (21 healthy subjects), we defined 10 resting state networks consisting of 169 brain regions: visual-fovea and visual-periphery, sensory-motor, auditory, dorsal attention, ventral attention, language, fronto-parietal control, cingulo-opercular control, and default mode. We correlated the neglect factor score with the strength of resting state functional connectivity within and across the 10 resting state networks. All damaged brain voxels were removed from the functional connectivity:behaviour correlational analysis. We found that the correlated behavioural deficits summarized by the factor score were associated with correlated multi-network patterns of abnormal functional connectivity involving large swaths of cortex. Specifically, dorsal attention and sensory-motor networks showed: (i) reduced interhemispheric functional connectivity; (ii) reduced anti-correlation with fronto-parietal and default mode networks in the right hemisphere; and (iii) increased intrahemispheric connectivity with the basal ganglia. These patterns of functional connectivity:behaviour correlations were stronger in patients with right- as compared to left-hemisphere damage and were independent of lesion volume. Our findings identify large-scale changes in resting state network interactions that are a physiological signature of spatial neglect and may relate to its right hemisphere lateralization. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Differentiating unipolar and bipolar depression by alterations in large-scale brain networks.

PubMed

Goya-Maldonado, Roberto; Brodmann, Katja; Keil, Maria; Trost, Sarah; Dechent, Peter; Gruber, Oliver

2016-02-01

Misdiagnosing bipolar depression can lead to very deleterious consequences of mistreatment. Although depressive symptoms may be similarly expressed in unipolar and bipolar disorder, changes in specific brain networks could be very distinct, being therefore informative markers for the differential diagnosis. We aimed to characterize specific alterations in candidate large-scale networks (frontoparietal, cingulo-opercular, and default mode) in symptomatic unipolar and bipolar patients using resting state fMRI, a cognitively low demanding paradigm ideal to investigate patients. Networks were selected after independent component analysis, compared across 40 patients acutely depressed (20 unipolar, 20 bipolar), and 20 controls well-matched for age, gender, and education levels, and alterations were correlated to clinical parameters. Despite comparable symptoms, patient groups were robustly differentiated by large-scale network alterations. Differences were driven in bipolar patients by increased functional connectivity in the frontoparietal network, a central executive and externally-oriented network. Conversely, unipolar patients presented increased functional connectivity in the default mode network, an introspective and self-referential network, as much as reduced connectivity of the cingulo-opercular network to default mode regions, a network involved in detecting the need to switch between internally and externally oriented demands. These findings were mostly unaffected by current medication, comorbidity, and structural changes. Moreover, network alterations in unipolar patients were significantly correlated to the number of depressive episodes. Unipolar and bipolar groups displaying similar symptomatology could be clearly distinguished by characteristic changes in large-scale networks, encouraging further investigation of network fingerprints for clinical use. Hum Brain Mapp 37:808-818, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

NASA Astrophysics Data System (ADS)

Lamoš, Martin; Mareček, Radek; Slavíček, Tomáš; Mikl, Michal; Rektor, Ivan; Jan, Jiří

2018-06-01

Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

Maturation of metabolic connectivity of the adolescent rat brain

PubMed Central

Choi, Hongyoon; Choi, Yoori; Kim, Kyu Wan; Kang, Hyejin; Hwang, Do Won; Kim, E Edmund; Chung, June-Key; Lee, Dong Soo

2015-01-01

Neuroimaging has been used to examine developmental changes of the brain. While PET studies revealed maturation-related changes, maturation of metabolic connectivity of the brain is not yet understood. Here, we show that rat brain metabolism is reconfigured to achieve long-distance connections with higher energy efficiency during maturation. Metabolism increased in anterior cerebrum and decreased in thalamus and cerebellum during maturation. When functional covariance patterns of PET images were examined, metabolic networks including default mode network (DMN) were extracted. Connectivity increased between the anterior and posterior parts of DMN and sensory-motor cortices during maturation. Energy efficiency, a ratio of connectivity strength to metabolism of a region, increased in medial prefrontal and retrosplenial cortices. Our data revealed that metabolic networks mature to increase metabolic connections and establish its efficiency between large-scale spatial components from childhood to early adulthood. Neurodevelopmental diseases might be understood by abnormal reconfiguration of metabolic connectivity and efficiency. DOI: http://dx.doi.org/10.7554/eLife.11571.001 PMID:26613413

Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.

PubMed

Jordan, Jakob; Ippen, Tammo; Helias, Moritz; Kitayama, Itaru; Sato, Mitsuhisa; Igarashi, Jun; Diesmann, Markus; Kunkel, Susanne

2018-01-01

State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.

Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers

PubMed Central

Jordan, Jakob; Ippen, Tammo; Helias, Moritz; Kitayama, Itaru; Sato, Mitsuhisa; Igarashi, Jun; Diesmann, Markus; Kunkel, Susanne

2018-01-01

State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems. PMID:29503613

Large-scale changes in network interactions as a physiological signature of spatial neglect

PubMed Central

Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L.; Callejas, Alicia; Astafiev, Serguei V.; Metcalf, Nicholas V.; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z.; Carter, Alex R.; Shulman, Gordon L.

2014-01-01

The relationship between spontaneous brain activity and behaviour following focal injury is not well understood. Here, we report a large-scale study of resting state functional connectivity MRI and spatial neglect following stroke in a large (n = 84) heterogeneous sample of first-ever stroke patients (within 1–2 weeks). Spatial neglect, which is typically more severe after right than left hemisphere injury, includes deficits of spatial attention and motor actions contralateral to the lesion, and low general attention due to impaired vigilance/arousal. Patients underwent structural and resting state functional MRI scans, and spatial neglect was measured using the Posner spatial cueing task, and Mesulam and Behavioural Inattention Test cancellation tests. A principal component analysis of the behavioural tests revealed a main factor accounting for 34% of variance that captured three correlated behavioural deficits: visual neglect of the contralesional visual field, visuomotor neglect of the contralesional field, and low overall performance. In an independent sample (21 healthy subjects), we defined 10 resting state networks consisting of 169 brain regions: visual-fovea and visual-periphery, sensory-motor, auditory, dorsal attention, ventral attention, language, fronto-parietal control, cingulo-opercular control, and default mode. We correlated the neglect factor score with the strength of resting state functional connectivity within and across the 10 resting state networks. All damaged brain voxels were removed from the functional connectivity:behaviour correlational analysis. We found that the correlated behavioural deficits summarized by the factor score were associated with correlated multi-network patterns of abnormal functional connectivity involving large swaths of cortex. Specifically, dorsal attention and sensory-motor networks showed: (i) reduced interhemispheric functional connectivity; (ii) reduced anti-correlation with fronto-parietal and default mode networks in the right hemisphere; and (iii) increased intrahemispheric connectivity with the basal ganglia. These patterns of functional connectivity:behaviour correlations were stronger in patients with right- as compared to left-hemisphere damage and were independent of lesion volume. Our findings identify large-scale changes in resting state network interactions that are a physiological signature of spatial neglect and may relate to its right hemisphere lateralization. PMID:25367028

Influence of Wiring Cost on the Large-Scale Architecture of Human Cortical Connectivity

PubMed Central

Samu, David; Seth, Anil K.; Nowotny, Thomas

2014-01-01

In the past two decades some fundamental properties of cortical connectivity have been discovered: small-world structure, pronounced hierarchical and modular organisation, and strong core and rich-club structures. A common assumption when interpreting results of this kind is that the observed structural properties are present to enable the brain's function. However, the brain is also embedded into the limited space of the skull and its wiring has associated developmental and metabolic costs. These basic physical and economic aspects place separate, often conflicting, constraints on the brain's connectivity, which must be characterized in order to understand the true relationship between brain structure and function. To address this challenge, here we ask which, and to what extent, aspects of the structural organisation of the brain are conserved if we preserve specific spatial and topological properties of the brain but otherwise randomise its connectivity. We perform a comparative analysis of a connectivity map of the cortical connectome both on high- and low-resolutions utilising three different types of surrogate networks: spatially unconstrained (‘random’), connection length preserving (‘spatial’), and connection length optimised (‘reduced’) surrogates. We find that unconstrained randomisation markedly diminishes all investigated architectural properties of cortical connectivity. By contrast, spatial and reduced surrogates largely preserve most properties and, interestingly, often more so in the reduced surrogates. Specifically, our results suggest that the cortical network is less tightly integrated than its spatial constraints would allow, but more strongly segregated than its spatial constraints would necessitate. We additionally find that hierarchical organisation and rich-club structure of the cortical connectivity are largely preserved in spatial and reduced surrogates and hence may be partially attributable to cortical wiring constraints. In contrast, the high modularity and strong s-core of the high-resolution cortical network are significantly stronger than in the surrogates, underlining their potential functional relevance in the brain. PMID:24699277

Disruption of posteromedial large-scale neural communication predicts recovery from coma

PubMed Central

de Pasquale, Francesco; Vuillaume, Corine; Riu, Beatrice; Loubinoux, Isabelle; Geeraerts, Thomas; Seguin, Thierry; Bounes, Vincent; Fourcade, Olivier; Demonet, Jean-Francois; Péran, Patrice

2015-01-01

Objective: We hypothesize that the major consciousness deficit observed in coma is due to the breakdown of long-range neuronal communication supported by precuneus and posterior cingulate cortex (PCC), and that prognosis depends on a specific connectivity pattern in these networks. Methods: We compared 27 prospectively recruited comatose patients who had severe brain injury (Glasgow Coma Scale score <8; 14 traumatic and 13 anoxic cases) with 14 age-matched healthy participants. Standardized clinical assessment and fMRI were performed on average 4 ± 2 days after withdrawal of sedation. Analysis of resting-state fMRI connectivity involved a hypothesis-driven, region of interest–based strategy. We assessed patient outcome after 3 months using the Coma Recovery Scale–Revised (CRS-R). Results: Patients who were comatose showed a significant disruption of functional connectivity of brain areas spontaneously synchronized with PCC, globally notwithstanding etiology. The functional connectivity strength between PCC and medial prefrontal cortex (mPFC) was significantly different between comatose patients who went on to recover and those who eventually scored an unfavorable outcome 3 months after brain injury (Kruskal-Wallis test, p < 0.001; linear regression between CRS-R and PCC-mPFC activity coupling at rest, Spearman ρ = 0.93, p < 0.003). Conclusion: In both etiology groups (traumatic and anoxic), changes in the connectivity of PCC-centered, spontaneously synchronized, large-scale networks account for the loss of external and internal self-centered awareness observed during coma. Sparing of functional connectivity between PCC and mPFC may predict patient outcome, and further studies are needed to substantiate this potential prognosis biomarker. PMID:26561296

Cognitive Flexibility through Metastable Neural Dynamics Is Disrupted by Damage to the Structural Connectome.

PubMed

Hellyer, Peter J; Scott, Gregory; Shanahan, Murray; Sharp, David J; Leech, Robert

2015-06-17

Current theory proposes that healthy neural dynamics operate in a metastable regime, where brain regions interact to simultaneously maximize integration and segregation. Metastability may confer important behavioral properties, such as cognitive flexibility. It is increasingly recognized that neural dynamics are constrained by the underlying structural connections between brain regions. An important challenge is, therefore, to relate structural connectivity, neural dynamics, and behavior. Traumatic brain injury (TBI) is a pre-eminent structural disconnection disorder whereby traumatic axonal injury damages large-scale connectivity, producing characteristic cognitive impairments, including slowed information processing speed and reduced cognitive flexibility, that may be a result of disrupted metastable dynamics. Therefore, TBI provides an experimental and theoretical model to examine how metastable dynamics relate to structural connectivity and cognition. Here, we use complementary empirical and computational approaches to investigate how metastability arises from the healthy structural connectome and relates to cognitive performance. We found reduced metastability in large-scale neural dynamics after TBI, measured with resting-state functional MRI. This reduction in metastability was associated with damage to the connectome, measured using diffusion MRI. Furthermore, decreased metastability was associated with reduced cognitive flexibility and information processing. A computational model, defined by empirically derived connectivity data, demonstrates how behaviorally relevant changes in neural dynamics result from structural disconnection. Our findings suggest how metastable dynamics are important for normal brain function and contingent on the structure of the human connectome. Copyright © 2015 the authors 0270-6474/15/359050-14$15.00/0.

Integration and segregation of large-scale brain networks during short-term task automatization

PubMed Central

Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F.; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

2016-01-01

The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes. PMID:27808095

Novel Neuroimaging Methods to Understand How HIV Affects the Brain

PubMed Central

Thompson, Paul

2015-01-01

In much of the developed world, the HIV epidemic has largely been controlled by anti-retroviral treatment. Even so, there is growing concern that HIV-infected individuals may be at risk for accelerated brain aging, and a range of cognitive impairments. What promotes or resists these changes is largely unknown. There is also interest in discovering factors that promote resilience to HIV, and combat its adverse effects in children. Here we review recent developments in brain imaging that reveal how the virus affects the brain. We relate these brain changes to changes in blood markers, cognitive function, and other patient outcomes or symptoms, such as apathy or neuropathic pain. We focus on new and emerging techniques, including new variants of brain MRI. Diffusion tensor imaging, for example, can map the brain’s structural connections while fMRI can uncover functional connections. Finally, we suggest how large-scale global research alliances, such as ENIGMA, may resolve controversies over effects where evidence is now lacking. These efforts pool scans from tens of thousands of individuals, and offer a source of power not previously imaginable for brain imaging studies. PMID:25902966

Cerebral cartography and connectomics

PubMed Central

Sporns, Olaf

2015-01-01

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics. PMID:25823870

Rewiring the connectome: Evidence and effects.

PubMed

Bennett, Sophie H; Kirby, Alastair J; Finnerty, Gerald T

2018-05-01

Neuronal connections form the physical basis for communication in the brain. Recently, there has been much interest in mapping the "connectome" to understand how brain structure gives rise to brain function, and ultimately, to behaviour. These attempts to map the connectome have largely assumed that connections are stable once formed. Recent studies, however, indicate that connections in mammalian brains may undergo rewiring during learning and experience-dependent plasticity. This suggests that the connectome is more dynamic than previously thought. To what extent can neural circuitry be rewired in the healthy adult brain? The connectome has been subdivided into multiple levels of scale, from synapses and microcircuits through to long-range tracts. Here, we examine the evidence for rewiring at each level. We then consider the role played by rewiring during learning. We conclude that harnessing rewiring offers new avenues to treat brain diseases. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Brain functional network abnormality extends beyond the sensorimotor network in brachial plexus injury patients.

PubMed

Feng, Jun-Tao; Liu, Han-Qiu; Hua, Xu-Yun; Gu, Yu-Dong; Xu, Jian-Guang; Xu, Wen-Dong

2016-12-01

Brachial plexus injury (BPI) is a type of severe peripheral nerve trauma that leads to central remodeling in the brain, as revealed by functional MRI analysis. However, previously reported remodeling is mostly restricted to sensorimotor areas of the brain. Whether this disturbance in the sensorimotor network leads to larger-scale functional remodeling remains unknown. We sought to explore the higher-level brain functional abnormality pattern of BPI patients from a large-scale network function connectivity dimension in 15 right-handed BPI patients. Resting-state functional MRI data were collected and analyzed using independent component analysis methods. Five components of interest were recognized and compared between patients and healthy subjects. Patients showed significantly altered brain local functional activities in the bilateral fronto-parietal network (FPN), sensorimotor network (SMN), and executive-control network (ECN) compared with healthy subjects. Moreover, functional connectivity between SMN and ECN were significantly less in patients compared with healthy subjects, and connectivity strength between ECN and SMN was negatively correlated with patients' residual function of the affected limb. Functional connectivity between SMN and right FPN were also significantly less than in controls, although connectivity between ECN and default mode network (DMN) was greater than in controls. These data suggested that brain functional disturbance in BPI patients extends beyond the sensorimotor network and cascades serial remodeling in the brain, which significantly correlates with residual hand function of the paralyzed limb. Furthermore, functional remodeling in these higher-level functional networks may lead to cognitive alterations in complex tasks.

Role of local network oscillations in resting-state functional connectivity.

PubMed

Cabral, Joana; Hugues, Etienne; Sporns, Olaf; Deco, Gustavo

2011-07-01

Spatio-temporally organized low-frequency fluctuations (<0.1 Hz), observed in BOLD fMRI signal during rest, suggest the existence of underlying network dynamics that emerge spontaneously from intrinsic brain processes. Furthermore, significant correlations between distinct anatomical regions-or functional connectivity (FC)-have led to the identification of several widely distributed resting-state networks (RSNs). This slow dynamics seems to be highly structured by anatomical connectivity but the mechanism behind it and its relationship with neural activity, particularly in the gamma frequency range, remains largely unknown. Indeed, direct measurements of neuronal activity have revealed similar large-scale correlations, particularly in slow power fluctuations of local field potential gamma frequency range oscillations. To address these questions, we investigated neural dynamics in a large-scale model of the human brain's neural activity. A key ingredient of the model was a structural brain network defined by empirically derived long-range brain connectivity together with the corresponding conduction delays. A neural population, assumed to spontaneously oscillate in the gamma frequency range, was placed at each network node. When these oscillatory units are integrated in the network, they behave as weakly coupled oscillators. The time-delayed interaction between nodes is described by the Kuramoto model of phase oscillators, a biologically-based model of coupled oscillatory systems. For a realistic setting of axonal conduction speed, we show that time-delayed network interaction leads to the emergence of slow neural activity fluctuations, whose patterns correlate significantly with the empirically measured FC. The best agreement of the simulated FC with the empirically measured FC is found for a set of parameters where subsets of nodes tend to synchronize although the network is not globally synchronized. Inside such clusters, the simulated BOLD signal between nodes is found to be correlated, instantiating the empirically observed RSNs. Between clusters, patterns of positive and negative correlations are observed, as described in experimental studies. These results are found to be robust with respect to a biologically plausible range of model parameters. In conclusion, our model suggests how resting-state neural activity can originate from the interplay between the local neural dynamics and the large-scale structure of the brain. Copyright © 2011 Elsevier Inc. All rights reserved.

Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function.

PubMed

Lerman, Caryn; Gu, Hong; Loughead, James; Ruparel, Kosha; Yang, Yihong; Stein, Elliot A

2014-05-01

Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. To test the hypothesis that the strength of coupling among 3 large-scale brain networks--salience, executive control, and default mode--will reflect the state of nicotine withdrawal (vs smoking satiety) and will predict abstinence-induced craving and cognitive deficits and to develop a resource allocation index (RAI) that reflects the combined strength of interactions among the 3 large-scale networks. A within-subject functional magnetic resonance imaging study in an academic medical center compared resting-state functional connectivity coherence strength after 24 hours of abstinence and after smoking satiety. We examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural functions. We included 37 healthy smoking volunteers, aged 19 to 61 years, for analyses. Twenty-four hours of abstinence vs smoking satiety. Inter-network connectivity strength (primary) and the relationship with subjective, behavioral, and neural measures of nicotine withdrawal during abstinence vs smoking satiety states (secondary). The RAI was significantly lower in the abstinent compared with the smoking satiety states (left RAI, P = .002; right RAI, P = .04), suggesting weaker inhibition between the default mode and salience networks. Weaker inter-network connectivity (reduced RAI) predicted abstinence-induced cravings to smoke (r = -0.59; P = .007) and less suppression of default mode activity during performance of a subsequent working memory task (ventromedial prefrontal cortex, r = -0.66, P = .003; posterior cingulate cortex, r = -0.65, P = .001). Alterations in coupling of the salience and default mode networks and the inability to disengage from the default mode network may be critical in cognitive/affective alterations that underlie nicotine dependence.

Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder.

PubMed

Cao, Miao; Shu, Ni; Cao, Qingjiu; Wang, Yufeng; He, Yong

2014-12-01

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopment disorders in childhood. Clinically, the core symptoms of this disorder include inattention, hyperactivity, and impulsivity. Previous studies have documented that these behavior deficits in ADHD children are associated with not only regional brain abnormalities but also changes in functional and structural connectivity among regions. In the past several years, our understanding of how ADHD affects the brain's connectivity has been greatly advanced by mapping topological alterations of large-scale brain networks (i.e., connectomes) using noninvasive neurophysiological and neuroimaging techniques (e.g., electroencephalograph, functional MRI, and diffusion MRI) in combination with graph theoretical approaches. In this review, we summarize the recent progresses of functional and structural brain connectomics in ADHD, focusing on graphic analysis of large-scale brain systems. Convergent evidence suggests that children with ADHD had abnormal small-world properties in both functional and structural brain networks characterized by higher local clustering and lower global integrity, suggesting a disorder-related shift of network topology toward regular configurations. Moreover, ADHD children showed the redistribution of regional nodes and connectivity involving the default-mode, attention, and sensorimotor systems. Importantly, these ADHD-associated alterations significantly correlated with behavior disturbances (e.g., inattention and hyperactivity/impulsivity symptoms) and exhibited differential patterns between clinical subtypes. Together, these connectome-based studies highlight brain network dysfunction in ADHD, thus opening up a new window into our understanding of the pathophysiological mechanisms of this disorder. These works might also have important implications on the development of imaging-based biomarkers for clinical diagnosis and treatment evaluation in ADHD.

Large-scale brain network associated with creative insight: combined voxel-based morphometry and resting-state functional connectivity analyses.

PubMed

Ogawa, Takeshi; Aihara, Takatsugu; Shimokawa, Takeaki; Yamashita, Okito

2018-04-24

Creative insight occurs with an "Aha!" experience when solving a difficult problem. Here, we investigated large-scale networks associated with insight problem solving. We recruited 232 healthy participants aged 21-69 years old. Participants completed a magnetic resonance imaging study (MRI; structural imaging and a 10 min resting-state functional MRI) and an insight test battery (ITB) consisting of written questionnaires (matchstick arithmetic task, remote associates test, and insight problem solving task). To identify the resting-state functional connectivity (RSFC) associated with individual creative insight, we conducted an exploratory voxel-based morphometry (VBM)-constrained RSFC analysis. We identified positive correlations between ITB score and grey matter volume (GMV) in the right insula and middle cingulate cortex/precuneus, and a negative correlation between ITB score and GMV in the left cerebellum crus 1 and right supplementary motor area. We applied seed-based RSFC analysis to whole brain voxels using the seeds obtained from the VBM and identified insight-positive/negative connections, i.e. a positive/negative correlation between the ITB score and individual RSFCs between two brain regions. Insight-specific connections included motor-related regions whereas creative-common connections included a default mode network. Our results indicate that creative insight requires a coupling of multiple networks, such as the default mode, semantic and cerebral-cerebellum networks.

Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks

PubMed Central

Hwang, Seong Jae; Adluru, Nagesh; Collins, Maxwell D.; Ravi, Sathya N.; Bendlin, Barbara B.; Johnson, Sterling C.; Singh, Vikas

2016-01-01

There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function. To do so, one typically performs so-called tractography procedures on diffusion MR brain images and derives measures of brain connectivity expressed as graphs. The nodes correspond to distinct brain regions and the edges encode the strength of the connection. The scientific interest is in characterizing the evolution of these graphs over time or from healthy individuals to diseased. We pose this important question in terms of the Laplacian of the connectivity graphs derived from various longitudinal or disease time points — quantifying its progression is then expressed in terms of coupling the harmonic bases of a full set of Laplacians. We derive a coupled system of generalized eigenvalue problems (and corresponding numerical optimization schemes) whose solution helps characterize the full life cycle of brain connectivity evolution in a given dataset. Finally, we show a set of results on a diffusion MR imaging dataset of middle aged people at risk for Alzheimer’s disease (AD), who are cognitively healthy. In such asymptomatic adults, we find that a framework for characterizing brain connectivity evolution provides the ability to predict cognitive scores for individual subjects, and for estimating the progression of participant’s brain connectivity into the future. PMID:27812274

Structural and functional connectivity of the human brain in autism spectrum disorders and attention-deficit/hyperactivity disorder: A rich club organization study

PubMed Central

Ray, Siddharth; Miller, Meghan; Karalunas, Sarah; Robertson, C.J.; Grayson, David; Cary, Paul; Hawkey, Elizabeth; Painter, Julia G.; Kriz, Daniel; Fombonne, Eric; Nigg, Joel T.; Fair, Damien A.

2015-01-01

Attention deficit hyperactive disorder (ADHD) and Autism spectrum disorders (ASD) are two of the most common and vexing neurodevelopmental disorders among children. Although the two disorders share many behavioral and neuropsychological characteristics, most MRI studies examine only one of the disorders at a time. Using graph theory combined with structural and functional connectivity, we examined the large-scale network organization among three groups of children: a group with ADHD (8-12 years, n = 20), a group with ASD (7-13 years, n = 16), and typically developing controls (TD) (8-12 years, n = 20). We apply the concept of the rich-club organization, whereby central, highly connected hub regions are also highly connected to themselves. We examine the brain into two different network domains: (1) inside a rich-club network phenomena, and (2) outside a rich-club network phenomena. ASD and ADHD populations had markedly different patterns of rich club and non rich-club connections in both functional and structural data. The ASD group exhibited higher connectivity in structural and functional networks but only inside the rich-club networks. These findings were replicated using the autism brain imaging data exchange (ABIDE) dataset with ASD (n = 85) and TD (n = 101). The ADHD group exhibited a lower generalized fractional anisotropy (GFA) and functional connectivity inside the rich-club networks, but a higher number of axonal fibers and correlation coefficient values outside the rich-club. Despite some shared biological features and frequent comorbity, these data suggest ADHD and ASD exhibit distinct large-scale connectivity patterns in middle childhood. PMID:25116862

Network structure shapes spontaneous functional connectivity dynamics.

PubMed

Shen, Kelly; Hutchison, R Matthew; Bezgin, Gleb; Everling, Stefan; McIntosh, Anthony R

2015-04-08

The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture. Copyright © 2015 the authors 0270-6474/15/355579-10$15.00/0.

ELUCIDATING BRAIN CONNECTIVITY NETWORKS IN MAJOR DEPRESSIVE DISORDER USING CLASSIFICATION-BASED SCORING.

PubMed

Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

2014-04-01

Graph theory is increasingly used in the field of neuroscience to understand the large-scale network structure of the human brain. There is also considerable interest in applying machine learning techniques in clinical settings, for example, to make diagnoses or predict treatment outcomes. Here we used support-vector machines (SVMs), in conjunction with whole-brain tractography, to identify graph metrics that best differentiate individuals with Major Depressive Disorder (MDD) from nondepressed controls. To do this, we applied a novel feature-scoring procedure that incorporates iterative classifier performance to assess feature robustness. We found that small-worldness , a measure of the balance between global integration and local specialization, most reliably differentiated MDD from nondepressed individuals. Post-hoc regional analyses suggested that heightened connectivity of the subcallosal cingulate gyrus (SCG) in MDDs contributes to these differences. The current study provides a novel way to assess the robustness of classification features and reveals anomalies in large-scale neural networks in MDD.

Large-scale topology and the default mode network in the mouse connectome

PubMed Central

Stafford, James M.; Jarrett, Benjamin R.; Miranda-Dominguez, Oscar; Mills, Brian D.; Cain, Nicholas; Mihalas, Stefan; Lahvis, Garet P.; Lattal, K. Matthew; Mitchell, Suzanne H.; David, Stephen V.; Fryer, John D.; Nigg, Joel T.; Fair, Damien A.

2014-01-01

Noninvasive functional imaging holds great promise for serving as a translational bridge between human and animal models of various neurological and psychiatric disorders. However, despite a depth of knowledge of the cellular and molecular underpinnings of atypical processes in mouse models, little is known about the large-scale functional architecture measured by functional brain imaging, limiting translation to human conditions. Here, we provide a robust processing pipeline to generate high-resolution, whole-brain resting-state functional connectivity MRI (rs-fcMRI) images in the mouse. Using a mesoscale structural connectome (i.e., an anterograde tracer mapping of axonal projections across the mouse CNS), we show that rs-fcMRI in the mouse has strong structural underpinnings, validating our procedures. We next directly show that large-scale network properties previously identified in primates are present in rodents, although they differ in several ways. Last, we examine the existence of the so-called default mode network (DMN)—a distributed functional brain system identified in primates as being highly important for social cognition and overall brain function and atypically functionally connected across a multitude of disorders. We show the presence of a potential DMN in the mouse brain both structurally and functionally. Together, these studies confirm the presence of basic network properties and functional networks of high translational importance in structural and functional systems in the mouse brain. This work clears the way for an important bridge measurement between human and rodent models, enabling us to make stronger conclusions about how regionally specific cellular and molecular manipulations in mice relate back to humans. PMID:25512496

Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy.

PubMed

de Campos, Brunno Machado; Coan, Ana Carolina; Lin Yasuda, Clarissa; Casseb, Raphael Fernandes; Cendes, Fernando

2016-09-01

Mesial temporal lobe epilepsy (MTLE) with hippocampus sclerosis (HS) is associated with functional and structural alterations extending beyond the temporal regions and abnormal pattern of brain resting state networks (RSNs) connectivity. We hypothesized that the interaction of large-scale RSNs is differently affected in patients with right- and left-MTLE with HS compared to controls. We aimed to determine and characterize these alterations through the analysis of 12 RSNs, functionally parceled in 70 regions of interest (ROIs), from resting-state functional-MRIs of 99 subjects (52 controls, 26 right- and 21 left-MTLE patients with HS). Image preprocessing and statistical analysis were performed using UF(2) C-toolbox, which provided ROI-wise results for intranetwork and internetwork connectivity. Intranetwork abnormalities were observed in the dorsal default mode network (DMN) in both groups of patients and in the posterior salience network in right-MTLE. Both groups showed abnormal correlation between the dorsal-DMN and the posterior salience, as well as between the dorsal-DMN and the executive-control network. Patients with left-MTLE also showed reduced correlation between the dorsal-DMN and visuospatial network and increased correlation between bilateral thalamus and the posterior salience network. The ipsilateral hippocampus stood out as a central area of abnormalities. Alterations on left-MTLE expressed a low cluster coefficient, whereas the altered connections on right-MTLE showed low cluster coefficient in the DMN but high in the posterior salience regions. Both right- and left-MTLE patients with HS have widespread abnormal interactions of large-scale brain networks; however, all parameters evaluated indicate that left-MTLE has a more intricate bihemispheric dysfunction compared to right-MTLE. Hum Brain Mapp 37:3137-3152, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Network diffusion accurately models the relationship between structural and functional brain connectivity networks

PubMed Central

Abdelnour, Farras; Voss, Henning U.; Raj, Ashish

2014-01-01

The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain’s long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways. PMID:24384152

Transfer of Learning Relates to Intrinsic Connectivity between Hippocampus, Ventromedial Prefrontal Cortex, and Large-Scale Networks

PubMed Central

Gerraty, Raphael T.; Davidow, Juliet Y.; Wimmer, G. Elliott; Kahn, Itamar

2014-01-01

An important aspect of adaptive learning is the ability to flexibly use past experiences to guide new decisions. When facing a new decision, some people automatically leverage previously learned associations, while others do not. This variability in transfer of learning across individuals has been demonstrated repeatedly and has important implications for understanding adaptive behavior, yet the source of these individual differences remains poorly understood. In particular, it is unknown why such variability in transfer emerges even among homogeneous groups of young healthy participants who do not vary on other learning-related measures. Here we hypothesized that individual differences in the transfer of learning could be related to relatively stable differences in intrinsic brain connectivity, which could constrain how individuals learn. To test this, we obtained a behavioral measure of memory-based transfer outside of the scanner and on a separate day acquired resting-state functional MRI images in 42 participants. We then analyzed connectivity across independent component analysis-derived brain networks during rest, and tested whether intrinsic connectivity in learning-related networks was associated with transfer. We found that individual differences in transfer were related to intrinsic connectivity between the hippocampus and the ventromedial prefrontal cortex, and between these regions and large-scale functional brain networks. Together, the findings demonstrate a novel role for intrinsic brain dynamics in flexible learning-guided behavior, both within a set of functionally specific regions known to be important for learning, as well as between these regions and the default and frontoparietal networks, which are thought to serve more general cognitive functions. PMID:25143610

Robust prediction of individual creative ability from brain functional connectivity.

PubMed

Beaty, Roger E; Kenett, Yoed N; Christensen, Alexander P; Rosenberg, Monica D; Benedek, Mathias; Chen, Qunlin; Fink, Andreas; Qiu, Jiang; Kwapil, Thomas R; Kane, Michael J; Silvia, Paul J

2018-01-30

People's ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis-connectome-based predictive modeling-to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences ( r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems-intrinsic functional networks that tend to work in opposition-suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

Stepwise Connectivity of the Modal Cortex Reveals the Multimodal Organization of the Human Brain

PubMed Central

Sepulcre, Jorge; Sabuncu, Mert R.; Yeo, Thomas B.; Liu, Hesheng; Johnson, Keith A.

2012-01-01

How human beings integrate information from external sources and internal cognition to produce a coherent experience is still not well understood. During the past decades, anatomical, neurophysiological and neuroimaging research in multimodal integration have stood out in the effort to understand the perceptual binding properties of the brain. Areas in the human lateral occipito-temporal, prefrontal and posterior parietal cortices have been associated with sensory multimodal processing. Even though this, rather patchy, organization of brain regions gives us a glimpse of the perceptual convergence, the articulation of the flow of information from modality-related to the more parallel cognitive processing systems remains elusive. Using a method called Stepwise Functional Connectivity analysis, the present study analyzes the functional connectome and transitions from primary sensory cortices to higher-order brain systems. We identify the large-scale multimodal integration network and essential connectivity axes for perceptual integration in the human brain. PMID:22855814

Low-frequency connectivity is associated with mild traumatic brain injury.

PubMed

Dunkley, B T; Da Costa, L; Bethune, A; Jetly, R; Pang, E W; Taylor, M J; Doesburg, S M

2015-01-01

Mild traumatic brain injury (mTBI) occurs from a closed-head impact. Often referred to as concussion, about 20% of cases complain of secondary psychological sequelae, such as disorders of attention and memory. Known as post-concussive symptoms (PCS), these problems can severely disrupt the patient's quality of life. Changes in local spectral power, particularly low-frequency amplitude increases and/or peak alpha slowing have been reported in mTBI, but large-scale connectivity metrics based on inter-regional amplitude correlations relevant for integration and segregation in functional brain networks, and their association with disorders in cognition and behaviour, remain relatively unexplored. Here, we used non-invasive neuroimaging with magnetoencephalography to examine functional connectivity in a resting-state protocol in a group with mTBI (n = 20), and a control group (n = 21). We observed a trend for atypical slow-wave power changes in subcortical, temporal and parietal regions in mTBI, as well as significant long-range increases in amplitude envelope correlations among deep-source, temporal, and frontal regions in the delta, theta, and alpha bands. Subsequently, we conducted an exploratory analysis of patterns of connectivity most associated with variability in secondary symptoms of mTBI, including inattention, anxiety, and depression. Differential patterns of altered resting state neurophysiological network connectivity were found across frequency bands. This indicated that multiple network and frequency specific alterations in large scale brain connectivity may contribute to overlapping cognitive sequelae in mTBI. In conclusion, we show that local spectral power content can be supplemented with measures of correlations in amplitude to define general networks that are atypical in mTBI, and suggest that certain cognitive difficulties are mediated by disturbances in a variety of alterations in network interactions which are differentially expressed across canonical neurophysiological frequency ranges.

Large-Scale Hypoconnectivity Between Resting-State Functional Networks in Unmedicated Adolescent Major Depressive Disorder.

PubMed

Sacchet, Matthew D; Ho, Tiffany C; Connolly, Colm G; Tymofiyeva, Olga; Lewinn, Kaja Z; Han, Laura Km; Blom, Eva H; Tapert, Susan F; Max, Jeffrey E; Frank, Guido Kw; Paulus, Martin P; Simmons, Alan N; Gotlib, Ian H; Yang, Tony T

2016-11-01

Major depressive disorder (MDD) often emerges during adolescence, a critical period of brain development. Recent resting-state fMRI studies of adults suggest that MDD is associated with abnormalities within and between resting-state networks (RSNs). Here we tested whether adolescent MDD is characterized by abnormalities in interactions among RSNs. Participants were 55 unmedicated adolescents diagnosed with MDD and 56 matched healthy controls. Functional connectivity was mapped using resting-state fMRI. We used the network-based statistic (NBS) to compare large-scale connectivity between groups and also compared the groups on graph metrics. We further assessed whether group differences identified using nodes defined from functionally defined RSNs were also evident when using anatomically defined nodes. In addition, we examined relations between network abnormalities and depression severity and duration. Finally, we compared intranetwork connectivity between groups and assessed the replication of previously reported MDD-related abnormalities in connectivity. The NBS indicated that, compared with controls, depressed adolescents exhibited reduced connectivity (p<0.024, corrected) between a specific set of RSNs, including components of the attention, central executive, salience, and default mode networks. The NBS did not identify group differences in network connectivity when using anatomically defined nodes. Longer duration of depression was significantly correlated with reduced connectivity in this set of network interactions (p=0.020, corrected), specifically with reduced connectivity between components of the dorsal attention network. The dorsal attention network was also characterized by reduced intranetwork connectivity in the MDD group. Finally, we replicated previously reported abnormal connectivity in individuals with MDD. In summary, adolescents with MDD show hypoconnectivity between large-scale brain networks compared with healthy controls. Given that connectivity among these networks typically increases during adolescent neurodevelopment, these results suggest that adolescent depression is associated with abnormalities in neural systems that are still developing during this critical period.

Large-Scale Hypoconnectivity Between Resting-State Functional Networks in Unmedicated Adolescent Major Depressive Disorder

PubMed Central

Sacchet, Matthew D; Ho, Tiffany C; Connolly, Colm G; Tymofiyeva, Olga; Lewinn, Kaja Z; Han, Laura KM; Blom, Eva H; Tapert, Susan F; Max, Jeffrey E; Frank, Guido KW; Paulus, Martin P; Simmons, Alan N; Gotlib, Ian H; Yang, Tony T

2016-01-01

Major depressive disorder (MDD) often emerges during adolescence, a critical period of brain development. Recent resting-state fMRI studies of adults suggest that MDD is associated with abnormalities within and between resting-state networks (RSNs). Here we tested whether adolescent MDD is characterized by abnormalities in interactions among RSNs. Participants were 55 unmedicated adolescents diagnosed with MDD and 56 matched healthy controls. Functional connectivity was mapped using resting-state fMRI. We used the network-based statistic (NBS) to compare large-scale connectivity between groups and also compared the groups on graph metrics. We further assessed whether group differences identified using nodes defined from functionally defined RSNs were also evident when using anatomically defined nodes. In addition, we examined relations between network abnormalities and depression severity and duration. Finally, we compared intranetwork connectivity between groups and assessed the replication of previously reported MDD-related abnormalities in connectivity. The NBS indicated that, compared with controls, depressed adolescents exhibited reduced connectivity (p<0.024, corrected) between a specific set of RSNs, including components of the attention, central executive, salience, and default mode networks. The NBS did not identify group differences in network connectivity when using anatomically defined nodes. Longer duration of depression was significantly correlated with reduced connectivity in this set of network interactions (p=0.020, corrected), specifically with reduced connectivity between components of the dorsal attention network. The dorsal attention network was also characterized by reduced intranetwork connectivity in the MDD group. Finally, we replicated previously reported abnormal connectivity in individuals with MDD. In summary, adolescents with MDD show hypoconnectivity between large-scale brain networks compared with healthy controls. Given that connectivity among these networks typically increases during adolescent neurodevelopment, these results suggest that adolescent depression is associated with abnormalities in neural systems that are still developing during this critical period. PMID:27238621

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients.

PubMed

Miller, Robyn L; Yaesoubi, Maziar; Turner, Jessica A; Mathalon, Daniel; Preda, Adrian; Pearlson, Godfrey; Adali, Tulay; Calhoun, Vince D

2016-01-01

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject's trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients

PubMed Central

Miller, Robyn L.; Yaesoubi, Maziar; Turner, Jessica A.; Mathalon, Daniel; Preda, Adrian; Pearlson, Godfrey; Adali, Tulay; Calhoun, Vince D.

2016-01-01

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject’s trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls. PMID:26981625

Weighted and directed interactions in evolving large-scale epileptic brain networks

NASA Astrophysics Data System (ADS)

Dickten, Henning; Porz, Stephan; Elger, Christian E.; Lehnertz, Klaus

2016-10-01

Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess—with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics—both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only - in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.

Cerebral cartography and connectomics.

PubMed

Sporns, Olaf

2015-05-19

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography

PubMed Central

Liu, Yaou; Duan, Yunyun; Li, Kuncheng

2015-01-01

The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain. PMID:26539535

Disrupted Brain Functional Organization in Epilepsy Revealed by Graph Theory Analysis.

PubMed

Song, Jie; Nair, Veena A; Gaggl, Wolfgang; Prabhakaran, Vivek

2015-06-01

The human brain is a complex and dynamic system that can be modeled as a large-scale brain network to better understand the reorganizational changes secondary to epilepsy. In this study, we developed a brain functional network model using graph theory methods applied to resting-state fMRI data acquired from a group of epilepsy patients and age- and gender-matched healthy controls. A brain functional network model was constructed based on resting-state functional connectivity. A minimum spanning tree combined with proportional thresholding approach was used to obtain sparse connectivity matrices for each subject, which formed the basis of brain networks. We examined the brain reorganizational changes in epilepsy thoroughly at the level of the whole brain, the functional network, and individual brain regions. At the whole-brain level, local efficiency was significantly decreased in epilepsy patients compared with the healthy controls. However, global efficiency was significantly increased in epilepsy due to increased number of functional connections between networks (although weakly connected). At the functional network level, there were significant proportions of newly formed connections between the default mode network and other networks and between the subcortical network and other networks. There was a significant proportion of decreasing connections between the cingulo-opercular task control network and other networks. Individual brain regions from different functional networks, however, showed a distinct pattern of reorganizational changes in epilepsy. These findings suggest that epilepsy alters brain efficiency in a consistent pattern at the whole-brain level, yet alters brain functional networks and individual brain regions differently.

Dynamic functional connectivity: Promise, issues, and interpretations

PubMed Central

Hutchison, R. Matthew; Womelsdorf, Thilo; Allen, Elena A.; Bandettini, Peter A.; Calhoun, Vince D.; Corbetta, Maurizio; Penna, Stefania Della; Duyn, Jeff H.; Glover, Gary H.; Gonzalez-Castillo, Javier; Handwerker, Daniel A.; Keilholz, Shella; Kiviniemi, Vesa; Leopold, David A.; de Pasquale, Francesco; Sporns, Olaf; Walter, Martin; Chang, Catie

2013-01-01

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations. PMID:23707587

Alcohol Affects the Brain's Resting-State Network in Social Drinkers

PubMed Central

Lithari, Chrysa; Klados, Manousos A.; Pappas, Costas; Albani, Maria; Kapoukranidou, Dorothea; Kovatsi, Leda

2012-01-01

Acute alcohol intake is known to enhance inhibition through facilitation of GABAA receptors, which are present in 40% of the synapses all over the brain. Evidence suggests that enhanced GABAergic transmission leads to increased large-scale brain connectivity. Our hypothesis is that acute alcohol intake would increase the functional connectivity of the human brain resting-state network (RSN). To test our hypothesis, electroencephalographic (EEG) measurements were recorded from healthy social drinkers at rest, during eyes-open and eyes-closed sessions, after administering to them an alcoholic beverage or placebo respectively. Salivary alcohol and cortisol served to measure the inebriation and stress levels. By calculating Magnitude Square Coherence (MSC) on standardized Low Resolution Electromagnetic Tomography (sLORETA) solutions, we formed cortical networks over several frequency bands, which were then analyzed in the context of functional connectivity and graph theory. MSC was increased (p<0.05, corrected with False Discovery Rate, FDR corrected) in alpha, beta (eyes-open) and theta bands (eyes-closed) following acute alcohol intake. Graph parameters were accordingly altered in these bands quantifying the effect of alcohol on the structure of brain networks; global efficiency and density were higher and path length was lower during alcohol (vs. placebo, p<0.05). Salivary alcohol concentration was positively correlated with the density of the network in beta band. The degree of specific nodes was elevated following alcohol (vs. placebo). Our findings support the hypothesis that short-term inebriation considerably increases large-scale connectivity in the RSN. The increased baseline functional connectivity can -at least partially- be attributed to the alcohol-induced disruption of the delicate balance between inhibitory and excitatory neurotransmission in favor of inhibitory influences. Thus, it is suggested that short-term inebriation is associated, as expected, to increased GABA transmission and functional connectivity, while long-term alcohol consumption may be linked to exactly the opposite effect. PMID:23119078

Altered brain structural connectivity in post-traumatic stress disorder: a diffusion tensor imaging tractography study.

PubMed

Long, Zhiliang; Duan, Xujun; Xie, Bing; Du, Handan; Li, Rong; Xu, Qiang; Wei, Luqing; Zhang, Shao-xiang; Wu, Yi; Gao, Qing; Chen, Huafu

2013-09-25

Post-traumatic stress disorder (PTSD) is characterized by dysfunction of several discrete brain regions such as medial prefrontal gyrus with hypoactivation and amygdala with hyperactivation. However, alterations of large-scale whole brain topological organization of structural networks remain unclear. Seventeen patients with PTSD in motor vehicle accident survivors and 15 normal controls were enrolled in our study. Large-scale structural connectivity network (SCN) was constructed using diffusion tensor tractography, followed by thresholding the mean factional anisotropy matrix of 90 brain regions. Graph theory analysis was then employed to investigate their aberrant topological properties. Both patient and control group showed small-world topology in their SCNs. However, patients with PTSD exhibited abnormal global properties characterized by significantly decreased characteristic shortest path length and normalized characteristic shortest path length. Furthermore, the patient group showed enhanced nodal centralities predominately in salience network including bilateral anterior cingulate and pallidum, and hippocampus/parahippocamus gyrus, and decreased nodal centralities mainly in medial orbital part of superior frontal gyrus. The main limitation of this study is the small sample of PTSD patients, which may lead to decrease the statistic power. Consequently, this study should be considered an exploratory analysis. These results are consistent with the notion that PTSD can be understood by investigating the dysfunction of large-scale, spatially distributed neural networks, and also provide structural evidences for further exploration of neurocircuitry models in PTSD. © 2013 Elsevier B.V. All rights reserved.

Exact solution for the optimal neuronal layout problem.

PubMed

Chklovskii, Dmitri B

2004-10-01

Evolution perfected brain design by maximizing its functionality while minimizing costs associated with building and maintaining it. Assumption that brain functionality is specified by neuronal connectivity, implemented by costly biological wiring, leads to the following optimal design problem. For a given neuronal connectivity, find a spatial layout of neurons that minimizes the wiring cost. Unfortunately, this problem is difficult to solve because the number of possible layouts is often astronomically large. We argue that the wiring cost may scale as wire length squared, reducing the optimal layout problem to a constrained minimization of a quadratic form. For biologically plausible constraints, this problem has exact analytical solutions, which give reasonable approximations to actual layouts in the brain. These solutions make the inverse problem of inferring neuronal connectivity from neuronal layout more tractable.

Transfer of learning relates to intrinsic connectivity between hippocampus, ventromedial prefrontal cortex, and large-scale networks.

PubMed

Gerraty, Raphael T; Davidow, Juliet Y; Wimmer, G Elliott; Kahn, Itamar; Shohamy, Daphna

2014-08-20

An important aspect of adaptive learning is the ability to flexibly use past experiences to guide new decisions. When facing a new decision, some people automatically leverage previously learned associations, while others do not. This variability in transfer of learning across individuals has been demonstrated repeatedly and has important implications for understanding adaptive behavior, yet the source of these individual differences remains poorly understood. In particular, it is unknown why such variability in transfer emerges even among homogeneous groups of young healthy participants who do not vary on other learning-related measures. Here we hypothesized that individual differences in the transfer of learning could be related to relatively stable differences in intrinsic brain connectivity, which could constrain how individuals learn. To test this, we obtained a behavioral measure of memory-based transfer outside of the scanner and on a separate day acquired resting-state functional MRI images in 42 participants. We then analyzed connectivity across independent component analysis-derived brain networks during rest, and tested whether intrinsic connectivity in learning-related networks was associated with transfer. We found that individual differences in transfer were related to intrinsic connectivity between the hippocampus and the ventromedial prefrontal cortex, and between these regions and large-scale functional brain networks. Together, the findings demonstrate a novel role for intrinsic brain dynamics in flexible learning-guided behavior, both within a set of functionally specific regions known to be important for learning, as well as between these regions and the default and frontoparietal networks, which are thought to serve more general cognitive functions. Copyright © 2014 the authors 0270-6474/14/3411297-07$15.00/0.

Individual differences and time-varying features of modular brain architecture.

PubMed

Liao, Xuhong; Cao, Miao; Xia, Mingrui; He, Yong

2017-05-15

Recent studies have suggested that human brain functional networks are topologically organized into functionally specialized but inter-connected modules to facilitate efficient information processing and highly flexible cognitive function. However, these studies have mainly focused on group-level network modularity analyses using "static" functional connectivity approaches. How these extraordinary modular brain structures vary across individuals and spontaneously reconfigure over time remain largely unknown. Here, we employed multiband resting-state functional MRI data (N=105) from the Human Connectome Project and a graph-based modularity analysis to systematically investigate individual variability and dynamic properties in modular brain networks. We showed that the modular structures of brain networks dramatically vary across individuals, with higher modular variability primarily in the association cortex (e.g., fronto-parietal and attention systems) and lower variability in the primary systems. Moreover, brain regions spontaneously changed their module affiliations on a temporal scale of seconds, which cannot be simply attributable to head motion and sampling error. Interestingly, the spatial pattern of intra-subject dynamic modular variability largely overlapped with that of inter-subject modular variability, both of which were highly reproducible across repeated scanning sessions. Finally, the regions with remarkable individual/temporal modular variability were closely associated with network connectors and the number of cognitive components, suggesting a potential contribution to information integration and flexible cognitive function. Collectively, our findings highlight individual modular variability and the notable dynamic characteristics in large-scale brain networks, which enhance our understanding of the neural substrates underlying individual differences in a variety of cognition and behaviors. Copyright © 2017 Elsevier Inc. All rights reserved.

The Rich Get Richer: Brain Injury Elicits Hyperconnectivity in Core Subnetworks

PubMed Central

Hillary, Frank G.; Rajtmajer, Sarah M.; Roman, Cristina A.; Medaglia, John D.; Slocomb-Dluzen, Julia E.; Calhoun, Vincent D.; Good, David C.; Wylie, Glenn R.

2014-01-01

There remains much unknown about how large-scale neural networks accommodate neurological disruption, such as moderate and severe traumatic brain injury (TBI). A primary goal in this study was to examine the alterations in network topology occurring during the first year of recovery following TBI. To do so we examined 21 individuals with moderate and severe TBI at 3 and 6 months after resolution of posttraumatic amnesia and 15 age- and education-matched healthy adults using functional MRI and graph theoretical analyses. There were two central hypotheses in this study: 1) physical disruption results in increased functional connectivity, or hyperconnectivity, and 2) hyperconnectivity occurs in regions typically observed to be the most highly connected cortical hubs, or the “rich club”. The current findings generally support the hyperconnectivity hypothesis showing that during the first year of recovery after TBI, neural networks show increased connectivity, and this change is disproportionately represented in brain regions belonging to the brain's core subnetworks. The selective increases in connectivity observed here are consistent with the preferential attachment model underlying scale-free network development. This study is the largest of its kind and provides the unique opportunity to examine how neural systems adapt to significant neurological disruption during the first year after injury. PMID:25121760

The rich get richer: brain injury elicits hyperconnectivity in core subnetworks.

PubMed

Hillary, Frank G; Rajtmajer, Sarah M; Roman, Cristina A; Medaglia, John D; Slocomb-Dluzen, Julia E; Calhoun, Vincent D; Good, David C; Wylie, Glenn R

2014-01-01

There remains much unknown about how large-scale neural networks accommodate neurological disruption, such as moderate and severe traumatic brain injury (TBI). A primary goal in this study was to examine the alterations in network topology occurring during the first year of recovery following TBI. To do so we examined 21 individuals with moderate and severe TBI at 3 and 6 months after resolution of posttraumatic amnesia and 15 age- and education-matched healthy adults using functional MRI and graph theoretical analyses. There were two central hypotheses in this study: 1) physical disruption results in increased functional connectivity, or hyperconnectivity, and 2) hyperconnectivity occurs in regions typically observed to be the most highly connected cortical hubs, or the "rich club". The current findings generally support the hyperconnectivity hypothesis showing that during the first year of recovery after TBI, neural networks show increased connectivity, and this change is disproportionately represented in brain regions belonging to the brain's core subnetworks. The selective increases in connectivity observed here are consistent with the preferential attachment model underlying scale-free network development. This study is the largest of its kind and provides the unique opportunity to examine how neural systems adapt to significant neurological disruption during the first year after injury.

Multi-scale integration and predictability in resting state brain activity

PubMed Central

Kolchinsky, Artemy; van den Heuvel, Martijn P.; Griffa, Alessandra; Hagmann, Patric; Rocha, Luis M.; Sporns, Olaf; Goñi, Joaquín

2014-01-01

The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales. PMID:25104933

Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

DSouza, Adora M.; Abidin, Anas Z.; Leistritz, Lutz; Wismüller, Axel

2017-02-01

We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

Beyond Scale-Free Small-World Networks: Cortical Columns for Quick Brains

NASA Astrophysics Data System (ADS)

Stoop, Ralph; Saase, Victor; Wagner, Clemens; Stoop, Britta; Stoop, Ruedi

2013-03-01

We study to what extent cortical columns with their particular wiring boost neural computation. Upon a vast survey of columnar networks performing various real-world cognitive tasks, we detect no signs of enhancement. It is on a mesoscopic—intercolumnar—scale that the existence of columns, largely irrespective of their inner organization, enhances the speed of information transfer and minimizes the total wiring length required to bind distributed columnar computations towards spatiotemporally coherent results. We suggest that brain efficiency may be related to a doubly fractal connectivity law, resulting in networks with efficiency properties beyond those by scale-free networks.

Resting-State fMRI Functional Connectivity Is Associated with Sleepiness, Imagery, and Discontinuity of Mind

PubMed Central

Chen, Gang; den Braber, Anouk; van ‘t Ent, Dennis; Boomsma, Dorret I.; Mansvelder, Huibert D.; de Geus, Eco; Van Someren, Eus J. W.; Linkenkaer-Hansen, Klaus

2015-01-01

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to investigate the functional architecture of the healthy human brain and how it is affected by learning, lifelong development, brain disorders or pharmacological intervention. Non-sensory experiences are prevalent during rest and must arise from ongoing brain activity, yet little is known about this relationship. Here, we used two runs of rs-fMRI both immediately followed by the Amsterdam Resting-State Questionnaire (ARSQ) to investigate the relationship between functional connectivity within ten large-scale functional brain networks and ten dimensions of thoughts and feelings experienced during the scan in 106 healthy participants. We identified 11 positive associations between brain-network functional connectivity and ARSQ dimensions. ‘Sleepiness’ exhibited significant associations with functional connectivity within Visual, Sensorimotor and Default Mode networks. Similar associations were observed for ‘Visual Thought’ and ‘Discontinuity of Mind’, which may relate to variation in imagery and thought control mediated by arousal fluctuations. Our findings show that self-reports of thoughts and feelings experienced during a rs-fMRI scan help understand the functional significance of variations in functional connectivity, which should be of special relevance to clinical studies. PMID:26540239

Risk seeking for losses modulates the functional connectivity of the default mode and left frontoparietal networks in young males.

PubMed

Deza Araujo, Yacila I; Nebe, Stephan; Neukam, Philipp T; Pooseh, Shakoor; Sebold, Miriam; Garbusow, Maria; Heinz, Andreas; Smolka, Michael N

2018-06-01

Value-based decision making (VBDM) is a principle that states that humans and other species adapt their behavior according to the dynamic subjective values of the chosen or unchosen options. The neural bases of this process have been extensively investigated using task-based fMRI and lesion studies. However, the growing field of resting-state functional connectivity (RSFC) may shed light on the organization and function of brain connections across different decision-making domains. With this aim, we used independent component analysis to study the brain network dynamics in a large cohort of young males (N = 145) and the relationship of these dynamics with VBDM. Participants completed a battery of behavioral tests that evaluated delay aversion, risk seeking for losses, risk aversion for gains, and loss aversion, followed by an RSFC scan session. We identified a set of large-scale brain networks and conducted our analysis only on the default mode network (DMN) and networks comprising cognitive control, appetitive-driven, and reward-processing regions. Higher risk seeking for losses was associated with increased connectivity between medial temporal regions, frontal regions, and the DMN. Higher risk seeking for losses was also associated with increased coupling between the left frontoparietal network and occipital cortices. These associations illustrate the participation of brain regions involved in prospective thinking, affective decision making, and visual processing in participants who are greater risk-seekers, and they demonstrate the sensitivity of RSFC to detect brain connectivity differences associated with distinct VBDM parameters.

Large-Scale Brain Network Coupling Predicts Acute Nicotine Abstinence Effects on Craving and Cognitive Function

PubMed Central

Lerman, Caryn; Gu, Hong; Loughead, James; Ruparel, Kosha; Yang, Yihong; Stein, Elliot A.

2014-01-01

IMPORTANCE Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. OBJECTIVES To test the hypothesis that the strength of coupling among 3 large-scale brain networks–salience, executive control, and default mode–will reflect the state of nicotine withdrawal (vs smoking satiety) and will predict abstinence-induced craving and cognitive deficits and to develop a resource allocation index (RAI) that reflects the combined strength of interactions among the 3 large-scale networks. DESIGN, SETTING, AND PARTICIPANTS A within-subject functional magnetic resonance imaging study in an academic medical center compared resting-state functional connectivity coherence strength after 24 hours of abstinence and after smoking satiety. We examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural functions. We included 37 healthy smoking volunteers, aged 19 to 61 years, for analyses. INTERVENTIONS Twenty-four hours of abstinence vs smoking satiety. MAIN OUTCOMES AND MEASURES Inter-network connectivity strength (primary) and the relationship with subjective, behavioral, and neural measures of nicotine withdrawal during abstinence vs smoking satiety states (secondary). RESULTS The RAI was significantly lower in the abstinent compared with the smoking satiety states (left RAI, P = .002; right RAI, P = .04), suggesting weaker inhibition between the default mode and salience networks. Weaker inter-network connectivity (reduced RAI) predicted abstinence-induced cravings to smoke (r = −0.59; P = .007) and less suppression of default mode activity during performance of a subsequent working memory task (ventromedial prefrontal cortex, r = −0.66, P = .003; posterior cingulate cortex, r = −0.65, P = .001). CONCLUSIONS AND RELEVANCE Alterations in coupling of the salience and default mode networks and the inability to disengage from the default mode network may be critical in cognitive/affective alterations that underlie nicotine dependence. PMID:24622915

Influence of anesthesia on cerebral blood flow, cerebral metabolic rate, and brain functional connectivity.

PubMed

Bonhomme, Vincent; Boveroux, Pierre; Hans, Pol; Brichant, Jean François; Vanhaudenhuyse, Audrey; Boly, Melanie; Laureys, Steven

2011-10-01

To describe recent studies exploring brain function under the influence of hypnotic anesthetic agents, and their implications on the understanding of consciousness physiology and anesthesia-induced alteration of consciousness. Cerebral cortex is the primary target of the hypnotic effect of anesthetic agents, and higher-order association areas are more sensitive to this effect than lower-order processing regions. Increasing concentration of anesthetic agents progressively attenuates connectivity in the consciousness networks, while connectivity in lower-order sensory and motor networks is preserved. Alteration of thalamic sub-cortical regulation could compromise the cortical integration of information despite preserved thalamic activation by external stimuli. At concentrations producing unresponsiveness, the activity of consciousness networks becomes anticorrelated with thalamic activity, while connectivity in lower-order sensory networks persists, although with cross-modal interaction alterations. Accumulating evidence suggests that hypnotic anesthetic agents disrupt large-scale cerebral connectivity. This would result in an inability of the brain to generate and integrate information, while external sensory information is still processed at a lower order of complexity.

Connectivity and functional profiling of abnormal brain structures in pedophilia

PubMed Central

Poeppl, Timm B.; Eickhoff, Simon B.; Fox, Peter T.; Laird, Angela R.; Rupprecht, Rainer; Langguth, Berthold; Bzdok, Danilo

2015-01-01

Despite its 0.5–1% lifetime prevalence in men and its general societal relevance, neuroimaging investigations in pedophilia are scarce. Preliminary findings indicate abnormal brain structure and function. However, no study has yet linked structural alterations in pedophiles to both connectional and functional properties of the aberrant hotspots. The relationship between morphological alterations and brain function in pedophilia as well as their contribution to its psychopathology thus remain unclear. First, we assessed bimodal connectivity of structurally altered candidate regions using meta-analytic connectivity modeling (MACM) and resting-state correlations employing openly accessible data. We compared the ensuing connectivity maps to the activation likelihood estimation (ALE) maps of a recent quantitative meta-analysis of brain activity during processing of sexual stimuli. Second, we functionally characterized the structurally altered regions employing meta-data of a large-scale neuroimaging database. Candidate regions were functionally connected to key areas for processing of sexual stimuli. Moreover, we found that the functional role of structurally altered brain regions in pedophilia relates to nonsexual emotional as well as neurocognitive and executive functions, previously reported to be impaired in pedophiles. Our results suggest that structural brain alterations affect neural networks for sexual processing by way of disrupted functional connectivity, which may entail abnormal sexual arousal patterns. The findings moreover indicate that structural alterations account for common affective and neurocognitive impairments in pedophilia. The present multi-modal integration of brain structure and function analyses links sexual and nonsexual psychopathology in pedophilia. PMID:25733379

Hyperconnectivity is a fundamental response to neurological disruption.

PubMed

Hillary, Frank G; Roman, Cristina A; Venkatesan, Umesh; Rajtmajer, Sarah M; Bajo, Ricardo; Castellanos, Nazareth D

2015-01-01

In the cognitive and clinical neurosciences, the past decade has been marked by dramatic growth in a literature examining brain "connectivity" using noninvasive methods. We offer a critical review of the blood oxygen level dependent functional MRI (BOLD fMRI) literature examining neural connectivity changes in neurological disorders with focus on brain injury and dementia. The goal is to demonstrate that there are identifiable shifts in local and large-scale network connectivity that can be predicted by the degree of pathology. We anticipate that the most common network response to neurological insult is hyperconnectivity but that this response depends upon demand and resource availability. To examine this hypothesis, we initially reviewed the results from 1,426 studies examining functional brain connectivity in individuals diagnosed with multiple sclerosis, traumatic brain injury, mild cognitive impairment, and Alzheimer's disease. Based upon inclusionary criteria, 126 studies were included for detailed analysis. RESULTS from 126 studies examining local and whole brain connectivity demonstrated increased connectivity in traumatic brain injury and multiple sclerosis. This finding is juxtaposed with findings in mild cognitive impairment and Alzheimer's disease where there is a shift to diminished connectivity as degeneration progresses. This summary of the functional imaging literature using fMRI methods reveals that hyperconnectivity is a common response to neurological disruption and that it may be differentially observable across brain regions. We discuss the factors contributing to both hyper- and hypoconnectivity results after neurological disruption and the implications these findings have for network plasticity. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Connectivity and functional profiling of abnormal brain structures in pedophilia.

PubMed

Poeppl, Timm B; Eickhoff, Simon B; Fox, Peter T; Laird, Angela R; Rupprecht, Rainer; Langguth, Berthold; Bzdok, Danilo

2015-06-01

Despite its 0.5-1% lifetime prevalence in men and its general societal relevance, neuroimaging investigations in pedophilia are scarce. Preliminary findings indicate abnormal brain structure and function. However, no study has yet linked structural alterations in pedophiles to both connectional and functional properties of the aberrant hotspots. The relationship between morphological alterations and brain function in pedophilia as well as their contribution to its psychopathology thus remain unclear. First, we assessed bimodal connectivity of structurally altered candidate regions using meta-analytic connectivity modeling (MACM) and resting-state correlations employing openly accessible data. We compared the ensuing connectivity maps to the activation likelihood estimation (ALE) maps of a recent quantitative meta-analysis of brain activity during processing of sexual stimuli. Second, we functionally characterized the structurally altered regions employing meta-data of a large-scale neuroimaging database. Candidate regions were functionally connected to key areas for processing of sexual stimuli. Moreover, we found that the functional role of structurally altered brain regions in pedophilia relates to nonsexual emotional as well as neurocognitive and executive functions, previously reported to be impaired in pedophiles. Our results suggest that structural brain alterations affect neural networks for sexual processing by way of disrupted functional connectivity, which may entail abnormal sexual arousal patterns. The findings moreover indicate that structural alterations account for common affective and neurocognitive impairments in pedophilia. The present multimodal integration of brain structure and function analyses links sexual and nonsexual psychopathology in pedophilia. © 2015 Wiley Periodicals, Inc.

Large-scale Scanning Transmission Electron Microscopy (Nanotomy) of Healthy and Injured Zebrafish Brain.

PubMed

Kuipers, Jeroen; Kalicharan, Ruby D; Wolters, Anouk H G; van Ham, Tjakko J; Giepmans, Ben N G

2016-05-25

Large-scale 2D electron microscopy (EM), or nanotomy, is the tissue-wide application of nanoscale resolution electron microscopy. Others and we previously applied large scale EM to human skin pancreatic islets, tissue culture and whole zebrafish larvae(1-7). Here we describe a universally applicable method for tissue-scale scanning EM for unbiased detection of sub-cellular and molecular features. Nanotomy was applied to investigate the healthy and a neurodegenerative zebrafish brain. Our method is based on standardized EM sample preparation protocols: Fixation with glutaraldehyde and osmium, followed by epoxy-resin embedding, ultrathin sectioning and mounting of ultrathin-sections on one-hole grids, followed by post staining with uranyl and lead. Large-scale 2D EM mosaic images are acquired using a scanning EM connected to an external large area scan generator using scanning transmission EM (STEM). Large scale EM images are typically ~ 5 - 50 G pixels in size, and best viewed using zoomable HTML files, which can be opened in any web browser, similar to online geographical HTML maps. This method can be applied to (human) tissue, cross sections of whole animals as well as tissue culture(1-5). Here, zebrafish brains were analyzed in a non-invasive neuronal ablation model. We visualize within a single dataset tissue, cellular and subcellular changes which can be quantified in various cell types including neurons and microglia, the brain's macrophages. In addition, nanotomy facilitates the correlation of EM with light microscopy (CLEM)(8) on the same tissue, as large surface areas previously imaged using fluorescent microscopy, can subsequently be subjected to large area EM, resulting in the nano-anatomy (nanotomy) of tissues. In all, nanotomy allows unbiased detection of features at EM level in a tissue-wide quantifiable manner.

Large-scale Scanning Transmission Electron Microscopy (Nanotomy) of Healthy and Injured Zebrafish Brain

PubMed Central

Kuipers, Jeroen; Kalicharan, Ruby D.; Wolters, Anouk H. G.

2016-01-01

Large-scale 2D electron microscopy (EM), or nanotomy, is the tissue-wide application of nanoscale resolution electron microscopy. Others and we previously applied large scale EM to human skin pancreatic islets, tissue culture and whole zebrafish larvae1-7. Here we describe a universally applicable method for tissue-scale scanning EM for unbiased detection of sub-cellular and molecular features. Nanotomy was applied to investigate the healthy and a neurodegenerative zebrafish brain. Our method is based on standardized EM sample preparation protocols: Fixation with glutaraldehyde and osmium, followed by epoxy-resin embedding, ultrathin sectioning and mounting of ultrathin-sections on one-hole grids, followed by post staining with uranyl and lead. Large-scale 2D EM mosaic images are acquired using a scanning EM connected to an external large area scan generator using scanning transmission EM (STEM). Large scale EM images are typically ~ 5 - 50 G pixels in size, and best viewed using zoomable HTML files, which can be opened in any web browser, similar to online geographical HTML maps. This method can be applied to (human) tissue, cross sections of whole animals as well as tissue culture1-5. Here, zebrafish brains were analyzed in a non-invasive neuronal ablation model. We visualize within a single dataset tissue, cellular and subcellular changes which can be quantified in various cell types including neurons and microglia, the brain's macrophages. In addition, nanotomy facilitates the correlation of EM with light microscopy (CLEM)8 on the same tissue, as large surface areas previously imaged using fluorescent microscopy, can subsequently be subjected to large area EM, resulting in the nano-anatomy (nanotomy) of tissues. In all, nanotomy allows unbiased detection of features at EM level in a tissue-wide quantifiable manner. PMID:27285162

Controllability of structural brain networks

NASA Astrophysics Data System (ADS)

Gu, Shi; Pasqualetti, Fabio; Cieslak, Matthew; Telesford, Qawi K.; Yu, Alfred B.; Kahn, Ari E.; Medaglia, John D.; Vettel, Jean M.; Miller, Michael B.; Grafton, Scott T.; Bassett, Danielle S.

2015-10-01

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.

Interplay between Functional Connectivity and Scale-Free Dynamics in Intrinsic fMRI Networks

PubMed Central

Ciuciu, Philippe; Abry, Patrice; He, Biyu J.

2014-01-01

Studies employing functional connectivity-type analyses have established that spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals are organized within large-scale brain networks. Meanwhile, fMRI signals have been shown to exhibit 1/f-type power spectra – a hallmark of scale-free dynamics. We studied the interplay between functional connectivity and scale-free dynamics in fMRI signals, utilizing the fractal connectivity framework – a multivariate extension of the univariate fractional Gaussian noise model, which relies on a wavelet formulation for robust parameter estimation. We applied this framework to fMRI data acquired from healthy young adults at rest and performing a visual detection task. First, we found that scale-invariance existed beyond univariate dynamics, being present also in bivariate cross-temporal dynamics. Second, we observed that frequencies within the scale-free range do not contribute evenly to inter-regional connectivity, with a systematically stronger contribution of the lowest frequencies, both at rest and during task. Third, in addition to a decrease of the Hurst exponent and inter-regional correlations, task performance modified cross-temporal dynamics, inducing a larger contribution of the highest frequencies within the scale-free range to global correlation. Lastly, we found that across individuals, a weaker task modulation of the frequency contribution to inter-regional connectivity was associated with better task performance manifesting as shorter and less variable reaction times. These findings bring together two related fields that have hitherto been studied separately – resting-state networks and scale-free dynamics, and show that scale-free dynamics of human brain activity manifest in cross-regional interactions as well. PMID:24675649

Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI

PubMed Central

Wang, Lubin; Su, Longfei; Shen, Hui; Hu, Dewen

2012-01-01

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8–79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' “brain ages” from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI. PMID:22952990

Decoding lifespan changes of the human brain using resting-state functional connectivity MRI.

PubMed

Wang, Lubin; Su, Longfei; Shen, Hui; Hu, Dewen

2012-01-01

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8-79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' "brain ages" from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.

The development of Human Functional Brain Networks

PubMed Central

Power, Jonathan D; Fair, Damien A; Schlaggar, Bradley L

2010-01-01

Recent advances in MRI technology have enabled precise measurements of correlated activity throughout the brain, leading to the first comprehensive descriptions of functional brain networks in humans. This article reviews the growing literature on the development of functional networks, from infancy through adolescence, as measured by resting state functional connectivity MRI. We note several limitations of traditional approaches to describing brain networks, and describe a powerful framework for analyzing networks, called graph theory. We argue that characterization of the development of brain systems (e.g. the default mode network) should be comprehensive, considering not only relationships within a given system, but also how these relationships are situated within wider network contexts. We note that, despite substantial reorganization of functional connectivity, several large-scale network properties appear to be preserved across development, suggesting that functional brain networks, even in children, are organized in manners similar to other complex systems. PMID:20826306

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics.

PubMed

He, Bin; Sohrabpour, Abbas; Brown, Emery; Liu, Zhongming

2018-06-04

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

Intrinsic Functional Connectivity Patterns Predict Consciousness Level and Recovery Outcome in Acquired Brain Injury

PubMed Central

Wu, Xuehai; Zou, Qihong; Hu, Jin; Tang, Weijun; Mao, Ying; Gao, Liang; Zhu, Jianhong; Jin, Yi; Wu, Xin; Lu, Lu; Zhang, Yaojun; Zhang, Yao; Dai, Zhengjia; Gao, Jia-Hong; Weng, Xuchu; Northoff, Georg; Giacino, Joseph T.; He, Yong

2015-01-01

For accurate diagnosis and prognostic prediction of acquired brain injury (ABI), it is crucial to understand the neurobiological mechanisms underlying loss of consciousness. However, there is no consensus on which regions and networks act as biomarkers for consciousness level and recovery outcome in ABI. Using resting-state fMRI, we assessed intrinsic functional connectivity strength (FCS) of whole-brain networks in a large sample of 99 ABI patients with varying degrees of consciousness loss (including fully preserved consciousness state, minimally conscious state, unresponsive wakefulness syndrome/vegetative state, and coma) and 34 healthy control subjects. Consciousness level was evaluated using the Glasgow Coma Scale and Coma Recovery Scale-Revised on the day of fMRI scanning; recovery outcome was assessed using the Glasgow Outcome Scale 3 months after the fMRI scanning. One-way ANOVA of FCS, Spearman correlation analyses between FCS and the consciousness level and recovery outcome, and FCS-based multivariate pattern analysis were performed. We found decreased FCS with loss of consciousness primarily distributed in the posterior cingulate cortex/precuneus (PCC/PCU), medial prefrontal cortex, and lateral parietal cortex. The FCS values of these regions were significantly correlated with consciousness level and recovery outcome. Multivariate support vector machine discrimination analysis revealed that the FCS patterns predicted whether patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%, and the most discriminative region was the PCC/PCU. These findings suggest that intrinsic functional connectivity patterns of the human posteromedial cortex could serve as a potential indicator for consciousness level and recovery outcome in individuals with ABI. SIGNIFICANCE STATEMENT Varying degrees of consciousness loss and recovery are commonly observed in acquired brain injury patients, yet the underlying neurobiological mechanisms remain elusive. Using a large sample of patients with varying degrees of consciousness loss, we demonstrate that intrinsic functional connectivity strength in many brain regions, especially in the posterior cingulate cortex and precuneus, significantly correlated with consciousness level and recovery outcome. We further demonstrate that the functional connectivity pattern of these regions can predict patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%. Our study thus provides potentially important biomarkers of acquired brain injury in clinical diagnosis, prediction of recovery outcome, and decision making for treatment strategies for patients with severe loss of consciousness. PMID:26377477

Validity of semi-quantitative scale for brain MRI in unilateral cerebral palsy due to periventricular white matter lesions: Relationship with hand sensorimotor function and structural connectivity.

PubMed

Fiori, Simona; Guzzetta, Andrea; Pannek, Kerstin; Ware, Robert S; Rossi, Giuseppe; Klingels, Katrijn; Feys, Hilde; Coulthard, Alan; Cioni, Giovanni; Rose, Stephen; Boyd, Roslyn N

2015-01-01

To provide first evidence of construct validity of a semi-quantitative scale for brain structural MRI (sqMRI scale) in children with unilateral cerebral palsy (UCP) secondary to periventricular white matter (PWM) lesions, by examining the relationship with hand sensorimotor function and whole brain structural connectivity. Cross-sectional study of 50 children with UCP due to PWM lesions using 3 T (MRI), diffusion MRI and assessment of hand sensorimotor function. We explored the relationship of lobar, hemispheric and global scores on the sqMRI scale, with fractional anisotropy (FA), as a measure of brain white matter microstructure, and with hand sensorimotor measures (Assisting Hand Assessment, AHA; Jebsen-Taylor Test for Hand Function, JTTHF; Melbourne Assessment of Unilateral Upper Limb Function, MUUL; stereognosis; 2-point discrimination). Lobar and hemispheric scores on the sqMRI scale contralateral to the clinical side of hemiplegia correlated with sensorimotor paretic hand function measures and FA of a number of brain structural connections, including connections of brain areas involved in motor control (postcentral, precentral and paracentral gyri in the parietal lobe). More severe lesions correlated with lower sensorimotor performance, with the posterior limb of internal capsule score being the strongest contributor to impaired hand function. The sqMRI scale demonstrates first evidence of construct validity against impaired motor and sensory function measures and brain structural connectivity in a cohort of children with UCP due to PWM lesions. More severe lesions correlated with poorer paretic hand sensorimotor function and impaired structural connectivity in the hemisphere contralateral to the clinical side of hemiplegia. The quantitative structural MRI scoring may be a useful clinical tool for studying brain structure-function relationships but requires further validation in other populations of CP.

DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks

PubMed Central

Zhu, Dajiang; Guo, Lei; Jiang, Xi; Zhang, Tuo; Zhang, Degang; Chen, Hanbo; Deng, Fan; Faraco, Carlos; Jin, Changfeng; Wee, Chong-Yaw; Yuan, Yixuan; Lv, Peili; Yin, Yan; Hu, Xiaolei; Duan, Lian; Hu, Xintao; Han, Junwei; Wang, Lihong; Shen, Dinggang; Miller, L Stephen

2013-01-01

Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex. Here, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We report a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity–based Cortical Landmarks (DICCCOLs). Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. Our results have shown that these 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes, as demonstrated in this work. PMID:22490548

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

PubMed

Guo, Hao; Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

PubMed Central

Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%. PMID:29387141

Neural Signatures of Autism Spectrum Disorders: Insights into Brain Network Dynamics

PubMed Central

Hernandez, Leanna M; Rudie, Jeffrey D; Green, Shulamite A; Bookheimer, Susan; Dapretto, Mirella

2015-01-01

Neuroimaging investigations of autism spectrum disorders (ASDs) have advanced our understanding of atypical brain function and structure, and have recently converged on a model of altered network-level connectivity. Traditional task-based functional magnetic resonance imaging (MRI) and volume-based structural MRI studies have identified widespread atypicalities in brain regions involved in social behavior and other core ASD-related behavioral deficits. More recent advances in MR-neuroimaging methods allow for quantification of brain connectivity using diffusion tensor imaging, functional connectivity, and graph theoretic methods. These newer techniques have moved the field toward a systems-level understanding of ASD etiology, integrating functional and structural measures across distal brain regions. Neuroimaging findings in ASD as a whole have been mixed and at times contradictory, likely due to the vast genetic and phenotypic heterogeneity characteristic of the disorder. Future longitudinal studies of brain development will be crucial to yield insights into mechanisms of disease etiology in ASD sub-populations. Advances in neuroimaging methods and large-scale collaborations will also allow for an integrated approach linking neuroimaging, genetics, and phenotypic data. PMID:25011468

Rest but busy: Aberrant resting-state functional connectivity of triple network model in insomnia.

PubMed

Dong, Xiaojuan; Qin, Haixia; Wu, Taoyu; Hu, Hua; Liao, Keren; Cheng, Fei; Gao, Dong; Lei, Xu

2018-02-01

One classical hypothesis among many models to explain the etiology and maintenance of insomnia disorder (ID) is hyperarousal. Aberrant functional connectivity among resting-state large-scale brain networks may be the underlying neurological mechanisms of this hypothesis. The aim of current study was to investigate the functional network connectivity (FNC) among large-scale brain networks in patients with insomnia disorder (ID) during resting state. In the present study, the resting-state fMRI was used to evaluate whether patients with ID showed aberrant FNC among dorsal attention network (DAN), frontoparietal control network (FPC), anterior default mode network (aDMN), and posterior default mode network (pDMN) compared with healthy good sleepers (HGSs). The Pearson's correlation analysis was employed to explore whether the abnormal FNC observed in patients with ID was associated with sleep parameters, cognitive and emotional scores, and behavioral performance assessed by questionnaires and tasks. Patients with ID had worse subjective thought control ability measured by Thought Control Ability Questionnaire (TCAQ) and more negative affect than HGSs. Intriguingly, relative to HGSs, patients with ID showed a significant increase in FNC between DAN and FPC, but a significant decrease in FNC between aDMN and pDMN. Exploratory analysis in patients with ID revealed a significantly positive correlation between the DAN-FPC FNC and reaction time (RT) of psychomotor vigilance task (PVT). The current study demonstrated that even during the resting state, the task-activated and task-deactivated large-scale brain networks in insomniacs may still maintain a hyperarousal state, looking quite similar to the pattern in a task condition with external stimuli. Those results support the hyperarousal model of insomnia.

Cortical parcellation based on structural connectivity: A case for generative models.

PubMed

Tittgemeyer, Marc; Rigoux, Lionel; Knösche, Thomas R

2018-06-01

One of the major challenges in systems neuroscience is to identify brain networks and unravel their significance for brain function -this has led to the concept of the 'connectome'. Connectomes are currently extensively studied in large-scale international efforts at multiple scales, and follow different definitions with respect to their connections as well as their elements. Perhaps the most promising avenue for defining the elements of connectomes originates from the notion that individual brain areas maintain distinct (long-range) connection profiles. These connectivity patterns determine the areas' functional properties and also allow for their anatomical delineation and mapping. This rationale has motivated the concept of connectivity-based cortex parcellation. In the past ten years, non-invasive mapping of human brain connectivity has led to immense advances in the development of parcellation techniques and their applications. Unfortunately, many of these approaches primarily aim for confirmation of well-known, existing architectonic maps and, to that end, unsuitably incorporate prior knowledge and frequently build on circular argumentation. Often, current approaches also tend to disregard the specific apertures of connectivity measurements, as well as the anatomical specificities of cortical areas, such as spatial compactness, regional heterogeneity, inter-subject variability, the multi-scaling nature of connectivity information, and potential hierarchical organisation. From a methodological perspective, however, a useful framework that regards all of these aspects in an unbiased way is technically demanding. In this commentary, we first outline the concept of connectivity-based cortex parcellation and discuss its prospects and limitations in particular with respect to structural connectivity. To improve reliability and efficiency, we then strongly advocate for connectivity-based cortex parcellation as a modelling approach; that is, an approximation of the data based on (model) parameter inference. As such, a parcellation algorithm can be formally tested for robustness -the precision of its predictions can be quantified and statistics about potential generalization of the results can be derived. Such a framework also allows the question of model constraints to be reformulated in terms of hypothesis testing through model selection and offers a formative way to integrate anatomical knowledge in terms of prior distributions. Copyright © 2018 Elsevier Inc. All rights reserved.

Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation.

PubMed

Tsvetanov, Kamen A; Henson, Richard N A; Tyler, Lorraine K; Razi, Adeel; Geerligs, Linda; Ham, Timothy E; Rowe, James B

2016-03-16

The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort (n = 602, 18-88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability. Copyright © 2016 Tsvetanov et al.

Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation

PubMed Central

Henson, Richard N.A.; Tyler, Lorraine K.; Razi, Adeel; Geerligs, Linda; Ham, Timothy E.; Rowe, James B.

2016-01-01

The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. SIGNIFICANCE STATEMENT Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort (n = 602, 18–88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability. PMID:26985024

Maternal IL-6 during pregnancy can be estimated from newborn brain connectivity and predicts future working memory in offspring.

PubMed

Rudolph, Marc D; Graham, Alice M; Feczko, Eric; Miranda-Dominguez, Oscar; Rasmussen, Jerod M; Nardos, Rahel; Entringer, Sonja; Wadhwa, Pathik D; Buss, Claudia; Fair, Damien A

2018-05-01

Several lines of evidence support the link between maternal inflammation during pregnancy and increased likelihood of neurodevelopmental and psychiatric disorders in offspring. This longitudinal study seeks to advance understanding regarding implications of systemic maternal inflammation during pregnancy, indexed by plasma interleukin-6 (IL-6) concentrations, for large-scale brain system development and emerging executive function skills in offspring. We assessed maternal IL-6 during pregnancy, functional magnetic resonance imaging acquired in neonates, and working memory (an important component of executive function) at 2 years of age. Functional connectivity within and between multiple neonatal brain networks can be modeled to estimate maternal IL-6 concentrations during pregnancy. Brain regions heavily weighted in these models overlap substantially with those supporting working memory in a large meta-analysis. Maternal IL-6 also directly accounts for a portion of the variance of working memory at 2 years of age. Findings highlight the association of maternal inflammation during pregnancy with the developing functional architecture of the brain and emerging executive function.

Abnormal rich club organization and functional brain dynamics in schizophrenia.

PubMed

van den Heuvel, Martijn P; Sporns, Olaf; Collin, Guusje; Scheewe, Thomas; Mandl, René C W; Cahn, Wiepke; Goñi, Joaquín; Hulshoff Pol, Hilleke E; Kahn, René S

2013-08-01

The human brain forms a large-scale structural network of regions and interregional pathways. Recent studies have reported the existence of a selective set of highly central and interconnected hub regions that may play a crucial role in the brain's integrative processes, together forming a central backbone for global brain communication. Abnormal brain connectivity may have a key role in the pathophysiology of schizophrenia. To examine the structure of the rich club in schizophrenia and its role in global functional brain dynamics. Structural diffusion tensor imaging and resting-state functional magnetic resonance imaging were performed in patients with schizophrenia and matched healthy controls. Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands. Forty-eight patients and 45 healthy controls participated in the study. An independent replication data set of 41 patients and 51 healthy controls was included to replicate and validate significant findings. MAIN OUTCOME(S) AND MEASURES: Measures of rich club organization, connectivity density of rich club connections and connections linking peripheral regions to brain hubs, measures of global brain network efficiency, and measures of coupling between brain structure and functional dynamics. Rich club organization between high-degree hub nodes was significantly affected in patients, together with a reduced density of rich club connections predominantly comprising the white matter pathways that link the midline frontal, parietal, and insular hub regions. This reduction in rich club density was found to be associated with lower levels of global communication capacity, a relationship that was absent for other white matter pathways. In addition, patients had an increase in the strength of structural connectivity-functional connectivity coupling. Our findings provide novel biological evidence that schizophrenia is characterized by a selective disruption of brain connectivity among central hub regions of the brain, potentially leading to reduced communication capacity and altered functional brain dynamics.

The Impact of Serotonin Transporter (5-HTTLPR) Genotype on the Development of Resting-State Functional Connectivity in Children and Adolescents: A Preliminary Report

PubMed Central

Wiggins, Jillian Lee; Bedoyan, Jirair K.; Peltier, Scott J.; Ashinoff, Samantha; Carrasco, Melisa; Weng, Shih-Jen; Welsh, Robert C.; Martin, Donna M.; Monk, Christopher S.

2011-01-01

A fundamental component of brain development is the formation of large-scale networks across the cortex. One such network, the default network, undergoes a protracted development, displaying weak connectivity in childhood that strengthens in adolescence and becomes most robust in adulthood. Little is known about the genetic contributions to default network connectivity in adulthood or during development. Alterations in connectivity between posterior and frontal portions of the default network have been associated with several psychological disorders, including anxiety, autism spectrum disorders, schizophrenia, depression, and attention-deficit/hyperactivity disorder. These disorders have also been linked to variants of the serotonin transporter linked polymorphic region (5-HTTLPR). The LA allele of 5-HTTLPR results in higher serotonin transporter expression than the S allele or the rarer LG allele. 5-HTTLPR may influence default network connectivity, as the superior medial frontal region has been shown to be sensitive to changes in serotonin. Also, serotonin as a growth factor early in development may alter large-scale networks such as the default network. The present study examined the influence of 5-HTTLPR variants on connectivity between the posterior and frontal structures and its development in a cross-sectional study of 39 healthy children and adolescents. We found that children and adolescents homozygous for the S allele (S/S, n = 10) showed weaker connectivity in the superior medial frontal cortex compared to those homozygous for the LA allele (LA/LA, n = 13) or heterozygotes (S/LA, S/LG, n = 16). Moreover, there was an age-by-genotype interaction, such that those with LA/LA genotype had the steepest age-related increase in connectivity between the posterior hub and superior medial frontal cortex, followed by heterozygotes. In contrast, individuals with the S/S genotype had the least age-related increase in connectivity strength. This preliminary report expands our understanding of the genetic influences on the development of large-scale brain connectivity and lays down the foundation for future research and replication of the results with a larger sample. PMID:22032950

Consciousness, cognition and brain networks: New perspectives.

PubMed

Aldana, E M; Valverde, J L; Fábregas, N

2016-10-01

A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks. Copyright © 2014 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Publicado por Elsevier España, S.L.U. All rights reserved.

Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

PubMed Central

Krienen, Fenna M.; Yeo, B. T. Thomas; Ge, Tian; Buckner, Randy L.; Sherwood, Chet C.

2016-01-01

The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute’s human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections. PMID:26739559

Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain.

PubMed

Krienen, Fenna M; Yeo, B T Thomas; Ge, Tian; Buckner, Randy L; Sherwood, Chet C

2016-01-26

The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute's human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.

Regional homogeneity of the resting-state brain activity correlates with individual intelligence.

PubMed

Wang, Leiqiong; Song, Ming; Jiang, Tianzi; Zhang, Yunting; Yu, Chunshui

2011-01-25

Resting-state functional magnetic resonance imaging has confirmed that the strengths of the long distance functional connectivity between different brain areas are correlated with individual differences in intelligence. However, the association between the local connectivity within a specific brain region and intelligence during rest remains largely unknown. The aim of this study is to investigate the relationship between local connectivity and intelligence. Fifty-nine right-handed healthy adults participated in the study. The regional homogeneity (ReHo) was used to assess the strength of local connectivity. The associations between ReHo and full-scale intelligence quotient (FSIQ) scores were studied in a voxel-wise manner using partial correlation analysis controlling for age and sex. We found that the FSIQ scores were positively correlated with the ReHo values of the bilateral inferior parietal lobules, middle frontal, parahippocampal and inferior temporal gyri, the right thalamus, superior frontal and fusiform gyri, and the left superior parietal lobule. The main findings are consistent with the parieto-frontal integration theory (P-FIT) of intelligence, supporting the view that general intelligence involves multiple brain regions throughout the brain. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Efficiency at rest: magnetoencephalographic resting-state connectivity and individual differences in verbal working memory.

PubMed

del Río, David; Cuesta, Pablo; Bajo, Ricardo; García-Pacios, Javier; López-Higes, Ramón; del-Pozo, Francisco; Maestú, Fernando

2012-11-01

Inter-individual differences in cognitive performance are based on an efficient use of task-related brain resources. However, little is known yet on how these differences might be reflected on resting-state brain networks. Here we used Magnetoencephalography resting-state recordings to assess the relationship between a behavioral measurement of verbal working memory and functional connectivity as measured through Mutual Information. We studied theta (4-8 Hz), low alpha (8-10 Hz), high alpha (10-13 Hz), low beta (13-18 Hz) and high beta (18-30 Hz) frequency bands. A higher verbal working memory capacity was associated with a lower mutual information in the low alpha band, prominently among right-anterior and left-lateral sensors. The results suggest that an efficient brain organization in the domain of verbal working memory might be related to a lower resting-state functional connectivity across large-scale brain networks possibly involving right prefrontal and left perisylvian areas. Copyright © 2012 Elsevier B.V. All rights reserved.

Human connectome module pattern detection using a new multi-graph MinMax cut model.

PubMed

De, Wang; Wang, Yang; Nie, Feiping; Yan, Jingwen; Cai, Weidong; Saykin, Andrew J; Shen, Li; Huang, Heng

2014-01-01

Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.

Childhood poverty and stress reactivity are associated with aberrant functional connectivity in default mode network.

PubMed

Sripada, Rebecca K; Swain, James E; Evans, Gary W; Welsh, Robert C; Liberzon, Israel

2014-08-01

Convergent research suggests that childhood poverty is associated with perturbation in the stress response system. This might extend to aberrations in the connectivity of large-scale brain networks, which subserve key cognitive and emotional functions. Resting-state brain activity was measured in adults with a documented history of childhood poverty (n=26) and matched controls from middle-income families (n=26). Participants also underwent a standard laboratory social stress test and provided saliva samples for cortisol assay. Childhood poverty was associated with reduced default mode network (DMN) connectivity. This, in turn, was associated with higher cortisol levels in anticipation of social stress. These results suggest a possible brain basis for exaggerated stress sensitivity in low-income individuals. Alterations in DMN may be associated with less efficient cognitive processing or greater risk for development of stress-related psychopathology among individuals who experienced the adversity of chronic childhood poverty.

Biocytin-Derived MRI Contrast Agent for Longitudinal Brain Connectivity Studies

PubMed Central

2011-01-01

To investigate the connectivity of brain networks noninvasively and dynamically, we have developed a new strategy to functionalize neuronal tracers and designed a biocompatible probe that can be visualized in vivo using magnetic resonance imaging (MRI). Furthermore, the multimodal design used allows combined ex vivo studies with microscopic spatial resolution by conventional histochemical techniques. We present data on the functionalization of biocytin, a well-known neuronal tract tracer, and demonstrate the validity of the approach by showing brain networks of cortical connectivity in live rats under MRI, together with the corresponding microscopic details, such as fibers and neuronal morphology under light microscopy. We further demonstrate that the developed molecule is the first MRI-visible probe to preferentially trace retrograde connections. Our study offers a new platform for the development of multimodal molecular imaging tools of broad interest in neuroscience, that capture in vivo the dynamics of large scale neural networks together with their microscopic characteristics, thereby spanning several organizational levels. PMID:22860157

Dual-labeling method for electron microscopy to characterize synaptic connectivity using genetically encoded fluorescent reporters in Drosophila

PubMed Central

Tanaka, Nobuaki K.; Dye, Louis; Stopfer, Mark

2010-01-01

Light and electron microscopy (LM and EM) both offer important advantages for characterizing neuronal circuitry in intact brains: LM can reveal the general patterns neurons trace between brain areas, and EM can confirm synaptic connections between identified neurons within a small area. In a few species, genetic labeling with fluorescent proteins has been used with LM to visualize many kinds of neurons and to analyze their morphologies and projection patterns. However, combining these large-scale patterns with the fine detail available in EM analysis has been a technical challenge. To analyze the synaptic connectivity of neurons expressing fluorescent markers with EM, we developed a dual-labeling method for use with pre-embedded brains. In Drosophila expressing genetic labels and also injected with markers we visualized synaptic connections among two populations of neurons in the AL, one of which has been shown to mediate a specific function, odor evoked neural oscillation. PMID:21074556

Large-scale functional networks connect differently for processing words and symbol strings.

PubMed

Liljeström, Mia; Vartiainen, Johanna; Kujala, Jan; Salmelin, Riitta

2018-01-01

Reconfigurations of synchronized large-scale networks are thought to be central neural mechanisms that support cognition and behavior in the human brain. Magnetoencephalography (MEG) recordings together with recent advances in network analysis now allow for sub-second snapshots of such networks. In the present study, we compared frequency-resolved functional connectivity patterns underlying reading of single words and visual recognition of symbol strings. Word reading emphasized coherence in a left-lateralized network with nodes in classical perisylvian language regions, whereas symbol processing recruited a bilateral network, including connections between frontal and parietal regions previously associated with spatial attention and visual working memory. Our results illustrate the flexible nature of functional networks, whereby processing of different form categories, written words vs. symbol strings, leads to the formation of large-scale functional networks that operate at distinct oscillatory frequencies and incorporate task-relevant regions. These results suggest that category-specific processing should be viewed not so much as a local process but as a distributed neural process implemented in signature networks. For words, increased coherence was detected particularly in the alpha (8-13 Hz) and high gamma (60-90 Hz) frequency bands, whereas increased coherence for symbol strings was observed in the high beta (21-29 Hz) and low gamma (30-45 Hz) frequency range. These findings attest to the role of coherence in specific frequency bands as a general mechanism for integrating stimulus-dependent information across brain regions.

Dual Anterograde and Retrograde Viral Tracing of Reciprocal Connectivity.

PubMed

Haberl, Matthias G; Ginger, Melanie; Frick, Andreas

2017-01-01

Current large-scale approaches in neuroscience aim to unravel the complete connectivity map of specific neuronal circuits, or even the entire brain. This emerging research discipline has been termed connectomics. Recombinant glycoprotein-deleted rabies virus (RABV ∆G) has become an important tool for the investigation of neuronal connectivity in the brains of a variety of species. Neuronal infection with even a single RABV ∆G particle results in high-level transgene expression, revealing the fine-detailed morphology of all neuronal features-including dendritic spines, axonal processes, and boutons-on a brain-wide scale. This labeling is eminently suitable for subsequent post-hoc morphological analysis, such as semiautomated reconstruction in 3D. Here we describe the use of a recently developed anterograde RABV ∆G variant together with a retrograde RABV ∆G for the investigation of projections both to, and from, a particular brain region. In addition to the automated reconstruction of a dendritic tree, we also give as an example the volume measurements of axonal boutons following RABV ∆G-mediated fluorescent marker expression. In conclusion RABV ∆G variants expressing a combination of markers and/or tools for stimulating/monitoring neuronal activity, used together with genetic or behavioral animal models, promise important insights in the structure-function relationship of neural circuits.

Multivariate Heteroscedasticity Models for Functional Brain Connectivity.

PubMed

Seiler, Christof; Holmes, Susan

2017-01-01

Functional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI). We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP) comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.

Network-dependent modulation of brain activity during sleep.

PubMed

Watanabe, Takamitsu; Kan, Shigeyuki; Koike, Takahiko; Misaki, Masaya; Konishi, Seiki; Miyauchi, Satoru; Miyahsita, Yasushi; Masuda, Naoki

2014-09-01

Brain activity dynamically changes even during sleep. A line of neuroimaging studies has reported changes in functional connectivity and regional activity across different sleep stages such as slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. However, it remains unclear whether and how the large-scale network activity of human brains changes within a given sleep stage. Here, we investigated modulation of network activity within sleep stages by applying the pairwise maximum entropy model to brain activity obtained by functional magnetic resonance imaging from sleeping healthy subjects. We found that the brain activity of individual brain regions and functional interactions between pairs of regions significantly increased in the default-mode network during SWS and decreased during REM sleep. In contrast, the network activity of the fronto-parietal and sensory-motor networks showed the opposite pattern. Furthermore, in the three networks, the amount of the activity changes throughout REM sleep was negatively correlated with that throughout SWS. The present findings suggest that the brain activity is dynamically modulated even in a sleep stage and that the pattern of modulation depends on the type of the large-scale brain networks. Copyright © 2014 Elsevier Inc. All rights reserved.

Changes of intranetwork and internetwork functional connectivity in Alzheimer’s disease and mild cognitive impairment

NASA Astrophysics Data System (ADS)

Zhu, Haoze; Zhou, Peng; Alcauter, Sarael; Chen, Yuanyuan; Cao, Hongbao; Tian, Miao; Ming, Dong; Qi, Hongzhi; Wang, Xuemin; Zhao, Xin; He, Feng; Ni, Hongyan; Gao, Wei

2016-08-01

Objective. Alzheimer’s disease (AD) is a serious neurodegenerative disorder characterized by deficits of working memory, attention, language and many other cognitive functions. Although different stages of the disease are relatively well characterized by clinical criteria, stage-specific pathological changes in the brain remain relatively poorly understood, especially at the level of large-scale functional networks. In this study, we aimed to characterize the potential disruptions of large-scale functional brain networks based on a sample including amnestic mild cognition impairment (aMCI) and AD patients to help delineate the underlying stage-dependent AD pathology. Approach. We sought to identify the neural connectivity mechanisms of aMCI and AD through examination of both intranetwork and internetwork interactions among four of the brain’s key networks, namely dorsal attention network (DAN), default mode network (DMN), executive control network (ECN) and salience network (SAL). We analyzed functional connectivity based on resting-state functional magnetic resonance imaging (rs-fMRI) data from 25 Alzheimer’s disease patients, 20 aMCI patients and 35 elderly normal controls (NC). Main results. Intranetwork functional disruptions within the DAN and ECN were detected in both aMCI and AD patients. Disrupted intranetwork connectivity of DMN and anti-correlation between DAN and DMN were observed in AD patients. Moreover, aMCI-specific alterations in the internetwork functional connectivity of SAL were observed. Significance. Our results confirmed previous findings that AD pathology was related to dysconnectivity both within and between resting-state networks but revealed more spatial details. Moreover, the SAL network, reportedly flexibly coupling either with the DAN or DMN networks during different brain states, demonstrated interesting alterations specifically in the early stage of the disease.

Communication efficiency and congestion of signal traffic in large-scale brain networks.

PubMed

Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R

2014-01-01

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a "rich club" of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.

Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks

PubMed Central

Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R.

2014-01-01

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a “rich club” of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication. PMID:24415931

Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy.

PubMed

Wirsich, Jonathan; Perry, Alistair; Ridley, Ben; Proix, Timothée; Golos, Mathieu; Bénar, Christian; Ranjeva, Jean-Philippe; Bartolomei, Fabrice; Breakspear, Michael; Jirsa, Viktor; Guye, Maxime

2016-01-01

The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

Massive cortical reorganization in sighted Braille readers.

PubMed

Siuda-Krzywicka, Katarzyna; Bola, Łukasz; Paplińska, Małgorzata; Sumera, Ewa; Jednoróg, Katarzyna; Marchewka, Artur; Śliwińska, Magdalena W; Amedi, Amir; Szwed, Marcin

2016-03-15

The brain is capable of large-scale reorganization in blindness or after massive injury. Such reorganization crosses the division into separate sensory cortices (visual, somatosensory...). As its result, the visual cortex of the blind becomes active during tactile Braille reading. Although the possibility of such reorganization in the normal, adult brain has been raised, definitive evidence has been lacking. Here, we demonstrate such extensive reorganization in normal, sighted adults who learned Braille while their brain activity was investigated with fMRI and transcranial magnetic stimulation (TMS). Subjects showed enhanced activity for tactile reading in the visual cortex, including the visual word form area (VWFA) that was modulated by their Braille reading speed and strengthened resting-state connectivity between visual and somatosensory cortices. Moreover, TMS disruption of VWFA activity decreased their tactile reading accuracy. Our results indicate that large-scale reorganization is a viable mechanism recruited when learning complex skills.

Large-scale imaging in small brains

PubMed Central

Ahrens, Misha B.; Engert, Florian

2016-01-01

The dense connectivity in the brain and arrangements of cells into circuits means that one neuron’s activity can influence many others. To observe this interconnected system comprehensively, an aspiration within neuroscience is to record from as many neurons as possible at the same time. There are two useful routes toward this goal: one is to expand the spatial extent of functional imaging techniques, and the second is to use animals with small brains. Here we review recent progress toward imaging many neurons and complete populations of identified neurons in small vertebrates and invertebrates. PMID:25636154

Large-scale imaging in small brains.

PubMed

Ahrens, Misha B; Engert, Florian

2015-06-01

The dense connectivity in the brain means that one neuron's activity can influence many others. To observe this interconnected system comprehensively, an aspiration within neuroscience is to record from as many neurons as possible at the same time. There are two useful routes toward this goal: one is to expand the spatial extent of functional imaging techniques, and the second is to use animals with small brains. Here we review recent progress toward imaging many neurons and complete populations of identified neurons in small vertebrates and invertebrates. Copyright © 2015 Elsevier Ltd. All rights reserved.

Validity of semi-quantitative scale for brain MRI in unilateral cerebral palsy due to periventricular white matter lesions: Relationship with hand sensorimotor function and structural connectivity

PubMed Central

Fiori, Simona; Guzzetta, Andrea; Pannek, Kerstin; Ware, Robert S.; Rossi, Giuseppe; Klingels, Katrijn; Feys, Hilde; Coulthard, Alan; Cioni, Giovanni; Rose, Stephen; Boyd, Roslyn N.

2015-01-01

Aim To provide first evidence of construct validity of a semi-quantitative scale for brain structural MRI (sqMRI scale) in children with unilateral cerebral palsy (UCP) secondary to periventricular white matter (PWM) lesions, by examining the relationship with hand sensorimotor function and whole brain structural connectivity. Methods Cross-sectional study of 50 children with UCP due to PWM lesions using 3 T (MRI), diffusion MRI and assessment of hand sensorimotor function. We explored the relationship of lobar, hemispheric and global scores on the sqMRI scale, with fractional anisotropy (FA), as a measure of brain white matter microstructure, and with hand sensorimotor measures (Assisting Hand Assessment, AHA; Jebsen–Taylor Test for Hand Function, JTTHF; Melbourne Assessment of Unilateral Upper Limb Function, MUUL; stereognosis; 2-point discrimination). Results Lobar and hemispheric scores on the sqMRI scale contralateral to the clinical side of hemiplegia correlated with sensorimotor paretic hand function measures and FA of a number of brain structural connections, including connections of brain areas involved in motor control (postcentral, precentral and paracentral gyri in the parietal lobe). More severe lesions correlated with lower sensorimotor performance, with the posterior limb of internal capsule score being the strongest contributor to impaired hand function. Conclusion The sqMRI scale demonstrates first evidence of construct validity against impaired motor and sensory function measures and brain structural connectivity in a cohort of children with UCP due to PWM lesions. More severe lesions correlated with poorer paretic hand sensorimotor function and impaired structural connectivity in the hemisphere contralateral to the clinical side of hemiplegia. The quantitative structural MRI scoring may be a useful clinical tool for studying brain structure–function relationships but requires further validation in other populations of CP. PMID:26106533

Resting-State Network Topology Differentiates Task Signals across the Adult Life Span.

PubMed

Chan, Micaela Y; Alhazmi, Fahd H; Park, Denise C; Savalia, Neil K; Wig, Gagan S

2017-03-08

Brain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) subnetworks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20-89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system ("non-connector" nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems ("connector" nodes). This "activation selectivity" was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the "dedifferentiation" in brain activity observed in aging. SIGNIFICANCE STATEMENT Similar to other real-world networks, the organization of brain networks impacts their function. As brain network connectivity patterns differ across individuals, we hypothesized that individual differences in network connectivity would relate to differences in brain activity. Using functional MRI in a group of individuals sampled across the adult life span (20-89 years), we measured correlations at rest and related the functional connectivity patterns to measurements of functional activity during two independent tasks. Brain activity varied in relation to connectivity patterns revealed by large-scale network analysis. This relationship tracked the differences in connectivity patterns accompanied by older age, providing important evidence for a link between the topology of areal connectivity measured at rest and the functional recruitment of these areas during task performance. Copyright © 2017 Chan et al.

Cerebral energy metabolism and the brain's functional network architecture: an integrative review.

PubMed

Lord, Louis-David; Expert, Paul; Huckins, Jeremy F; Turkheimer, Federico E

2013-09-01

Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain's 'functional connectivity' is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks.

Functional organization of intrinsic connectivity networks in Chinese-chess experts.

PubMed

Duan, Xujun; Long, Zhiliang; Chen, Huafu; Liang, Dongmei; Qiu, Lihua; Huang, Xiaoqi; Liu, Timon Cheng-Yi; Gong, Qiyong

2014-04-16

The functional architecture of the human brain has been extensively described in terms of functional connectivity networks, detected from the low-frequency coherent neuronal fluctuations during a resting state condition. Accumulating evidence suggests that the overall organization of functional connectivity networks is associated with individual differences in cognitive performance and prior experience. Such an association raises the question of how cognitive expertise exerts an influence on the topological properties of large-scale functional networks. To address this question, we examined the overall organization of brain functional networks in 20 grandmaster and master level Chinese-chess players (GM/M) and twenty novice players, by means of resting-state functional connectivity and graph theoretical analyses. We found that, relative to novices, functional connectivity was increased in GM/Ms between basal ganglia, thalamus, hippocampus, and several parietal and temporal areas, suggesting the influence of cognitive expertise on intrinsic connectivity networks associated with learning and memory. Furthermore, we observed economical small-world topology in the whole-brain functional connectivity networks in both groups, but GM/Ms exhibited significantly increased values of normalized clustering coefficient which resulted in increased small-world topology. These findings suggest an association between the functional organization of brain networks and individual differences in cognitive expertise, which might provide further evidence of the mechanisms underlying expert behavior. Copyright © 2014 Elsevier B.V. All rights reserved.

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities

PubMed Central

Santangelo, Valerio

2018-01-01

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior. PMID:29535614

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities.

PubMed

Santangelo, Valerio

2018-01-01

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior.

A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks

PubMed Central

Sotiropoulos, Stamatios N.; Brookes, Matthew J.; Woolrich, Mark W.

2018-01-01

Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP. PMID:29474352

Intrinsic brain connectivity predicts impulse control disorders in patients with Parkinson's disease.

PubMed

Tessitore, Alessandro; De Micco, Rosa; Giordano, Alfonso; di Nardo, Federica; Caiazzo, Giuseppina; Siciliano, Mattia; De Stefano, Manuela; Russo, Antonio; Esposito, Fabrizio; Tedeschi, Gioacchino

2017-12-01

Impulse control disorders can be triggered by dopamine replacement therapies in patients with PD. Using resting-state functional MRI, we investigated the intrinsic brain network connectivity at baseline in a cohort of drug-naive PD patients who successively developed impulse control disorders over a 36-month follow-up period compared with patients who did not. Baseline 3-Tesla MRI images of 30 drug-naive PD patients and 20 matched healthy controls were analyzed. The impulse control disorders' presence and severity at follow-up were assessed by the Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease Rating Scale. Single-subject and group-level independent component analysis was used to investigate functional connectivity differences within the major resting-state networks. We also compared internetwork connectivity between patients. Finally, a multivariate Cox regression model was used to investigate baseline predictors of impulse control disorder development. At baseline, decreased connectivity in the default-mode and right central executive networks and increased connectivity in the salience network were detected in PD patients with impulse control disorders at follow-up compared with those without. Increased default-mode/central executive internetwork connectivity was significantly associated with impulse control disorders development (P < 0.05). Our findings demonstrated that abnormal brain connectivity in the three large-scale networks characterizes drug-naive PD patients who will eventually develop impulse control disorders while on dopaminergic treatment. We hypothesize that these divergent cognitive and limbic network connectivity changes could represent a potential biomarker and an additional risk factor for the emergence of impulse control disorders. © 2017 International Parkinson and Movement Disorder Society. © 2017 International Parkinson and Movement Disorder Society.

Altered Resting State Effective Connectivity of Anterior Insula in Depression.

PubMed

Kandilarova, Sevdalina; Stoyanov, Drozdstoy; Kostianev, Stefan; Specht, Karsten

2018-01-01

Depression has been associated with changes in both functional and effective connectivity of large scale brain networks, including the default mode network, executive network, and salience network. However, studies of effective connectivity by means of spectral dynamic causal modeling (spDCM) are still rare and the interaction between the different resting state networks has not been investigated in detail. Thus, we aimed at exploring differences in effective connectivity among eight right hemisphere brain areas-anterior insula, inferior frontal gyrus, middle frontal gyrus (MFG), frontal eye field, anterior cingulate cortex, superior parietal lobe, amygdala, and hippocampus, between a group of healthy controls ( N  = 20) and medicated depressed patients ( N  = 20). We found that patients not only had significantly reduced strength of the connection from the anterior insula to the MFG (i.e., dorsolateral prefrontal cortex) but also a significant connection between the amygdala and the anterior insula. Moreover, depression severity correlated with connectivity of the hippocampal node. In conclusion, the results from this resting state spDCM study support and enrich previous data on the role of the right anterior insula in the pathophysiology of depression. Furthermore, our findings add to the growing evidence of an association between depression severity and disturbances of the hippocampal function in terms of impaired connectivity with other brain regions.

Altered Resting State Effective Connectivity of Anterior Insula in Depression

PubMed Central

Kandilarova, Sevdalina; Stoyanov, Drozdstoy; Kostianev, Stefan; Specht, Karsten

2018-01-01

Depression has been associated with changes in both functional and effective connectivity of large scale brain networks, including the default mode network, executive network, and salience network. However, studies of effective connectivity by means of spectral dynamic causal modeling (spDCM) are still rare and the interaction between the different resting state networks has not been investigated in detail. Thus, we aimed at exploring differences in effective connectivity among eight right hemisphere brain areas—anterior insula, inferior frontal gyrus, middle frontal gyrus (MFG), frontal eye field, anterior cingulate cortex, superior parietal lobe, amygdala, and hippocampus, between a group of healthy controls (N = 20) and medicated depressed patients (N = 20). We found that patients not only had significantly reduced strength of the connection from the anterior insula to the MFG (i.e., dorsolateral prefrontal cortex) but also a significant connection between the amygdala and the anterior insula. Moreover, depression severity correlated with connectivity of the hippocampal node. In conclusion, the results from this resting state spDCM study support and enrich previous data on the role of the right anterior insula in the pathophysiology of depression. Furthermore, our findings add to the growing evidence of an association between depression severity and disturbances of the hippocampal function in terms of impaired connectivity with other brain regions. PMID:29599728

Episodic memory in aspects of large-scale brain networks

PubMed Central

Jeong, Woorim; Chung, Chun Kee; Kim, June Sic

2015-01-01

Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL) structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network (DMN). Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network (RSN). Altered patterns of functional connectivity (FC) among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment. PMID:26321939

Spatial embedding of structural similarity in the cerebral cortex

PubMed Central

Song, H. Francis; Kennedy, Henry; Wang, Xiao-Jing

2014-01-01

Recent anatomical tracing studies have yielded substantial amounts of data on the areal connectivity underlying distributed processing in cortex, yet the fundamental principles that govern the large-scale organization of cortex remain unknown. Here we show that functional similarity between areas as defined by the pattern of shared inputs or outputs is a key to understanding the areal network of cortex. In particular, we report a systematic relation in the monkey, human, and mouse cortex between the occurrence of connections from one area to another and their similarity distance. This characteristic relation is rooted in the wiring distance dependence of connections in the brain. We introduce a weighted, spatially embedded random network model that robustly gives rise to this structure, as well as many other spatial and topological properties observed in cortex. These include features that were not accounted for in any previous model, such as the wide range of interareal connection weights. Connections in the model emerge from an underlying distribution of spatially embedded axons, thereby integrating the two scales of cortical connectivity—individual axons and interareal pathways—into a common geometric framework. These results provide insights into the origin of large-scale connectivity in cortex and have important implications for theories of cortical organization. PMID:25368200

Treatment effect of methylphenidate on intrinsic functional brain network in medication-naïve ADHD children: A multivariate analysis.

PubMed

Yoo, Jae Hyun; Kim, Dohyun; Choi, Jeewook; Jeong, Bumseok

2018-04-01

Methylphenidate is a first-line therapeutic option for treating attention-deficit/hyperactivity disorder (ADHD); however, elicited changes on resting-state functional networks (RSFNs) are not well understood. This study investigated the treatment effect of methylphenidate using a variety of RSFN analyses and explored the collaborative influences of treatment-relevant RSFN changes in children with ADHD. Resting-state functional magnetic resonance imaging was acquired from 20 medication-naïve ADHD children before methylphenidate treatment and twelve weeks later. Changes in large-scale functional connectivity were defined using independent component analysis with dual regression and graph theoretical analysis. The amplitude of low frequency fluctuation (ALFF) was measured to investigate local spontaneous activity alteration. Finally, significant findings were recruited to random forest regression to identify the feature subset that best explains symptom improvement. After twelve weeks of methylphenidate administration, large-scale connectivity was increased between the left fronto-parietal RSFN and the left insula cortex and the right fronto-parietal and the brainstem, while the clustering coefficient (CC) of the global network and nodes, the left fronto-parietal, cerebellum, and occipital pole-visual network, were decreased. ALFF was increased in the bilateral superior parietal cortex and decreased in the right inferior fronto-temporal area. The subset of the local and large-scale RSFN changes, including widespread ALFF changes, the CC of the global network and the cerebellum, could explain the 27.1% variance of the ADHD Rating Scale and 13.72% of the Conner's Parent Rating Scale. Our multivariate approach suggests that the neural mechanism of methylphenidate treatment could be associated with alteration of spontaneous activity in the superior parietal cortex or widespread brain regions as well as functional segregation of the large-scale intrinsic functional network.

Estimating individual contribution from group-based structural correlation networks.

PubMed

Saggar, Manish; Hosseini, S M Hadi; Bruno, Jennifer L; Quintin, Eve-Marie; Raman, Mira M; Kesler, Shelli R; Reiss, Allan L

2015-10-15

Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Functional connectivity with the retrosplenial cortex predicts cognitive aging in rats.

PubMed

Ash, Jessica A; Lu, Hanbing; Taxier, Lisa R; Long, Jeffrey M; Yang, Yihong; Stein, Elliot A; Rapp, Peter R

2016-10-25

Changes in the functional connectivity (FC) of large-scale brain networks are a prominent feature of brain aging, but defining their relationship to variability along the continuum of normal and pathological cognitive outcomes has proved challenging. Here we took advantage of a well-characterized rat model that displays substantial individual differences in hippocampal memory during aging, uncontaminated by slowly progressive, spontaneous neurodegenerative disease. By this approach, we aimed to interrogate the underlying neural network substrates that mediate aging as a uniquely permissive condition and the primary risk for neurodegeneration. Using resting state (rs) blood oxygenation level-dependent fMRI and a restrosplenial/posterior cingulate cortex seed, aged rats demonstrated a large-scale network that had a spatial distribution similar to the default mode network (DMN) in humans, consistent with earlier findings in younger animals. Between-group whole brain contrasts revealed that aged subjects with documented deficits in memory (aged impaired) displayed widespread reductions in cortical FC, prominently including many areas outside the DMN, relative to both young adults (Y) and aged rats with preserved memory (aged unimpaired, AU). Whereas functional connectivity was relatively preserved in AU rats, they exhibited a qualitatively distinct network signature, comprising the loss of an anticorrelated network observed in Y adults. Together the findings demonstrate that changes in rs-FC are specifically coupled to variability in the cognitive outcome of aging, and that successful neurocognitive aging is associated with adaptive remodeling, not simply the persistence of youthful network dynamics.

Functional Connectivity in Multiple Cortical Networks Is Associated with Performance Across Cognitive Domains in Older Adults.

PubMed

Shaw, Emily E; Schultz, Aaron P; Sperling, Reisa A; Hedden, Trey

2015-10-01

Intrinsic functional connectivity MRI has become a widely used tool for measuring integrity in large-scale cortical networks. This study examined multiple cortical networks using Template-Based Rotation (TBR), a method that applies a priori network and nuisance component templates defined from an independent dataset to test datasets of interest. A priori templates were applied to a test dataset of 276 older adults (ages 65-90) from the Harvard Aging Brain Study to examine the relationship between multiple large-scale cortical networks and cognition. Factor scores derived from neuropsychological tests represented processing speed, executive function, and episodic memory. Resting-state BOLD data were acquired in two 6-min acquisitions on a 3-Tesla scanner and processed with TBR to extract individual-level metrics of network connectivity in multiple cortical networks. All results controlled for data quality metrics, including motion. Connectivity in multiple large-scale cortical networks was positively related to all cognitive domains, with a composite measure of general connectivity positively associated with general cognitive performance. Controlling for the correlations between networks, the frontoparietal control network (FPCN) and executive function demonstrated the only significant association, suggesting specificity in this relationship. Further analyses found that the FPCN mediated the relationships of the other networks with cognition, suggesting that this network may play a central role in understanding individual variation in cognition during aging.

Functional Brain Connectivity as a New Feature for P300 Speller.

PubMed

Kabbara, Aya; Khalil, Mohamad; El-Falou, Wassim; Eid, Hassan; Hassan, Mahmoud

2016-01-01

The brain is a large-scale complex network often referred to as the "connectome". Cognitive functions and information processing are mainly based on the interactions between distant brain regions. However, most of the 'feature extraction' methods used in the context of Brain Computer Interface (BCI) ignored the possible functional relationships between different signals recorded from distinct brain areas. In this paper, the functional connectivity quantified by the phase locking value (PLV) was introduced to characterize the evoked responses (ERPs) obtained in the case of target and non-targets visual stimuli. We also tested the possibility of using the functional connectivity in the context of 'P300 speller'. The proposed approach was compared to the well-known methods proposed in the state of the art of "P300 Speller", mainly the peak picking, the area, time/frequency based features, the xDAWN spatial filtering and the stepwise linear discriminant analysis (SWLDA). The electroencephalographic (EEG) signals recorded from ten subjects were analyzed offline. The results indicated that phase synchrony offers relevant information for the classification in a P300 speller. High synchronization between the brain regions was clearly observed during target trials, although no significant synchronization was detected for a non-target trial. The results showed also that phase synchrony provides higher performance than some existing methods for letter classification in a P300 speller principally when large number of trials is available. Finally, we tested the possible combination of both approaches (classical features and phase synchrony). Our findings showed an overall improvement of the performance of the P300-speller when using Peak picking, the area and frequency based features. Similar performances were obtained compared to xDAWN and SWLDA when using large number of trials.

The Conundrum of Functional Brain Networks: Small-World Efficiency or Fractal Modularity

PubMed Central

Gallos, Lazaros K.; Sigman, Mariano; Makse, Hernán A.

2012-01-01

The human brain has been studied at multiple scales, from neurons, circuits, areas with well-defined anatomical and functional boundaries, to large-scale functional networks which mediate coherent cognition. In a recent work, we addressed the problem of the hierarchical organization in the brain through network analysis. Our analysis identified functional brain modules of fractal structure that were inter-connected in a small-world topology. Here, we provide more details on the use of network science tools to elaborate on this behavior. We indicate the importance of using percolation theory to highlight the modular character of the functional brain network. These modules present a fractal, self-similar topology, identified through fractal network methods. When we lower the threshold of correlations to include weaker ties, the network as a whole assumes a small-world character. These weak ties are organized precisely as predicted by theory maximizing information transfer with minimal wiring costs. PMID:22586406

Characterizing attention with predictive network models

PubMed Central

Rosenberg, M. D.; Finn, E. S.; Scheinost, D.; Constable, R. T.; Chun, M. M.

2017-01-01

Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals’ attentional abilities. Some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that (1) attention is a network property of brain computation, (2) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task, and (3) this architecture supports a general attentional ability common to several lab-based tasks and impaired in attention deficit hyperactivity disorder. Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. PMID:28238605

Complex network analysis of brain functional connectivity under a multi-step cognitive task

NASA Astrophysics Data System (ADS)

Cai, Shi-Min; Chen, Wei; Liu, Dong-Bai; Tang, Ming; Chen, Xun

2017-01-01

Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a multi-step cognitive task involving consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed based on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based approach. We find that at voxel level the functional brain network shows robust small-worldness and scale-free characteristics, while its assortativity and rich-club organization are slightly restricted to the order of behaviors performed. More interestingly, the functional connectivity of brain network in activated ROIs strongly correlates with behaviors and is obviously restricted to the order of behaviors performed. These empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse functional connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain.

Complexity of low-frequency blood oxygen level-dependent fluctuations covaries with local connectivity.

PubMed

Anderson, Jeffrey S; Zielinski, Brandon A; Nielsen, Jared A; Ferguson, Michael A

2014-04-01

Very low-frequency blood oxygen level-dependent (BOLD) fluctuations have emerged as a valuable tool for describing brain anatomy, neuropathology, and development. Such fluctuations exhibit power law frequency dynamics, with largest amplitude at lowest frequencies. The biophysical mechanisms generating such fluctuations are poorly understood. Using publicly available data from 1,019 subjects of age 7-30, we show that BOLD fluctuations exhibit temporal complexity that is linearly related to local connectivity (regional homogeneity), consistently and significantly covarying across subjects and across gray matter regions. This relationship persisted independently of covariance with gray matter density or standard deviation of BOLD signal. During late neurodevelopment, BOLD fluctuations were unchanged with age in association cortex while becoming more random throughout the rest of the brain. These data suggest that local interconnectivity may play a key role in establishing the complexity of low-frequency BOLD fluctuations underlying functional magnetic resonance imaging connectivity. Stable low-frequency power dynamics may emerge through segmentation and integration of connectivity during development of distributed large-scale brain networks. Copyright © 2013 Wiley Periodicals, Inc.

The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism

PubMed Central

Di Martino, Adriana; Yan, Chao-Gan; Li, Qingyang; Denio, Erin; Castellanos, Francisco X.; Alaerts, Kaat; Anderson, Jeffrey S.; Assaf, Michal; Bookheimer, Susan Y.; Dapretto, Mirella; Deen, Ben; Delmonte, Sonja; Dinstein, Ilan; Ertl-Wagner, Birgit; Fair, Damien A.; Gallagher, Louise; Kennedy, Daniel P.; Keown, Christopher L.; Keysers, Christian; Lainhart, Janet E.; Lord, Catherine; Luna, Beatriz; Menon, Vinod; Minshew, Nancy; Monk, Christopher S.; Mueller, Sophia; Müller, Ralph-Axel; Nebel, Mary Beth; Nigg, Joel T.; O’Hearn, Kirsten; Pelphrey, Kevin A.; Peltier, Scott J.; Rudie, Jeffrey D.; Sunaert, Stefan; Thioux, Marc; Tyszka, J. Michael; Uddin, Lucina Q.; Verhoeven, Judith S.; Wenderoth, Nicole; Wiggins, Jillian L.; Mostofsky, Stewart H.; Milham, Michael P.

2014-01-01

Autism spectrum disorders (ASD) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, life-long nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. While the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE) – a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) datasets with corresponding structural MRI and phenotypic information from 539 individuals with ASD and 573 age-matched typical controls (TC; 7–64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 males with ASD and 403 male age-matched TC. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo and hyperconnectivity in the ASD literature; both were detected, though hypoconnectivity dominated, particularly for cortico-cortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASD (mid and posterior insula, posterior cingulate cortex), and highlighted less commonly explored regions such as thalamus. The survey of the ABIDE R-fMRI datasets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international datasets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies. PMID:23774715

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.

PubMed

Di Martino, A; Yan, C-G; Li, Q; Denio, E; Castellanos, F X; Alaerts, K; Anderson, J S; Assaf, M; Bookheimer, S Y; Dapretto, M; Deen, B; Delmonte, S; Dinstein, I; Ertl-Wagner, B; Fair, D A; Gallagher, L; Kennedy, D P; Keown, C L; Keysers, C; Lainhart, J E; Lord, C; Luna, B; Menon, V; Minshew, N J; Monk, C S; Mueller, S; Müller, R-A; Nebel, M B; Nigg, J T; O'Hearn, K; Pelphrey, K A; Peltier, S J; Rudie, J D; Sunaert, S; Thioux, M; Tyszka, J M; Uddin, L Q; Verhoeven, J S; Wenderoth, N; Wiggins, J L; Mostofsky, S H; Milham, M P

2014-06-01

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

PubMed Central

Chung, Ji Ryang; Sung, Chul; Mayerich, David; Kwon, Jaerock; Miller, Daniel E.; Huffman, Todd; Keyser, John; Abbott, Louise C.; Choe, Yoonsuck

2011-01-01

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions. PMID:22275895

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

PubMed Central

Shen, Xilin; Finn, Emily S.; Scheinost, Dustin; Rosenberg, Monica D.; Chun, Marvin M.; Papademetris, Xenophon; Constable, R Todd

2017-01-01

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results. PMID:28182017

Modeling brain circuitry over a wide range of scales.

PubMed

Fua, Pascal; Knott, Graham W

2015-01-01

If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other. Electron Microscopes (EM) can now provide the nanometer resolution that is needed to image synapses, and therefore connections, while Light Microscopes (LM) see at the micrometer resolution required to model the 3D structure of the dendritic network. Since both the topology and the connection strength are integral parts of the brain's wiring diagram, being able to combine these two modalities is critically important. In fact, these microscopes now routinely produce high-resolution imagery in such large quantities that the bottleneck becomes automated processing and interpretation, which is needed for such data to be exploited to its full potential. In this paper, we briefly review the Computer Vision techniques we have developed at EPFL to address this need. They include delineating dendritic arbors from LM imagery, segmenting organelles from EM, and combining the two into a consistent representation.

Modeling brain circuitry over a wide range of scales

PubMed Central

Fua, Pascal; Knott, Graham W.

2015-01-01

If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other. Electron Microscopes (EM) can now provide the nanometer resolution that is needed to image synapses, and therefore connections, while Light Microscopes (LM) see at the micrometer resolution required to model the 3D structure of the dendritic network. Since both the topology and the connection strength are integral parts of the brain's wiring diagram, being able to combine these two modalities is critically important. In fact, these microscopes now routinely produce high-resolution imagery in such large quantities that the bottleneck becomes automated processing and interpretation, which is needed for such data to be exploited to its full potential. In this paper, we briefly review the Computer Vision techniques we have developed at EPFL to address this need. They include delineating dendritic arbors from LM imagery, segmenting organelles from EM, and combining the two into a consistent representation. PMID:25904852

Reconfiguration of parietal circuits with cognitive tutoring in elementary school children

PubMed Central

Jolles, Dietsje; Supekar, Kaustubh; Richardson, Jennifer; Tenison, Caitlin; Ashkenazi, Sarit; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2016-01-01

Cognitive development is shaped by brain plasticity during childhood, yet little is known about changes in large-scale functional circuits associated with learning in academically relevant cognitive domains such as mathematics. Here, we investigate plasticity of intrinsic brain circuits associated with one-on-one math tutoring and its relation to individual differences in children’s learning. We focused on functional circuits associated with the intraparietal sulcus (IPS) and angular gyrus (AG), cytoarchitectonically distinct subdivisions of the human parietal cortex with different roles in numerical cognition. Tutoring improved performance and strengthened IPS connectivity with the lateral prefrontal cortex, ventral temporal-occipital cortex, and hippocampus. Crucially, increased IPS connectivity was associated with individual performance gains, highlighting the behavioral significance of plasticity in IPS circuits. Tutoring-related changes in IPS connectivity were distinct from those of the adjacent AG, which did not predict performance gains. Our findings provide new insights into plasticity of functional brain circuits associated with the development of specialized cognitive skills in children. PMID:27618765

Reconfiguration of parietal circuits with cognitive tutoring in elementary school children.

PubMed

Jolles, Dietsje; Supekar, Kaustubh; Richardson, Jennifer; Tenison, Caitlin; Ashkenazi, Sarit; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2016-10-01

Cognitive development is shaped by brain plasticity during childhood, yet little is known about changes in large-scale functional circuits associated with learning in academically relevant cognitive domains such as mathematics. Here, we investigate plasticity of intrinsic brain circuits associated with one-on-one math tutoring and its relation to individual differences in children's learning. We focused on functional circuits associated with the intraparietal sulcus (IPS) and angular gyrus (AG), cytoarchitectonically distinct subdivisions of the human parietal cortex with different roles in numerical cognition. Tutoring improved performance and strengthened IPS connectivity with the lateral prefrontal cortex, ventral temporal-occipital cortex, and hippocampus. Crucially, increased IPS connectivity was associated with individual performance gains, highlighting the behavioral significance of plasticity in IPS circuits. Tutoring-related changes in IPS connectivity were distinct from those of the adjacent AG, which did not predict performance gains. Our findings provide new insights into plasticity of functional brain circuits associated with the development of specialized cognitive skills in children. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.

PubMed

Schmidt, Christoph; Pester, Britta; Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz

2016-01-01

Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

Fine-Grained Parcellation of Brain Connectivity Improves Differentiation of States of Consciousness During Graded Propofol Sedation.

PubMed

Liu, Xiaolin; Lauer, Kathryn K; Ward, B Douglas; Roberts, Christopher J; Liu, Suyan; Gollapudy, Suneeta; Rohloff, Robert; Gross, William; Xu, Zhan; Chen, Guangyu; Binder, Jeffrey R; Li, Shi-Jiang; Hudetz, Anthony G

2017-08-01

Conscious perception relies on interactions between spatially and functionally distinct modules of the brain at various spatiotemporal scales. These interactions are altered by anesthesia, an intervention that leads to fading consciousness. Relatively little is known about brain functional connectivity and its anesthetic modulation at a fine spatial scale. Here, we used functional imaging to examine propofol-induced changes in functional connectivity in brain networks defined at a fine-grained parcellation based on a combination of anatomical and functional features. Fifteen healthy volunteers underwent resting-state functional imaging in wakeful baseline, mild sedation, deep sedation, and recovery of consciousness. Compared with wakeful baseline, propofol produced widespread, dose-dependent functional connectivity changes that scaled with the extent to which consciousness was altered. The dominant changes in connectivity were associated with the frontal lobes. By examining node pairs that demonstrated a trend of functional connectivity change between wakefulness and deep sedation, quadratic discriminant analysis differentiated the states of consciousness in individual participants more accurately at a fine-grained parcellation (e.g., 2000 nodes) than at a coarse-grained parcellation (e.g., 116 anatomical nodes). Our study suggests that defining brain networks at a high granularity may provide a superior imaging-based distinction of the graded effect of anesthesia on consciousness.

Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury

PubMed Central

De Simoni, Sara; Jenkins, Peter O; Bourke, Niall J; Fleminger, Jessica J; Jolly, Amy E; Patel, Maneesh C; Leech, Robert; Sharp, David J

2018-01-01

Abstract Traumatic brain injury often produces executive dysfunction. This characteristic cognitive impairment often causes long-term problems with behaviour and personality. Frontal lobe injuries are associated with executive dysfunction, but it is unclear how these injuries relate to corticostriatal interactions that are known to play an important role in behavioural control. We hypothesized that executive dysfunction after traumatic brain injury would be associated with abnormal corticostriatal interactions, a question that has not previously been investigated. We used structural and functional MRI measures of connectivity to investigate this. Corticostriatal functional connectivity in healthy individuals was initially defined using a data-driven approach. A constrained independent component analysis approach was applied in 100 healthy adult dataset from the Human Connectome Project. Diffusion tractography was also performed to generate white matter tracts. The output of this analysis was used to compare corticostriatal functional connectivity and structural integrity between groups of 42 patients with traumatic brain injury and 21 age-matched controls. Subdivisions of the caudate and putamen had distinct patterns of functional connectivity. Traumatic brain injury patients showed disruption to functional connectivity between the caudate and a distributed set of cortical regions, including the anterior cingulate cortex. Cognitive impairments in the patients were mainly seen in processing speed and executive function, as well as increased levels of apathy and fatigue. Abnormalities of caudate functional connectivity correlated with these cognitive impairments, with reductions in right caudate connectivity associated with increased executive dysfunction, information processing speed and memory impairment. Structural connectivity, measured using diffusion tensor imaging between the caudate and anterior cingulate cortex was impaired and this also correlated with measures of executive dysfunction. We show for the first time that altered subcortical connectivity is associated with large-scale network disruption in traumatic brain injury and that this disruption is related to the cognitive impairments seen in these patients. PMID:29186356

Computational Modeling of Resting-State Activity Demonstrates Markers of Normalcy in Children with Prenatal or Perinatal Stroke

PubMed Central

Raja Beharelle, Anjali; Griffa, Alessandra; Hagmann, Patric; Solodkin, Ana; McIntosh, Anthony R.; Small, Steven L.; Deco, Gustavo

2015-01-01

Children who sustain a prenatal or perinatal brain injury in the form of a stroke develop remarkably normal cognitive functions in certain areas, with a particular strength in language skills. A dominant explanation for this is that brain regions from the contralesional hemisphere “take over” their functions, whereas the damaged areas and other ipsilesional regions play much less of a role. However, it is difficult to tease apart whether changes in neural activity after early brain injury are due to damage caused by the lesion or by processes related to postinjury reorganization. We sought to differentiate between these two causes by investigating the functional connectivity (FC) of brain areas during the resting state in human children with early brain injury using a computational model. We simulated a large-scale network consisting of realistic models of local brain areas coupled through anatomical connectivity information of healthy and injured participants. We then compared the resulting simulated FC values of healthy and injured participants with the empirical ones. We found that the empirical connectivity values, especially of the damaged areas, correlated better with simulated values of a healthy brain than those of an injured brain. This result indicates that the structural damage caused by an early brain injury is unlikely to have an adverse and sustained impact on the functional connections, albeit during the resting state, of damaged areas. Therefore, these areas could continue to play a role in the development of near-normal function in certain domains such as language in these children. PMID:26063923

Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia.

PubMed

Damaraju, E; Allen, E A; Belger, A; Ford, J M; McEwen, S; Mathalon, D H; Mueller, B A; Pearlson, G D; Potkin, S G; Preda, A; Turner, J A; Vaidya, J G; van Erp, T G; Calhoun, V D

2014-01-01

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical-subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.

Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia

PubMed Central

Damaraju, E.; Allen, E.A.; Belger, A.; Ford, J.M.; McEwen, S.; Mathalon, D.H.; Mueller, B.A.; Pearlson, G.D.; Potkin, S.G.; Preda, A.; Turner, J.A.; Vaidya, J.G.; van Erp, T.G.; Calhoun, V.D.

2014-01-01

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical–subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences. PMID:25161896

Dimensionless, Scale Invariant, Edge Weight Metric for the Study of Complex Structural Networks

PubMed Central

Colon-Perez, Luis M.; Spindler, Caitlin; Goicochea, Shelby; Triplett, William; Parekh, Mansi; Montie, Eric; Carney, Paul R.; Price, Catherine; Mareci, Thomas H.

2015-01-01

High spatial and angular resolution diffusion weighted imaging (DWI) with network analysis provides a unique framework for the study of brain structure in vivo. DWI-derived brain connectivity patterns are best characterized with graph theory using an edge weight to quantify the strength of white matter connections between gray matter nodes. Here a dimensionless, scale-invariant edge weight is introduced to measure node connectivity. This edge weight metric provides reasonable and consistent values over any size scale (e.g. rodents to humans) used to quantify the strength of connection. Firstly, simulations were used to assess the effects of tractography seed point density and random errors in the estimated fiber orientations; with sufficient signal-to-noise ratio (SNR), edge weight estimates improve as the seed density increases. Secondly to evaluate the application of the edge weight in the human brain, ten repeated measures of DWI in the same healthy human subject were analyzed. Mean edge weight values within the cingulum and corpus callosum were consistent and showed low variability. Thirdly, using excised rat brains to study the effects of spatial resolution, the weight of edges connecting major structures in the temporal lobe were used to characterize connectivity in this local network. The results indicate that with adequate resolution and SNR, connections between network nodes are characterized well by this edge weight metric. Therefore this new dimensionless, scale-invariant edge weight metric provides a robust measure of network connectivity that can be applied in any size regime. PMID:26173147

Dimensionless, Scale Invariant, Edge Weight Metric for the Study of Complex Structural Networks.

PubMed

Colon-Perez, Luis M; Spindler, Caitlin; Goicochea, Shelby; Triplett, William; Parekh, Mansi; Montie, Eric; Carney, Paul R; Price, Catherine; Mareci, Thomas H

2015-01-01

High spatial and angular resolution diffusion weighted imaging (DWI) with network analysis provides a unique framework for the study of brain structure in vivo. DWI-derived brain connectivity patterns are best characterized with graph theory using an edge weight to quantify the strength of white matter connections between gray matter nodes. Here a dimensionless, scale-invariant edge weight is introduced to measure node connectivity. This edge weight metric provides reasonable and consistent values over any size scale (e.g. rodents to humans) used to quantify the strength of connection. Firstly, simulations were used to assess the effects of tractography seed point density and random errors in the estimated fiber orientations; with sufficient signal-to-noise ratio (SNR), edge weight estimates improve as the seed density increases. Secondly to evaluate the application of the edge weight in the human brain, ten repeated measures of DWI in the same healthy human subject were analyzed. Mean edge weight values within the cingulum and corpus callosum were consistent and showed low variability. Thirdly, using excised rat brains to study the effects of spatial resolution, the weight of edges connecting major structures in the temporal lobe were used to characterize connectivity in this local network. The results indicate that with adequate resolution and SNR, connections between network nodes are characterized well by this edge weight metric. Therefore this new dimensionless, scale-invariant edge weight metric provides a robust measure of network connectivity that can be applied in any size regime.

38 CFR 3.310 - Disabilities that are proximately due to, or aggravated by, service-connected disease or injury.

Code of Federal Regulations, 2014 CFR

2014-07-01

...) Traumatic brain injury. (1) In a veteran who has a service-connected traumatic brain injury, the following shall be held to be the proximate result of the service-connected traumatic brain injury (TBI), in the.../mental state. PTA—Post-traumatic amnesia. GCS—Glasgow Coma Scale. (For purposes of injury stratification...

Construction of multi-scale consistent brain networks: methods and applications.

PubMed

Ge, Bao; Tian, Yin; Hu, Xintao; Chen, Hanbo; Zhu, Dajiang; Zhang, Tuo; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.

Relationship Between Large-Scale Functional and Structural Covariance Networks in Idiopathic Generalized Epilepsy

PubMed Central

Zhang, Zhiqiang; Mantini, Dante; Xu, Qiang; Wang, Zhengge; Chen, Guanghui; Jiao, Qing; Zang, Yu-Feng

2013-01-01

Abstract The human brain can be modeled as a network, whose structure can be revealed by either anatomical or functional connectivity analyses. Little is known, so far, about the topological features of the large-scale interregional functional covariance network (FCN) in the brain. Further, the relationship between the FCN and the structural covariance network (SCN) has not been characterized yet, in the intact as well as in the diseased brain. Here, we studied 59 patients with idiopathic generalized epilepsy characterized by tonic–clonic seizures and 59 healthy controls. We estimated the FCN and the SCN by measuring amplitude of low-frequency fluctuations (ALFF) and gray matter volume (GMV), respectively, and then we conducted graph theoretical analyses. Our ALFF-based FCN and GMV-based results revealed that the normal human brain is characterized by specific topological properties such as small worldness and highly-connected hub regions. The patients had an altered overall topology compared to the controls, suggesting that epilepsy is primarily a disorder of the cerebral network organization. Further, the patients had altered nodal characteristics in the subcortical and medial temporal regions and default-mode regions, for both the FCN and SCN. Importantly, the correspondence between the FCN and SCN was significantly larger in patients than in the controls. These results support the hypothesis that the SCN reflects shared long-term trophic mechanisms within functionally synchronous systems. They can also provide crucial information for understanding the interactions between the whole-brain network organization and pathology in generalized tonic–clonic seizures. PMID:23510272

The circuit architecture of whole brains at the mesoscopic scale.

PubMed

Mitra, Partha P

2014-09-17

Vertebrate brains of even moderate size are composed of astronomically large numbers of neurons and show a great degree of individual variability at the microscopic scale. This variation is presumably the result of phenotypic plasticity and individual experience. At a larger scale, however, relatively stable species-typical spatial patterns are observed in neuronal architecture, e.g., the spatial distributions of somata and axonal projection patterns, probably the result of a genetically encoded developmental program. The mesoscopic scale of analysis of brain architecture is the transitional point between a microscopic scale where individual variation is prominent and the macroscopic level where a stable, species-typical neural architecture is observed. The empirical existence of this scale, implicit in neuroanatomical atlases, combined with advances in computational resources, makes studying the circuit architecture of entire brains a practical task. A methodology has previously been proposed that employs a shotgun-like grid-based approach to systematically cover entire brain volumes with injections of neuronal tracers. This methodology is being employed to obtain mesoscale circuit maps in mouse and should be applicable to other vertebrate taxa. The resulting large data sets raise issues of data representation, analysis, and interpretation, which must be resolved. Even for data representation the challenges are nontrivial: the conventional approach using regional connectivity matrices fails to capture the collateral branching patterns of projection neurons. Future success of this promising research enterprise depends on the integration of previous neuroanatomical knowledge, partly through the development of suitable computational tools that encapsulate such expertise. Copyright © 2014 Elsevier Inc. All rights reserved.

Altered default mode, fronto-parietal and salience networks in adolescents with Internet addiction.

PubMed

Wang, Lubin; Shen, Hui; Lei, Yu; Zeng, Ling-Li; Cao, Fenglin; Su, Linyan; Yang, Zheng; Yao, Shuqiao; Hu, Dewen

2017-07-01

Internet addiction (IA) is a condition characterized by loss of control over Internet use, leading to a variety of negative psychosocial consequences. Recent neuroimaging studies have begun to identify IA-related changes in specific brain regions and connections. However, whether and how the interactions within and between the large-scale brain networks are disrupted in individuals with IA remain largely unexplored. Using group independent component analysis, we extracted five intrinsic connectivity networks (ICNs) from the resting-state fMRI data of 26 adolescents with IA and 43 controls, including the anterior and posterior default mode network (DMN), left and right fronto-parietal network (FPN), and salience network (SN). We then examined the possible group differences in the functional connectivity within each ICN and between the ICNs. We found that, compared with controls, IA subjects showed: (1) reduced inter-hemispheric functional connectivity of the right FPN, whereas increased intra-hemispheric functional connectivity of the left FPN; (2) reduced functional connectivity in the dorsal medial prefrontal cortex (mPFC) of the anterior DMN; (3) reduced functional connectivity between the SN and anterior DMN. Our findings suggest that IA is associated with imbalanced interactions among the DMN, FPN and SN, which may serve as system-level neural underpinnings for the uncontrollable Internet-using behaviors. Copyright © 2017 Elsevier Ltd. All rights reserved.

EEG functional connectivity is partially predicted by underlying white matter connectivity

PubMed Central

Chu, CJ; Tanaka, N; Diaz, J; Edlow, BL; Wu, O; Hämäläinen, M; Stufflebeam, S; Cash, SS; Kramer, MA.

2015-01-01

Over the past decade, networks have become a leading model to illustrate both the anatomical relationships (structural networks) and the coupling of dynamic physiology (functional networks) linking separate brain regions. The relationship between these two levels of description remains incompletely understood and an area of intense research interest. In particular, it is unclear how cortical currents relate to underlying brain structural architecture. In addition, although theory suggests that brain communication is highly frequency dependent, how structural connections influence overlying functional connectivity in different frequency bands has not been previously explored. Here we relate functional networks inferred from statistical associations between source imaging of EEG activity and underlying cortico-cortical structural brain connectivity determined by probabilistic white matter tractography. We evaluate spontaneous fluctuating cortical brain activity over a long time scale (minutes) and relate inferred functional networks to underlying structural connectivity for broadband signals, as well as in seven distinct frequency bands. We find that cortical networks derived from source EEG estimates partially reflect both direct and indirect underlying white matter connectivity in all frequency bands evaluated. In addition, we find that when structural support is absent, functional connectivity is significantly reduced for high frequency bands compared to low frequency bands. The association between cortical currents and underlying white matter connectivity highlights the obligatory interdependence of functional and structural networks in the human brain. The increased dependence on structural support for the coupling of higher frequency brain rhythms provides new evidence for how underlying anatomy directly shapes emergent brain dynamics at fast time scales. PMID:25534110

Massive cortical reorganization in sighted Braille readers

PubMed Central

Siuda-Krzywicka, Katarzyna; Bola, Łukasz; Paplińska, Małgorzata; Sumera, Ewa; Jednoróg, Katarzyna; Marchewka, Artur; Śliwińska, Magdalena W; Amedi, Amir; Szwed, Marcin

2016-01-01

The brain is capable of large-scale reorganization in blindness or after massive injury. Such reorganization crosses the division into separate sensory cortices (visual, somatosensory...). As its result, the visual cortex of the blind becomes active during tactile Braille reading. Although the possibility of such reorganization in the normal, adult brain has been raised, definitive evidence has been lacking. Here, we demonstrate such extensive reorganization in normal, sighted adults who learned Braille while their brain activity was investigated with fMRI and transcranial magnetic stimulation (TMS). Subjects showed enhanced activity for tactile reading in the visual cortex, including the visual word form area (VWFA) that was modulated by their Braille reading speed and strengthened resting-state connectivity between visual and somatosensory cortices. Moreover, TMS disruption of VWFA activity decreased their tactile reading accuracy. Our results indicate that large-scale reorganization is a viable mechanism recruited when learning complex skills. DOI: http://dx.doi.org/10.7554/eLife.10762.001 PMID:26976813

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex.

PubMed

Romero-Garcia, Rafael; Whitaker, Kirstie J; Váša, František; Seidlitz, Jakob; Shinn, Maxwell; Fonagy, Peter; Dolan, Raymond J; Jones, Peter B; Goodyer, Ian M; Bullmore, Edward T; Vértes, Petra E

2018-05-01

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we used this to define, transcriptomic brain networks by estimating gene co-expression between pairs of cortical regions. Finally, we explored the hypothesis that transcriptional networks and structural MRI connectomes are coupled. A transcriptional brain network (TBN) and a structural covariance network (SCN) were correlated across connection weights and showed qualitatively similar complex topological properties: assortativity, small-worldness, modularity, and a rich-club. In both networks, the weight of an edge was inversely related to the anatomical (Euclidean) distance between regions. There were differences between networks in degree and distance distributions: the transcriptional network had a less fat-tailed degree distribution and a less positively skewed distance distribution than the SCN. However, cortical areas connected to each other within modules of the SCN had significantly higher levels of whole genome co-expression than expected by chance. Nodes connected in the SCN had especially high levels of expression and co-expression of a human supragranular enriched (HSE) gene set that has been specifically located to supragranular layers of human cerebral cortex and is known to be important for large-scale, long-distance cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not entirely accounted for by the common constraint of physical distance on both networks. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Dexmedetomidine Disrupts the Local and Global Efficiencies of Large-scale Brain Networks.

PubMed

Hashmi, Javeria A; Loggia, Marco L; Khan, Sheraz; Gao, Lei; Kim, Jieun; Napadow, Vitaly; Brown, Emery N; Akeju, Oluwaseun

2017-03-01

A clear understanding of the neural basis of consciousness is fundamental to research in clinical and basic neuroscience disciplines and anesthesia. Recently, decreased efficiency of information integration was suggested as a core network feature of propofol-induced unconsciousness. However, it is unclear whether this finding can be generalized to dexmedetomidine, which has a different molecular target. Dexmedetomidine was administered as a 1-μg/kg bolus over 10 min, followed by a 0.7-μg · kg · h infusion to healthy human volunteers (age range, 18 to 36 yr; n = 15). Resting-state functional magnetic resonance imaging data were acquired during baseline, dexmedetomidine-induced altered arousal, and recovery states. Zero-lag correlations between resting-state functional magnetic resonance imaging signals extracted from 131 brain parcellations were used to construct weighted brain networks. Network efficiency, degree distribution, and node strength were computed using graph analysis. Parcellated brain regions were also mapped to known resting-state networks to study functional connectivity changes. Dexmedetomidine significantly reduced the local and global efficiencies of graph theory-derived networks. Dexmedetomidine also reduced the average brain connectivity strength without impairing the degree distribution. Functional connectivity within and between all resting-state networks was modulated by dexmedetomidine. Dexmedetomidine is associated with a significant drop in the capacity for efficient information transmission at both the local and global levels. These changes result from reductions in the strength of connectivity and also manifest as reduced within and between resting-state network connectivity. These findings strengthen the hypothesis that conscious processing relies on an efficient system of information transfer in the brain.

Creative Cognition and Brain Network Dynamics

PubMed Central

Beaty, Roger E.; Benedek, Mathias; Silvia, Paul J.; Schacter, Daniel L.

2015-01-01

Creative thinking is central to the arts, sciences, and everyday life. How does the brain produce creative thought? A series of recently published papers has begun to provide insight into this question, reporting a strikingly similar pattern of brain activity and connectivity across a range of creative tasks and domains, from divergent thinking to poetry composition to musical improvisation. This research suggests that creative thought involves dynamic interactions of large-scale brain systems, with the most compelling finding being that the default and executive control networks, which can show an antagonistic relationship, actually cooperate during creative cognition and artistic performance. These findings have implications for understanding how brain networks interact to support complex cognitive processes, particularly those involving goal-directed, self-generated thought. PMID:26553223

Altered Insula Connectivity under MDMA.

PubMed

Walpola, Ishan C; Nest, Timothy; Roseman, Leor; Erritzoe, David; Feilding, Amanda; Nutt, David J; Carhart-Harris, Robin L

2017-10-01

Recent work with noninvasive human brain imaging has started to investigate the effects of 3,4-methylenedioxymethamphetamine (MDMA) on large-scale patterns of brain activity. MDMA, a potent monoamine-releaser with particularly pronounced serotonin- releasing properties, has unique subjective effects that include: marked positive mood, pleasant/unusual bodily sensations and pro-social, empathic feelings. However, the neurobiological basis for these effects is not properly understood, and the present analysis sought to address this knowledge gap. To do this, we administered MDMA-HCl (100 mg p.o.) and, separately, placebo (ascorbic acid) in a randomized, double-blind, repeated-measures design with twenty-five healthy volunteers undergoing fMRI scanning. We then employed a measure of global resting-state functional brain connectivity and follow-up seed-to-voxel analysis to the fMRI data we acquired. Results revealed decreased right insula/salience network functional connectivity under MDMA. Furthermore, these decreases in right insula/salience network connectivity correlated with baseline trait anxiety and acute experiences of altered bodily sensations under MDMA. The present findings highlight insular disintegration (ie, compromised salience network membership) as a neurobiological signature of the MDMA experience, and relate this brain effect to trait anxiety and acutely altered bodily sensations-both of which are known to be associated with insular functioning.

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity.

PubMed

Yee, Yohan; Fernandes, Darren J; French, Leon; Ellegood, Jacob; Cahill, Lindsay S; Vousden, Dulcie A; Spencer Noakes, Leigh; Scholz, Jan; van Eede, Matthijs C; Nieman, Brian J; Sled, John G; Lerch, Jason P

2018-05-18

An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings. Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed. We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance. Copyright © 2018. Published by Elsevier Inc.

Towards the understanding of network information processing in biology

NASA Astrophysics Data System (ADS)

Singh, Vijay

Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.

PubMed

Aerts, Hannelore; Schirner, Michael; Jeurissen, Ben; Van Roost, Dirk; Achten, Eric; Ritter, Petra; Marinazzo, Daniele

2018-01-01

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

Brain modularity controls the critical behavior of spontaneous activity.

PubMed

Russo, R; Herrmann, H J; de Arcangelis, L

2014-03-13

The human brain exhibits a complex structure made of scale-free highly connected modules loosely interconnected by weaker links to form a small-world network. These features appear in healthy patients whereas neurological diseases often modify this structure. An important open question concerns the role of brain modularity in sustaining the critical behaviour of spontaneous activity. Here we analyse the neuronal activity of a model, successful in reproducing on non-modular networks the scaling behaviour observed in experimental data, on a modular network implementing the main statistical features measured in human brain. We show that on a modular network, regardless the strength of the synaptic connections or the modular size and number, activity is never fully scale-free. Neuronal avalanches can invade different modules which results in an activity depression, hindering further avalanche propagation. Critical behaviour is solely recovered if inter-module connections are added, modifying the modular into a more random structure.

Large-scale DCMs for resting-state fMRI.

PubMed

Razi, Adeel; Seghier, Mohamed L; Zhou, Yuan; McColgan, Peter; Zeidman, Peter; Park, Hae-Jeong; Sporns, Olaf; Rees, Geraint; Friston, Karl J

2017-01-01

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity . This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM-with functional connectivity priors-is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

Computational modeling of resting-state activity demonstrates markers of normalcy in children with prenatal or perinatal stroke.

PubMed

Adhikari, Mohit H; Raja Beharelle, Anjali; Griffa, Alessandra; Hagmann, Patric; Solodkin, Ana; McIntosh, Anthony R; Small, Steven L; Deco, Gustavo

2015-06-10

Children who sustain a prenatal or perinatal brain injury in the form of a stroke develop remarkably normal cognitive functions in certain areas, with a particular strength in language skills. A dominant explanation for this is that brain regions from the contralesional hemisphere "take over" their functions, whereas the damaged areas and other ipsilesional regions play much less of a role. However, it is difficult to tease apart whether changes in neural activity after early brain injury are due to damage caused by the lesion or by processes related to postinjury reorganization. We sought to differentiate between these two causes by investigating the functional connectivity (FC) of brain areas during the resting state in human children with early brain injury using a computational model. We simulated a large-scale network consisting of realistic models of local brain areas coupled through anatomical connectivity information of healthy and injured participants. We then compared the resulting simulated FC values of healthy and injured participants with the empirical ones. We found that the empirical connectivity values, especially of the damaged areas, correlated better with simulated values of a healthy brain than those of an injured brain. This result indicates that the structural damage caused by an early brain injury is unlikely to have an adverse and sustained impact on the functional connections, albeit during the resting state, of damaged areas. Therefore, these areas could continue to play a role in the development of near-normal function in certain domains such as language in these children. Copyright © 2015 the authors 0270-6474/15/358914-11$15.00/0.

Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools.

PubMed

Siettos, Constantinos; Starke, Jens

2016-09-01

The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

Cognitive benefit and cost of acute stress is differentially modulated by individual brain state.

PubMed

Kohn, Nils; Hermans, Erno J; Fernández, Guillén

2017-07-01

Acute stress is associated with beneficial as well as detrimental effects on cognition in different individuals. However, it is not yet known how stress can have such opposing effects. Stroop-like tasks typically show this dissociation: stress diminishes speed, but improves accuracy. We investigated accuracy and speed during a stroop-like task of 120 healthy male subjects after an experimental stress induction or control condition in a randomized, counter-balanced cross-over design; we assessed brain-behavior associations and determined the influence of individual brain connectivity patterns on these associations, which may moderate the effect and help identify stress resilience factors. In the mean, stress was associated to increase in accuracy, but decrease in speed. Accuracy was associated to brain activation in a distributed set of brain regions overlapping with the executive control network (ECN) and speed to temporo-parietal activation. In line with a stress-related large-scale network reconfiguration, individuals showing an upregulation of the salience and down-regulation of the executive-control network under stress displayed increased speed, but decreased performance. In contrast, individuals who upregulate their ECN under stress show improved performance. Our results indicate that the individual large-scale brain network balance under acute stress moderates cognitive consequences of threat. © The Author (2017). Published by Oxford University Press.

Abnormalities in Structural Covariance of Cortical Gyrification in Parkinson's Disease.

PubMed

Xu, Jinping; Zhang, Jiuquan; Zhang, Jinlei; Wang, Yue; Zhang, Yanling; Wang, Jian; Li, Guanglin; Hu, Qingmao; Zhang, Yuanchao

2017-01-01

Although abnormal cortical morphology and connectivity between brain regions (structural covariance) have been reported in Parkinson's disease (PD), the topological organizations of large-scale structural brain networks are still poorly understood. In this study, we investigated large-scale structural brain networks in a sample of 37 PD patients and 34 healthy controls (HC) by assessing the structural covariance of cortical gyrification with local gyrification index (lGI). We demonstrated prominent small-world properties of the structural brain networks for both groups. Compared with the HC group, PD patients showed significantly increased integrated characteristic path length and integrated clustering coefficient, as well as decreased integrated global efficiency in structural brain networks. Distinct distributions of hub regions were identified between the two groups, showing more hub regions in the frontal cortex in PD patients. Moreover, the modular analyses revealed significantly decreased integrated regional efficiency in lateral Fronto-Insula-Temporal module, and increased integrated regional efficiency in Parieto-Temporal module in the PD group as compared to the HC group. In summary, our study demonstrated altered topological properties of structural networks at a global, regional and modular level in PD patients. These findings suggests that the structural networks of PD patients have a suboptimal topological organization, resulting in less effective integration of information between brain regions.

Mapping human brain networks with cortico-cortical evoked potentials

PubMed Central

Keller, Corey J.; Honey, Christopher J.; Mégevand, Pierre; Entz, Laszlo; Ulbert, Istvan; Mehta, Ashesh D.

2014-01-01

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex. PMID:25180306

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

NASA Astrophysics Data System (ADS)

Zamora-López, Gorka; Chen, Yuhan; Deco, Gustavo; Kringelbach, Morten L.; Zhou, Changsong

2016-12-01

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks.

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

PubMed Central

Zamora-López, Gorka; Chen, Yuhan; Deco, Gustavo; Kringelbach, Morten L.; Zhou, Changsong

2016-01-01

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks. PMID:27917958

Brain Network Analysis from High-Resolution EEG Signals

NASA Astrophysics Data System (ADS)

de Vico Fallani, Fabrizio; Babiloni, Fabio

Over the last decade, there has been a growing interest in the detection of the functional connectivity in the brain from different neuroelectromagnetic and hemodynamic signals recorded by several neuro-imaging devices such as the functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalography (EEG) and magnetoencephalography (MEG) apparatus. Many methods have been proposed and discussed in the literature with the aim of estimating the functional relationships among different cerebral structures. However, the necessity of an objective comprehension of the network composed by the functional links of different brain regions is assuming an essential role in the Neuroscience. Consequently, there is a wide interest in the development and validation of mathematical tools that are appropriate to spot significant features that could describe concisely the structure of the estimated cerebral networks. The extraction of salient characteristics from brain connectivity patterns is an open challenging topic, since often the estimated cerebral networks have a relative large size and complex structure. Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach seems relevant and useful as firstly demonstrated on a set of anatomical brain networks. In those studies, the authors have employed two characteristic measures, the average shortest path L and the clustering index C, to extract respectively the global and local properties of the network structure. They have found that anatomical brain networks exhibit many local connections (i.e. a high C) and few random long distance connections (i.e. a low L). These values identify a particular model that interpolate between a regular lattice and a random structure. Such a model has been designated as "small-world" network in analogy with the concept of the small-world phenomenon observed more than 30 years ago in social systems. In a similar way, many types of functional brain networks have been analyzed according to this mathematical approach. In particular, several studies based on different imaging techniques (fMRI, MEG and EEG) have found that the estimated functional networks showed small-world characteristics. In the functional brain connectivity context, these properties have been demonstrated to reflect an optimal architecture for the information processing and propagation among the involved cerebral structures. However, the performance of cognitive and motor tasks as well as the presence of neural diseases has been demonstrated to affect such a small-world topology, as revealed by the significant changes of L and C. Moreover, some functional brain networks have been mostly found to be very unlike the random graphs in their degree-distribution, which gives information about the allocation of the functional links within the connectivity pattern. It was demonstrated that the degree distributions of these networks follow a power-law trend. For this reason those networks are called "scale-free". They still exhibit the small-world phenomenon but tend to contain few nodes that act as highly connected "hubs". Scale-free networks are known to show resistance to failure, facility of synchronization and fast signal processing. Hence, it would be important to see whether the scaling properties of the functional brain networks are altered under various pathologies or experimental tasks. The present Chapter proposes a theoretical graph approach in order to evaluate the functional connectivity patterns obtained from high-resolution EEG signals. In this way, the "Brain Network Analysis" (in analogy with the Social Network Analysis that has emerged as a key technique in modern sociology) represents an effective methodology improving the comprehension of the complex interactions in the brain.

BRAPH: A graph theory software for the analysis of brain connectivity

PubMed Central

Mijalkov, Mite; Kakaei, Ehsan; Pereira, Joana B.; Westman, Eric; Volpe, Giovanni

2017-01-01

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment. PMID:28763447

BRAPH: A graph theory software for the analysis of brain connectivity.

PubMed

Mijalkov, Mite; Kakaei, Ehsan; Pereira, Joana B; Westman, Eric; Volpe, Giovanni

2017-01-01

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH-BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer's disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson's patients with mild cognitive impairment.

Efficiency of weak brain connections support general cognitive functioning.

PubMed

Santarnecchi, Emiliano; Galli, Giulia; Polizzotto, Nicola Riccardo; Rossi, Alessandro; Rossi, Simone

2014-09-01

Brain network topology provides valuable information on healthy and pathological brain functioning. Novel approaches for brain network analysis have shown an association between topological properties and cognitive functioning. Under the assumption that "stronger is better", the exploration of brain properties has generally focused on the connectivity patterns of the most strongly correlated regions, whereas the role of weaker brain connections has remained obscure for years. Here, we assessed whether the different strength of connections between brain regions may explain individual differences in intelligence. We analyzed-functional connectivity at rest in ninety-eight healthy individuals of different age, and correlated several connectivity measures with full scale, verbal, and performance Intelligent Quotients (IQs). Our results showed that the variance in IQ levels was mostly explained by the distributed communication efficiency of brain networks built using moderately weak, long-distance connections, with only a smaller contribution of stronger connections. The variability in individual IQs was associated with the global efficiency of a pool of regions in the prefrontal lobes, hippocampus, temporal pole, and postcentral gyrus. These findings challenge the traditional view of a prominent role of strong functional brain connections in brain topology, and highlight the importance of both strong and weak connections in determining the functional architecture responsible for human intelligence variability. Copyright © 2014 Wiley Periodicals, Inc.

EEG functional connectivity, axon delays and white matter disease.

PubMed

Nunez, Paul L; Srinivasan, Ramesh; Fields, R Douglas

2015-01-01

Both structural and functional brain connectivities are closely linked to white matter disease. We discuss several such links of potential interest to neurologists, neurosurgeons, radiologists, and non-clinical neuroscientists. Treatment of brains as genuine complex systems suggests major emphasis on the multi-scale nature of brain connectivity and dynamic behavior. Cross-scale interactions of local, regional, and global networks are apparently responsible for much of EEG's oscillatory behaviors. Finite axon propagation speed, often assumed to be infinite in local network models, is central to our conceptual framework. Myelin controls axon speed, and the synchrony of impulse traffic between distant cortical regions appears to be critical for optimal mental performance and learning. Several experiments suggest that axon conduction speed is plastic, thereby altering the regional and global white matter connections that facilitate binding of remote local networks. Combined EEG and high resolution EEG can provide distinct multi-scale estimates of functional connectivity in both healthy and diseased brains with measures like frequency and phase spectra, covariance, and coherence. White matter disease may profoundly disrupt normal EEG coherence patterns, but currently these kinds of studies are rare in scientific labs and essentially missing from clinical environments. Copyright © 2014 International Federation of Clinical Neurophysiology. All rights reserved.

Tau, amyloid, and cascading network failure across the Alzheimer's disease spectrum.

PubMed

Jones, David T; Graff-Radford, Jonathan; Lowe, Val J; Wiste, Heather J; Gunter, Jeffrey L; Senjem, Matthew L; Botha, Hugo; Kantarci, Kejal; Boeve, Bradley F; Knopman, David S; Petersen, Ronald C; Jack, Clifford R

2017-12-01

Functionally related brain regions are selectively vulnerable to Alzheimer's disease pathophysiology. However, molecular markers of this pathophysiology (i.e., beta-amyloid and tau aggregates) have discrepant spatial and temporal patterns of progression within these selectively vulnerable brain regions. Existing reductionist pathophysiologic models cannot account for these large-scale spatiotemporal inconsistencies. Within the framework of the recently proposed cascading network failure model of Alzheimer's disease, however, these large-scale patterns are to be expected. This model postulates the following: 1) a tau-associated, circumscribed network disruption occurs in brain regions specific to a given phenotype in clinically normal individuals; 2) this disruption can trigger phenotype independent, stereotypic, and amyloid-associated compensatory brain network changes indexed by changes in the default mode network; 3) amyloid deposition marks a saturation of functional compensation and portends an acceleration of the inciting phenotype specific, and tau-associated, network failure. With the advent of in vivo molecular imaging of tau pathology, combined with amyloid and functional network imaging, it is now possible to investigate the relationship between functional brain networks, tau, and amyloid across the disease spectrum within these selectively vulnerable brain regions. In a large cohort (n = 218) spanning the Alzheimer's disease spectrum from young, amyloid negative, cognitively normal subjects to Alzheimer's disease dementia, we found several distinct spatial patterns of tau deposition, including 'Braak-like' and 'non-Braak-like', across functionally related brain regions. Rather than arising focally and spreading sequentially, elevated tau signal seems to occur system-wide based on inferences made from multiple cross-sectional analyses we conducted looking at regional patterns of tau signal. Younger age-of-disease-onset was associated with 'non-Braak-like' patterns of tau, suggesting an association with atypical clinical phenotypes. As predicted by the cascading network failure model of Alzheimer's disease, we found that amyloid is a partial mediator of the relationship between functional network failure and tau deposition in functionally connected brain regions. This study implicates large-scale brain networks in the pathophysiology of tau deposition and offers support to models incorporating large-scale network physiology into disease models linking tau and amyloid, such as the cascading network failure model of Alzheimer's disease. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

PubMed Central

Cerliani, Leonardo; Mennes, Maarten; Thomas, Rajat M.; Di Martino, Adriana; Thioux, Marc; Keysers, Christian

2016-01-01

Importance Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks. Objectives To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories. Design, Setting, and Participants We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012. Main Outcomes and Measures We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale. Results Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t ≥ 3.13, P < .001 corrected). The strength of such connections was associated with the severity of autistic traits in the ASD group (all r ≥ 0.21, P < .0067 corrected). In addition, subcortico-cortical interaction decreased with age in the entire sample (all r ≤ −0.09, P < .012 corrected), although this association was significant only in typically developing participants (all r ≤ −0.13, P < .009 corrected). Conclusions and Relevance Our results showing ASD-related impairment in the interaction between primary sensory cortices and subcortical regions suggest that the sensory processes they subserve abnormally influence brain information processing in individuals with ASD. This might contribute to the occurrence of hyposensitivity or hypersensitivity and of difficulties in top-down regulation of behavior. PMID:26061743

Heterogeneous fractionation profiles of meta-analytic coactivation networks.

PubMed

Laird, Angela R; Riedel, Michael C; Okoe, Mershack; Jianu, Radu; Ray, Kimberly L; Eickhoff, Simon B; Smith, Stephen M; Fox, Peter T; Sutherland, Matthew T

2017-04-01

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication. Copyright © 2017 Elsevier Inc. All rights reserved.

Heterogeneous fractionation profiles of meta-analytic coactivation networks

PubMed Central

Laird, Angela R.; Riedel, Michael C.; Okoe, Mershack; Jianu, Radu; Ray, Kimberly L.; Eickhoff, Simon B.; Smith, Stephen M.; Fox, Peter T.; Sutherland, Matthew T.

2017-01-01

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d = 20 to 300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how “parent” functional brain systems decompose into constituent “child” sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication. PMID:28222386

Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects.

PubMed

Rashid, Barnaly; Damaraju, Eswar; Pearlson, Godfrey D; Calhoun, Vince D

2014-01-01

Schizophrenia (SZ) and bipolar disorder (BP) share significant overlap in clinical symptoms, brain characteristics, and risk genes, and both are associated with dysconnectivity among large-scale brain networks. Resting state functional magnetic resonance imaging (rsfMRI) data facilitates studying macroscopic connectivity among distant brain regions. Standard approaches to identifying such connectivity include seed-based correlation and data-driven clustering methods such as independent component analysis (ICA) but typically focus on average connectivity. In this study, we utilize ICA on rsfMRI data to obtain intrinsic connectivity networks (ICNs) in cohorts of healthy controls (HCs) and age matched SZ and BP patients. Subsequently, we investigated difference in functional network connectivity, defined as pairwise correlations among the timecourses of ICNs, between HCs and patients. We quantified differences in both static (average) and dynamic (windowed) connectivity during the entire scan duration. Disease-specific differences were identified in connectivity within different dynamic states. Notably, results suggest that patients make fewer transitions to some states (states 1, 2, and 4) compared to HCs, with most such differences confined to a single state. SZ patients showed more differences from healthy subjects than did bipolars, including both hyper and hypo connectivity in one common connectivity state (dynamic state 3). Also group differences between SZ and bipolar patients were identified in patterns (states) of connectivity involving the frontal (dynamic state 1) and frontal-parietal regions (dynamic state 3). Our results provide new information about these illnesses and strongly suggest that state-based analyses are critical to avoid averaging together important factors that can help distinguish these clinical groups.

Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals

PubMed Central

Berényi, Antal; Somogyvári, Zoltán; Nagy, Anett J.; Roux, Lisa; Long, John D.; Fujisawa, Shigeyoshi; Stark, Eran; Leonardo, Anthony; Harris, Timothy D.

2013-01-01

Monitoring representative fractions of neurons from multiple brain circuits in behaving animals is necessary for understanding neuronal computation. Here, we describe a system that allows high-channel-count recordings from a small volume of neuronal tissue using a lightweight signal multiplexing headstage that permits free behavior of small rodents. The system integrates multishank, high-density recording silicon probes, ultraflexible interconnects, and a miniaturized microdrive. These improvements allowed for simultaneous recordings of local field potentials and unit activity from hundreds of sites without confining free movements of the animal. The advantages of large-scale recordings are illustrated by determining the electroanatomic boundaries of layers and regions in the hippocampus and neocortex and constructing a circuit diagram of functional connections among neurons in real anatomic space. These methods will allow the investigation of circuit operations and behavior-dependent interregional interactions for testing hypotheses of neural networks and brain function. PMID:24353300

Imbalance in subregional connectivity of the right temporoparietal junction in major depression.

PubMed

Poeppl, Timm B; Müller, Veronika I; Hoffstaedter, Felix; Bzdok, Danilo; Laird, Angela R; Fox, Peter T; Langguth, Berthold; Rupprecht, Rainer; Sorg, Christian; Riedl, Valentin; Goya-Maldonado, Roberto; Gruber, Oliver; Eickhoff, Simon B

2016-08-01

Major depressive disorder (MDD) involves impairment in cognitive and interpersonal functioning. The right temporoparietal junction (RTPJ) is a key brain region subserving cognitive-attentional and social processes. Yet, findings on the involvement of the RTPJ in the pathophysiology of MDD have so far been controversial. Recent connectivity-based parcellation data revealed a topofunctional dualism within the RTPJ, linking its anterior and posterior part (aRTPJ/pRTPJ) to antagonistic brain networks for attentional and social processing, respectively. Comparing functional resting-state connectivity of the aRTPJ and pRTPJ in 72 MDD patients and 76 well-matched healthy controls, we found a seed (aRTPJ/pRTPJ) × diagnosis (MDD/controls) interaction in functional connectivity for eight regions. Employing meta-data from a large-scale neuroimaging database, functional characterization of these regions exhibiting differentially altered connectivity with the aRTPJ/pRTPJ revealed associations with cognitive (dorsolateral prefrontal cortex, parahippocampus) and behavioral (posterior medial frontal cortex) control, visuospatial processing (dorsal visual cortex), reward (subgenual anterior cingulate cortex, medial orbitofrontal cortex, posterior cingulate cortex), as well as memory retrieval and social cognition (precuneus). These findings suggest that an imbalance in connectivity of subregions, rather than disturbed connectivity of the RTPJ as a whole, characterizes the connectional disruption of the RTPJ in MDD. This imbalance may account for key symptoms of MDD in cognitive, emotional, and social domains. Hum Brain Mapp 37:2931-2942, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Impaired development of intrinsic connectivity networks in children with medically intractable localization-related epilepsy.

PubMed

Ibrahim, George M; Morgan, Benjamin R; Lee, Wayne; Smith, Mary Lou; Donner, Elizabeth J; Wang, Frank; Beers, Craig A; Federico, Paolo; Taylor, Margot J; Doesburg, Sam M; Rutka, James T; Snead, O Carter

2014-11-01

Typical childhood development is characterized by the emergence of intrinsic connectivity networks (ICNs) by way of internetwork segregation and intranetwork integration. The impact of childhood epilepsy on the maturation of ICNs is, however, poorly understood. The developmental trajectory of ICNs in 26 children (8-17 years) with localization-related epilepsy and 28 propensity-score matched controls was evaluated using graph theoretical analysis of whole brain connectomes from resting-state functional magnetic resonance imaging (fMRI) data. Children with epilepsy demonstrated impaired development of regional hubs in nodes of the salience and default mode networks (DMN). Seed-based connectivity and hierarchical clustering analysis revealed significantly decreased intranetwork connections, and greater internetwork connectivity in children with epilepsy compared to controls. Significant interactions were identified between epilepsy duration and the expected developmental trajectory of ICNs, indicating that prolonged epilepsy may cause progressive alternations in large-scale networks throughout childhood. DMN integration was also associated with better working memory, whereas internetwork segregation was associated with higher full-scale intelligence quotient scores. Furthermore, subgroup analyses revealed the thalamus, hippocampus, and caudate were weaker hubs in children with secondarily generalized seizures, relative to other patient subgroups. Our findings underscore that epilepsy interferes with the developmental trajectory of brain networks underlying cognition, providing evidence supporting the early treatment of affected children. Copyright © 2014 Wiley Periodicals, Inc.

Superconducting Optoelectronic Circuits for Neuromorphic Computing

NASA Astrophysics Data System (ADS)

Shainline, Jeffrey M.; Buckley, Sonia M.; Mirin, Richard P.; Nam, Sae Woo

2017-03-01

Neural networks have proven effective for solving many difficult computational problems, yet implementing complex neural networks in software is computationally expensive. To explore the limits of information processing, it is necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons. Here we propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes fanout and parasitic constraints on electrical signals while simultaneously introducing physical degrees of freedom which can be employed for computation. The use of supercurrents achieves the low power density (1 mW /cm2 at 20-MHz firing rate) necessary to scale to systems with enormous entropy. Estimates comparing the proposed hardware platform to a human brain show that with the same number of neurons (1 011) and 700 independent connections per neuron, the hardware presented here may achieve an order of magnitude improvement in synaptic events per second per watt.

A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex

PubMed Central

Chaudhuri, Rishidev; Knoblauch, Kenneth; Gariel, Marie-Alice; Kennedy, Henry; Wang, Xiao-Jing

2015-01-01

We developed a large-scale dynamical model of the macaque neocortex, which is based on recently acquired directed- and weighted-connectivity data from tract-tracing experiments, and which incorporates heterogeneity across areas. A hierarchy of timescales naturally emerges from this system: sensory areas show brief, transient responses to input (appropriate for sensory processing), whereas association areas integrate inputs over time and exhibit persistent activity (suitable for decision-making and working memory). The model displays multiple temporal hierarchies, as evidenced by contrasting responses to visual versus somatosensory stimulation. Moreover, slower prefrontal and temporal areas have a disproportionate impact on global brain dynamics. These findings establish a circuit mechanism for “temporal receptive windows” that are progressively enlarged along the cortical hierarchy, suggest an extension of time integration in decision-making from local to large circuits, and should prompt a re-evaluation of the analysis of functional connectivity (measured by fMRI or EEG/MEG) by taking into account inter-areal heterogeneity. PMID:26439530

Characterizing Attention with Predictive Network Models.

PubMed

Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

2017-04-01

Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

Creative Cognition and Brain Network Dynamics.

PubMed

Beaty, Roger E; Benedek, Mathias; Silvia, Paul J; Schacter, Daniel L

2016-02-01

Creative thinking is central to the arts, sciences, and everyday life. How does the brain produce creative thought? A series of recently published papers has begun to provide insight into this question, reporting a strikingly similar pattern of brain activity and connectivity across a range of creative tasks and domains, from divergent thinking to poetry composition to musical improvisation. This research suggests that creative thought involves dynamic interactions of large-scale brain systems, with the most compelling finding being that the default and executive control networks, which can show an antagonistic relation, tend to cooperate during creative cognition and artistic performance. These findings have implications for understanding how brain networks interact to support complex cognitive processes, particularly those involving goal-directed, self-generated thought. Copyright © 2015 Elsevier Ltd. All rights reserved.

Sex differences in normal age trajectories of functional brain networks.

PubMed

Scheinost, Dustin; Finn, Emily S; Tokoglu, Fuyuze; Shen, Xilin; Papademetris, Xenophon; Hampson, Michelle; Constable, R Todd

2015-04-01

Resting-state functional magnetic resonance image (rs-fMRI) is increasingly used to study functional brain networks. Nevertheless, variability in these networks due to factors such as sex and aging is not fully understood. This study explored sex differences in normal age trajectories of resting-state networks (RSNs) using a novel voxel-wise measure of functional connectivity, the intrinsic connectivity distribution (ICD). Males and females showed differential patterns of changing connectivity in large-scale RSNs during normal aging from early adulthood to late middle-age. In some networks, such as the default-mode network, males and females both showed decreases in connectivity with age, albeit at different rates. In other networks, such as the fronto-parietal network, males and females showed divergent connectivity trajectories with age. Main effects of sex and age were found in many of the same regions showing sex-related differences in aging. Finally, these sex differences in aging trajectories were robust to choice of preprocessing strategy, such as global signal regression. Our findings resolve some discrepancies in the literature, especially with respect to the trajectory of connectivity in the default mode, which can be explained by our observed interactions between sex and aging. Overall, results indicate that RSNs show different aging trajectories for males and females. Characterizing effects of sex and age on RSNs are critical first steps in understanding the functional organization of the human brain. © 2014 Wiley Periodicals, Inc.

Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder.

PubMed

Cao, Qingjiu; Shu, Ni; An, Li; Wang, Peng; Sun, Li; Xia, Ming-Rui; Wang, Jin-Hui; Gong, Gao-Lang; Zang, Yu-Feng; Wang, Yu-Feng; He, Yong

2013-06-26

Attention-deficit/hyperactivity disorder (ADHD), which is characterized by core symptoms of inattention and hyperactivity/impulsivity, is one of the most common neurodevelopmental disorders of childhood. Neuroimaging studies have suggested that these behavioral disturbances are associated with abnormal functional connectivity among brain regions. However, the alterations in the structural connections that underlie these behavioral and functional deficits remain poorly understood. Here, we used diffusion magnetic resonance imaging and probabilistic tractography method to examine whole-brain white matter (WM) structural connectivity in 30 drug-naive boys with ADHD and 30 healthy controls. The WM networks of the human brain were constructed by estimating inter-regional connectivity probability. The topological properties of the resultant networks (e.g., small-world and network efficiency) were then analyzed using graph theoretical approaches. Nonparametric permutation tests were applied for between-group comparisons of these graphic metrics. We found that both the ADHD and control groups showed an efficient small-world organization in the whole-brain WM networks, suggesting a balance between structurally segregated and integrated connectivity patterns. However, relative to controls, patients with ADHD exhibited decreased global efficiency and increased shortest path length, with the most pronounced efficiency decreases in the left parietal, frontal, and occipital cortices. Intriguingly, the ADHD group showed decreased structural connectivity in the prefrontal-dominant circuitry and increased connectivity in the orbitofrontal-striatal circuitry, and these changes significantly correlated with the inattention and hyperactivity/impulsivity symptoms, respectively. The present study shows disrupted topological organization of large-scale WM networks in ADHD, extending our understanding of how structural disruptions of neuronal circuits underlie behavioral disturbances in patients with ADHD.

The Rich Club of the C. elegans Neuronal Connectome

PubMed Central

Vértes, Petra E.; Ahnert, Sebastian E.; Schafer, William R.; Bullmore, Edward T.

2013-01-01

There is increasing interest in topological analysis of brain networks as complex systems, with researchers often using neuroimaging to represent the large-scale organization of nervous systems without precise cellular resolution. Here we used graph theory to investigate the neuronal connectome of the nematode worm Caenorhabditis elegans, which is defined anatomically at a cellular scale as 2287 synaptic connections between 279 neurons. We identified a small number of highly connected neurons as a rich club (N = 11) interconnected with high efficiency and high connection distance. Rich club neurons comprise almost exclusively the interneurons of the locomotor circuits, with known functional importance for coordinated movement. The rich club neurons are connector hubs, with high betweenness centrality, and many intermodular connections to nodes in different modules. On identifying the shortest topological paths (motifs) between pairs of peripheral neurons, the motifs that are found most frequently traverse the rich club. The rich club neurons are born early in development, before visible movement of the animal and before the main phase of developmental elongation of its body. We conclude that the high wiring cost of the globally integrative rich club of neurons in the C. elegans connectome is justified by the adaptive value of coordinated movement of the animal. The economical trade-off between physical cost and behavioral value of rich club organization in a cellular connectome confirms theoretical expectations and recapitulates comparable results from human neuroimaging on much larger scale networks, suggesting that this may be a general and scale-invariant principle of brain network organization. PMID:23575836

Information processing architecture of functionally defined clusters in the macaque cortex.

PubMed

Shen, Kelly; Bezgin, Gleb; Hutchison, R Matthew; Gati, Joseph S; Menon, Ravi S; Everling, Stefan; McIntosh, Anthony R

2012-11-28

Computational and empirical neuroimaging studies have suggested that the anatomical connections between brain regions primarily constrain their functional interactions. Given that the large-scale organization of functional networks is determined by the temporal relationships between brain regions, the structural limitations may extend to the global characteristics of functional networks. Here, we explored the extent to which the functional network community structure is determined by the underlying anatomical architecture. We directly compared macaque (Macaca fascicularis) functional connectivity (FC) assessed using spontaneous blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) to directed anatomical connectivity derived from macaque axonal tract tracing studies. Consistent with previous reports, FC increased with increasing strength of anatomical connection, and FC was also present between regions that had no direct anatomical connection. We observed moderate similarity between the FC of each region and its anatomical connectivity. Notably, anatomical connectivity patterns, as described by structural motifs, were different within and across functional modules: partitioning of the functional network was supported by dense bidirectional anatomical connections within clusters and unidirectional connections between clusters. Together, our data directly demonstrate that the FC patterns observed in resting-state BOLD-fMRI are dictated by the underlying neuroanatomical architecture. Importantly, we show how this architecture contributes to the global organizational principles of both functional specialization and integration.

The functional organization of human epileptic hippocampus

PubMed Central

Klimes, Petr; Duque, Juliano J.; Brinkmann, Ben; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Halamek, Josef; Jurak, Pavel

2016-01-01

The function and connectivity of human brain is disrupted in epilepsy. We previously reported that the region of epileptic brain generating focal seizures, i.e., the seizure onset zone (SOZ), is functionally isolated from surrounding brain regions in focal neocortical epilepsy. The modulatory effect of behavioral state on the spatial and spectral scales over which the reduced functional connectivity occurs, however, is unclear. Here we use simultaneous sleep staging from scalp EEG with intracranial EEG recordings from medial temporal lobe to investigate how behavioral state modulates the spatial and spectral scales of local field potential synchrony in focal epileptic hippocampus. The local field spectral power and linear correlation between adjacent electrodes provide measures of neuronal population synchrony at different spatial scales, ∼1 and 10 mm, respectively. Our results show increased connectivity inside the SOZ and low connectivity between electrodes in SOZ and outside the SOZ. During slow-wave sleep, we observed decreased connectivity for ripple and fast ripple frequency bands within the SOZ at the 10 mm spatial scale, while the local synchrony remained high at the 1 mm spatial scale. Further study of these phenomena may prove useful for SOZ localization and help understand seizure generation, and the functional deficits seen in epileptic eloquent cortex. PMID:27030735

Emotional speech synchronizes brains across listeners and engages large-scale dynamic brain networks

PubMed Central

Nummenmaa, Lauri; Saarimäki, Heini; Glerean, Enrico; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P.; Hari, Riitta; Sams, Mikko

2014-01-01

Speech provides a powerful means for sharing emotions. Here we implement novel intersubject phase synchronization and whole-brain dynamic connectivity measures to show that networks of brain areas become synchronized across participants who are listening to emotional episodes in spoken narratives. Twenty participants' hemodynamic brain activity was measured with functional magnetic resonance imaging (fMRI) while they listened to 45-s narratives describing unpleasant, neutral, and pleasant events spoken in neutral voice. After scanning, participants listened to the narratives again and rated continuously their feelings of pleasantness–unpleasantness (valence) and of arousal–calmness. Instantaneous intersubject phase synchronization (ISPS) measures were computed to derive both multi-subject voxel-wise similarity measures of hemodynamic activity and inter-area functional dynamic connectivity (seed-based phase synchronization, SBPS). Valence and arousal time series were subsequently used to predict the ISPS and SBPS time series. High arousal was associated with increased ISPS in the auditory cortices and in Broca's area, and negative valence was associated with enhanced ISPS in the thalamus, anterior cingulate, lateral prefrontal, and orbitofrontal cortices. Negative valence affected functional connectivity of fronto-parietal, limbic (insula, cingulum) and fronto-opercular circuitries, and positive arousal affected the connectivity of the striatum, amygdala, thalamus, cerebellum, and dorsal frontal cortex. Positive valence and negative arousal had markedly smaller effects. We propose that high arousal synchronizes the listeners' sound-processing and speech-comprehension networks, whereas negative valence synchronizes circuitries supporting emotional and self-referential processing. PMID:25128711

Network analysis of mesoscale optical recordings to assess regional, functional connectivity.

PubMed

Lim, Diana H; LeDue, Jeffrey M; Murphy, Timothy H

2015-10-01

With modern optical imaging methods, it is possible to map structural and functional connectivity. Optical imaging studies that aim to describe large-scale neural connectivity often need to handle large and complex datasets. In order to interpret these datasets, new methods for analyzing structural and functional connectivity are being developed. Recently, network analysis, based on graph theory, has been used to describe and quantify brain connectivity in both experimental and clinical studies. We outline how to apply regional, functional network analysis to mesoscale optical imaging using voltage-sensitive-dye imaging and channelrhodopsin-2 stimulation in a mouse model. We include links to sample datasets and an analysis script. The analyses we employ can be applied to other types of fluorescence wide-field imaging, including genetically encoded calcium indicators, to assess network properties. We discuss the benefits and limitations of using network analysis for interpreting optical imaging data and define network properties that may be used to compare across preparations or other manipulations such as animal models of disease.

Disconnection of network hubs and cognitive impairment after traumatic brain injury.

PubMed

Fagerholm, Erik D; Hellyer, Peter J; Scott, Gregory; Leech, Robert; Sharp, David J

2015-06-01

Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an injury can then be estimated by calculating graph metrics of network structure and function. Here we test which graph metrics best predict the presence of traumatic axonal injury, as well as which are most highly associated with cognitive impairment. A comprehensive range of graph metrics was calculated from structural connectivity measures for 52 patients with traumatic brain injury, 21 of whom had microbleed evidence of traumatic axonal injury, and 25 age-matched controls. White matter connections between 165 grey matter brain regions were defined using tractography, and structural connectivity matrices calculated from skeletonized diffusion tensor imaging data. This technique estimates injury at the centre of tract, but is insensitive to damage at tract edges. Graph metrics were calculated from the resulting connectivity matrices and machine-learning techniques used to select the metrics that best predicted the presence of traumatic brain injury. In addition, we used regularization and variable selection via the elastic net to predict patient behaviour on tests of information processing speed, executive function and associative memory. Support vector machines trained with graph metrics of white matter connectivity matrices from the microbleed group were able to identify patients with a history of traumatic brain injury with 93.4% accuracy, a result robust to different ways of sampling the data. Graph metrics were significantly associated with cognitive performance: information processing speed (R(2) = 0.64), executive function (R(2) = 0.56) and associative memory (R(2) = 0.25). These results were then replicated in a separate group of patients without microbleeds. The most influential graph metrics were betweenness centrality and eigenvector centrality, which provide measures of the extent to which a given brain region connects other regions in the network. Reductions in betweenness centrality and eigenvector centrality were particularly evident within hub regions including the cingulate cortex and caudate. Our results demonstrate that betweenness centrality and eigenvector centrality are reduced within network hubs, due to the impact of traumatic axonal injury on network connections. The dominance of betweenness centrality and eigenvector centrality suggests that cognitive impairment after traumatic brain injury results from the disconnection of network hubs by traumatic axonal injury. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

Neural basis of economic bubble behavior.

PubMed

Ogawa, A; Onozaki, T; Mizuno, T; Asamizuya, T; Ueno, K; Cheng, K; Iriki, A

2014-04-18

Throughout human history, economic bubbles have formed and burst. As a bubble grows, microeconomic behavior ceases to be constrained by realistic predictions. This contradicts the basic assumption of economics that agents have rational expectations. To examine the neural basis of behavior during bubbles, we performed functional magnetic resonance imaging while participants traded shares in a virtual stock exchange with two non-bubble stocks and one bubble stock. The price was largely deflected from the fair price in one of the non-bubble stocks, but not in the other. Their fair prices were specified. The price of the bubble stock showed a large increase and battering, as based on a real stock-market bust. The imaging results revealed modulation of the brain circuits that regulate trade behavior under different market conditions. The premotor cortex was activated only under a market condition in which the price was largely deflected from the fair price specified. During the bubble, brain regions associated with the cognitive processing that supports order decisions were identified. The asset preference that might bias the decision was associated with the ventrolateral prefrontal cortex and the dorsolateral prefrontal cortex (DLPFC). The activity of the inferior parietal lobule (IPL) was correlated with the score of future time perspective, which would bias the estimation of future price. These regions were deemed to form a distinctive network during the bubble. A functional connectivity analysis showed that the connectivity between the DLPFC and the IPL was predominant compared with other connectivities only during the bubble. These findings indicate that uncertain and unstable market conditions changed brain modes in traders. These brain mechanisms might lead to a loss of control caused by wishful thinking, and to microeconomic bubbles that expand, on the macroscopic scale, toward bust. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Towards a comprehensive atlas of cortical connections in a primate brain: Mapping tracer injection studies of the common marmoset into a reference digital template.

PubMed

Majka, Piotr; Chaplin, Tristan A; Yu, Hsin-Hao; Tolpygo, Alexander; Mitra, Partha P; Wójcik, Daniel K; Rosa, Marcello G P

2016-08-01

The marmoset is an emerging animal model for large-scale attempts to understand primate brain connectivity, but achieving this aim requires the development and validation of procedures for normalization and integration of results from many neuroanatomical experiments. Here we describe a computational pipeline for coregistration of retrograde tracing data on connections of cortical areas into a 3D marmoset brain template, generated from Nissl-stained sections. The procedure results in a series of spatial transformations that are applied to the coordinates of labeled neurons in the different cases, bringing them into common stereotaxic space. We applied this procedure to 17 injections, placed in the frontal lobe of nine marmosets as part of earlier studies. Visualizations of cortical patterns of connections revealed by these injections are supplied as Supplementary Materials. Comparison between the results of the automated and human-based processing of these cases reveals that the centers of injection sites can be reconstructed, on average, to within 0.6 mm of coordinates estimated by an experienced neuroanatomist. Moreover, cell counts obtained in different areas by the automated approach are highly correlated (r = 0.83) with those obtained by an expert, who examined in detail histological sections for each individual. The present procedure enables comparison and visualization of large datasets, which in turn opens the way for integration and analysis of results from many animals. Its versatility, including applicability to archival materials, may reduce the number of additional experiments required to produce the first detailed cortical connectome of a primate brain. J. Comp. Neurol. 524:2161-2181, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

Neurobiomimetic constructs for intelligent unmanned systems and robotics

NASA Astrophysics Data System (ADS)

Braun, Jerome J.; Shah, Danelle C.; DeAngelus, Marianne A.

2014-06-01

This paper discusses a paradigm we refer to as neurobiomimetic, which involves emulations of brain neuroanatomy and neurobiology aspects and processes. Neurobiomimetic constructs include rudimentary and down-scaled computational representations of brain regions, sub-regions, and synaptic connectivity. Many different instances of neurobiomimetic constructs are possible, depending on various aspects such as the initial conditions of synaptic connectivity, number of neuron elements in regions, connectivity specifics, and more, and we refer to these instances as `animats'. While downscaled for computational feasibility, the animats are very large constructs; the animats implemented in this work contain over 47,000 neuron elements and over 720,000 synaptic connections. The paper outlines aspects of the animats implemented, spatial memory and learning cognitive task, the virtual-reality environment constructed to study the animat performing that task, and discussion of results. In a broad sense, we argue that the neurobiomimetic paradigm pursued in this work constitutes a particularly promising path to artificial cognition and intelligent unmanned systems. Biological brains readily cope with challenges of real-life tasks that consistently prove beyond even the most sophisticated algorithmic approaches known. At the cross-over point of neuroscience, cognitive science and computer science, paradigms such as the one pursued in this work aim to mimic the mechanisms of biological brains and as such, we argue, may lead to machines with abilities closer to those of biological species.

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

PubMed Central

Hu, Xintao; Zhu, Dajiang; Lv, Peili; Li, Kaiming; Han, Junwei; Wang, Lihong; Shen, Dinggang; Guo, Lei; Liu, Tianming

2014-01-01

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures. PMID:23319242

Mapping the Alzheimer’s Brain with Connectomics

PubMed Central

Xie, Teng; He, Yong

2012-01-01

Alzheimer’s disease (AD) is the most common form of dementia. As an incurable, progressive, and neurodegenerative disease, it causes cognitive and memory deficits. However, the biological mechanisms underlying the disease are not thoroughly understood. In recent years, non-invasive neuroimaging and neurophysiological techniques [e.g., structural magnetic resonance imaging (MRI), diffusion MRI, functional MRI, and EEG/MEG] and graph theory based network analysis have provided a new perspective on structural and functional connectivity patterns of the human brain (i.e., the human connectome) in health and disease. Using these powerful approaches, several recent studies of patients with AD exhibited abnormal topological organization in both global and regional properties of neuronal networks, indicating that AD not only affects specific brain regions, but also alters the structural and functional associations between distinct brain regions. Specifically, disruptive organization in the whole-brain networks in AD is involved in the loss of small-world characters and the re-organization of hub distributions. These aberrant neuronal connectivity patterns were associated with cognitive deficits in patients with AD, even with genetic factors in healthy aging. These studies provide empirical evidence to support the existence of an aberrant connectome of AD. In this review we will summarize recent advances discovered in large-scale brain network studies of AD, mainly focusing on graph theoretical analysis of brain connectivity abnormalities. These studies provide novel insights into the pathophysiological mechanisms of AD and could be helpful in developing imaging biomarkers for disease diagnosis and monitoring. PMID:22291664

Functional hypergraph uncovers novel covariant structures over neurodevelopment.

PubMed

Gu, Shi; Yang, Muzhi; Medaglia, John D; Gur, Ruben C; Gur, Raquel E; Satterthwaite, Theodore D; Bassett, Danielle S

2017-08-01

Brain development during adolescence is marked by substantial changes in brain structure and function, leading to a stable network topology in adulthood. However, most prior work has examined the data through the lens of brain areas connected to one another in large-scale functional networks. Here, we apply a recently developed hypergraph approach that treats network connections (edges) rather than brain regions as the unit of interest, allowing us to describe functional network topology from a fundamentally different perspective. Capitalizing on a sample of 780 youth imaged as part of the Philadelphia Neurodevelopmental Cohort, this hypergraph representation of resting-state functional MRI data reveals three distinct classes of subnetworks (hyperedges): clusters, bridges, and stars, which respectively represent homogeneously connected, bipartite, and focal architectures. Cluster hyperedges show a strong resemblance to previously-described functional modules of the brain including somatomotor, visual, default mode, and salience systems. In contrast, star hyperedges represent highly localized subnetworks centered on a small set of regions, and are distributed across the entire cortex. Finally, bridge hyperedges link clusters and stars in a core-periphery organization. Notably, developmental changes within hyperedges are ordered in a similar core-periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. Taken together, these results reveal a novel decomposition of the network organization of human brain, and further provide a new perspective on the role of local structures that emerge across neurodevelopment. Hum Brain Mapp 38:3823-3835, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

PubMed Central

Gonzalez-Castillo, Javier; Hoy, Colin W.; Handwerker, Daniel A.; Robinson, Meghan E.; Buchanan, Laura C.; Saad, Ziad S.; Bandettini, Peter A.

2015-01-01

Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configurations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to group-level, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC information decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition. PMID:26124112

Cognitive benefit and cost of acute stress is differentially modulated by individual brain state

PubMed Central

Hermans, Erno J.; Fernández, Guillén

2017-01-01

Abstract Acute stress is associated with beneficial as well as detrimental effects on cognition in different individuals. However, it is not yet known how stress can have such opposing effects. Stroop-like tasks typically show this dissociation: stress diminishes speed, but improves accuracy. We investigated accuracy and speed during a stroop-like task of 120 healthy male subjects after an experimental stress induction or control condition in a randomized, counter-balanced cross-over design; we assessed brain–behavior associations and determined the influence of individual brain connectivity patterns on these associations, which may moderate the effect and help identify stress resilience factors. In the mean, stress was associated to increase in accuracy, but decrease in speed. Accuracy was associated to brain activation in a distributed set of brain regions overlapping with the executive control network (ECN) and speed to temporo-parietal activation. In line with a stress-related large-scale network reconfiguration, individuals showing an upregulation of the salience and down-regulation of the executive-control network under stress displayed increased speed, but decreased performance. In contrast, individuals who upregulate their ECN under stress show improved performance. Our results indicate that the individual large-scale brain network balance under acute stress moderates cognitive consequences of threat. PMID:28402480

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series

PubMed Central

Fransson, Peter

2016-01-01

Abstract Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box–Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed. PMID:27784176

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series.

PubMed

Thompson, William Hedley; Fransson, Peter

2016-12-01

Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box-Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed.

Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex.

PubMed

Ulloa, Antonio; Horwitz, Barry

2016-01-01

A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were "non-task-specific" (NS) neurons that served as noise generators to "task-specific" neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional connectivities using the hybrid LSNM/TVB model and the original LSNM. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors.

Large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

2016-03-01

We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.

Mapping human brain networks with cortico-cortical evoked potentials.

PubMed

Keller, Corey J; Honey, Christopher J; Mégevand, Pierre; Entz, Laszlo; Ulbert, Istvan; Mehta, Ashesh D

2014-10-05

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Overlap and Differences in Brain Networks Underlying the Processing of Complex Sentence Structures in Second Language Users Compared with Native Speakers.

PubMed

Weber, Kirsten; Luther, Lisa; Indefrey, Peter; Hagoort, Peter

2016-05-01

When we learn a second language later in life, do we integrate it with the established neural networks in place for the first language or is at least a partially new network recruited? While there is evidence that simple grammatical structures in a second language share a system with the native language, the story becomes more multifaceted for complex sentence structures. In this study, we investigated the underlying brain networks in native speakers compared with proficient second language users while processing complex sentences. As hypothesized, complex structures were processed by the same large-scale inferior frontal and middle temporal language networks of the brain in the second language, as seen in native speakers. These effects were seen both in activations and task-related connectivity patterns. Furthermore, the second language users showed increased task-related connectivity from inferior frontal to inferior parietal regions of the brain, regions related to attention and cognitive control, suggesting less automatic processing for these structures in a second language.

CoCoMac 2.0 and the future of tract-tracing databases

PubMed Central

Bakker, Rembrandt; Wachtler, Thomas; Diesmann, Markus

2012-01-01

The CoCoMac database contains the results of several hundred published axonal tract-tracing studies in the macaque monkey brain. The combined results are used for constructing the macaque macro-connectome. Here we discuss the redevelopment of CoCoMac and compare it to six connectome-related projects: two online resources that provide full access to raw tracing data in rodents, a connectome viewer for advanced 3D graphics, a partial but highly detailed rat connectome, a brain data management system that generates custom connectivity matrices, and a software package that covers the complete pipeline from connectivity data to large-scale brain simulations. The second edition of CoCoMac features many enhancements over the original. For example, a search wizard is provided for full access to all tables and their nested dependencies. Connectivity matrices can be computed on demand in a user-selected nomenclature. A new data entry system is available as a preview, and is to become a generic solution for community-driven data entry in manually collated databases. We conclude with the question whether neuronal tracing will remain the gold standard to uncover the wiring of brains, thereby highlighting developments in human connectome construction, tracer substances, polarized light imaging, and serial block-face scanning electron microscopy. PMID:23293600

CoCoMac 2.0 and the future of tract-tracing databases.

PubMed

Bakker, Rembrandt; Wachtler, Thomas; Diesmann, Markus

2012-01-01

The CoCoMac database contains the results of several hundred published axonal tract-tracing studies in the macaque monkey brain. The combined results are used for constructing the macaque macro-connectome. Here we discuss the redevelopment of CoCoMac and compare it to six connectome-related projects: two online resources that provide full access to raw tracing data in rodents, a connectome viewer for advanced 3D graphics, a partial but highly detailed rat connectome, a brain data management system that generates custom connectivity matrices, and a software package that covers the complete pipeline from connectivity data to large-scale brain simulations. The second edition of CoCoMac features many enhancements over the original. For example, a search wizard is provided for full access to all tables and their nested dependencies. Connectivity matrices can be computed on demand in a user-selected nomenclature. A new data entry system is available as a preview, and is to become a generic solution for community-driven data entry in manually collated databases. We conclude with the question whether neuronal tracing will remain the gold standard to uncover the wiring of brains, thereby highlighting developments in human connectome construction, tracer substances, polarized light imaging, and serial block-face scanning electron microscopy.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

PubMed

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others' facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity

PubMed Central

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition. PMID:29615882

Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults.

PubMed

Li, Lin; Cazzell, Mary; Babawale, Olajide; Liu, Hanli

2016-10-01

Atlas-guided diffuse optical tomography (atlas-DOT) is a computational means to image changes in cortical hemodynamic signals during human brain activities. Graph theory analysis (GTA) is a network analysis tool commonly used in functional neuroimaging to study brain networks. Atlas-DOT has not been analyzed with GTA to derive large-scale brain connectivity/networks based on near-infrared spectroscopy (NIRS) measurements. We introduced an automated voxel classification (AVC) method that facilitated the use of GTA with atlas-DOT images by grouping unequal-sized finite element voxels into anatomically meaningful regions of interest within the human brain. The overall approach included volume segmentation, AVC, and cross-correlation. To demonstrate the usefulness of AVC, we applied reproducibility analysis to resting-state functional connectivity measurements conducted from 15 young adults in a two-week period. We also quantified and compared changes in several brain network metrics between young and older adults, which were in agreement with those reported by a previous positron emission tomography study. Overall, this study demonstrated that AVC is a useful means for facilitating integration or combination of atlas-DOT with GTA and thus for quantifying NIRS-based, voxel-wise resting-state functional brain networks.

Compensation through Functional Hyperconnectivity: A Longitudinal Connectome Assessment of Mild Traumatic Brain Injury

PubMed Central

Iraji, Armin; Chen, Hanbo; Wiseman, Natalie; Welch, Robert D.; O'Neil, Brian J.; Haacke, E. Mark; Liu, Tianming; Kou, Zhifeng

2016-01-01

Mild traumatic brain injury (mTBI) is a major public health concern. Functional MRI has reported alterations in several brain networks following mTBI. However, the connectome-scale brain network changes are still unknown. In this study, sixteen mTBI patients were prospectively recruited from an emergency department and followed up at 4–6 weeks after injury. Twenty-four healthy controls were also scanned twice with the same time interval. Three hundred fifty-eight brain landmarks that preserve structural and functional correspondence of brain networks across individuals were used to investigate longitudinal brain connectivity. Network-based statistic (NBS) analysis did not find significant difference in the group-by-time interaction and time effects. However, 258 functional pairs show group differences in which mTBI patients have higher functional connectivity. Meta-analysis showed that “Action” and “Cognition” are the most affected functional domains. Categorization of connectomic signatures using multiview group-wise cluster analysis identified two patterns of functional hyperconnectivity among mTBI patients: (I) between the posterior cingulate cortex and the association areas of the brain and (II) between the occipital and the frontal lobes of the brain. Our results demonstrate that brain concussion renders connectome-scale brain network connectivity changes, and the brain tends to be hyperactivated to compensate the pathophysiological disturbances. PMID:26819765

Compensation through Functional Hyperconnectivity: A Longitudinal Connectome Assessment of Mild Traumatic Brain Injury.

PubMed

Iraji, Armin; Chen, Hanbo; Wiseman, Natalie; Welch, Robert D; O'Neil, Brian J; Haacke, E Mark; Liu, Tianming; Kou, Zhifeng

2016-01-01

Mild traumatic brain injury (mTBI) is a major public health concern. Functional MRI has reported alterations in several brain networks following mTBI. However, the connectome-scale brain network changes are still unknown. In this study, sixteen mTBI patients were prospectively recruited from an emergency department and followed up at 4-6 weeks after injury. Twenty-four healthy controls were also scanned twice with the same time interval. Three hundred fifty-eight brain landmarks that preserve structural and functional correspondence of brain networks across individuals were used to investigate longitudinal brain connectivity. Network-based statistic (NBS) analysis did not find significant difference in the group-by-time interaction and time effects. However, 258 functional pairs show group differences in which mTBI patients have higher functional connectivity. Meta-analysis showed that "Action" and "Cognition" are the most affected functional domains. Categorization of connectomic signatures using multiview group-wise cluster analysis identified two patterns of functional hyperconnectivity among mTBI patients: (I) between the posterior cingulate cortex and the association areas of the brain and (II) between the occipital and the frontal lobes of the brain. Our results demonstrate that brain concussion renders connectome-scale brain network connectivity changes, and the brain tends to be hyperactivated to compensate the pathophysiological disturbances.

Critical dynamics on a large human Open Connectome network

NASA Astrophysics Data System (ADS)

Ódor, Géza

2016-12-01

Extended numerical simulations of threshold models have been performed on a human brain network with N =836 733 connected nodes available from the Open Connectome Project. While in the case of simple threshold models a sharp discontinuous phase transition without any critical dynamics arises, variable threshold models exhibit extended power-law scaling regions. This is attributed to fact that Griffiths effects, stemming from the topological or interaction heterogeneity of the network, can become relevant if the input sensitivity of nodes is equalized. I have studied the effects of link directness, as well as the consequence of inhibitory connections. Nonuniversal power-law avalanche size and time distributions have been found with exponents agreeing with the values obtained in electrode experiments of the human brain. The dynamical critical region occurs in an extended control parameter space without the assumption of self-organized criticality.

Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex.

PubMed

Mejias, Jorge F; Murray, John D; Kennedy, Henry; Wang, Xiao-Jing

2016-11-01

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

PubMed Central

Mejias, Jorge F.; Murray, John D.; Kennedy, Henry; Wang, Xiao-Jing

2016-01-01

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions. PMID:28138530

Self-organized dynamical complexity in human wakefulness and sleep: different critical brain-activity feedback for conscious and unconscious states.

PubMed

Allegrini, Paolo; Paradisi, Paolo; Menicucci, Danilo; Laurino, Marco; Piarulli, Andrea; Gemignani, Angelo

2015-09-01

Criticality reportedly describes brain dynamics. The main critical feature is the presence of scale-free neural avalanches, whose auto-organization is determined by a critical branching ratio of neural-excitation spreading. Other features, directly associated to second-order phase transitions, are: (i) scale-free-network topology of functional connectivity, stemming from suprathreshold pairwise correlations, superimposable, in waking brain activity, with that of ferromagnets at Curie temperature; (ii) temporal long-range memory associated to renewal intermittency driven by abrupt fluctuations in the order parameters, detectable in human brain via spatially distributed phase or amplitude changes in EEG activity. Herein we study intermittent events, extracted from 29 night EEG recordings, including presleep wakefulness and all phases of sleep, where different levels of mentation and consciousness are present. We show that while critical avalanching is unchanged, at least qualitatively, intermittency and functional connectivity, present during conscious phases (wakefulness and REM sleep), break down during both shallow and deep non-REM sleep. We provide a theory for fragmentation-induced intermittency breakdown and suggest that the main difference between conscious and unconscious states resides in the backwards causation, namely on the constraints that the emerging properties at large scale induce to the lower scales. In particular, while in conscious states this backwards causation induces a critical slowing down, preserving spatiotemporal correlations, in dreamless sleep we see a self-organized maintenance of moduli working in parallel. Critical avalanches are still present, and establish transient auto-organization, whose enhanced fluctuations are able to trigger sleep-protecting mechanisms that reinstate parallel activity. The plausible role of critical avalanches in dreamless sleep is to provide a rapid recovery of consciousness, if stimuli are highly arousing.

Self-organized dynamical complexity in human wakefulness and sleep: Different critical brain-activity feedback for conscious and unconscious states

NASA Astrophysics Data System (ADS)

Allegrini, Paolo; Paradisi, Paolo; Menicucci, Danilo; Laurino, Marco; Piarulli, Andrea; Gemignani, Angelo

2015-09-01

Criticality reportedly describes brain dynamics. The main critical feature is the presence of scale-free neural avalanches, whose auto-organization is determined by a critical branching ratio of neural-excitation spreading. Other features, directly associated to second-order phase transitions, are: (i) scale-free-network topology of functional connectivity, stemming from suprathreshold pairwise correlations, superimposable, in waking brain activity, with that of ferromagnets at Curie temperature; (ii) temporal long-range memory associated to renewal intermittency driven by abrupt fluctuations in the order parameters, detectable in human brain via spatially distributed phase or amplitude changes in EEG activity. Herein we study intermittent events, extracted from 29 night EEG recordings, including presleep wakefulness and all phases of sleep, where different levels of mentation and consciousness are present. We show that while critical avalanching is unchanged, at least qualitatively, intermittency and functional connectivity, present during conscious phases (wakefulness and REM sleep), break down during both shallow and deep non-REM sleep. We provide a theory for fragmentation-induced intermittency breakdown and suggest that the main difference between conscious and unconscious states resides in the backwards causation, namely on the constraints that the emerging properties at large scale induce to the lower scales. In particular, while in conscious states this backwards causation induces a critical slowing down, preserving spatiotemporal correlations, in dreamless sleep we see a self-organized maintenance of moduli working in parallel. Critical avalanches are still present, and establish transient auto-organization, whose enhanced fluctuations are able to trigger sleep-protecting mechanisms that reinstate parallel activity. The plausible role of critical avalanches in dreamless sleep is to provide a rapid recovery of consciousness, if stimuli are highly arousing.

Enhancement of Spike Synchrony in Hindmarsh-Rose Neural Networks by Randomly Rewiring Connections

NASA Astrophysics Data System (ADS)

Yang, Renhuan; Song, Aiguo; Yuan, Wujie

Spike synchrony of the neural system is thought to have very dichotomous roles. On the one hand, it is ubiquitously present in the healthy brain and is thought to underlie feature binding during information processing. On the other hand, large scale synchronization is an underlying mechanism of epileptic seizures. In this paper, we investigate the spike synchrony of Hindmarsh-Rose (HR) neural networks. Our focus is the influence of the network connections on the spike synchrony of the neural networks. The simulations show that desynchronization in the nearest-neighbor coupled network evolves into accurate synchronization with connection-rewiring probability p increasing. We uncover a phenomenon of enhancement of spike synchrony by randomly rewiring connections. With connection strength c and average connection number m increasing spike synchrony is enhanced but it is not the whole story. Furthermore, the possible mechanism behind such synchronization is also addressed.

Large scale digital atlases in neuroscience

NASA Astrophysics Data System (ADS)

Hawrylycz, M.; Feng, D.; Lau, C.; Kuan, C.; Miller, J.; Dang, C.; Ng, L.

2014-03-01

Imaging in neuroscience has revolutionized our current understanding of brain structure, architecture and increasingly its function. Many characteristics of morphology, cell type, and neuronal circuitry have been elucidated through methods of neuroimaging. Combining this data in a meaningful, standardized, and accessible manner is the scope and goal of the digital brain atlas. Digital brain atlases are used today in neuroscience to characterize the spatial organization of neuronal structures, for planning and guidance during neurosurgery, and as a reference for interpreting other data modalities such as gene expression and connectivity data. The field of digital atlases is extensive and in addition to atlases of the human includes high quality brain atlases of the mouse, rat, rhesus macaque, and other model organisms. Using techniques based on histology, structural and functional magnetic resonance imaging as well as gene expression data, modern digital atlases use probabilistic and multimodal techniques, as well as sophisticated visualization software to form an integrated product. Toward this goal, brain atlases form a common coordinate framework for summarizing, accessing, and organizing this knowledge and will undoubtedly remain a key technology in neuroscience in the future. Since the development of its flagship project of a genome wide image-based atlas of the mouse brain, the Allen Institute for Brain Science has used imaging as a primary data modality for many of its large scale atlas projects. We present an overview of Allen Institute digital atlases in neuroscience, with a focus on the challenges and opportunities for image processing and computation.

The mean-variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI.

PubMed

Thompson, William H; Fransson, Peter

2015-01-01

When studying brain connectivity using fMRI, signal intensity time-series are typically correlated with each other in time to compute estimates of the degree of interaction between different brain regions and/or networks. In the static connectivity case, the problem of defining which connections that should be considered significant in the analysis can be addressed in a rather straightforward manner by a statistical thresholding that is based on the magnitude of the correlation coefficients. More recently, interest has come to focus on the dynamical aspects of brain connectivity and the problem of deciding which brain connections that are to be considered relevant in the context of dynamical changes in connectivity provides further options. Since we, in the dynamical case, are interested in changes in connectivity over time, the variance of the correlation time-series becomes a relevant parameter. In this study, we discuss the relationship between the mean and variance of brain connectivity time-series and show that by studying the relation between them, two conceptually different strategies to analyze dynamic functional brain connectivity become available. Using resting-state fMRI data from a cohort of 46 subjects, we show that the mean of fMRI connectivity time-series scales negatively with its variance. This finding leads to the suggestion that magnitude- versus variance-based thresholding strategies will induce different results in studies of dynamic functional brain connectivity. Our assertion is exemplified by showing that the magnitude-based strategy is more sensitive to within-resting-state network (RSN) connectivity compared to between-RSN connectivity whereas the opposite holds true for a variance-based analysis strategy. The implications of our findings for dynamical functional brain connectivity studies are discussed.

Canceled connections: Lesion-derived network mapping helps explain differences in performance on a complex decision-making task

PubMed Central

Sutterer, Matthew J.; Bruss, Joel; Boes, Aaron D.; Voss, Michelle W.; Bechara, Antoine; Tranel, Daniel

2016-01-01

Studies of patients with brain damage have highlighted a broad neural network of limbic and prefrontal areas as important for adaptive decision-making. However, some patients with damage outside these regions have impaired decision-making behavior, and the behavioral impairments observed in these cases are often attributed to the general variability in behavior following brain damage, rather than a deficit in a specific brain-behavior relationship. A novel approach, lesion-derived network mapping, uses healthy subject resting-state functional connectivity (RSFC) data to infer the areas that would be connected with each patient’s lesion area in healthy adults. Here, we used this approach to investigate whether there was a systematic pattern of connectivity associated with decision-making performance in patients with focal damage in areas not classically associated with decision-making. These patients were categorized a priori into “impaired” or “unimpaired” groups based on their performance on the Iowa Gambling Task (IGT). Lesion-derived network maps based on the impaired patients showed overlap in somatosensory, motor and insula cortices, to a greater extent than patients who showed unimpaired IGT performance. Akin to the classic concept of “diaschisis” (von Monakow, 1914), this focus on the remote effects that focal damage can have on large-scale distributed brain networks has the potential to inform not only differences in decision-making behavior, but also other cognitive functions or neurological syndromes where a distinct phenotype has eluded neuroanatomical classification and brain-behavior relationships appear highly heterogeneous. PMID:26994344

Multimodal MR-imaging reveals large-scale structural and functional connectivity changes in profound early blindness

PubMed Central

Bauer, Corinna M.; Hirsch, Gabriella V.; Zajac, Lauren; Koo, Bang-Bon; Collignon, Olivier

2017-01-01

In the setting of profound ocular blindness, numerous lines of evidence demonstrate the existence of dramatic anatomical and functional changes within the brain. However, previous studies based on a variety of distinct measures have often provided inconsistent findings. To help reconcile this issue, we used a multimodal magnetic resonance (MR)-based imaging approach to provide complementary structural and functional information regarding this neuroplastic reorganization. This included gray matter structural morphometry, high angular resolution diffusion imaging (HARDI) of white matter connectivity and integrity, and resting state functional connectivity MRI (rsfcMRI) analysis. When comparing the brains of early blind individuals to sighted controls, we found evidence of co-occurring decreases in cortical volume and cortical thickness within visual processing areas of the occipital and temporal cortices respectively. Increases in cortical volume in the early blind were evident within regions of parietal cortex. Investigating white matter connections using HARDI revealed patterns of increased and decreased connectivity when comparing both groups. In the blind, increased white matter connectivity (indexed by increased fiber number) was predominantly left-lateralized, including between frontal and temporal areas implicated with language processing. Decreases in structural connectivity were evident involving frontal and somatosensory regions as well as between occipital and cingulate cortices. Differences in white matter integrity (as indexed by quantitative anisotropy, or QA) were also in general agreement with observed pattern changes in the number of white matter fibers. Analysis of resting state sequences showed evidence of both increased and decreased functional connectivity in the blind compared to sighted controls. Specifically, increased connectivity was evident between temporal and inferior frontal areas. Decreases in functional connectivity were observed between occipital and frontal and somatosensory-motor areas and between temporal (mainly fusiform and parahippocampus) and parietal, frontal, and other temporal areas. Correlations in white matter connectivity and functional connectivity observed between early blind and sighted controls showed an overall high degree of association. However, comparing the relative changes in white matter and functional connectivity between early blind and sighted controls did not show a significant correlation. In summary, these findings provide complimentary evidence, as well as highlight potential contradictions, regarding the nature of regional and large scale neuroplastic reorganization resulting from early onset blindness. PMID:28328939

Consensus between Pipelines in Structural Brain Networks

PubMed Central

Parker, Christopher S.; Deligianni, Fani; Cardoso, M. Jorge; Daga, Pankaj; Modat, Marc; Dayan, Michael; Clark, Chris A.

2014-01-01

Structural brain networks may be reconstructed from diffusion MRI tractography data and have great potential to further our understanding of the topological organisation of brain structure in health and disease. Network reconstruction is complex and involves a series of processesing methods including anatomical parcellation, registration, fiber orientation estimation and whole-brain fiber tractography. Methodological choices at each stage can affect the anatomical accuracy and graph theoretical properties of the reconstructed networks, meaning applying different combinations in a network reconstruction pipeline may produce substantially different networks. Furthermore, the choice of which connections are considered important is unclear. In this study, we assessed the similarity between structural networks obtained using two independent state-of-the-art reconstruction pipelines. We aimed to quantify network similarity and identify the core connections emerging most robustly in both pipelines. Similarity of network connections was compared between pipelines employing different atlases by merging parcels to a common and equivalent node scale. We found a high agreement between the networks across a range of fiber density thresholds. In addition, we identified a robust core of highly connected regions coinciding with a peak in similarity across network density thresholds, and replicated these results with atlases at different node scales. The binary network properties of these core connections were similar between pipelines but showed some differences in atlases across node scales. This study demonstrates the utility of applying multiple structural network reconstrution pipelines to diffusion data in order to identify the most important connections for further study. PMID:25356977

Whole Brain Functional Connectivity Pattern Homogeneity Mapping.

PubMed

Wang, Lijie; Xu, Jinping; Wang, Chao; Wang, Jiaojian

2018-01-01

Mounting studies have demonstrated that brain functions are determined by its external functional connectivity patterns. However, how to characterize the voxel-wise similarity of whole brain functional connectivity pattern is still largely unknown. In this study, we introduced a new method called functional connectivity homogeneity (FcHo) to delineate the voxel-wise similarity of whole brain functional connectivity patterns. FcHo was defined by measuring the whole brain functional connectivity patterns similarity of a given voxel with its nearest 26 neighbors using Kendall's coefficient concordance (KCC). The robustness of this method was tested in four independent datasets selected from a large repository of MRI. Furthermore, FcHo mapping results were further validated using the nearest 18 and six neighbors and intra-subject reproducibility with each subject scanned two times. We also compared FcHo distribution patterns with local regional homogeneity (ReHo) to identify the similarity and differences of the two methods. Finally, FcHo method was used to identify the differences of whole brain functional connectivity patterns between professional Chinese chess players and novices to test its application. FcHo mapping consistently revealed that the high FcHo was mainly distributed in association cortex including parietal lobe, frontal lobe, occipital lobe and default mode network (DMN) related areas, whereas the low FcHo was mainly found in unimodal cortex including primary visual cortex, sensorimotor cortex, paracentral lobule and supplementary motor area. These results were further supported by analyses of the nearest 18 and six neighbors and intra-subject similarity. Moreover, FcHo showed both similar and different whole brain distribution patterns compared to ReHo. Finally, we demonstrated that FcHo can effectively identify the whole brain functional connectivity pattern differences between professional Chinese chess players and novices. Our findings indicated that FcHo is a reliable method to delineate the whole brain functional connectivity pattern similarity and may provide a new way to study the functional organization and to reveal neuropathological basis for brain disorders.

Frequency-specific alterations in functional connectivity in treatment-resistant and -sensitive major depressive disorder.

PubMed

He, Zongling; Cui, Qian; Zheng, Junjie; Duan, Xujun; Pang, Yajing; Gao, Qing; Han, Shaoqiang; Long, Zhiliang; Wang, Yifeng; Li, Jiao; Wang, Xiao; Zhao, Jingping; Chen, Huafu

2016-11-01

Major depressive disorder (MDD) may involve alterations in brain functional connectivity in multiple neural circuits and present large-scale network dysfunction. Patients with treatment-resistant depression (TRD) and treatment-sensitive depression (TSD) show different responses to antidepressants and aberrant brain functions. This study aims to investigate functional connectivity patterns of TRD and TSD at the whole brain resting state. Seventeen patients with TRD, 17 patients with TSD, and 17 healthy controls matched with age, gender, and years of education were recruited in this study. The brain was divided using an automated anatomical labeling atlas into 90 regions of interest, which were used to construct the entire brain functional networks. An analysis method called network-based statistic was used to explore the dysconnected subnetworks of TRD and TSD at different frequency bands. At resting state, TSD and TRD present characteristic patterns of network dysfunction at special frequency bands. The dysconnected subnetwork of TSD mainly lies in the fronto-parietal top-down control network. Moreover, the abnormal neural circuits of TRD are extensive and complex. These circuits not only depend on the abnormal affective network but also involve other networks, including salience network, auditory network, visual network, and language processing cortex. Our findings reflect that the pathological mechanism of TSD may refer to impairment in cognitive control, whereas TRD mainly triggers the dysfunction of emotion processing and affective cognition. This study reveals that differences in brain functional connectivity at resting state reflect distinct pathophysiological mechanisms in TSD and TRD. These findings may be helpful in differentiating two types of MDD and predicting treatment responses. Copyright © 2016 Elsevier Ltd. All rights reserved.

Baseline effects of transcranial direct current stimulation on glutamatergic neurotransmission and large-scale network connectivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hunter, Michael A.; Coffman, Brian A.; Gasparovic, Charles

Transcranial direct current stimulation (tDCS) modulates glutamatergic neurotransmission and can be utilized as a novel treatment intervention for a multitude of populations. However, the exact mechanism by which tDCS modulates the brain's neural architecture, from the micro to macro scales, have yet to be investigated. In this paper, using a within-subjects design, resting-state functional magnetic resonance imaging (rs-fMRI) and proton magnetic resonance spectroscopy ( 1H MRS) were performed immediately before and after the administration of anodal tDCS over right parietal cortex. Group independent component analysis (ICA) was used to decompose fMRI scans into 75 brain networks, from which 12 resting-statemore » networks were identified that had significant voxel-wise functional connectivity to anatomical regions of interest. 1H MRS was used to obtain estimates of combined glutamate and glutamine (Glx) concentrations from bilateral intraparietal sulcus. Paired sample t-tests showed significantly increased Glx under the anodal electrode, but not in homologous regions of the contralateral hemisphere. Increases of within-network connectivity were observed within the superior parietal, inferior parietal, left frontal–parietal, salience and cerebellar intrinsic networks, and decreases in connectivity were observed in the anterior cingulate and the basal ganglia ( p<0.05, FDR-corrected). Individual differences in Glx concentrations predicted network connectivity in most of these networks. Finally, the observed relationships between glutamatergic neurotransmission and network connectivity may be used to guide future tDCS protocols that aim to target and alter neuroplastic mechanisms in healthy individuals as well as those with psychiatric and neurologic disorders.« less

Baseline effects of transcranial direct current stimulation on glutamatergic neurotransmission and large-scale network connectivity

DOE PAGES

Hunter, Michael A.; Coffman, Brian A.; Gasparovic, Charles; ...

2014-10-12

Transcranial direct current stimulation (tDCS) modulates glutamatergic neurotransmission and can be utilized as a novel treatment intervention for a multitude of populations. However, the exact mechanism by which tDCS modulates the brain's neural architecture, from the micro to macro scales, have yet to be investigated. In this paper, using a within-subjects design, resting-state functional magnetic resonance imaging (rs-fMRI) and proton magnetic resonance spectroscopy ( 1H MRS) were performed immediately before and after the administration of anodal tDCS over right parietal cortex. Group independent component analysis (ICA) was used to decompose fMRI scans into 75 brain networks, from which 12 resting-statemore » networks were identified that had significant voxel-wise functional connectivity to anatomical regions of interest. 1H MRS was used to obtain estimates of combined glutamate and glutamine (Glx) concentrations from bilateral intraparietal sulcus. Paired sample t-tests showed significantly increased Glx under the anodal electrode, but not in homologous regions of the contralateral hemisphere. Increases of within-network connectivity were observed within the superior parietal, inferior parietal, left frontal–parietal, salience and cerebellar intrinsic networks, and decreases in connectivity were observed in the anterior cingulate and the basal ganglia ( p<0.05, FDR-corrected). Individual differences in Glx concentrations predicted network connectivity in most of these networks. Finally, the observed relationships between glutamatergic neurotransmission and network connectivity may be used to guide future tDCS protocols that aim to target and alter neuroplastic mechanisms in healthy individuals as well as those with psychiatric and neurologic disorders.« less

Whole-brain functional connectivity identification of functional dyspepsia.

PubMed

Nan, Jiaofen; Liu, Jixin; Li, Guoying; Xiong, Shiwei; Yan, Xuemei; Yin, Qing; Zeng, Fang; von Deneen, Karen M; Liang, Fanrong; Gong, Qiyong; Qin, Wei; Tian, Jie

2013-01-01

Recent neuroimaging studies have shown local brain aberrations in functional dyspepsia (FD) patients, yet little attention has been paid to the whole-brain resting-state functional network abnormalities. The purpose of this study was to investigate whether FD disrupts the patterns of whole-brain networks and the abnormal functional connectivity could reflect the severity of the disease. The dysfunctional interactions between brain regions at rest were investigated in FD patients as compared with 40 age- and gender- matched healthy controls. Multivariate pattern analysis was used to evaluate the discriminative power of our results for classifying patients from controls. In our findings, the abnormal brain functional connections were mainly situated within or across the limbic/paralimbic system, the prefrontal cortex, the tempo-parietal areas and the visual cortex. About 96% of the subjects among the original dataset were correctly classified by a leave one-out cross-validation approach, and 88% accuracy was also validated in a replication dataset. The classification features were significantly associated with the patients' dyspepsia symptoms, the self-rating depression scale and self-rating anxiety scale, but it was not correlated with duration of FD patients (p>0.05). Our results may indicate the effectiveness of the altered brain functional connections reflecting the disease pathophysiology underling FD. These dysfunctional connections may be the epiphenomena or causative agents of FD, which may be affected by clinical severity and its related emotional dimension of the disease rather than the clinical course.

Brain hyperconnectivity in children with autism and its links to social deficits.

PubMed

Supekar, Kaustubh; Uddin, Lucina Q; Khouzam, Amirah; Phillips, Jennifer; Gaillard, William D; Kenworthy, Lauren E; Yerys, Benjamin E; Vaidya, Chandan J; Menon, Vinod

2013-11-14

Autism spectrum disorder (ASD), a neurodevelopmental disorder affecting nearly 1 in 88 children, is thought to result from aberrant brain connectivity. Remarkably, there have been no systematic attempts to characterize whole-brain connectivity in children with ASD. Here, we use neuroimaging to show that there are more instances of greater functional connectivity in the brains of children with ASD in comparison to those of typically developing children. Hyperconnectivity in ASD was observed at the whole-brain and subsystems levels, across long- and short-range connections, and was associated with higher levels of fluctuations in regional brain signals. Brain hyperconnectivity predicted symptom severity in ASD, such that children with greater functional connectivity exhibited more severe social deficits. We replicated these findings in two additional independent cohorts, demonstrating again that at earlier ages, the brain of children with ASD is largely functionally hyperconnected in ways that contribute to social dysfunction. Our findings provide unique insights into brain mechanisms underlying childhood autism. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Increased corticolimbic connectivity in cocaine dependence versus pathological gambling is associated with drug severity and emotion-related impulsivity.

PubMed

Contreras-Rodríguez, Oren; Albein-Urios, Natalia; Vilar-López, Raquel; Perales, Jose C; Martínez-Gonzalez, Jose M; Fernández-Serrano, Maria J; Lozano-Rojas, Oscar; Clark, Luke; Verdejo-García, Antonio

2016-05-01

Neural biomarkers for the active detrimental effects of cocaine dependence (CD) are lacking. Direct comparisons of brain connectivity in cocaine-targeted networks between CD and behavioural addictions (i.e. pathological gambling, PG) may be informative. This study therefore contrasted the resting-state functional connectivity networks of 20 individuals with CD, 19 individuals with PG and 21 healthy individuals (controls). Study groups were assessed to rule out psychiatric co-morbidities (except alcohol abuse and nicotine dependence) and current substance use or gambling (except PG). We first examined global connectivity differences in the corticolimbic reward network and then utilized seed-based analyses to characterize the connectivity of regions displaying between-group differences. We examined the relationships between seed-based connectivity and trait impulsivity and cocaine severity. CD compared with PG displayed increased global functional connectivity in a large-scale ventral corticostriatal network involving the orbitofrontal cortex, caudate, thalamus and amygdala. Seed-based analyses showed that CD compared with PG exhibited enhanced connectivity between the orbitofrontal and subgenual cingulate cortices and between caudate and lateral prefrontal cortex, which are involved in representing the value of decision-making feedback. CD and PG compared with controls showed overlapping connectivity changes between the orbitofrontal and dorsomedial prefrontal cortices and between amygdala and insula, which are involved in stimulus-outcome learning. Orbitofrontal-subgenual cingulate cortical connectivity correlated with impulsivity and caudate/amygdala connectivity correlated with cocaine severity. We conclude that CD is linked to enhanced connectivity in a large-scale ventral corticostriatal-amygdala network that is relevant to decision making and likely to reflect an active cocaine detrimental effect. © 2015 Society for the Study of Addiction.

Activity flow over resting-state networks shapes cognitive task activations.

PubMed

Cole, Michael W; Ito, Takuya; Bassett, Danielle S; Schultz, Douglas H

2016-12-01

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

Activity flow over resting-state networks shapes cognitive task activations

PubMed Central

Cole, Michael W.; Ito, Takuya; Bassett, Danielle S.; Schultz, Douglas H.

2016-01-01

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allows prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations. PMID:27723746

Functional connectivity in the resting brain as biological correlate of the Affective Neuroscience Personality Scales.

PubMed

Deris, Nadja; Montag, Christian; Reuter, Martin; Weber, Bernd; Markett, Sebastian

2017-02-15

According to Jaak Panksepp's Affective Neuroscience Theory and the derived self-report measure, the Affective Neuroscience Personality Scales (ANPS), differences in the responsiveness of primary emotional systems form the basis of human personality. In order to investigate neuronal correlates of personality, the underlying neuronal circuits of the primary emotional systems were analyzed in the present fMRI-study by associating the ANPS to functional connectivity in the resting brain. N=120 healthy participants were invited for the present study. The results were reinvestigated in an independent, smaller sample of N=52 participants. A seed-based whole brain approach was conducted with seed-regions bilaterally in the basolateral and superficial amygdalae. The selection of seed-regions was based on meta-analytic data on affective processing and the Juelich histological atlas. Multiple regression analyses on the functional connectivity maps revealed associations with the SADNESS-scale in both samples. Functional resting-state connectivity between the left basolateral amygdala and a cluster in the postcentral gyrus, and between the right basolateral amygdala and clusters in the superior parietal lobe and subgyral in the parietal lobe was associated with SADNESS. No other ANPS-scale revealed replicable results. The present findings give first insights into the neuronal basis of the SADNESS-scale of the ANPS and support the idea of underlying neuronal circuits. In combination with previous research on genetic associations of the ANPS functional resting-state connectivity is discussed as a possible endophenotype of personality. Copyright © 2016 Elsevier Inc. All rights reserved.

Review of thalamocortical resting-state fMRI studies in schizophrenia

PubMed Central

Giraldo-Chica, Monica; Woodward, Neil D.

2017-01-01

Brain circuitry underlying cognition, emotion, and perception is abnormal in schizophrenia. There is considerable evidence that the neuropathology of schizophrenia includes the thalamus, a key hub of cortical-subcortical circuitry and an important regulator of cortical activity. However, the thalamus is a heterogeneous structure composed of several nuclei with distinct inputs and cortical connections. Limitations of conventional neuroimaging methods and conflicting findings from post-mortem investigations have made it difficult to determine if thalamic pathology in schizophrenia is widespread or limited to specific thalamocortical circuits. Resting-state fMRI has proven invaluable for understanding the large-scale functional organization of the brain and investigating neural circuitry relevant to psychiatric disorders. This article summarizes resting-state fMRI investigations of thalamocortical functional connectivity in schizophrenia. Particular attention is paid to the course, diagnostic specificity, and clinical correlates of thalamocortical network dysfunction. PMID:27531067

Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

PubMed Central

Li, Xiang; Lim, Chulwoo; Li, Kaiming; Guo, Lei; Liu, Tianming

2013-01-01

Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. PMID:22941508

Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

PubMed Central

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. PMID:27341204

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

PubMed

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

Financial Exploitation Is Associated With Structural and Functional Brain Differences in Healthy Older Adults

PubMed Central

Spreng, R. Nathan; Cassidy, Benjamin N; Darboh, Bri S; DuPre, Elizabeth; Lockrow, Amber W; Setton, Roni; Turner, Gary R

2017-01-01

Abstract Background Age-related brain changes leading to altered socioemotional functioning may increase vulnerability to financial exploitation. If confirmed, this would suggest a novel mechanism leading to heightened financial exploitation risk in older adults. Development of predictive neural markers could facilitate increased vigilance and prevention. In this preliminary study, we sought to identify structural and functional brain differences associated with financial exploitation in older adults. Methods Financially exploited older adults (n = 13, 7 female) and a matched cohort of older adults who had been exposed to, but avoided, a potentially exploitative situation (n = 13, 7 female) were evaluated. Using magnetic resonance imaging, we examined cortical thickness and resting state functional connectivity. Behavioral data were collected using standardized cognitive assessments, self-report measures of mood and social functioning. Results The exploited group showed cortical thinning in anterior insula and posterior superior temporal cortices, regions associated with processing affective and social information, respectively. Functional connectivity encompassing these regions, within default and salience networks, was reduced, while between network connectivity was increased. Self-reported anger and hostility was higher for the exploited group. Conclusions We observed financial exploitation associated with brain differences in regions involved in socioemotional functioning. These exploratory and preliminary findings suggest that alterations in brain regions implicated in socioemotional functioning may be a marker of financial exploitation risk. Large-scale, prospective studies are necessary to validate this neural mechanism, and develop predictive markers for use in clinical practice. PMID:28369260

Effective connectivity of brain regions underlying third-party punishment: Functional MRI and Granger causality evidence.

PubMed

Bellucci, Gabriele; Chernyak, Sergey; Hoffman, Morris; Deshpande, Gopikrishna; Dal Monte, Olga; Knutson, Kristine M; Grafman, Jordan; Krueger, Frank

2017-04-01

Third-party punishment (TPP) for norm violations is an essential deterrent in large-scale human societies, and builds on two essential cognitive functions: evaluating legal responsibility and determining appropriate punishment. Despite converging evidence that TPP is mediated by a specific set of brain regions, little is known about their effective connectivity (direction and strength of connections). Applying parametric event-related functional MRI in conjunction with multivariate Granger causality analysis, we asked healthy participants to estimate how much punishment a hypothetical perpetrator deserves for intentionally committing criminal offenses varying in levels of harm. Our results confirmed that TPP legal decisions are based on two domain-general networks: the mentalizing network for evaluating legal responsibility and the central-executive network for determining appropriate punishment. Further, temporal pole (TP) and dorsomedial prefrontal cortex (PFC) emerged as hubs of the mentalizing network, uniquely generating converging output connections to ventromedial PFC, temporo-parietal junction, and posterior cingulate. In particular, dorsomedial PFC received inputs only from TP and both its activation and its connectivity to dorsolateral PFC correlated with degree of punishment. This supports the hypothesis that dorsomedial PFC acts as the driver of the TPP activation pattern, leading to the decision on the appropriate punishment. In conclusion, these results advance our understanding of the organizational elements of the TPP brain networks and provide better insights into the mental states of judges and jurors tasked with blaming and punishing legal wrongs.

Intensive reasoning training alters patterns of brain connectivity at rest

PubMed Central

Mackey, Allyson P.; Miller Singley, Alison T.; Bunge, Silvia A.

2013-01-01

Patterns of correlated activity among brain regions reflect functionally relevant networks that are widely assumed to be stable over time. We hypothesized that if these correlations reflect the prior history of co-activation of brain regions, then a marked shift in cognition could alter the strength of coupling between these regions. We sought to test whether intensive reasoning training in humans would result in tighter coupling among regions in the lateral fronto-parietal network, as measured with resting-state fMRI (rs-fMRI). Rather than designing an artificial training program, we studied individuals who were preparing for a standardized test that places heavy demands on relational reasoning, the Law School Admissions Test (LSAT). LSAT questions require test-takers to group or sequence items according to a set of complex rules. We recruited young adults who were enrolled in an LSAT course that offers 70 hours of reasoning instruction (n=25), and age- and IQ-matched controls intending to take the LSAT in the future (n=24). Rs-fMRI data were collected for all subjects during two scanning sessions separated by 90 days. An analysis of pairwise correlations between brain regions implicated in reasoning showed that fronto-parietal connections were strengthened, along with parietal-striatal connections. These findings provide strong evidence for neural plasticity at the level of large-scale networks supporting high-level cognition. PMID:23486950

Relationship between individual differences in functional connectivity and facial-emotion recognition abilities in adults with traumatic brain injury.

PubMed

Rigon, A; Voss, M W; Turkstra, L S; Mutlu, B; Duff, M C

2017-01-01

Although several studies have demonstrated that facial-affect recognition impairment is common following moderate-severe traumatic brain injury (TBI), and that there are diffuse alterations in large-scale functional brain networks in TBI populations, little is known about the relationship between the two. Here, in a sample of 26 participants with TBI and 20 healthy comparison participants (HC) we measured facial-affect recognition abilities and resting-state functional connectivity (rs-FC) using fMRI. We then used network-based statistics to examine (A) the presence of rs-FC differences between individuals with TBI and HC within the facial-affect processing network, and (B) the association between inter-individual differences in emotion recognition skills and rs-FC within the facial-affect processing network. We found that participants with TBI showed significantly lower rs-FC in a component comprising homotopic and within-hemisphere, anterior-posterior connections within the facial-affect processing network. In addition, within the TBI group, participants with higher emotion-labeling skills showed stronger rs-FC within a network comprised of intra- and inter-hemispheric bilateral connections. Findings indicate that the ability to successfully recognize facial-affect after TBI is related to rs-FC within components of facial-affective networks, and provide new evidence that further our understanding of the mechanisms underlying emotion recognition impairment in TBI.

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

PubMed Central

Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

2018-01-01

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes. PMID:29642483

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks.

PubMed

Murakami, Masaya; Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

2018-04-08

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

The convergence of maturational change and structural covariance in human cortical networks.

PubMed

Alexander-Bloch, Aaron; Raznahan, Armin; Bullmore, Ed; Giedd, Jay

2013-02-13

Large-scale covariance of cortical thickness or volume in distributed brain regions has been consistently reported by human neuroimaging studies. The mechanism of this population covariance of regional cortical anatomy has been hypothetically related to synchronized maturational changes in anatomically connected neuronal populations. Brain regions that grow together, i.e., increase or decrease in volume at the same rate over the course of years in the same individual, are thus expected to demonstrate strong structural covariance or anatomical connectivity across individuals. To test this prediction, we used a structural MRI dataset on healthy young people (N = 108; aged 9-22 years at enrollment), comprising 3-6 longitudinal scans on each participant over 6-12 years of follow-up. At each of 360 regional nodes, and for each participant, we estimated the following: (1) the cortical thickness in the median scan and (2) the linear rate of change in cortical thickness over years of serial scanning. We constructed structural and maturational association matrices and networks from these measurements. Both structural and maturational networks shared similar global and nodal topological properties, as well as mesoscopic features including a modular community structure, a relatively small number of highly connected hub regions, and a bias toward short distance connections. Using resting-state functional magnetic resonance imaging data on a subset of the sample (N = 32), we also demonstrated that functional connectivity and network organization was somewhat predictable by structural/maturational networks but demonstrated a stronger bias toward short distance connections and greater topological segregation. Brain structural covariance networks are likely to reflect synchronized developmental change in distributed cortical regions.

Brain corticostriatal systems and the major clinical symptom dimensions of obsessive-compulsive disorder.

PubMed

Harrison, Ben J; Pujol, Jesus; Cardoner, Narcis; Deus, Joan; Alonso, Pino; López-Solà, Marina; Contreras-Rodríguez, Oren; Real, Eva; Segalàs, Cinto; Blanco-Hinojo, Laura; Menchon, José M; Soriano-Mas, Carles

2013-02-15

Functional neuroimaging studies have provided consistent support for the idea that obsessive-compulsive disorder (OCD) is associated with disturbances of brain corticostriatal systems. However, in general, these studies have not sought to account for the disorder's prominent clinical heterogeneity. To address these concerns, we investigated the influence of major OCD symptom dimensions on brain corticostriatal functional systems in a large sample of OCD patients (n = 74) and control participants (n = 74) examined with resting-state functional magnetic resonance imaging. We employed a valid method for mapping ventral and dorsal striatal functional connectivity, which supported both standard group comparisons and linear regression analyses with patients' scores on the Dimensional Yale-Brown Obsessive-Compulsive Scale. Consistent with past findings, patients demonstrated a common connectivity alteration involving the ventral striatum and orbitofrontal cortex that predicted overall illness severity levels. This common alteration was independent of the effect of particular symptom dimensions. Instead, we observed distinct anatomical relationships between the severity of symptom dimensions and striatal functional connectivity. Aggression symptoms modulated connectivity between the ventral striatum, amygdala, and ventromedial frontal cortex, while sexual/religious symptoms had a specific influence on ventral striatal-insular connectivity. Hoarding modulated the strength of ventral and dorsal striatal connectivity with distributed frontal regions. Taken together, these results suggest that pathophysiological changes among orbitofrontal-striatal regions may be common to all forms of OCD. They suggest that a further examination of certain dimensional relationships will also be relevant for advancing current neurobiological models of the disorder. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Dynamic reconfiguration of frontal brain networks during executive cognition in humans

PubMed Central

Braun, Urs; Schäfer, Axel; Walter, Henrik; Erk, Susanne; Romanczuk-Seiferth, Nina; Haddad, Leila; Schweiger, Janina I.; Grimm, Oliver; Heinz, Andreas; Tost, Heike; Meyer-Lindenberg, Andreas; Bassett, Danielle S.

2015-01-01

The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of “dynamic network neuroscience” to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the “n-back” task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes “network flexibility,” employs transient and heterogeneous connectivity between frontal systems, which we refer to as “integration.” Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia. PMID:26324898

Effects of training strategies implemented in a complex videogame on functional connectivity of attentional networks.

PubMed

Voss, Michelle W; Prakash, Ruchika Shaurya; Erickson, Kirk I; Boot, Walter R; Basak, Chandramallika; Neider, Mark B; Simons, Daniel J; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

2012-01-02

We used the Space Fortress videogame, originally developed by cognitive psychologists to study skill acquisition, as a platform to examine learning-induced plasticity of interacting brain networks. Novice videogame players learned Space Fortress using one of two training strategies: (a) focus on all aspects of the game during learning (fixed priority), or (b) focus on improving separate game components in the context of the whole game (variable priority). Participants were scanned during game play using functional magnetic resonance imaging (fMRI), both before and after 20 h of training. As expected, variable priority training enhanced learning, particularly for individuals who initially performed poorly. Functional connectivity analysis revealed changes in brain network interaction reflective of more flexible skill learning and retrieval with variable priority training, compared to procedural learning and skill implementation with fixed priority training. These results provide the first evidence for differences in the interaction of large-scale brain networks when learning with different training strategies. Our approach and findings also provide a foundation for exploring the brain plasticity involved in transfer of trained abilities to novel real-world tasks such as driving, sport, or neurorehabilitation. Copyright © 2011 Elsevier Inc. All rights reserved.

Spherical Harmonics Reveal Standing EEG Waves and Long-Range Neural Synchronization during Non-REM Sleep.

PubMed

Sivakumar, Siddharth S; Namath, Amalia G; Galán, Roberto F

2016-01-01

Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale of neural dynamics, we have analyzed EEG data recorded with the standard 10-20 electrode system from 41 neurologically normal children and adolescents during stage 2, non-REM sleep. We show that the principal components of EEG spindles, or sigma waves (10-16 Hz), reveal non-propagating, standing waves in the form of spherical harmonics. We mathematically demonstrate that standing EEG waves exist when the spatial covariance and the Laplacian operator on the head's surface commute. This in turn implies that the covariance between two EEG channels decreases as the inverse of their relative distance; a relationship that we corroborate with empirical data. Using volume conduction theory, we then demonstrate that superficial current sources are more synchronized at larger distances, and determine the characteristic length of large-scale neural synchronization as 1.31 times the head radius, on average. Moreover, consistent with the hypothesis that EEG spindles are driven by thalamo-cortical rather than cortico-cortical loops, we also show that 8 additional patients with hypoplasia or complete agenesis of the corpus callosum, i.e., with deficient or no connectivity between cortical hemispheres, similarly exhibit standing EEG waves in the form of spherical harmonics. We conclude that spherical harmonics are a hallmark of spontaneous, large-scale synchronization of neural activity in the brain, which are associated with unconscious, light sleep. The analogy with spherical harmonics in quantum mechanics suggests that the variances (eigenvalues) of the principal components follow a Boltzmann distribution, or equivalently, that standing waves are in a sort of "thermodynamic" equilibrium during non-REM sleep. By extension, we speculate that consciousness emerges as the brain dynamics deviate from such equilibrium.

Spherical Harmonics Reveal Standing EEG Waves and Long-Range Neural Synchronization during Non-REM Sleep

PubMed Central

Sivakumar, Siddharth S.; Namath, Amalia G.; Galán, Roberto F.

2016-01-01

Previous work from our lab has demonstrated how the connectivity of brain circuits constrains the repertoire of activity patterns that those circuits can display. Specifically, we have shown that the principal components of spontaneous neural activity are uniquely determined by the underlying circuit connections, and that although the principal components do not uniquely resolve the circuit structure, they do reveal important features about it. Expanding upon this framework on a larger scale of neural dynamics, we have analyzed EEG data recorded with the standard 10–20 electrode system from 41 neurologically normal children and adolescents during stage 2, non-REM sleep. We show that the principal components of EEG spindles, or sigma waves (10–16 Hz), reveal non-propagating, standing waves in the form of spherical harmonics. We mathematically demonstrate that standing EEG waves exist when the spatial covariance and the Laplacian operator on the head's surface commute. This in turn implies that the covariance between two EEG channels decreases as the inverse of their relative distance; a relationship that we corroborate with empirical data. Using volume conduction theory, we then demonstrate that superficial current sources are more synchronized at larger distances, and determine the characteristic length of large-scale neural synchronization as 1.31 times the head radius, on average. Moreover, consistent with the hypothesis that EEG spindles are driven by thalamo-cortical rather than cortico-cortical loops, we also show that 8 additional patients with hypoplasia or complete agenesis of the corpus callosum, i.e., with deficient or no connectivity between cortical hemispheres, similarly exhibit standing EEG waves in the form of spherical harmonics. We conclude that spherical harmonics are a hallmark of spontaneous, large-scale synchronization of neural activity in the brain, which are associated with unconscious, light sleep. The analogy with spherical harmonics in quantum mechanics suggests that the variances (eigenvalues) of the principal components follow a Boltzmann distribution, or equivalently, that standing waves are in a sort of “thermodynamic” equilibrium during non-REM sleep. By extension, we speculate that consciousness emerges as the brain dynamics deviate from such equilibrium. PMID:27445777

Functional brain networks in schizophrenia: a review.

PubMed

Calhoun, Vince D; Eichele, Tom; Pearlson, Godfrey

2009-01-01

Functional magnetic resonance imaging (fMRI) has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event-related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA) which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large-scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their inter-relationships with fMRI has great potential to improve our understanding of schizophrenia.

Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex

PubMed Central

Ulloa, Antonio; Horwitz, Barry

2016-01-01

A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were “non-task-specific” (NS) neurons that served as noise generators to “task-specific” neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional connectivities using the hybrid LSNM/TVB model and the original LSNM. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors. PMID:27536235

A comparative study of theoretical graph models for characterizing structural networks of human brain.

PubMed

Li, Xiaojin; Hu, Xintao; Jin, Changfeng; Han, Junwei; Liu, Tianming; Guo, Lei; Hao, Wei; Li, Lingjiang

2013-01-01

Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

Self-similarity and quasi-idempotence in neural networks and related dynamical systems.

PubMed

Minati, Ludovico; Winkel, Julia; Bifone, Angelo; Oświęcimka, Paweł; Jovicich, Jorge

2017-04-01

Self-similarity across length scales is pervasively observed in natural systems. Here, we investigate topological self-similarity in complex networks representing diverse forms of connectivity in the brain and some related dynamical systems, by considering the correlation between edges directly connecting any two nodes in a network and indirect connection between the same via all triangles spanning the rest of the network. We note that this aspect of self-similarity, which is distinct from hierarchically nested connectivity (coarse-grain similarity), is closely related to idempotence of the matrix representing the graph. We introduce two measures, ι(1) and ι(∞), which represent the element-wise correlation coefficients between the initial matrix and the ones obtained after squaring it once or infinitely many times, and term the matrices which yield large values of these parameters "quasi-idempotent". These measures delineate qualitatively different forms of "shallow" and "deep" quasi-idempotence, which are influenced by nodal strength heterogeneity. A high degree of quasi-idempotence was observed for partially synchronized mean-field Kuramoto oscillators with noise, electronic chaotic oscillators, and cultures of dissociated neurons, wherein the expression of quasi-idempotence correlated strongly with network maturity. Quasi-idempotence was also detected for macro-scale brain networks representing axonal connectivity, synchronization of slow activity fluctuations during idleness, and co-activation across experimental tasks, and preliminary data indicated that quasi-idempotence of structural connectivity may decrease with ageing. This initial study highlights that the form of network self-similarity indexed by quasi-idempotence is detectable in diverse dynamical systems, and draws attention to it as a possible basis for measures representing network "collectivity" and pattern formation.

Self-similarity and quasi-idempotence in neural networks and related dynamical systems

NASA Astrophysics Data System (ADS)

Minati, Ludovico; Winkel, Julia; Bifone, Angelo; Oświecimka, Paweł; Jovicich, Jorge

2017-04-01

Self-similarity across length scales is pervasively observed in natural systems. Here, we investigate topological self-similarity in complex networks representing diverse forms of connectivity in the brain and some related dynamical systems, by considering the correlation between edges directly connecting any two nodes in a network and indirect connection between the same via all triangles spanning the rest of the network. We note that this aspect of self-similarity, which is distinct from hierarchically nested connectivity (coarse-grain similarity), is closely related to idempotence of the matrix representing the graph. We introduce two measures, ι ( 1 ) and ι ( ∞ ) , which represent the element-wise correlation coefficients between the initial matrix and the ones obtained after squaring it once or infinitely many times, and term the matrices which yield large values of these parameters "quasi-idempotent". These measures delineate qualitatively different forms of "shallow" and "deep" quasi-idempotence, which are influenced by nodal strength heterogeneity. A high degree of quasi-idempotence was observed for partially synchronized mean-field Kuramoto oscillators with noise, electronic chaotic oscillators, and cultures of dissociated neurons, wherein the expression of quasi-idempotence correlated strongly with network maturity. Quasi-idempotence was also detected for macro-scale brain networks representing axonal connectivity, synchronization of slow activity fluctuations during idleness, and co-activation across experimental tasks, and preliminary data indicated that quasi-idempotence of structural connectivity may decrease with ageing. This initial study highlights that the form of network self-similarity indexed by quasi-idempotence is detectable in diverse dynamical systems, and draws attention to it as a possible basis for measures representing network "collectivity" and pattern formation.

Functional connectivity and graph theory in preclinical Alzheimer's disease.

PubMed

Brier, Matthew R; Thomas, Jewell B; Fagan, Anne M; Hassenstab, Jason; Holtzman, David M; Benzinger, Tammie L; Morris, John C; Ances, Beau M

2014-04-01

Alzheimer's disease (AD) has a long preclinical phase in which amyloid and tau cerebral pathology accumulate without producing cognitive symptoms. Resting state functional connectivity magnetic resonance imaging has demonstrated that brain networks degrade during symptomatic AD. It is unclear to what extent these degradations exist before symptomatic onset. In this study, we investigated graph theory metrics of functional integration (path length), functional segregation (clustering coefficient), and functional distinctness (modularity) as a function of disease severity. Further, we assessed whether these graph metrics were affected in cognitively normal participants with cerebrospinal fluid evidence of preclinical AD. Clustering coefficient and modularity, but not path length, were reduced in AD. Cognitively normal participants who harbored AD biomarker pathology also showed reduced values in these graph measures, demonstrating brain changes similar to, but smaller than, symptomatic AD. Only modularity was significantly affected by age. We also demonstrate that AD has a particular effect on hub-like regions in the brain. We conclude that AD causes large-scale disconnection that is present before onset of symptoms. Copyright © 2014 Elsevier Inc. All rights reserved.

On consciousness, resting state fMRI, and neurodynamics

PubMed Central

2010-01-01

Background During the last years, functional magnetic resonance imaging (fMRI) of the brain has been introduced as a new tool to measure consciousness, both in a clinical setting and in a basic neurocognitive research. Moreover, advanced mathematical methods and theories have arrived the field of fMRI (e.g. computational neuroimaging), and functional and structural brain connectivity can now be assessed non-invasively. Results The present work deals with a pluralistic approach to "consciousness'', where we connect theory and tools from three quite different disciplines: (1) philosophy of mind (emergentism and global workspace theory), (2) functional neuroimaging acquisitions, and (3) theory of deterministic and statistical neurodynamics – in particular the Wilson-Cowan model and stochastic resonance. Conclusions Based on recent experimental and theoretical work, we believe that the study of large-scale neuronal processes (activity fluctuations, state transitions) that goes on in the living human brain while examined with functional MRI during "resting state", can deepen our understanding of graded consciousness in a clinical setting, and clarify the concept of "consiousness" in neurocognitive and neurophilosophy research. PMID:20522270

Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer's disease.

PubMed

Chong, Joanna Su Xian; Liu, Siwei; Loke, Yng Miin; Hilal, Saima; Ikram, Mohammad Kamran; Xu, Xin; Tan, Boon Yeow; Venketasubramanian, Narayanaswamy; Chen, Christopher Li-Hsian; Zhou, Juan

2017-11-01

Network-sensitive neuroimaging methods have been used to characterize large-scale brain network degeneration in Alzheimer's disease and its prodrome. However, few studies have investigated the combined effect of Alzheimer's disease and cerebrovascular disease on brain network degeneration. Our study sought to examine the intrinsic functional connectivity and structural covariance network changes in 235 prodromal and clinical Alzheimer's disease patients with and without cerebrovascular disease. We focused particularly on two higher-order cognitive networks-the default mode network and the executive control network. We found divergent functional connectivity and structural covariance patterns in Alzheimer's disease patients with and without cerebrovascular disease. Alzheimer's disease patients without cerebrovascular disease, but not Alzheimer's disease patients with cerebrovascular disease, showed reductions in posterior default mode network functional connectivity. By comparison, while both groups exhibited parietal reductions in executive control network functional connectivity, only Alzheimer's disease patients with cerebrovascular disease showed increases in frontal executive control network connectivity. Importantly, these distinct executive control network changes were recapitulated in prodromal Alzheimer's disease patients with and without cerebrovascular disease. Across Alzheimer's disease patients with and without cerebrovascular disease, higher default mode network functional connectivity z-scores correlated with greater hippocampal volumes while higher executive control network functional connectivity z-scores correlated with greater white matter changes. In parallel, only Alzheimer's disease patients without cerebrovascular disease showed increased default mode network structural covariance, while only Alzheimer's disease patients with cerebrovascular disease showed increased executive control network structural covariance compared to controls. Our findings demonstrate the differential neural network structural and functional changes in Alzheimer's disease with and without cerebrovascular disease, suggesting that the underlying pathology of Alzheimer's disease patients with cerebrovascular disease might differ from those without cerebrovascular disease and reflect a combination of more severe cerebrovascular disease and less severe Alzheimer's disease network degeneration phenotype. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

A pairwise maximum entropy model accurately describes resting-state human brain networks

PubMed Central

Watanabe, Takamitsu; Hirose, Satoshi; Wada, Hiroyuki; Imai, Yoshio; Machida, Toru; Shirouzu, Ichiro; Konishi, Seiki; Miyashita, Yasushi; Masuda, Naoki

2013-01-01

The resting-state human brain networks underlie fundamental cognitive functions and consist of complex interactions among brain regions. However, the level of complexity of the resting-state networks has not been quantified, which has prevented comprehensive descriptions of the brain activity as an integrative system. Here, we address this issue by demonstrating that a pairwise maximum entropy model, which takes into account region-specific activity rates and pairwise interactions, can be robustly and accurately fitted to resting-state human brain activities obtained by functional magnetic resonance imaging. Furthermore, to validate the approximation of the resting-state networks by the pairwise maximum entropy model, we show that the functional interactions estimated by the pairwise maximum entropy model reflect anatomical connexions more accurately than the conventional functional connectivity method. These findings indicate that a relatively simple statistical model not only captures the structure of the resting-state networks but also provides a possible method to derive physiological information about various large-scale brain networks. PMID:23340410

Joint Pairing and Structured Mapping of Convolutional Brain Morphological Multiplexes for Early Dementia Diagnosis.

PubMed

Lisowska, Anna; Rekik, Islem

2018-06-21

Diagnosis of brain dementia, particularly early mild cognitive impairment (eMCI), is critical for early intervention to prevent the onset of Alzheimer's Disease (AD), where cognitive decline is severe and irreversible. There is a large body of machine-learning based research investigating how dementia alters brain connectivity, mainly using structural (derived from diffusion MRI) and functional (derived from resting-state functional MRI) brain connectomic data. However, how early dementia affects cortical brain connections in morphology remains largely unexplored. To fill this gap, we propose a joint morphological brain multiplexes pairing and mapping strategy for early MCI detection, where a brain multiplex not only encodes the similarity in morphology between pairs of brain regions, but also a pair of brain morphological networks. Experimental results confirm that the proposed framework outperforms in classification accuracy several state-of-the-art methods. More importantly, we unprecedentedly identified most discriminative brain morphological networks between eMCI and NC, which included the paired views derived from maximum principal curvature and the sulcal depth for the left hemisphere and sulcal depth and the average curvature for the right hemisphere. We also identified the most highly correlated morphological brain connections in our cohort, which included the (pericalcarine cortex, insula cortex) on the maximum principal curvature view, (entorhinal cortex, insula cortex) on the mean sulcal depth view, and (entorhinal cortex, pericalcarine cortex) on the mean average curvature view, for both hemispheres. These highly correlated morphological connections might serve as biomarkers for early MCI diagnosis.

Emergence of Rich-Club Topology and Coordinated Dynamics in Development of Hippocampal Functional Networks In Vitro

PubMed Central

Charlesworth, Paul; Kitzbichler, Manfred G.; Paulsen, Ole

2015-01-01

Recent studies demonstrated that the anatomical network of the human brain shows a “rich-club” organization. This complex topological feature implies that highly connected regions, hubs of the large-scale brain network, are more densely interconnected with each other than expected by chance. Rich-club nodes were traversed by a majority of short paths between peripheral regions, underlining their potential importance for efficient global exchange of information between functionally specialized areas of the brain. Network hubs have also been described at the microscale of brain connectivity (so-called “hub neurons”). Their role in shaping synchronous dynamics and forming microcircuit wiring during development, however, is not yet fully understood. The present study aimed to investigate the role of hubs during network development, using multi-electrode arrays and functional connectivity analysis during spontaneous multi-unit activity (MUA) of dissociated primary mouse hippocampal neurons. Over the first 4 weeks in vitro, functional connectivity significantly increased in strength, density, and size, with mature networks demonstrating a robust modular and small-world topology. As expected by a “rich-get-richer” growth rule of network evolution, MUA graphs were found to form rich-clubs at an early stage in development (14 DIV). Later on, rich-club nodes were a consistent topological feature of MUA graphs, demonstrating high nodal strength, efficiency, and centrality. Rich-club nodes were also found to be crucial for MUA dynamics. They often served as broker of spontaneous activity flow, confirming that hub nodes and rich-clubs may play an important role in coordinating functional dynamics at the microcircuit level. PMID:25855164

Large-Scale Functional Brain Network Reorganization During Taoist Meditation.

PubMed

Jao, Tun; Li, Chia-Wei; Vértes, Petra E; Wu, Changwei Wesley; Achard, Sophie; Hsieh, Chao-Hsien; Liou, Chien-Hui; Chen, Jyh-Horng; Bullmore, Edward T

2016-02-01

Meditation induces a distinct and reversible mental state that provides insights into brain correlates of consciousness. We explored brain network changes related to meditation by graph theoretical analysis of resting-state functional magnetic resonance imaging data. Eighteen Taoist meditators with varying levels of expertise were scanned using a within-subjects counterbalanced design during resting and meditation states. State-related differences in network topology were measured globally and at the level of individual nodes and edges. Although measures of global network topology, such as small-worldness, were unchanged, meditation was characterized by an extensive and expertise-dependent reorganization of the hubs (highly connected nodes) and edges (functional connections). Areas of sensory cortex, especially the bilateral primary visual and auditory cortices, and the bilateral temporopolar areas, which had the highest degree (or connectivity) during the resting state, showed the biggest decrease during meditation. Conversely, bilateral thalamus and components of the default mode network, mainly the bilateral precuneus and posterior cingulate cortex, had low degree in the resting state but increased degree during meditation. Additionally, these changes in nodal degree were accompanied by reorganization of anatomical orientation of the edges. During meditation, long-distance longitudinal (antero-posterior) edges increased proportionally, whereas orthogonal long-distance transverse (right-left) edges connecting bilaterally homologous cortices decreased. Our findings suggest that transient changes in consciousness associated with meditation introduce convergent changes in the topological and spatial properties of brain functional networks, and the anatomical pattern of integration might be as important as the global level of integration when considering the network basis for human consciousness.

Attention reorganizes connectivity across networks in a frequency specific manner.

PubMed

Kwon, Soyoung; Watanabe, Masataka; Fischer, Elvira; Bartels, Andreas

2017-01-01

Attention allows our brain to focus its limited resources on a given task. It does so by selective modulation of neural activity and of functional connectivity (FC) across brain-wide networks. While there is extensive literature on activity changes, surprisingly few studies examined brain-wide FC modulations that can be cleanly attributed to attention compared to matched visual processing. In contrast to prior approaches, we used an ultra-long trial design that avoided transients from trial onsets, included slow fluctuations (<0.1Hz) that carry important information on FC, and allowed for frequency-segregated analyses. We found that FC derived from long blocks had a nearly two-fold higher gain compared to FC derived from traditional (short) block designs. Second, attention enhanced intrinsic (negative or positive) correlations across networks, such as between the default-mode network (DMN), the dorsal attention network (DAN), and the visual system (VIS). In contrast attention de-correlated the intrinsically correlated visual regions. Third, the de-correlation within VIS was driven primarily by high frequencies, whereas the increase in DAN-VIS predominantly by low frequencies. These results pinpoint two fundamentally distinct effects of attention on connectivity. Information flow increases between distinct large-scale networks, and de-correlation within sensory cortex indicates decreased redundancy. Copyright © 2016 Elsevier Inc. All rights reserved.

Inferring multi-scale neural mechanisms with brain network modelling

PubMed Central

Schirner, Michael; McIntosh, Anthony Randal; Jirsa, Viktor; Deco, Gustavo

2018-01-01

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies. PMID:29308767

Analyzing the association between functional connectivity of the brain and intellectual performance

PubMed Central

Pamplona, Gustavo S. P.; Santos Neto, Gérson S.; Rosset, Sara R. E.; Rogers, Baxter P.; Salmon, Carlos E. G.

2015-01-01

Measurements of functional connectivity support the hypothesis that the brain is composed of distinct networks with anatomically separated nodes but common functionality. A few studies have suggested that intellectual performance may be associated with greater functional connectivity in the fronto-parietal network and enhanced global efficiency. In this fMRI study, we performed an exploratory analysis of the relationship between the brain's functional connectivity and intelligence scores derived from the Portuguese language version of the Wechsler Adult Intelligence Scale (WAIS-III) in a sample of 29 people, born and raised in Brazil. We examined functional connectivity between 82 regions, including graph theoretic properties of the overall network. Some previous findings were extended to the Portuguese-speaking population, specifically the presence of small-world organization of the brain and relationships of intelligence with connectivity of frontal, pre-central, parietal, occipital, fusiform and supramarginal gyrus, and caudate nucleus. Verbal comprehension was associated with global network efficiency, a new finding. PMID:25713528

Multimodal imaging of the self-regulating developing brain.

PubMed

Fjell, Anders M; Walhovd, Kristine Beate; Brown, Timothy T; Kuperman, Joshua M; Chung, Yoonho; Hagler, Donald J; Venkatraman, Vijay; Roddey, J Cooper; Erhart, Matthew; McCabe, Connor; Akshoomoff, Natacha; Amaral, David G; Bloss, Cinnamon S; Libiger, Ondrej; Darst, Burcu F; Schork, Nicholas J; Casey, B J; Chang, Linda; Ernst, Thomas M; Gruen, Jeffrey R; Kaufmann, Walter E; Kenet, Tal; Frazier, Jean; Murray, Sarah S; Sowell, Elizabeth R; van Zijl, Peter; Mostofsky, Stewart; Jernigan, Terry L; Dale, Anders M

2012-11-27

Self-regulation refers to the ability to control behavior, cognition, and emotions, and self-regulation failure is related to a range of neuropsychiatric problems. It is poorly understood how structural maturation of the brain brings about the gradual improvement in self-regulation during childhood. In a large-scale multicenter effort, 735 children (4-21 y) underwent structural MRI for quantification of cortical thickness and surface area and diffusion tensor imaging for quantification of the quality of major fiber connections. Brain development was related to a standardized measure of cognitive control (the flanker task from the National Institutes of Health Toolbox), a critical component of self-regulation. Ability to inhibit responses and impose cognitive control increased rapidly during preteen years. Surface area of the anterior cingulate cortex accounted for a significant proportion of the variance in cognitive performance. This finding is intriguing, because characteristics of the anterior cingulum are shown to be related to impulse, attention, and executive problems in neurodevelopmental disorders, indicating a neural foundation for self-regulation abilities along a continuum from normality to pathology. The relationship was strongest in the younger children. Properties of large-fiber connections added to the picture by explaining additional variance in cognitive control. Although cognitive control was related to surface area of the anterior cingulate independently of basic processes of mental speed, the relationship between white matter quality and cognitive control could be fully accounted for by speed. The results underscore the need for integration of different aspects of brain maturation to understand the foundations of cognitive development.

Probing Intrinsic Resting-State Networks in the Infant Rat Brain

PubMed Central

Bajic, Dusica; Craig, Michael M.; Borsook, David; Becerra, Lino

2016-01-01

Resting-state functional magnetic resonance imaging (rs-fMRI) measures spontaneous fluctuations in blood oxygenation level-dependent (BOLD) signal in the absence of external stimuli. It has become a powerful tool for mapping large-scale brain networks in humans and animal models. Several rs-fMRI studies have been conducted in anesthetized and awake adult rats, reporting consistent patterns of brain activity at the systems level. However, the evolution to adult patterns of resting-state activity has not yet been evaluated and quantified in the developing rat brain. In this study, we hypothesized that large-scale intrinsic networks would be easily detectable but not fully established as specific patterns of activity in lightly anesthetized 2-week-old rats (N = 11). Independent component analysis (ICA) identified 8 networks in 2-week-old-rats. These included Default mode, Sensory (Exteroceptive), Salience (Interoceptive), Basal Ganglia-Thalamic-Hippocampal, Basal Ganglia, Autonomic, Cerebellar, as well as Thalamic-Brainstem networks. Many of these networks consisted of more than one component, possibly indicative of immature, underdeveloped networks at this early time point. Except for the Autonomic network, infant rat networks showed reduced connectivity with subcortical structures in comparison to previously published adult networks. Reported slow fluctuations in the BOLD signal that correspond to functionally relevant resting-state networks in 2-week-old rats can serve as an important tool for future studies of brain development in the settings of different pharmacological applications or disease. PMID:27803653

Using large-scale Granger causality to study changes in brain network properties in the Clinically Isolated Syndrome (CIS) stage of multiple sclerosis

NASA Astrophysics Data System (ADS)

Abidin, Anas Z.; Chockanathan, Udaysankar; DSouza, Adora M.; Inglese, Matilde; Wismüller, Axel

2017-03-01

Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination occurring in the CNS can manifest as a change in neuronal metabolism, with multiple asymptomatic white matter lesions detected in clinical MRI. Such damage may induce topological changes of brain networks, which can be captured by advanced functional MRI (fMRI) analysis techniques. We test this hypothesis by capturing the effective relationships of 90 brain regions, defined in the Automated Anatomic Labeling (AAL) atlas, using a large-scale Granger Causality (lsGC) framework. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We study for differences in these properties in network graphs obtained for 18 subjects (10 male and 8 female, 9 with CIS and 9 healthy controls). Global network properties captured trending differences with modularity and clustering coefficient (p<0.1). Additionally, local network properties, such as local efficiency and the strength of connections, captured statistically significant (p<0.01) differences in some regions of the inferior frontal and parietal lobe. We conclude that multivariate analysis of fMRI time-series can reveal interesting information about changes occurring in the brain in early stages of MS.

Abnormal small-world architecture of top–down control networks in obsessive–compulsive disorder

PubMed Central

Zhang, Tijiang; Wang, Jinhui; Yang, Yanchun; Wu, Qizhu; Li, Bin; Chen, Long; Yue, Qiang; Tang, Hehan; Yan, Chaogan; Lui, Su; Huang, Xiaoqi; Chan, Raymond C.K.; Zang, Yufeng; He, Yong; Gong, Qiyong

2011-01-01

Background Obsessive–compulsive disorder (OCD) is a common neuropsychiatric disorder that is characterized by recurrent intrusive thoughts, ideas or images and repetitive ritualistic behaviours. Although focal structural and functional abnormalities in specific brain regions have been widely studied in populations with OCD, changes in the functional relations among them remain poorly understood. This study examined OCD–related alterations in functional connectivity patterns in the brain’s top–down control network. Methods We applied resting-state functional magnetic resonance imaging to investigate the correlation patterns of intrinsic or spontaneous blood oxygen level–dependent signal fluctuations in 18 patients with OCD and 16 healthy controls. The brain control networks were first constructed by thresholding temporal correlation matrices of 39 brain regions associated with top–down control and then analyzed using graph theory-based approaches. Results Compared with healthy controls, the patients with OCD showed decreased functional connectivity in the posterior temporal regions and increased connectivity in various control regions such as the cingulate, precuneus, thalamus and cerebellum. Furthermore, the brain’s control networks in the healthy controls showed small-world architecture (high clustering coefficients and short path lengths), suggesting an optimal balance between modularized and distributed information processing. In contrast, the patients with OCD showed significantly higher local clustering, implying abnormal functional organization in the control network. Further analysis revealed that the changes in network properties occurred in regions of increased functional connectivity strength in patients with OCD. Limitations The patient group in the present study was heterogeneous in terms of symptom clusters, and most of the patients with OCD were medicated. Conclusion Our preliminary results suggest that the organizational patterns of intrinsic brain activity in the control networks are altered in patients with OCD and thus provide empirical evidence for aberrant functional connectivity in the large-scale brain systems in people with this disorder. PMID:20964957

Convergent and divergent intranetwork and internetwork connectivity patterns in patients with remitted late-life depression and amnestic mild cognitive impairment.

PubMed

Chen, Jiu; Shu, Hao; Wang, Zan; Zhan, Yafeng; Liu, Duan; Liao, Wenxiang; Xu, Lin; Liu, Yong; Zhang, Zhijun

2016-10-01

Both remitted late-life depression (rLLD) and amnesiac mild cognitive impairment (aMCI) alter brain functions in specific regions of the brain. They are also disconnection syndromes that are associated with a high risk of developing Alzheimer's disease (AD). Resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) was performed to define the shared and distinct aberrant patterns in intranetwork and internetwork connectivity between rLLD and aMCI and to determine how knowledge of these differences might contribute to our essential understanding of the altered sequences involved in functional systems both inside and outside of resting-state networks. We used rs-fcMRI to investigate in five functionally well-defined brain networks in two large cohorts of subjects at high risk for AD (55 rLLD and 87 aMCI) and 114 healthy controls (HC). A reduced degree of functional connectivity was observed in the bilateral inferior temporal cortex and supplemental motor area, and reduced correlations were observed within the sensory-motor network (SMN) and in the default mode network (DMN)-control network (CON) pair in the rLLD group than the HC group. The aMCI group showed only focal functional changes in regions of interest pairs, a trend toward increased correlations within the salience network and SMN, and a trend toward a reduced correlation in the DMN-CON pair. Furthermore, the rLLD group exhibited more severely altered functional connectivity than the aMCI group. Interestingly, these altered connectivities were associated with specific multi-domain cognitive and behavioral functions in both rLLD and aMCI. The degree of functional connectivity in the right primary auditory areas was negatively correlated with Hamilton Depression Scale scores in rLLD. Notably, altered connectivity between the right middle temporal cortex and the posterior cerebellum was negatively correlated with Mattis Dementia Rating Scale scores in both rLLD and aMCI. These results demonstrate that rLLD and aMCI may share convergent and divergent aberrant intranetwork and internetwork connectivity patterns as a potential continuous spectrum of the same disease. They further suggest that dysfunctions in the right specific temporal-cerebellum neural circuit may contribute to the similarities observed in rLLD and aMCI conversion to AD. Copyright © 2016 Elsevier Ltd. All rights reserved.

Inferring cortical function in the mouse visual system through large-scale systems neuroscience.

PubMed

Hawrylycz, Michael; Anastassiou, Costas; Arkhipov, Anton; Berg, Jim; Buice, Michael; Cain, Nicholas; Gouwens, Nathan W; Gratiy, Sergey; Iyer, Ramakrishnan; Lee, Jung Hoon; Mihalas, Stefan; Mitelut, Catalin; Olsen, Shawn; Reid, R Clay; Teeter, Corinne; de Vries, Saskia; Waters, Jack; Zeng, Hongkui; Koch, Christof

2016-07-05

The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort.

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity.

PubMed

Finn, Emily S; Shen, Xilin; Scheinost, Dustin; Rosenberg, Monica D; Huang, Jessica; Chun, Marvin M; Papademetris, Xenophon; Constable, R Todd

2015-11-01

Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.

Dynamic Functional Connectivity States Reflecting Psychotic-like Experiences.

PubMed

Barber, Anita D; Lindquist, Martin A; DeRosse, Pamela; Karlsgodt, Katherine H

2018-05-01

Psychotic-like experiences (PLEs) are associated with lower social and occupational functioning, and lower executive function. Emerging evidence also suggests that PLEs reflect neural dysfunction resembling that of psychotic disorders. The present study examined dynamic connectivity related to a measure of PLEs derived from the Achenbach Adult Self-Report, in an otherwise-healthy sample of adults from the Human Connectome Project. A total of 76 PLE-endorsing and 153 control participants were included in the final sample. To characterize network dysfunction, dynamic connectivity states were examined across large-scale resting-state networks using dynamic conditional correlation and k-means clustering. Three dynamic states were identified. The PLE-endorsing group spent more time than the control group in state 1, a state reflecting hyperconnectivity within visual regions and hypoconnectivity within the default mode network, and less time in state 2, a state characterized by robust within-network connectivity for all networks and strong default mode network anticorrelations. Within the PLE-endorsing group, worse executive function was associated with more time spent in and more transitions into state 1 and less time spent in and fewer transitions into state 3. PLEs are associated with altered large-scale brain dynamics, which tip the system away from spending more time in states reflecting more "typical" connectivity patterns toward more time in states reflecting visual hyperconnectivity and default mode hypoconnectivity. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Network topology and functional connectivity disturbances precede the onset of Huntington's disease.

PubMed

Harrington, Deborah L; Rubinov, Mikail; Durgerian, Sally; Mourany, Lyla; Reece, Christine; Koenig, Katherine; Bullmore, Ed; Long, Jeffrey D; Paulsen, Jane S; Rao, Stephen M

2015-08-01

Cognitive, motor and psychiatric changes in prodromal Huntington's disease have nurtured the emergent need for early interventions. Preventive clinical trials for Huntington's disease, however, are limited by a shortage of suitable measures that could serve as surrogate outcomes. Measures of intrinsic functional connectivity from resting-state functional magnetic resonance imaging are of keen interest. Yet recent studies suggest circumscribed abnormalities in resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease, despite the spectrum of behavioural changes preceding a manifest diagnosis. The present study used two complementary analytical approaches to examine whole-brain resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease. Network topology was studied using graph theory and simple functional connectivity amongst brain regions was explored using the network-based statistic. Participants consisted of gene-negative controls (n = 16) and prodromal Huntington's disease individuals (n = 48) with various stages of disease progression to examine the influence of disease burden on intrinsic connectivity. Graph theory analyses showed that global network interconnectivity approximated a random network topology as proximity to diagnosis neared and this was associated with decreased connectivity amongst highly-connected rich-club network hubs, which integrate processing from diverse brain regions. However, functional segregation within the global network (average clustering) was preserved. Functional segregation was also largely maintained at the local level, except for the notable decrease in the diversity of anterior insula intermodular-interconnections (participation coefficient), irrespective of disease burden. In contrast, network-based statistic analyses revealed patterns of weakened frontostriatal connections and strengthened frontal-posterior connections that evolved as disease burden increased. These disturbances were often related to long-range connections involving peripheral nodes and interhemispheric connections. A strong association was found between weaker connectivity and decreased rich-club organization, indicating that whole-brain simple connectivity partially expressed disturbances in the communication of highly-connected hubs. However, network topology and network-based statistic connectivity metrics did not correlate with key markers of executive dysfunction (Stroop Test, Trail Making Test) in prodromal Huntington's disease, which instead were related to whole-brain connectivity disturbances in nodes (right inferior parietal, right thalamus, left anterior cingulate) that exhibited multiple aberrant connections and that mediate executive control. Altogether, our results show for the first time a largely disease burden-dependent functional reorganization of whole-brain networks in prodromal Huntington's disease. Both analytic approaches provided a unique window into brain reorganization that was not related to brain atrophy or motor symptoms. Longitudinal studies currently in progress will chart the course of functional changes to determine the most sensitive markers of disease progression. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Establishing a link between sex-related differences in the structural connectome and behaviour.

PubMed

Tunç, Birkan; Solmaz, Berkan; Parker, Drew; Satterthwaite, Theodore D; Elliott, Mark A; Calkins, Monica E; Ruparel, Kosha; Gur, Raquel E; Gur, Ruben C; Verma, Ragini

2016-02-19

Recent years have witnessed an increased attention to studies of sex differences, partly because such differences offer important considerations for personalized medicine. While the presence of sex differences in human behaviour is well documented, our knowledge of their anatomical foundations in the brain is still relatively limited. As a natural gateway to fathom the human mind and behaviour, studies concentrating on the human brain network constitute an important segment of the research effort to investigate sex differences. Using a large sample of healthy young individuals, each assessed with diffusion MRI and a computerized neurocognitive battery, we conducted a comprehensive set of experiments examining sex-related differences in the meso-scale structures of the human connectome and elucidated how these differences may relate to sex differences at the level of behaviour. Our results suggest that behavioural sex differences, which indicate complementarity of males and females, are accompanied by related differences in brain structure across development. When using subnetworks that are defined over functional and behavioural domains, we observed increased structural connectivity related to the motor, sensory and executive function subnetworks in males. In females, subnetworks associated with social motivation, attention and memory tasks had higher connectivity. Males showed higher modularity compared to females, with females having higher inter-modular connectivity. Applying multivariate analysis, we showed an increasing separation between males and females in the course of development, not only in behavioural patterns but also in brain structure. We also showed that these behavioural and structural patterns correlate with each other, establishing a reliable link between brain and behaviour. © 2016 The Author(s).

Financial Exploitation Is Associated With Structural and Functional Brain Differences in Healthy Older Adults.

PubMed

Spreng, R Nathan; Cassidy, Benjamin N; Darboh, Bri S; DuPre, Elizabeth; Lockrow, Amber W; Setton, Roni; Turner, Gary R

2017-10-01

Age-related brain changes leading to altered socioemotional functioning may increase vulnerability to financial exploitation. If confirmed, this would suggest a novel mechanism leading to heightened financial exploitation risk in older adults. Development of predictive neural markers could facilitate increased vigilance and prevention. In this preliminary study, we sought to identify structural and functional brain differences associated with financial exploitation in older adults. Financially exploited older adults (n = 13, 7 female) and a matched cohort of older adults who had been exposed to, but avoided, a potentially exploitative situation (n = 13, 7 female) were evaluated. Using magnetic resonance imaging, we examined cortical thickness and resting state functional connectivity. Behavioral data were collected using standardized cognitive assessments, self-report measures of mood and social functioning. The exploited group showed cortical thinning in anterior insula and posterior superior temporal cortices, regions associated with processing affective and social information, respectively. Functional connectivity encompassing these regions, within default and salience networks, was reduced, while between network connectivity was increased. Self-reported anger and hostility was higher for the exploited group. We observed financial exploitation associated with brain differences in regions involved in socioemotional functioning. These exploratory and preliminary findings suggest that alterations in brain regions implicated in socioemotional functioning may be a marker of financial exploitation risk. Large-scale, prospective studies are necessary to validate this neural mechanism, and develop predictive markers for use in clinical practice. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America.

Salience network integrity predicts default mode network function after traumatic brain injury

PubMed Central

Bonnelle, Valerie; Ham, Timothy E.; Leech, Robert; Kinnunen, Kirsi M.; Mehta, Mitul A.; Greenwood, Richard J.; Sharp, David J.

2012-01-01

Efficient behavior involves the coordinated activity of large-scale brain networks, but the way in which these networks interact is uncertain. One theory is that the salience network (SN)—which includes the anterior cingulate cortex, presupplementary motor area, and anterior insulae—regulates dynamic changes in other networks. If this is the case, then damage to the structural connectivity of the SN should disrupt the regulation of associated networks. To investigate this hypothesis, we studied a group of 57 patients with cognitive impairments following traumatic brain injury (TBI) and 25 control subjects using the stop-signal task. The pattern of brain activity associated with stop-signal task performance was studied by using functional MRI, and the structural integrity of network connections was quantified by using diffusion tensor imaging. Efficient inhibitory control was associated with rapid deactivation within parts of the default mode network (DMN), including the precuneus and posterior cingulate cortex. TBI patients showed a failure of DMN deactivation, which was associated with an impairment of inhibitory control. TBI frequently results in traumatic axonal injury, which can disconnect brain networks by damaging white matter tracts. The abnormality of DMN function was specifically predicted by the amount of white matter damage in the SN tract connecting the right anterior insulae to the presupplementary motor area and dorsal anterior cingulate cortex. The results provide evidence that structural integrity of the SN is necessary for the efficient regulation of activity in the DMN, and that a failure of this regulation leads to inefficient cognitive control. PMID:22393019

Flexible Organic Electronics for Use in Neural Sensing

PubMed Central

Bink, Hank; Lai, Yuming; Saudari, Sangameshwar R.; Helfer, Brian; Viventi, Jonathan; Van der Spiegel, Jan; Litt, Brian; Kagan, Cherie

2016-01-01

Recent research in brain-machine interfaces and devices to treat neurological disease indicate that important network activity exists at temporal and spatial scales beyond the resolution of existing implantable devices. High density, active electrode arrays hold great promise in enabling high-resolution interface with the brain to access and influence this network activity. Integrating flexible electronic devices directly at the neural interface can enable thousands of multiplexed electrodes to be connected using many fewer wires. Active electrode arrays have been demonstrated using flexible, inorganic silicon transistors. However, these approaches may be limited in their ability to be cost-effectively scaled to large array sizes (8×8 cm). Here we show amplifiers built using flexible organic transistors with sufficient performance for neural signal recording. We also demonstrate a pathway for a fully integrated, amplified and multiplexed electrode array built from these devices. PMID:22255558

How a (sub)Cellular Coincidence Detection Mechanism Featuring Layer-5 Pyramidal Cells May Help Produce Various Visual Phenomena.

PubMed

Bachmann, Talis

2015-01-01

Perceptual phenomena such as spatio-temporal illusions and masking are typically explained by psychological (cognitive) processing theories or large-scale neural theories involving inter-areal connectivity and neural circuits comprising of hundreds or more interconnected single cells. Subcellular mechanisms are hardly used for such purpose. Here, a mechanistic theoretical view is presented on how a subcellular brain mechanism of integration of presynaptic signals that arrive at different compartments of layer-5 pyramidal neurons could explain a couple of spatiotemporal visual-phenomenal effects unfolding along very brief time intervals within the range of the sub-second temporal scale.

The Dynamical Balance of the Brain at Rest

PubMed Central

Deco, Gustavo; Corbetta, Maurizio

2014-01-01

We review evidence that spontaneous, i.e. not stimulus- or task-driven, activity in the brain is not noise, but orderly organized at the level of large scale systems in a series of functional networks that maintain at all times a high level of coherence. These networks of spontaneous activity correlation or resting state networks (RSN) are closely related to the underlying anatomical connectivity, but their topography is also gated by the history of prior task activation. Network coherence does not depend on covert cognitive activity, but its strength and integrity relates to behavioral performance. Some RSN are functionally organized as dynamically competing systems both at rest and during tasks. Computational studies show that one of such dynamics, the anti-correlation between networks, depends on noise driven transitions between different multi-stable cluster synchronization states. These multi-stable states emerge because of transmission delays between regions that are modeled as coupled oscillators systems. Large-scale systems dynamics are useful for keeping different functional sub-networks in a state of heightened competition, which can be stabilized and fired by even small modulations of either sensory or internal signals. PMID:21196530

Functional Brain Networks Develop from a “Local to Distributed” Organization

PubMed Central

Power, Jonathan D.; Dosenbach, Nico U. F.; Church, Jessica A.; Miezin, Francis M.; Schlaggar, Bradley L.; Petersen, Steven E.

2009-01-01

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways. PMID:19412534

Functional brain networks develop from a "local to distributed" organization.

PubMed

Fair, Damien A; Cohen, Alexander L; Power, Jonathan D; Dosenbach, Nico U F; Church, Jessica A; Miezin, Francis M; Schlaggar, Bradley L; Petersen, Steven E

2009-05-01

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.

Identification and functional characterization of HIV-associated neurocognitive disorders with large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

Chockanathan, Udaysankar; DSouza, Adora M.; Abidin, Anas Z.; Schifitto, Giovanni; Wismüller, Axel

2018-02-01

Resting-state functional MRI (rs-fMRI), coupled with advanced multivariate time-series analysis methods such as Granger causality, is a promising tool for the development of novel functional connectivity biomarkers of neurologic and psychiatric disease. Recently large-scale Granger causality (lsGC) has been proposed as an alternative to conventional Granger causality (cGC) that extends the scope of robust Granger causal analyses to high-dimensional systems such as the human brain. In this study, lsGC and cGC were comparatively evaluated on their ability to capture neurologic damage associated with HIV-associated neurocognitive disorders (HAND). Functional brain network models were constructed from rs-fMRI data collected from a cohort of HIV+ and HIV- subjects. Graph theoretic properties of the resulting networks were then used to train a support vector machine (SVM) model to predict clinically relevant parameters, such as HIV status and neuropsychometric (NP) scores. For the HIV+/- classification task, lsGC, which yielded a peak area under the receiver operating characteristic curve (AUC) of 0.83, significantly outperformed cGC, which yielded a peak AUC of 0.61, at all parameter settings tested. For the NP score regression task, lsGC, with a minimum mean squared error (MSE) of 0.75, significantly outperformed cGC, with a minimum MSE of 0.84 (p < 0.001, one-tailed paired t-test). These results show that, at optimal parameter settings, lsGC is better able to capture functional brain connectivity correlates of HAND than cGC. However, given the substantial variation in the performance of the two methods at different parameter settings, particularly for the regression task, improved parameter selection criteria are necessary and constitute an area for future research.

Connectivity in Autism: A review of MRI connectivity studies

PubMed Central

Rane, Pallavi; Cochran, David; Hodge, Steven M.; Haselgrove, Christian; Kennedy, David; Frazier, Jean A.

2016-01-01

Autism Spectrum Disorder (ASD) affects 1 in 50 children between the ages of 6–17 years as per a 2012 CDC survey of parents. The etiology of ASD is not precisely known. ASD is an umbrella term, which includes low (IQ<70) to high functioning (IQ>70) individuals. A better understanding of the disorder, and how it manifests in an individual subject can lead to more effective intervention plans to fulfill the individual’s treatment needs. Magnetic resonance imaging (MRI) is a non-invasive investigational tool that can help study the ways in which the brain develops and/or deviates from the typical developmental trajectory. MRI offers insights into the structure, function, and metabolism of the brain. In this article, we review published studies on brain connectivity changes in ASD using either resting state functional MRI or diffusion tensor imaging. The general findings of decreases in white matter integrity and long-range neural coherence are prevalent in ASD literature. However, there is somewhat less of a consensus in the detailed localization of these findings. There are even fewer studies linking these connectivity alterations with the behavioral phenotype of the disorder. Nevertheless, with the help of data sharing and large-scale analytic efforts, the field is advancing towards several convergent themes. These include reduced functional coherence of long-range intra-hemispheric cortico-cortical default mode circuitry, impaired inter-hemispheric regulation, and an associated, perhaps compensatory, increase in local and short-range cortico-subcortical coherence. PMID:26146755

Mind-Body Practice Changes Fractional Amplitude of Low Frequency Fluctuations in Intrinsic Control Networks

PubMed Central

Wei, Gao-Xia; Gong, Zhu-Qing; Yang, Zhi; Zuo, Xi-Nian

2017-01-01

Cognitive control impairment is a typical symptom largely reported in populations with neurological disorders. Previous studies have provided evidence about the changes in cognitive control induced by mind-body training. However, the neural correlates underlying the effect of extensive mind-body practice on cognitive control remain largely unknown. Using resting-state functional magnetic resonance imaging, we characterized dynamic fluctuations in large-scale intrinsic connectivity networks associated with mind-body practice, and examined their differences between healthy controls and Tai Chi Chuan (TCC) practitioners. Compared with a control group, the TCC group revealed significantly decreased fractional Amplitude of Low Frequency Fluctuations (fALFF) in the bilateral frontoparietal network, default mode network and dorsal prefrontal-angular gyri network. Furthermore, we detected a significant association between mind-body practice experience and fALFF in the default mode network, as well as an association between cognitive control performance and fALFF of the frontoparietal network. This provides the first evidence of large-scale functional connectivity in brain networks associated with mind-body practice, shedding light on the neural network changes that accompany intensive mind-body training. It also highlights the functionally plastic role of the frontoparietal network in the context of the “immune system” of mental health recently developed in relation to flexible hub theory. PMID:28736535

Mind-Body Practice Changes Fractional Amplitude of Low Frequency Fluctuations in Intrinsic Control Networks.

PubMed

Wei, Gao-Xia; Gong, Zhu-Qing; Yang, Zhi; Zuo, Xi-Nian

2017-01-01

Cognitive control impairment is a typical symptom largely reported in populations with neurological disorders. Previous studies have provided evidence about the changes in cognitive control induced by mind-body training. However, the neural correlates underlying the effect of extensive mind-body practice on cognitive control remain largely unknown. Using resting-state functional magnetic resonance imaging, we characterized dynamic fluctuations in large-scale intrinsic connectivity networks associated with mind-body practice, and examined their differences between healthy controls and Tai Chi Chuan (TCC) practitioners. Compared with a control group, the TCC group revealed significantly decreased fractional Amplitude of Low Frequency Fluctuations (fALFF) in the bilateral frontoparietal network, default mode network and dorsal prefrontal-angular gyri network. Furthermore, we detected a significant association between mind-body practice experience and fALFF in the default mode network, as well as an association between cognitive control performance and fALFF of the frontoparietal network. This provides the first evidence of large-scale functional connectivity in brain networks associated with mind-body practice, shedding light on the neural network changes that accompany intensive mind-body training. It also highlights the functionally plastic role of the frontoparietal network in the context of the "immune system" of mental health recently developed in relation to flexible hub theory.

A review of structural and functional brain networks: small world and atlas.

PubMed

Yao, Zhijun; Hu, Bin; Xie, Yuanwei; Moore, Philip; Zheng, Jiaxiang

2015-03-01

Brain networks can be divided into two categories: structural and functional networks. Many studies of neuroscience have reported that the complex brain networks are characterized by small-world or scale-free properties. The identification of nodes is the key factor in studying the properties of networks on the macro-, micro- or mesoscale in both structural and functional networks. In the study of brain networks, nodes are always determined by atlases. Therefore, the selection of atlases is critical, and appropriate atlases are helpful to combine the analyses of structural and functional networks. Currently, some problems still exist in the establishment or usage of atlases, which are often caused by the segmentation or the parcellation of the brain. We suggest that quantification of brain networks might be affected by the selection of atlases to a large extent. In the process of building atlases, the influences of single subjects and groups should be balanced. In this article, we focused on the effects of atlases on the analysis of brain networks and the improved divisions based on the tractography or connectivity in the parcellation of atlases.

A mesoscale connectome of the mouse brain

PubMed Central

Oh, Seung Wook; Harris, Julie A.; Ng, Lydia; Winslow, Brent; Cain, Nicholas; Mihalas, Stefan; Wang, Quanxin; Lau, Chris; Kuan, Leonard; Henry, Alex M.; Mortrud, Marty T.; Ouellette, Benjamin; Nguyen, Thuc Nghi; Sorensen, Staci A.; Slaughterbeck, Clifford R.; Wakeman, Wayne; Li, Yang; Feng, David; Ho, Anh; Nicholas, Eric; Hirokawa, Karla E.; Bohn, Phillip; Joines, Kevin M.; Peng, Hanchuan; Hawrylycz, Michael J.; Phillips, John W.; Hohmann, John G.; Wohnoutka, Paul; Gerfen, Charles R.; Koch, Christof; Bernard, Amy; Dang, Chinh; Jones, Allan R.; Zeng, Hongkui

2016-01-01

Comprehensive knowledge of the brain’s wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease. PMID:24695228

Lateralized Feeding Behavior is Associated with Asymmetrical Neuroanatomy and Lateralized Gene Expressions in the Brain in Scale-Eating Cichlid Fish

PubMed Central

Lee, Hyuk Je; Schneider, Ralf F; Manousaki, Tereza; Kang, Ji Hyoun; Lein, Etienne; Franchini, Paolo

2017-01-01

Abstract Lateralized behavior (“handedness”) is unusual, but consistently found across diverse animal lineages, including humans. It is thought to reflect brain anatomical and/or functional asymmetries, but its neuro-molecular mechanisms remain largely unknown. Lake Tanganyika scale-eating cichlid fish, Perissodus microlepis show pronounced asymmetry in their jaw morphology as well as handedness in feeding behavior—biting scales preferentially only from one or the other side of their victims. This makes them an ideal model in which to investigate potential laterality in neuroanatomy and transcription in the brain in relation to behavioral handedness. After determining behavioral handedness in P. microlepis (preferred attack side), we estimated the volume of the hemispheres of brain regions and captured their gene expression profiles. Our analyses revealed that the degree of behavioral handedness is mirrored at the level of neuroanatomical asymmetry, particularly in the tectum opticum. Transcriptome analyses showed that different brain regions (tectum opticum, telencephalon, hypothalamus, and cerebellum) display distinct expression patterns, potentially reflecting their developmental interrelationships. For numerous genes in each brain region, their extent of expression differences between hemispheres was found to be correlated with the degree of behavioral lateralization. Interestingly, the tectum opticum and telencephalon showed divergent biases on the direction of up- or down-regulation of the laterality candidate genes (e.g., grm2) in the hemispheres, highlighting the connection of handedness with gene expression profiles and the different roles of these brain regions. Hence, handedness in predation behavior may be caused by asymmetric size of brain hemispheres and also by lateralized gene expressions in the brain. PMID:29069363

Lateralized Feeding Behavior is Associated with Asymmetrical Neuroanatomy and Lateralized Gene Expressions in the Brain in Scale-Eating Cichlid Fish.

PubMed

Lee, Hyuk Je; Schneider, Ralf F; Manousaki, Tereza; Kang, Ji Hyoun; Lein, Etienne; Franchini, Paolo; Meyer, Axel

2017-11-01

Lateralized behavior ("handedness") is unusual, but consistently found across diverse animal lineages, including humans. It is thought to reflect brain anatomical and/or functional asymmetries, but its neuro-molecular mechanisms remain largely unknown. Lake Tanganyika scale-eating cichlid fish, Perissodus microlepis show pronounced asymmetry in their jaw morphology as well as handedness in feeding behavior-biting scales preferentially only from one or the other side of their victims. This makes them an ideal model in which to investigate potential laterality in neuroanatomy and transcription in the brain in relation to behavioral handedness. After determining behavioral handedness in P. microlepis (preferred attack side), we estimated the volume of the hemispheres of brain regions and captured their gene expression profiles. Our analyses revealed that the degree of behavioral handedness is mirrored at the level of neuroanatomical asymmetry, particularly in the tectum opticum. Transcriptome analyses showed that different brain regions (tectum opticum, telencephalon, hypothalamus, and cerebellum) display distinct expression patterns, potentially reflecting their developmental interrelationships. For numerous genes in each brain region, their extent of expression differences between hemispheres was found to be correlated with the degree of behavioral lateralization. Interestingly, the tectum opticum and telencephalon showed divergent biases on the direction of up- or down-regulation of the laterality candidate genes (e.g., grm2) in the hemispheres, highlighting the connection of handedness with gene expression profiles and the different roles of these brain regions. Hence, handedness in predation behavior may be caused by asymmetric size of brain hemispheres and also by lateralized gene expressions in the brain. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Spatiotemporal dynamics of large-scale brain activity

NASA Astrophysics Data System (ADS)

Neuman, Jeremy

Understanding the dynamics of large-scale brain activity is a tough challenge. One reason for this is the presence of an incredible amount of complexity arising from having roughly 100 billion neurons connected via 100 trillion synapses. Because of the extremely high number of degrees of freedom in the nervous system, the question of how the brain manages to properly function and remain stable, yet also be adaptable, must be posed. Neuroscientists have identified many ways the nervous system makes this possible, of which synaptic plasticity is possibly the most notable one. On the other hand, it is vital to understand how the nervous system also loses stability, resulting in neuropathological diseases such as epilepsy, a disease which affects 1% of the population. In the following work, we seek to answer some of these questions from two different perspectives. The first uses mean-field theory applied to neuronal populations, where the variables of interest are the percentages of active excitatory and inhibitory neurons in a network, to consider how the nervous system responds to external stimuli, self-organizes and generates epileptiform activity. The second method uses statistical field theory, in the framework of single neurons on a lattice, to study the concept of criticality, an idea borrowed from physics which posits that in some regime the brain operates in a collectively stable or marginally stable manner. This will be examined in two different neuronal networks with self-organized criticality serving as the overarching theme for the union of both perspectives. One of the biggest problems in neuroscience is the question of to what extent certain details are significant to the functioning of the brain. These details give rise to various spatiotemporal properties that at the smallest of scales explain the interaction of single neurons and synapses and at the largest of scales describe, for example, behaviors and sensations. In what follows, we will shed some light on this issue.

Neuropsychology and cognitive neuroscience in the fMRI era: A recapitulation of localizationist and connectionist views.

PubMed

Sutterer, Matthew J; Tranel, Daniel

2017-11-01

We highlight the past 25 years of cognitive neuroscience and neuropsychology, focusing on the impact to the field of the introduction in 1992 of functional MRI (fMRI). We reviewed the past 25 years of literature in cognitive neuroscience and neuropsychology, focusing on the relation and interplay of fMRI studies and studies utilizing the "lesion method" in human participants with focal brain damage. Our review highlights the state of localist/connectionist research debates in cognitive neuroscience and neuropsychology circa 1992, and details how the introduction of fMRI into the field at that time catalyzed a new wave of efforts to map complex human behavior to specific brain regions. This, in turn, eventually evolved into many studies that focused on networks and connections between brain areas, culminating in recent years with large-scale investigations such as the Human Connectome Project. We argue that throughout the past 25 years, neuropsychology-and more precisely, the "lesion method" in humans-has continued to play a critical role in arbitrating conclusions and theories derived from inferred patterns of local brain activity or wide-spread connectivity from functional imaging approaches. We conclude by highlighting the future for neuropsychology in the context of an increasingly complex methodological armamentarium. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Joint representation of consistent structural and functional profiles for identification of common cortical landmarks.

PubMed

Zhang, Shu; Zhao, Yu; Jiang, Xi; Shen, Dinggang; Liu, Tianming

2018-06-01

In the brain mapping field, there have been significant interests in representation of structural/functional profiles to establish structural/functional landmark correspondences across individuals and populations. For example, from the structural perspective, our previous studies have identified hundreds of consistent DICCCOL (dense individualized and common connectivity-based cortical landmarks) landmarks across individuals and populations, each of which possess consistent DTI-derived fiber connection patterns. From the functional perspective, a large collection of well-characterized HAFNI (holistic atlases of functional networks and interactions) networks based on sparse representation of whole-brain fMRI signals have been identified in our prior studies. However, due to the remarkable variability of structural and functional architectures in the human brain, it is challenging for earlier studies to jointly represent the connectome-scale structural and functional profiles for establishing a common cortical architecture which can comprehensively encode both structural and functional characteristics across individuals. To address this challenge, we propose an effective computational framework to jointly represent the structural and functional profiles for identification of consistent and common cortical landmarks with both structural and functional correspondences across different brains based on DTI and fMRI data. Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified.

Importance of human right inferior frontoparietal network connected by inferior branch of superior longitudinal fasciculus tract in corporeal awareness of kinesthetic illusory movement.

PubMed

Amemiya, Kaoru; Naito, Eiichi

2016-05-01

It is generally believed that the human right cerebral hemisphere plays a dominant role in corporeal awareness, which is highly associated with conscious experience of the physical self. Prompted by our previous findings, we examined whether the right frontoparietal activations often observed when people experience kinesthetic illusory limb movement are supported by a large-scale brain network connected by a specific branch of the superior longitudinal fasciculus fiber tracts (SLF I, II, and III). We scanned brain activity with functional magnetic resonance imaging (MRI) while nineteen blindfolded healthy volunteers experienced illusory movement of the right stationary hand elicited by tendon vibration, which was replicated after the scanning. We also scanned brain activity when they executed and imagined right hand movement, and identified the active brain regions during illusion, execution, and imagery in relation to the SLF fiber tracts. We found that illusion predominantly activated the right inferior frontoparietal regions connected by SLF III, which were not substantially recruited during execution and imagery. Among these regions, activities in the right inferior parietal cortices and inferior frontal cortices showed right-side dominance and correlated well with the amount of illusion (kinesthetic illusory awareness) experienced by the participants. The results illustrated the predominant involvement of the right inferior frontoparietal network connected by SLF III when people recognize postural changes of their limb. We assume that the network bears a series of functions, specifically, monitoring the current status of the musculoskeletal system, and building-up and updating our postural model (body schema), which could be a basis for the conscious experience of the physical self. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Emergence of rich-club topology and coordinated dynamics in development of hippocampal functional networks in vitro.

PubMed

Schroeter, Manuel S; Charlesworth, Paul; Kitzbichler, Manfred G; Paulsen, Ole; Bullmore, Edward T

2015-04-08

Recent studies demonstrated that the anatomical network of the human brain shows a "rich-club" organization. This complex topological feature implies that highly connected regions, hubs of the large-scale brain network, are more densely interconnected with each other than expected by chance. Rich-club nodes were traversed by a majority of short paths between peripheral regions, underlining their potential importance for efficient global exchange of information between functionally specialized areas of the brain. Network hubs have also been described at the microscale of brain connectivity (so-called "hub neurons"). Their role in shaping synchronous dynamics and forming microcircuit wiring during development, however, is not yet fully understood. The present study aimed to investigate the role of hubs during network development, using multi-electrode arrays and functional connectivity analysis during spontaneous multi-unit activity (MUA) of dissociated primary mouse hippocampal neurons. Over the first 4 weeks in vitro, functional connectivity significantly increased in strength, density, and size, with mature networks demonstrating a robust modular and small-world topology. As expected by a "rich-get-richer" growth rule of network evolution, MUA graphs were found to form rich-clubs at an early stage in development (14 DIV). Later on, rich-club nodes were a consistent topological feature of MUA graphs, demonstrating high nodal strength, efficiency, and centrality. Rich-club nodes were also found to be crucial for MUA dynamics. They often served as broker of spontaneous activity flow, confirming that hub nodes and rich-clubs may play an important role in coordinating functional dynamics at the microcircuit level. Copyright © 2015 the authors 0270-6474/15/355459-12$15.00/0.

Genetic influences on functional connectivity associated with feedback processing and prediction error: Phase coupling of theta-band oscillations in twins.

PubMed

Demiral, Şükrü Barış; Golosheykin, Simon; Anokhin, Andrey P

2017-05-01

Detection and evaluation of the mismatch between the intended and actually obtained result of an action (reward prediction error) is an integral component of adaptive self-regulation of behavior. Extensive human and animal research has shown that evaluation of action outcome is supported by a distributed network of brain regions in which the anterior cingulate cortex (ACC) plays a central role, and the integration of distant brain regions into a unified feedback-processing network is enabled by long-range phase synchronization of cortical oscillations in the theta band. Neural correlates of feedback processing are associated with individual differences in normal and abnormal behavior, however, little is known about the role of genetic factors in the cerebral mechanisms of feedback processing. Here we examined genetic influences on functional cortical connectivity related to prediction error in young adult twins (age 18, n=399) using event-related EEG phase coherence analysis in a monetary gambling task. To identify prediction error-specific connectivity pattern, we compared responses to loss and gain feedback. Monetary loss produced a significant increase of theta-band synchronization between the frontal midline region and widespread areas of the scalp, particularly parietal areas, whereas gain resulted in increased synchrony primarily within the posterior regions. Genetic analyses showed significant heritability of frontoparietal theta phase synchronization (24 to 46%), suggesting that individual differences in large-scale network dynamics are under substantial genetic control. We conclude that theta-band synchronization of brain oscillations related to negative feedback reflects genetically transmitted differences in the neural mechanisms of feedback processing. To our knowledge, this is the first evidence for genetic influences on task-related functional brain connectivity assessed using direct real-time measures of neuronal synchronization. Copyright © 2016 Elsevier B.V. All rights reserved.

Complexity in relational processing predicts changes in functional brain network dynamics.

PubMed

Cocchi, Luca; Halford, Graeme S; Zalesky, Andrew; Harding, Ian H; Ramm, Brentyn J; Cutmore, Tim; Shum, David H K; Mattingley, Jason B

2014-09-01

The ability to link variables is critical to many high-order cognitive functions, including reasoning. It has been proposed that limits in relating variables depend critically on relational complexity, defined formally as the number of variables to be related in solving a problem. In humans, the prefrontal cortex is known to be important for reasoning, but recent studies have suggested that such processes are likely to involve widespread functional brain networks. To test this hypothesis, we used functional magnetic resonance imaging and a classic measure of deductive reasoning to examine changes in brain networks as a function of relational complexity. As expected, behavioral performance declined as the number of variables to be related increased. Likewise, increments in relational complexity were associated with proportional enhancements in brain activity and task-based connectivity within and between 2 cognitive control networks: A cingulo-opercular network for maintaining task set, and a fronto-parietal network for implementing trial-by-trial control. Changes in effective connectivity as a function of increased relational complexity suggested a key role for the left dorsolateral prefrontal cortex in integrating and implementing task set in a trial-by-trial manner. Our findings show that limits in relational processing are manifested in the brain as complexity-dependent modulations of large-scale networks. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Network topology and functional connectivity disturbances precede the onset of Huntington’s disease

PubMed Central

Harrington, Deborah L.; Rubinov, Mikail; Durgerian, Sally; Mourany, Lyla; Reece, Christine; Koenig, Katherine; Bullmore, Ed; Long, Jeffrey D.; Paulsen, Jane S.

2015-01-01

Cognitive, motor and psychiatric changes in prodromal Huntington’s disease have nurtured the emergent need for early interventions. Preventive clinical trials for Huntington’s disease, however, are limited by a shortage of suitable measures that could serve as surrogate outcomes. Measures of intrinsic functional connectivity from resting-state functional magnetic resonance imaging are of keen interest. Yet recent studies suggest circumscribed abnormalities in resting-state functional magnetic resonance imaging connectivity in prodromal Huntington’s disease, despite the spectrum of behavioural changes preceding a manifest diagnosis. The present study used two complementary analytical approaches to examine whole-brain resting-state functional magnetic resonance imaging connectivity in prodromal Huntington’s disease. Network topology was studied using graph theory and simple functional connectivity amongst brain regions was explored using the network-based statistic. Participants consisted of gene-negative controls (n = 16) and prodromal Huntington’s disease individuals (n = 48) with various stages of disease progression to examine the influence of disease burden on intrinsic connectivity. Graph theory analyses showed that global network interconnectivity approximated a random network topology as proximity to diagnosis neared and this was associated with decreased connectivity amongst highly-connected rich-club network hubs, which integrate processing from diverse brain regions. However, functional segregation within the global network (average clustering) was preserved. Functional segregation was also largely maintained at the local level, except for the notable decrease in the diversity of anterior insula intermodular-interconnections (participation coefficient), irrespective of disease burden. In contrast, network-based statistic analyses revealed patterns of weakened frontostriatal connections and strengthened frontal-posterior connections that evolved as disease burden increased. These disturbances were often related to long-range connections involving peripheral nodes and interhemispheric connections. A strong association was found between weaker connectivity and decreased rich-club organization, indicating that whole-brain simple connectivity partially expressed disturbances in the communication of highly-connected hubs. However, network topology and network-based statistic connectivity metrics did not correlate with key markers of executive dysfunction (Stroop Test, Trail Making Test) in prodromal Huntington’s disease, which instead were related to whole-brain connectivity disturbances in nodes (right inferior parietal, right thalamus, left anterior cingulate) that exhibited multiple aberrant connections and that mediate executive control. Altogether, our results show for the first time a largely disease burden-dependent functional reorganization of whole-brain networks in prodromal Huntington’s disease. Both analytic approaches provided a unique window into brain reorganization that was not related to brain atrophy or motor symptoms. Longitudinal studies currently in progress will chart the course of functional changes to determine the most sensitive markers of disease progression. PMID:26059655

Linking Inter-Individual Variability in Functional Brain Connectivity to Cognitive Ability in Elderly Individuals

PubMed Central

Li, Rui; Yin, Shufei; Zhu, Xinyi; Ren, Weicong; Yu, Jing; Wang, Pengyun; Zheng, Zhiwei; Niu, Ya-Nan; Huang, Xin; Li, Juan

2017-01-01

Increasing evidence suggests that functional brain connectivity is an important determinant of cognitive aging. However, the fundamental concept of inter-individual variations in functional connectivity in older individuals is not yet completely understood. It is essential to evaluate the extent to which inter-individual variability in connectivity impacts cognitive performance at an older age. In the current study, we aimed to characterize individual variability of functional connectivity in the elderly and to examine its significance to individual cognition. We mapped inter-individual variability of functional connectivity by analyzing whole-brain functional connectivity magnetic resonance imaging data obtained from a large sample of cognitively normal older adults. Our results demonstrated a gradual increase in variability in primary regions of the visual, sensorimotor, and auditory networks to specific subcortical structures, particularly the hippocampal formation, and the prefrontal and parietal cortices, which largely constitute the default mode and fronto-parietal networks, to the cerebellum. Further, the inter-individual variability of the functional connectivity correlated significantly with the degree of cognitive relevance. Regions with greater connectivity variability demonstrated more connections that correlated with cognitive performance. These results also underscored the crucial function of the long-range and inter-network connections in individual cognition. Thus, individual connectivity–cognition variability mapping findings may provide important information for future research on cognitive aging and neurocognitive diseases. PMID:29209203

The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas.

PubMed

Ecker, Joseph R; Geschwind, Daniel H; Kriegstein, Arnold R; Ngai, John; Osten, Pavel; Polioudakis, Damon; Regev, Aviv; Sestan, Nenad; Wickersham, Ian R; Zeng, Hongkui

2017-11-01

A comprehensive characterization of neuronal cell types, their distributions, and patterns of connectivity is critical for understanding the properties of neural circuits and how they generate behaviors. Here we review the experiences of the BRAIN Initiative Cell Census Consortium, ten pilot projects funded by the U.S. BRAIN Initiative, in developing, validating, and scaling up emerging genomic and anatomical mapping technologies for creating a complete inventory of neuronal cell types and their connections in multiple species and during development. These projects lay the foundation for a larger and longer-term effort to generate whole-brain cell atlases in species including mice and humans. Copyright © 2017 Elsevier Inc. All rights reserved.

Changes in Gait Balance and Brain Connectivity in Response to Equine-Assisted Activity and Training in Children with Attention Deficit Hyperactivity Disorder.

PubMed

Hyun, Gi Jung; Jung, Tae-Woon; Park, Jeong Ha; Kang, Kyoung Doo; Kim, Sun Mi; Son, Young Don; Cheong, Jae Hoon; Kim, Bung-Nyun; Han, Doug Hyun

2016-04-01

Equine-assisted activity and training (EAAT) is thought to improve body balance and clinical symptoms in children with attention deficit hyperactivity disorder (ADHD). The study hypostheses were that EAAT would improve the clinical symptoms and gait balance in children with ADHD and that these improvements would be associated with increased brain connectivity within the balance circuit. A total of 12 children with ADHD and 12 age- and sex-matched healthy control children were recruited. EAAT consisted of three training sessions, each 70 minutes long, once a week for 4 weeks. Brain functional connectivity was assessed by using functional magnetic resonance imaging. After 4 weeks of EAAT, children with ADHD showed improved scores on the Korean ADHD scale (K-ARS), while the K-ARS scores of healthy children did not change. During the 4 weeks, the plantar pressure difference between the left foot and right foot decreased in both the healthy control group and the ADHD group. After 4 weeks of EAAT, healthy controls showed increased brain connectivity from the cerebellum to the left occipital lingual gyrus, fusiform gyrus, right and left thalami, right caudate, right precentral gyrus, and right superior frontal gyrus. However, children with ADHD showed increased brain connectivity from the cerebellum to the right insular cortex, right middle temporal gyrus, left superior temporal gyrus, and right precentral gyrus. In contrast, children with ADHD exhibited decreased brain connectivity from the cerebellum to the left inferior frontal gyrus. EAAT may improve clinical symptoms, gait balance, and brain connectivity, the last of which controls gait balance, in children with ADHD. However, children with ADHD who have deficits in the fronto-cerebellar tract did not exhibit changes in brain connectivity as extensive as those in healthy children in response to EAAT.

Towards mapping the brain connectome in depression: functional connectivity by perfusion SPECT.

PubMed

Gardner, Ann; Åstrand, Disa; Öberg, Johanna; Jacobsson, Hans; Jonsson, Cathrine; Larsson, Stig; Pagani, Marco

2014-08-30

Several studies have demonstrated altered brain functional connectivity in the resting state in depression. However, no study has investigated interregional networking in patients with persistent depressive disorder (PDD). The aim of this study was to assess differences in brain perfusion distribution and connectivity between large groups of patients and healthy controls. Participants comprised 91 patients with PDD and 65 age- and sex-matched healthy controls. Resting state perfusion was investigated by single photon emission computed tomography, and group differences were assessed by Statistical Parametric Mapping. Brain connectivity was explored through a voxel-wise interregional correlation analysis using as covariate of interest the normalized values of clusters of voxels in which perfusion differences were found in group analysis. Significantly increased regional brain perfusion distribution covering a large part of the cerebellum was observed in patients as compared with controls. Patients showed a significant negative functional connectivity between the cerebellar cluster and caudate, bilaterally. This study demonstrated inverse relative perfusion between the cerebellum and the caudate in PDD. Functional uncoupling may be associated with a dysregulation between the role of the cerebellum in action control and of the caudate in action selection, initiation and decision making in the patients. The potential impact of the resting state condition and the possibility of mitochondrial impairment are discussed. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Detecting large-scale networks in the human brain using high-density electroencephalography.

PubMed

Liu, Quanying; Farahibozorg, Seyedehrezvan; Porcaro, Camillo; Wenderoth, Nicole; Mantini, Dante

2017-09-01

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A review on functional and structural brain connectivity in numerical cognition

PubMed Central

Moeller, Korbinian; Willmes, Klaus; Klein, Elise

2015-01-01

Only recently has the complex anatomo-functional system underlying numerical cognition become accessible to evaluation in the living brain. We identified 27 studies investigating brain connectivity in numerical cognition. Despite considerable heterogeneity regarding methodological approaches, populations investigated, and assessment procedures implemented, the results provided largely converging evidence regarding the underlying brain connectivity involved in numerical cognition. Analyses of both functional/effective as well as structural connectivity have consistently corroborated the assumption that numerical cognition is subserved by a fronto-parietal network including (intra)parietal as well as (pre)frontal cortex sites. Evaluation of structural connectivity has indicated the involvement of fronto-parietal association fibers encompassing the superior longitudinal fasciculus dorsally and the external capsule/extreme capsule system ventrally. Additionally, commissural fibers seem to connect the bilateral intraparietal sulci when number magnitude information is processed. Finally, the identification of projection fibers such as the superior corona radiata indicates connections between cortex and basal ganglia as well as the thalamus in numerical cognition. Studies on functional/effective connectivity further indicated a specific role of the hippocampus. These specifications of brain connectivity augment the triple-code model of number processing and calculation with respect to how gray matter areas associated with specific number-related representations may work together. PMID:26029075

Structural connectivity of the developing human amygdala.

PubMed

Saygin, Zeynep M; Osher, David E; Koldewyn, Kami; Martin, Rebecca E; Finn, Amy; Saxe, Rebecca; Gabrieli, John D E; Sheridan, Margaret

2015-01-01

A large corpus of research suggests that there are changes in the manner and degree to which the amygdala supports cognitive and emotional function across development. One possible basis for these developmental differences could be the maturation of amygdalar connections with the rest of the brain. Recent functional connectivity studies support this conclusion, but the structural connectivity of the developing amygdala and its different nuclei remains largely unstudied. We examined age related changes in the DWI connectivity fingerprints of the amygdala to the rest of the brain in 166 individuals of ages 5-30. We also developed a model to predict age based on individual-subject amygdala connectivity, and identified the connections that were most predictive of age. Finally, we segmented the amygdala into its four main nucleus groups, and examined the developmental changes in connectivity for each nucleus. We observed that with age, amygdalar connectivity becomes increasingly sparse and localized. Age related changes were largely localized to the subregions of the amygdala that are implicated in social inference and contextual memory (the basal and lateral nuclei). The central nucleus' connectivity also showed differences with age but these differences affected fewer target regions than the basal and lateral nuclei. The medial nucleus did not exhibit any age related changes. These findings demonstrate increasing specificity in the connectivity patterns of amygdalar nuclei across age.

Human seizures couple across spatial scales through travelling wave dynamics

NASA Astrophysics Data System (ADS)

Martinet, L.-E.; Fiddyment, G.; Madsen, J. R.; Eskandar, E. N.; Truccolo, W.; Eden, U. T.; Cash, S. S.; Kramer, M. A.

2017-04-01

Epilepsy--the propensity toward recurrent, unprovoked seizures--is a devastating disease affecting 65 million people worldwide. Understanding and treating this disease remains a challenge, as seizures manifest through mechanisms and features that span spatial and temporal scales. Here we address this challenge through the analysis and modelling of human brain voltage activity recorded simultaneously across microscopic and macroscopic spatial scales. We show that during seizure large-scale neural populations spanning centimetres of cortex coordinate with small neural groups spanning cortical columns, and provide evidence that rapidly propagating waves of activity underlie this increased inter-scale coupling. We develop a corresponding computational model to propose specific mechanisms--namely, the effects of an increased extracellular potassium concentration diffusing in space--that support the observed spatiotemporal dynamics. Understanding the multi-scale, spatiotemporal dynamics of human seizures--and connecting these dynamics to specific biological mechanisms--promises new insights to treat this devastating disease.

Nicotine increases brain functional network efficiency.

PubMed

Wylie, Korey P; Rojas, Donald C; Tanabe, Jody; Martin, Laura F; Tregellas, Jason R

2012-10-15

Despite the use of cholinergic therapies in Alzheimer's disease and the development of cholinergic strategies for schizophrenia, relatively little is known about how the system modulates the connectivity and structure of large-scale brain networks. To better understand how nicotinic cholinergic systems alter these networks, this study examined the effects of nicotine on measures of whole-brain network communication efficiency. Resting state fMRI was acquired from fifteen healthy subjects before and after the application of nicotine or placebo transdermal patches in a single blind, crossover design. Data, which were previously examined for default network activity, were analyzed with network topology techniques to measure changes in the communication efficiency of whole-brain networks. Nicotine significantly increased local efficiency, a parameter that estimates the network's tolerance to local errors in communication. Nicotine also significantly enhanced the regional efficiency of limbic and paralimbic areas of the brain, areas which are especially altered in diseases such as Alzheimer's disease and schizophrenia. These changes in network topology may be one mechanism by which cholinergic therapies improve brain function. Published by Elsevier Inc.

Nicotine Increases Brain Functional Network Efficiency

PubMed Central

Wylie, Korey P.; Rojas, Donald C.; Tanabe, Jody; Martin, Laura F.; Tregellas, Jason R.

2012-01-01

Despite the use of cholinergic therapies in Alzheimer’s disease and the development of cholinergic strategies for schizophrenia, relatively little is known about how the system modulates the connectivity and structure of large-scale brain networks. To better understand how nicotinic cholinergic systems alter these networks, this study examined the effects of nicotine on measures of whole-brain network communication efficiency. Resting-state fMRI was acquired from fifteen healthy subjects before and after the application of nicotine or placebo transdermal patches in a single blind, crossover design. Data, which were previously examined for default network activity, were analyzed with network topology techniques to measure changes in the communication efficiency of whole-brain networks. Nicotine significantly increased local efficiency, a parameter that estimates the network’s tolerance to local errors in communication. Nicotine also significantly enhanced the regional efficiency of limbic and paralimbic areas of the brain, areas which are especially altered in diseases such as Alzheimer’s disease and schizophrenia. These changes in network topology may be one mechanism by which cholinergic therapies improve brain function. PMID:22796985

Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity.

PubMed

Kaiser, Roselinde H; Andrews-Hanna, Jessica R; Wager, Tor D; Pizzagalli, Diego A

2015-06-01

Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. To investigate network dysfunction in MDD through a meta-analysis of rsFC studies. Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data. Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis. Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network. Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions. Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.

Large-scale network dysfunction in Major Depressive Disorder: Meta-analysis of resting-state functional connectivity

PubMed Central

Kaiser, Roselinde H.; Andrews-Hanna, Jessica R.; Wager, Tor D.; Pizzagalli, Diego A.

2015-01-01

IMPORTANCE Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. OBJECTIVE To investigate network dysfunction in MDD through the first meta-analysis of rsFC studies. DATA SOURCES Seed-based voxel-wise rsFC studies comparing MDD with healthy individuals (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web-of-Science, EMBASE), and authors contacted for additional data. STUDY SELECTION Twenty-seven datasets from 25 publications (556 MDD adults/teens; 518 controls) were included in the meta-analysis. DATA EXTRACTION AND SYNTHESIS Coordinates of seed regions-of-interest and between-group effects were extracted. Seeds were categorized into “seed-networks” by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive, or reduced negative, connectivity) or hypoconnectivity (increased negative, or reduced positive, connectivity) with each seed-network. RESULTS MDD was characterized by hypoconnectivity within the frontoparietal network (FN), a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network (DAN) involved in attending to the external environment. MDD was also associated with hyperconnectivity within the default network (DN), a network believed to support internally-oriented and self-referential thought, and hyperconnectivity between FN control systems and regions of DN. Finally, MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions. CONCLUSIONS AND RELEVANCE Reduced connectivity within frontoparietal control systems, and imbalanced connectivity between control systems and networks involved in internal- or external-attention, may reflect depressive biases towards internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression. PMID:25785575

Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity.

PubMed

Wang, Danny J J; Jann, Kay; Fan, Chang; Qiao, Yang; Zang, Yu-Feng; Lu, Hanbing; Yang, Yihong

2018-01-01

Recently, non-linear statistical measures such as multi-scale entropy (MSE) have been introduced as indices of the complexity of electrophysiology and fMRI time-series across multiple time scales. In this work, we investigated the neurophysiological underpinnings of complexity (MSE) of electrophysiology and fMRI signals and their relations to functional connectivity (FC). MSE and FC analyses were performed on simulated data using neural mass model based brain network model with the Brain Dynamics Toolbox, on animal models with concurrent recording of fMRI and electrophysiology in conjunction with pharmacological manipulations, and on resting-state fMRI data from the Human Connectome Project. Our results show that the complexity of regional electrophysiology and fMRI signals is positively correlated with network FC. The associations between MSE and FC are dependent on the temporal scales or frequencies, with higher associations between MSE and FC at lower temporal frequencies. Our results from theoretical modeling, animal experiment and human fMRI indicate that (1) Regional neural complexity and network FC may be two related aspects of brain's information processing: the more complex regional neural activity, the higher FC this region has with other brain regions; (2) MSE at high and low frequencies may represent local and distributed information processing across brain regions. Based on literature and our data, we propose that the complexity of regional neural signals may serve as an index of the brain's capacity of information processing-increased complexity may indicate greater transition or exploration between different states of brain networks, thereby a greater propensity for information processing.

Speech networks at rest and in action: interactions between functional brain networks controlling speech production.

PubMed

Simonyan, Kristina; Fuertinger, Stefan

2015-04-01

Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. Copyright © 2015 the American Physiological Society.

Asphyxia-activated corticocardiac signaling accelerates onset of cardiac arrest.

PubMed

Li, Duan; Mabrouk, Omar S; Liu, Tiecheng; Tian, Fangyun; Xu, Gang; Rengifo, Santiago; Choi, Sarah J; Mathur, Abhay; Crooks, Charles P; Kennedy, Robert T; Wang, Michael M; Ghanbari, Hamid; Borjigin, Jimo

2015-04-21

The mechanism by which the healthy heart and brain die rapidly in the absence of oxygen is not well understood. We performed continuous electrocardiography and electroencephalography in rats undergoing experimental asphyxia and analyzed cortical release of core neurotransmitters, changes in brain and heart electrical activity, and brain-heart connectivity. Asphyxia stimulates a robust and sustained increase of functional and effective cortical connectivity, an immediate increase in cortical release of a large set of neurotransmitters, and a delayed activation of corticocardiac functional and effective connectivity that persists until the onset of ventricular fibrillation. Blocking the brain's autonomic outflow significantly delayed terminal ventricular fibrillation and lengthened the duration of detectable cortical activities despite the continued absence of oxygen. These results demonstrate that asphyxia activates a brainstorm, which accelerates premature death of the heart and the brain.

Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity.

PubMed

Thompson, Garth John; Pan, Wen-Ju; Magnuson, Matthew Evan; Jaeger, Dieter; Keilholz, Shella Dawn

2014-01-01

Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders. © 2013.

Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity

PubMed Central

Thompson, Garth John; Pan, Wen-Ju; Magnuson, Matthew Evan; Jaeger, Dieter; Keilholz, Shella Dawn

2013-01-01

Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders. PMID:24071524

Corticocortical evoked potentials reveal projectors and integrators in human brain networks.

PubMed

Keller, Corey J; Honey, Christopher J; Entz, Laszlo; Bickel, Stephan; Groppe, David M; Toth, Emilia; Ulbert, Istvan; Lado, Fred A; Mehta, Ashesh D

2014-07-02

The cerebral cortex is composed of subregions whose functional specialization is largely determined by their incoming and outgoing connections with each other. In the present study, we asked which cortical regions can exert the greatest influence over other regions and the cortical network as a whole. Previous research on this question has relied on coarse anatomy (mapping large fiber pathways) or functional connectivity (mapping inter-regional statistical dependencies in ongoing activity). Here we combined direct electrical stimulation with recordings from the cortical surface to provide a novel insight into directed, inter-regional influence within the cerebral cortex of awake humans. These networks of directed interaction were reproducible across strength thresholds and across subjects. Directed network properties included (1) a decrease in the reciprocity of connections with distance; (2) major projector nodes (sources of influence) were found in peri-Rolandic cortex and posterior, basal and polar regions of the temporal lobe; and (3) major receiver nodes (receivers of influence) were found in anterolateral frontal, superior parietal, and superior temporal regions. Connectivity maps derived from electrical stimulation and from resting electrocorticography (ECoG) correlations showed similar spatial distributions for the same source node. However, higher-level network topology analysis revealed differences between electrical stimulation and ECoG that were partially related to the reciprocity of connections. Together, these findings inform our understanding of large-scale corticocortical influence as well as the interpretation of functional connectivity networks. Copyright © 2014 the authors 0270-6474/14/349152-12$15.00/0.

The Effects of Taekwondo Training on Brain Connectivity and Body Intelligence.

PubMed

Kim, Young Jae; Cha, Eun Joo; Kim, Sun Mi; Kang, Kyung Doo; Han, Doug Hyun

2015-07-01

Many studies have reported that Taekwondo training could improve body perception, control and brain activity, as assessed with an electroencephalogram. This study aimed to assess body intelligence and brain connectivity in children with Taekwondo training as compared to children without Taekwondo training. Fifteen children with Taekwondo training (TKD) and 13 age- and sex-matched children who had no previous experience of Taekwondo training (controls) were recruited. Body intelligence, clinical characteristics and brain connectivity in all children were assessed with the Body Intelligence Scale (BIS), self-report, and resting state functional magnetic resonance imaging. The mean BIS score in the TKD group was higher than that in the control group. The TKD group showed increased low-frequency fluctuations in the right frontal precentral gyrus and the right parietal precuneus, compared to the control group. The TKD group showed positive cerebellum vermis (lobe VII) seed to the right frontal, left frontal, and left parietal lobe. The control group showed positive cerebellum seed to the left frontal, parietal, and occipital cortex. Relative to the control group, the TKD group showed increased functional connectivity from cerebellum seed to the right inferior frontal gyrus. To the best of our knowledge, this is the first study to assess the effect of Taekwondo training on brain connectivity in children. Taekwondo training improved body intelligence and brain connectivity from the cerebellum to the parietal and frontal cortex.

Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.

PubMed

Lohse, Christian; Bassett, Danielle S; Lim, Kelvin O; Carlson, Jean M

2014-10-01

Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.

Synchronized delta oscillations correlate with the resting-state functional MRI signal

PubMed Central

Lu, Hanbing; Zuo, Yantao; Gu, Hong; Waltz, James A.; Zhan, Wang; Scholl, Clara A.; Rea, William; Yang, Yihong; Stein, Elliot A.

2007-01-01

Synchronized low-frequency spontaneous fluctuations of the functional MRI (fMRI) signal have recently been applied to investigate large-scale neuronal networks of the brain in the absence of specific task instructions. However, the underlying neural mechanisms of these fluctuations remain largely unknown. To this end, electrophysiological recordings and resting-state fMRI measurements were conducted in α-chloralose-anesthetized rats. Using a seed-voxel analysis strategy, region-specific, anesthetic dose-dependent fMRI resting-state functional connectivity was detected in bilateral primary somatosensory cortex (S1FL) of the resting brain. Cortical electroencephalographic signals were also recorded from bilateral S1FL; a visual cortex locus served as a control site. Results demonstrate that, unlike the evoked fMRI response that correlates with power changes in the γ bands, the resting-state fMRI signal correlates with the power coherence in low-frequency bands, particularly the δ band. These data indicate that hemodynamic fMRI signal differentially registers specific electrical oscillatory frequency band activity, suggesting that fMRI may be able to distinguish the ongoing from the evoked activity of the brain. PMID:17991778

Resilience of developing brain networks to interictal epileptiform discharges is associated with cognitive outcome.

PubMed

Ibrahim, George M; Cassel, Daniel; Morgan, Benjamin R; Smith, Mary Lou; Otsubo, Hiroshi; Ochi, Ayako; Taylor, Margot; Rutka, James T; Snead, O Carter; Doesburg, Sam

2014-10-01

The effects of interictal epileptiform discharges on neurocognitive development in children with medically-intractable epilepsy are poorly understood. Such discharges may have a deleterious effect on the brain's intrinsic connectivity networks, which reflect the organization of functional networks at rest, and in turn on neurocognitive development. Using a combined functional magnetic resonance imaging-magnetoencephalography approach, we examine the effects of interictal epileptiform discharges on intrinsic connectivity networks and neurocognitive outcome. Functional magnetic resonance imaging was used to determine the location of regions comprising various intrinsic connectivity networks in 26 children (7-17 years), and magnetoencephalography data were reconstructed from these locations. Inter-regional phase synchronization was then calculated across interictal epileptiform discharges and graph theoretical analysis was applied to measure event-related changes in network topology in the peri-discharge period. The magnitude of change in network topology (network resilience/vulnerability) to interictal epileptiform discharges was associated with neurocognitive outcomes and functional magnetic resonance imaging networks using dual regression. Three main findings are reported: (i) large-scale network changes precede and follow interictal epileptiform discharges; (ii) the resilience of network topologies to interictal discharges is associated with stronger resting-state network connectivity; and (iii) vulnerability to interictal discharges is associated with worse neurocognitive outcomes. By combining the spatial resolution of functional magnetic resonance imaging with the temporal resolution of magnetoencephalography, we describe the effects of interictal epileptiform discharges on neurophysiological synchrony in intrinsic connectivity networks and establish the impact of interictal disruption of functional networks on cognitive outcome in children with epilepsy. The association between interictal discharges, network changes and neurocognitive outcomes suggests that it is of clinical importance to suppress discharges to foster more typical brain network development in children with focal epilepsy. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Inference in the age of big data: Future perspectives on neuroscience.

PubMed

Bzdok, Danilo; Yeo, B T Thomas

2017-07-15

Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging, and genetics). While growing data availability and information granularity have been amply discussed, we direct attention to a less explored question: How will the unprecedented data richness shape data analysis practices? Statistical reasoning is becoming more important to distill neurobiological knowledge from healthy and pathological brain measurements. We argue that large-scale data analysis will use more statistical models that are non-parametric, generative, and mixing frequentist and Bayesian aspects, while supplementing classical hypothesis testing with out-of-sample predictions. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Anterior insular cortex regulation in autism spectrum disorders

PubMed Central

Caria, Andrea; de Falco, Simona

2015-01-01

Autism spectrum disorders (ASDs) comprise a heterogeneous set of neurodevelopmental disorders characterized by dramatic impairments of interpersonal behavior, communication, and empathy. Recent neuroimaging studies suggested that ASD are disorders characterized by widespread abnormalities involving distributed brain network, though clear evidence of differences in large-scale brain network interactions underlying the cognitive and behavioral symptoms of ASD are still lacking. Consistent findings of anterior insula cortex hypoactivation and dysconnectivity during tasks related to emotional and social processing indicates its dysfunctional role in ASD. In parallel, increasing evidence showed that successful control of anterior insula activity can be attained using real-time fMRI paradigms. More importantly, successful regulation of this region was associated with changes in behavior and brain connectivity in both healthy individuals and psychiatric patients. Building on these results we here propose and discuss the use of real-time fMRI neurofeedback in ASD aiming at improving emotional and social behavior. PMID:25798096

Neural mechanisms of movement planning: motor cortex and beyond.

PubMed

Svoboda, Karel; Li, Nuo

2018-04-01

Neurons in motor cortex and connected brain regions fire in anticipation of specific movements, long before movement occurs. This neural activity reflects internal processes by which the brain plans and executes volitional movements. The study of motor planning offers an opportunity to understand how the structure and dynamics of neural circuits support persistent internal states and how these states influence behavior. Recent advances in large-scale neural recordings are beginning to decipher the relationship of the dynamics of populations of neurons during motor planning and movements. New behavioral tasks in rodents, together with quantified perturbations, link dynamics in specific nodes of neural circuits to behavior. These studies reveal a neural network distributed across multiple brain regions that collectively supports motor planning. We review recent advances and highlight areas where further work is needed to achieve a deeper understanding of the mechanisms underlying motor planning and related cognitive processes. Copyright © 2017. Published by Elsevier Ltd.

Focal temporal pole atrophy and network degeneration in semantic variant primary progressive aphasia

PubMed Central

Collins, Jessica A; Montal, Victor; Hochberg, Daisy; Quimby, Megan; Mandelli, Maria Luisa; Makris, Nikos; Seeley, William W; Gorno-Tempini, Maria Luisa; Dickerson, Bradford C

2017-01-01

Abstract A wealth of neuroimaging research has associated semantic variant primary progressive aphasia with distributed cortical atrophy that is most prominent in the left anterior temporal cortex; however, there is little consensus regarding which region within the anterior temporal cortex is most prominently damaged, which may indicate the putative origin of neurodegeneration. In this study, we localized the most prominent and consistent region of atrophy in semantic variant primary progressive aphasia using cortical thickness analysis in two independent patient samples (n = 16 and 28, respectively) relative to age-matched controls (n = 30). Across both samples the point of maximal atrophy was located in the same region of the left temporal pole. This same region was the point of maximal atrophy in 100% of individual patients in both semantic variant primary progressive aphasia samples. Using resting state functional connectivity in healthy young adults (n = 89), we showed that the seed region derived from the semantic variant primary progressive aphasia analysis was strongly connected with a large-scale network that closely resembled the distributed atrophy pattern in semantic variant primary progressive aphasia. In both patient samples, the magnitude of atrophy within a brain region was predicted by that region’s strength of functional connectivity to the temporopolar seed region in healthy adults. These findings suggest that cortical atrophy in semantic variant primary progressive aphasia may follow connectional pathways within a large-scale network that converges on the temporal pole. PMID:28040670

The positive effects of Xueshuan Xinmai tablets on brain functional connectivity in acute ischemic stroke: a placebo controlled randomized trial.

PubMed

Wei, Dongfeng; Xie, Daojun; Li, He; Chen, Yaojing; Qi, Di; Wang, Yujiao; Zhang, Yangjun; Chen, Kewei; Li, Chuanfu; Zhang, Zhanjun

2017-11-10

Through a placebo controlled randomized study, the purpose of this report was to investigate the effects of Xueshuan Xinmai tablets (XXMT) on neurologic deficits, quality of life and brain functional connectivity in acute ischemic stroke patients and to explore the mechanism of action of XXMT. In total, 44 acute ischemic stroke patients were randomly divided to the XXMT treatment group (n = 22) or the placebo group (n = 22) in a 2-week trial. Before and after the treatment, the neurological assessment and functional magnetic resonance imaging examinations were carried out. Compared to the placebo group, the scores of the National Institutes of Health Stroke Scale (NIHSS) and Stroke-Specific Quality of Life Scale (SSQOL) significantly improved in the treatment group. In addition, XXMT-treated patients demonstrated significantly enhanced functional connectivity within the default mode, frontal-parietal, and motor control networks. Furthermore, the changed connectivity in the left precuneus was positively correlated to the improvement of NIHSS and SSQOL scores. The present study indicated that XXMT treatment significantly improved the neurologic deficit and quality of life of acute ischemic stroke patients and that the therapeutic effect may be based on the modulation of XXMT on the functional connectivity of brain networks.

[Advances in mass spectrometry-based approaches for neuropeptide analysis].

PubMed

Ji, Qianyue; Ma, Min; Peng, Xin; Jia, Chenxi; Ji, Qianyue

2017-07-25

Neuropeptides are an important class of endogenous bioactive substances involved in the function of the nervous system, and connect the brain and other neural and peripheral organs. Mass spectrometry-based neuropeptidomics are designed to study neuropeptides in a large-scale manner and obtain important molecular information to further understand the mechanism of nervous system regulation and the pathogenesis of neurological diseases. This review summarizes the basic strategies for the study of neuropeptides using mass spectrometry, including sample preparation and processing, qualitative and quantitative methods, and mass spectrometry imagining.

Multimodal analysis of cortical chemoarchitecture and macroscale fMRI resting‐state functional connectivity

PubMed Central

Scholtens, Lianne H.; Turk, Elise; Mantini, Dante; Vanduffel, Wim; Feldman Barrett, Lisa

2016-01-01

Abstract The cerebral cortex is well known to display a large variation in excitatory and inhibitory chemoarchitecture, but the effect of this variation on global scale functional neural communication and synchronization patterns remains less well understood. Here, we provide evidence of the chemoarchitecture of cortical regions to be associated with large‐scale region‐to‐region resting‐state functional connectivity. We assessed the excitatory versus inhibitory chemoarchitecture of cortical areas as an ExIn ratio between receptor density mappings of excitatory (AMPA, M1) and inhibitory (GABAA, M2) receptors, computed on the basis of data collated from pioneering studies of autoradiography mappings as present in literature of the human (2 datasets) and macaque (1 dataset) cortex. Cortical variation in ExIn ratio significantly correlated with total level of functional connectivity as derived from resting‐state functional connectivity recordings of cortical areas across all three datasets (human I: P = 0.0004; human II: P = 0.0008; macaque: P = 0.0007), suggesting cortical areas with an overall more excitatory character to show higher levels of intrinsic functional connectivity during resting‐state. Our findings are indicative of the microscale chemoarchitecture of cortical regions to be related to resting‐state fMRI connectivity patterns at the global system's level of connectome organization. Hum Brain Mapp 37:3103–3113, 2016. © 2016 Wiley Periodicals, Inc. PMID:27207489

Understanding the brain through its spatial structure

NASA Astrophysics Data System (ADS)

Morrison, Will Zachary

The spatial location of cells in neural tissue can be easily extracted from many imaging modalities, but the information contained in spatial relationships between cells is seldom utilized. This is because of a lack of recognition of the importance of spatial relationships to some aspects of brain function, and the reflection in spatial statistics of other types of information. The mathematical tools necessary to describe spatial relationships are also unknown to many neuroscientists, and biologists in general. We analyze two cases, and show that spatial relationships can be used to understand the role of a particular type of cell, the astrocyte, in Alzheimer's disease, and that the geometry of axons in the brain's white matter sheds light on the process of establishing connectivity between areas of the brain. Astrocytes provide nutrients for neuronal metabolism, and regulate the chemical environment of the brain, activities that require manipulation of spatial distributions (of neurotransmitters, for example). We first show, through the use of a correlation function, that inter-astrocyte forces determine the size of independent regulatory domains in the cortex. By examining the spatial distribution of astrocytes in a mouse model of Alzheimer's Disease, we determine that astrocytes are not actively transported to fight the disease, as was previously thought. The paths axons take through the white matter determine which parts of the brain are connected, and how quickly signals are transmitted. The rules that determine these paths (i.e. shortest distance) are currently unknown. By measurement of axon orientation distributions using three-point correlation functions and the statistics of axon turning and branching, we reveal that axons are restricted to growth in three directions, like a taxicab traversing city blocks, albeit in three-dimensions. We show how geometric restrictions at the small scale are related to large-scale trajectories. Finally we discuss the implications of this finding for experimental and theoretical connectomics.

A versatile clearing agent for multi-modal brain imaging

PubMed Central

Costantini, Irene; Ghobril, Jean-Pierre; Di Giovanna, Antonino Paolo; Mascaro, Anna Letizia Allegra; Silvestri, Ludovico; Müllenbroich, Marie Caroline; Onofri, Leonardo; Conti, Valerio; Vanzi, Francesco; Sacconi, Leonardo; Guerrini, Renzo; Markram, Henry; Iannello, Giulio; Pavone, Francesco Saverio

2015-01-01

Extensive mapping of neuronal connections in the central nervous system requires high-throughput µm-scale imaging of large volumes. In recent years, different approaches have been developed to overcome the limitations due to tissue light scattering. These methods are generally developed to improve the performance of a specific imaging modality, thus limiting comprehensive neuroanatomical exploration by multi-modal optical techniques. Here, we introduce a versatile brain clearing agent (2,2′-thiodiethanol; TDE) suitable for various applications and imaging techniques. TDE is cost-efficient, water-soluble and low-viscous and, more importantly, it preserves fluorescence, is compatible with immunostaining and does not cause deformations at sub-cellular level. We demonstrate the effectiveness of this method in different applications: in fixed samples by imaging a whole mouse hippocampus with serial two-photon tomography; in combination with CLARITY by reconstructing an entire mouse brain with light sheet microscopy and in translational research by imaging immunostained human dysplastic brain tissue. PMID:25950610

Theta and Alpha Oscillations Are Traveling Waves in the Human Neocortex.

PubMed

Zhang, Honghui; Watrous, Andrew J; Patel, Ansh; Jacobs, Joshua

2018-06-01

Human cognition requires the coordination of neural activity across widespread brain networks. Here, we describe a new mechanism for large-scale coordination in the human brain: traveling waves of theta and alpha oscillations. Examining direct brain recordings from neurosurgical patients performing a memory task, we found contiguous clusters of cortex in individual patients with oscillations at specific frequencies within 2 to 15 Hz. These oscillatory clusters displayed spatial phase gradients, indicating that they formed traveling waves that propagated at ∼0.25-0.75 m/s. Traveling waves were relevant behaviorally because their propagation correlated with task events and was more consistent when subjects performed the task well. Human traveling theta and alpha waves can be modeled by a network of coupled oscillators because the direction of wave propagation correlated with the spatial orientation of local frequency gradients. Our findings suggest that oscillations support brain connectivity by organizing neural processes across space and time. Copyright © 2018 Elsevier Inc. All rights reserved.

Transition of the functional brain network related to increasing cognitive demands.

PubMed

Finc, Karolina; Bonna, Kamil; Lewandowska, Monika; Wolak, Tomasz; Nikadon, Jan; Dreszer, Joanna; Duch, Włodzisław; Kühn, Simone

2017-04-22

Network neuroscience provides tools that can easily be used to verify main assumptions of the global workspace theory (GWT), such as the existence of highly segregated information processing during effortless tasks performance, engagement of multiple distributed networks during effortful tasks and the critical role of long-range connections in workspace formation. A number of studies support the assumptions of GWT by showing the reorganization of the whole-brain functional network during cognitive task performance; however, the involvement of specific large scale networks in the formation of workspace is still not well-understood. (1) to examine changes in the whole-brain functional network under increased cognitive demands of working memory during an n-back task, and their relationship with behavioral outcomes; and (2) to provide a comprehensive description of local changes that may be involved in the formation of the global workspace, using hub detection and network-based statistic. Our results show that network modularity decreased with increasing cognitive demands, and this change allowed us to predict behavioral performance. The number of connector hubs increased, whereas the number of provincial hubs decreased when the task became more demanding. We also found that the default mode network (DMN) increased its connectivity to other networks while decreasing connectivity between its own regions. These results, apart from replicating previous findings, provide a valuable insight into the mechanisms of the formation of the global workspace, highlighting the role of the DMN in the processes of network integration. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Disrupted coupling of large-scale networks is associated with relapse behaviour in heroin-dependent men

PubMed Central

Li, Qiang; Liu, Jierong; Wang, Wei; Wang, Yarong; Li, Wei; Chen, Jiajie; Zhu, Jia; Yan, Xuejiao; Li, Yongbin; Li, Zhe; Ye, Jianjun; Wang, Wei

2018-01-01

Background It is unknown whether impaired coupling among 3 core large-scale brain networks (salience [SN], default mode [DMN] and executive control networks [ECN]) is associated with relapse behaviour in treated heroin-dependent patients. Methods We conducted a prospective resting-state functional MRI study comparing the functional connectivity strength among healthy controls and heroin-dependent men who had either relapsed or were in early remission. Men were considered to be either relapsed or in early remission based on urine drug screens during a 3-month follow-up period. We also examined how the coupling of large-scale networks correlated with relapse behaviour among heroin-dependent men. Results We included 20 controls and 50 heroin-dependent men (26 relapsed and 24 early remission) in our analyses. The relapsed men showed greater connectivity than the early remission and control groups between the dorsal anterior cingulate cortex (key node of the SN) and the dorsomedial prefrontal cortex (included in the DMN). The relapsed men and controls showed lower connectivity than the early remission group between the left dorsolateral prefrontal cortex (key node of the left ECN) and the dorsomedial prefrontal cortex. The percentage of positive urine drug screens positively correlated with the coupling between the dorsal anterior cingulate cortex and dorsomedial prefrontal cortex, but negatively correlated with the coupling between the left dorsolateral prefrontal cortex and dorsomedial prefrontal cortex. Limitations We examined deficits in only 3 core networks leading to relapse behaviour. Other networks may also contribute to relapse. Conclusion Greater coupling between the SN and DMN and lower coupling between the left ECN and DMN is associated with relapse behaviour. These findings may shed light on the development of new treatments for heroin addiction. PMID:29252165

Multimodal investigation of triple network connectivity in patients with 22q11DS and association with executive functions.

PubMed

Padula, Maria C; Schaer, Marie; Scariati, Elisa; Maeder, Johanna; Schneider, Maude; Eliez, Stephan

2017-04-01

Large-scale brain networks play a prominent role in cognitive abilities and their activity is impaired in psychiatric disorders, such as schizophrenia. Patients with 22q11.2 deletion syndrome (22q11DS) are at high risk of developing schizophrenia and present similar cognitive impairments, including executive functions deficits. Thus, 22q11DS represents a model for the study of neural biomarkers associated with schizophrenia. In this study, we investigated structural and functional connectivity within and between the Default Mode (DMN), the Central Executive (CEN), and the Saliency network (SN) in 22q11DS using resting-state fMRI and DTI. Furthermore, we investigated if triple network impairments were related to executive dysfunctions or the presence of psychotic symptoms. Sixty-three patients with 22q11DS and sixty-eighty controls (age 6-33 years) were included in the study. Structural connectivity between main nodes of DMN, CEN, and SN was computed using probabilistic tractography. Functional connectivity was computed as the partial correlation between the time courses extracted from each node. Structural and functional connectivity measures were then correlated to executive functions and psychotic symptom scores. Our results showed mainly reduced structural connectivity within the CEN, DMN, and SN, in patients with 22q11DS compared with controls as well as reduced between-network connectivity. Functional connectivity appeared to be more preserved, with impairments being evident only within the DMN. Structural connectivity impairments were also related to executive dysfunctions. These findings show an association between triple network structural alterations and executive deficits in patients with the microdeletion, suggesting that 22q11DS and schizophrenia share common psychopathological mechanisms. Hum Brain Mapp 38:2177-2189, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer’s disease

PubMed Central

Chong, Joanna Su Xian; Liu, Siwei; Loke, Yng Miin; Hilal, Saima; Ikram, Mohammad Kamran; Xu, Xin; Tan, Boon Yeow; Venketasubramanian, Narayanaswamy; Chen, Christopher Li-Hsian

2017-01-01

Abstract Network-sensitive neuroimaging methods have been used to characterize large-scale brain network degeneration in Alzheimer’s disease and its prodrome. However, few studies have investigated the combined effect of Alzheimer’s disease and cerebrovascular disease on brain network degeneration. Our study sought to examine the intrinsic functional connectivity and structural covariance network changes in 235 prodromal and clinical Alzheimer’s disease patients with and without cerebrovascular disease. We focused particularly on two higher-order cognitive networks—the default mode network and the executive control network. We found divergent functional connectivity and structural covariance patterns in Alzheimer’s disease patients with and without cerebrovascular disease. Alzheimer’s disease patients without cerebrovascular disease, but not Alzheimer’s disease patients with cerebrovascular disease, showed reductions in posterior default mode network functional connectivity. By comparison, while both groups exhibited parietal reductions in executive control network functional connectivity, only Alzheimer’s disease patients with cerebrovascular disease showed increases in frontal executive control network connectivity. Importantly, these distinct executive control network changes were recapitulated in prodromal Alzheimer’s disease patients with and without cerebrovascular disease. Across Alzheimer’s disease patients with and without cerebrovascular disease, higher default mode network functional connectivity z-scores correlated with greater hippocampal volumes while higher executive control network functional connectivity z-scores correlated with greater white matter changes. In parallel, only Alzheimer’s disease patients without cerebrovascular disease showed increased default mode network structural covariance, while only Alzheimer’s disease patients with cerebrovascular disease showed increased executive control network structural covariance compared to controls. Our findings demonstrate the differential neural network structural and functional changes in Alzheimer’s disease with and without cerebrovascular disease, suggesting that the underlying pathology of Alzheimer’s disease patients with cerebrovascular disease might differ from those without cerebrovascular disease and reflect a combination of more severe cerebrovascular disease and less severe Alzheimer’s disease network degeneration phenotype. PMID:29053778

The prestimulus default mode network state predicts cognitive task performance levels on a mental rotation task.

PubMed

Kamp, Tabea; Sorger, Bettina; Benjamins, Caroline; Hausfeld, Lars; Goebel, Rainer

2018-06-22

Linking individual task performance to preceding, regional brain activation is an ongoing goal of neuroscientific research. Recently, it could be shown that the activation and connectivity within large-scale brain networks prior to task onset influence performance levels. More specifically, prestimulus default mode network (DMN) effects have been linked to performance levels in sensory near-threshold tasks, as well as cognitive tasks. However, it still remains uncertain how the DMN state preceding cognitive tasks affects performance levels when the period between task trials is long and flexible, allowing participants to engage in different cognitive states. We here investigated whether the prestimulus activation and within-network connectivity of the DMN are predictive of the correctness and speed of task performance levels on a cognitive (match-to-sample) mental rotation task, employing a sparse event-related functional magnetic resonance imaging (fMRI) design. We found that prestimulus activation in the DMN predicted the speed of correct trials, with a higher amplitude preceding correct fast response trials compared to correct slow response trials. Moreover, we found higher connectivity within the DMN before incorrect trials compared to correct trials. These results indicate that pre-existing activation and connectivity states within the DMN influence task performance on cognitive tasks, both effecting the correctness and speed of task execution. The findings support existing theories and empirical work on relating mind-wandering and cognitive task performance to the DMN and expand these by establishing a relationship between the prestimulus DMN state and the speed of cognitive task performance. © 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

PubMed

Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

2015-06-12

During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. Copyright © 2015, American Association for the Advancement of Science.

Resolving Structural Variability in Network Models and the Brain

PubMed Central

Klimm, Florian; Bassett, Danielle S.; Carlson, Jean M.; Mucha, Peter J.

2014-01-01

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data. PMID:24675546

Direct modulation of aberrant brain network connectivity through real-time NeuroFeedback.

PubMed

Ramot, Michal; Kimmich, Sara; Gonzalez-Castillo, Javier; Roopchansingh, Vinai; Popal, Haroon; White, Emily; Gotts, Stephen J; Martin, Alex

2017-09-16

The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants' awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns.

Toward a standardized structural-functional group connectome in MNI space.

PubMed

Horn, Andreas; Blankenburg, Felix

2016-01-01

The analysis of the structural architecture of the human brain in terms of connectivity between its subregions has provided profound insights into its underlying functional organization and has coined the concept of the "connectome", a structural description of the elements forming the human brain and the connections among them. Here, as a proof of concept, we introduce a novel group connectome in standard space based on a large sample of 169 subjects from the Enhanced Nathan Kline Institute-Rockland Sample (eNKI-RS). Whole brain structural connectomes of each subject were estimated with a global tracking approach, and the resulting fiber tracts were warped into standard stereotactic (MNI) space using DARTEL. Employing this group connectome, the results of published tracking studies (i.e., the JHU white matter and Oxford thalamic connectivity atlas) could be largely reproduced directly within MNI space. In a second analysis, a study that examined structural connectivity between regions of a functional network, namely the default mode network, was reproduced. Voxel-wise structural centrality was then calculated and compared to others' findings. Furthermore, including additional resting-state fMRI data from the same subjects, structural and functional connectivity matrices between approximately forty thousand nodes of the brain were calculated. This was done to estimate structure-function agreement indices of voxel-wise whole brain connectivity. Taken together, the combination of a novel whole brain fiber tracking approach and an advanced normalization method led to a group connectome that allowed (at least heuristically) performing fiber tracking directly within MNI space. Such an approach may be used for various purposes like the analysis of structural connectivity and modeling experiments that aim at studying the structure-function relationship of the human connectome. Moreover, it may even represent a first step toward a standard DTI template of the human brain in stereotactic space. The standardized group connectome might thus be a promising new resource to better understand and further analyze the anatomical architecture of the human brain on a population level. Copyright © 2015 Elsevier Inc. All rights reserved.

Investigating the effects of streamline-based fiber tractography on matrix scaling in brain connective network.

PubMed

Jan, Hengtai; Chao, Yi-Ping; Cho, Kuan-Hung; Kuo, Li-Wei

2013-01-01

Investigating the brain connective network using the modern graph theory has been widely applied in cognitive and clinical neuroscience research. In this study, we aimed to investigate the effects of streamline-based fiber tractography on the change of network properties and established a systematic framework to understand how an adequate network matrix scaling can be determined. The network properties, including degree, efficiency and betweenness centrality, show similar tendency in both left and right hemispheres. By employing the curve-fitting process with exponential law and measuring the residuals, the association between changes of network properties and threshold of track numbers is found and an adequate range of investigating the lateralization of brain network is suggested. The proposed approach can be further applied in clinical applications to improve the diagnostic sensitivity using network analysis with graph theory.

Functional Connectivity of Insula, Basal Ganglia, and Prefrontal Executive Control Networks during Hypoglycemia in Type 1 Diabetes

PubMed Central

Simonson, Donald C.; Nickerson, Lisa D.; Flores, Veronica L.; Siracusa, Tamar; Hager, Brandon; Lyoo, In Kyoon; Renshaw, Perry F.; Jacobson, Alan M.

2015-01-01

Human brain networks mediating interoceptive, behavioral, and cognitive aspects of glycemic control are not well studied. Using group independent component analysis with dual-regression approach of functional magnetic resonance imaging data, we examined the functional connectivity changes of large-scale resting state networks during sequential euglycemic–hypoglycemic clamp studies in patients with type 1 diabetes and nondiabetic controls and how these changes during hypoglycemia were related to symptoms of hypoglycemia awareness and to concurrent glycosylated hemoglobin (HbA1c) levels. During hypoglycemia, diabetic patients showed increased functional connectivity of the right anterior insula and the prefrontal cortex within the executive control network, which was associated with higher HbA1c. Controls showed decreased functional connectivity of the right anterior insula with the cerebellum/basal ganglia network and of temporal regions within the temporal pole network and increased functional connectivity in the default mode and sensorimotor networks. Functional connectivity reductions in the right basal ganglia were correlated with increases of self-reported hypoglycemic symptoms in controls but not in patients. Resting state networks that showed different group functional connectivity during hypoglycemia may be most sensitive to glycemic environment, and their connectivity patterns may have adapted to repeated glycemic excursions present in type 1 diabetes. Our results suggest that basal ganglia and insula mediation of interoceptive awareness during hypoglycemia is altered in type 1 diabetes. These changes could be neuroplastic adaptations to frequent hypoglycemic experiences. Functional connectivity changes in the insula and prefrontal cognitive networks could also reflect an adaptation to changes in brain metabolic pathways associated with chronic hyperglycemia. SIGNIFICANCE STATEMENT The major factor limiting improved glucose control in type 1 diabetes is the significant increase in hypoglycemia associated with insulin treatment. Repeated exposure to hypoglycemia alters patients' ability to recognize the autonomic and neuroglycopenic symptoms associated with low plasma glucose levels. We examined brain resting state networks during the induction of hypoglycemia in diabetic and control subjects and found differences in networks involved in sensorimotor function, cognition, and interoceptive awareness that were related to chronic levels of glycemic control. These findings identify brain regions that are sensitive to variations in plasma glucose levels and may also provide a basis for understanding the mechanisms underlying the increased incidence of cognitive impairment and affective disorders seen in patients with diabetes. PMID:26245963

Transcallosal transfer of information and functional asymmetry of the human brain.

PubMed

Nowicka, Anna; Tacikowski, Pawel

2011-01-01

The corpus callosum is the largest commissure in the brain and acts as a "bridge" of nerve fibres connecting the two cerebral hemispheres. It plays a crucial role in interhemispheric integration and is responsible for normal communication and cooperation between the two hemispheres. Evolutionary pressures guiding brain size are accompanied by reduced interhemispheric and enhanced intrahemispheric connectivity. Some lines of evidence suggest that the speed of transcallosal conduction is limited in large brains (e.g., in humans), thus favouring intrahemispheric processing and brain lateralisation. Patterns of directional symmetry/asymmetry of transcallosal transfer time may be related to the degree of brain lateralisation. Neural network modelling and electrophysiological studies on interhemispheric transmission provide data supporting this supposition.

Longitudinal variations of brain functional connectivity: A case report study based on a mouse model of epilepsy.

PubMed

Erramuzpe, A; Encinas, J M; Sierra, A; Maletic-Savatic, M; Brewster, A L; Anderson, Anne E; Stramaglia, S; Cortes, Jesus M

2015-01-01

Brain Functional Connectivity (FC) quantifies statistical dependencies between areas of the brain. FC has been widely used to address altered function of brain circuits in control conditions compared to different pathological states, including epilepsy, a major neurological disorder. However, FC also has the as yet unexplored potential to help us understand the pathological transformation of the brain circuitry. Our hypothesis is that FC can differentiate global brain interactions across a time-scale of days. To this end, we present a case report study based on a mouse model for epilepsy and analyze longitudinal intracranial electroencephalography data of epilepsy to calculate FC changes from the initial insult (status epilepticus) and over the latent period, when epileptogenic networks emerge, and at chronic epilepsy, when unprovoked seizures occur as spontaneous events. We found that the overall network FC at low frequency bands decreased immediately after status epilepticus was provoked, and increased monotonously later on during the latent period. Overall, our results demonstrate the capacity of FC to address longitudinal variations of brain connectivity across the establishment of pathological states.

Spectral fingerprints of large-scale neuronal interactions.

PubMed

Siegel, Markus; Donner, Tobias H; Engel, Andreas K

2012-01-11

Cognition results from interactions among functionally specialized but widely distributed brain regions; however, neuroscience has so far largely focused on characterizing the function of individual brain regions and neurons therein. Here we discuss recent studies that have instead investigated the interactions between brain regions during cognitive processes by assessing correlations between neuronal oscillations in different regions of the primate cerebral cortex. These studies have opened a new window onto the large-scale circuit mechanisms underlying sensorimotor decision-making and top-down attention. We propose that frequency-specific neuronal correlations in large-scale cortical networks may be 'fingerprints' of canonical neuronal computations underlying cognitive processes.

Functional connectivity in the mouse brain imaged by B-mode photoacoustic microscopy

NASA Astrophysics Data System (ADS)

Nasiriavanaki, Mohammadreza; Xing, Wenxin; Xia, Jun; Wang, Lihong V.

2014-03-01

The increasing use of mouse models for human brain disease studies, coupled with the fact that existing functional imaging modalities cannot be easily applied to mice, presents an emerging need for a new functional imaging modality. Utilizing acoustic-resolution photoacoustic microscopy (AR-PAM), we imaged spontaneous cerebral hemodynamic fluctuations and their associated functional connections in the mouse brain. The images were acquired noninvasively in B-scan mode with a fast frame rate, a large field of view, and a high spatial resolution. At a location relative to the bregma 0, correlations were investigated inter-hemispherically between bilaterally homologous regions, as well as intra-hemispherically within the same functional regions. The functional connectivity in different functional regions was studied. The locations of these regions agreed well with the Paxinos mouse brain atlas. The functional connectivity map obtained in this study can then be used in the investigation of brain disorders such as stroke, Alzheimer's, schizophrenia, multiple sclerosis, autism, and epilepsy. Our experiments show that photoacoustic microscopy is capable to detect connectivities between different functional regions in B-scan mode, promising a powerful functional imaging modality for future brain research.

The social brain: scale-invariant layering of Erdős-Rényi networks in small-scale human societies.

PubMed

Harré, Michael S; Prokopenko, Mikhail

2016-05-01

The cognitive ability to form social links that can bind individuals together into large cooperative groups for safety and resource sharing was a key development in human evolutionary and social history. The 'social brain hypothesis' argues that the size of these social groups is based on a neurologically constrained capacity for maintaining long-term stable relationships. No model to date has been able to combine a specific socio-cognitive mechanism with the discrete scale invariance observed in ethnographic studies. We show that these properties result in nested layers of self-organizing Erdős-Rényi networks formed by each individual's ability to maintain only a small number of social links. Each set of links plays a specific role in the formation of different social groups. The scale invariance in our model is distinct from previous 'scale-free networks' studied using much larger social groups; here, the scale invariance is in the relationship between group sizes, rather than in the link degree distribution. We also compare our model with a dominance-based hierarchy and conclude that humans were probably egalitarian in hunter-gatherer-like societies, maintaining an average maximum of four or five social links connecting all members in a largest social network of around 132 people. © 2016 The Author(s).

Structural Connectivity of the Developing Human Amygdala

PubMed Central

Saygin, Zeynep M.; Osher, David E.; Koldewyn, Kami; Martin, Rebecca E.; Finn, Amy; Saxe, Rebecca; Gabrieli, John D.E.; Sheridan, Margaret

2015-01-01

A large corpus of research suggests that there are changes in the manner and degree to which the amygdala supports cognitive and emotional function across development. One possible basis for these developmental differences could be the maturation of amygdalar connections with the rest of the brain. Recent functional connectivity studies support this conclusion, but the structural connectivity of the developing amygdala and its different nuclei remains largely unstudied. We examined age related changes in the DWI connectivity fingerprints of the amygdala to the rest of the brain in 166 individuals of ages 5-30. We also developed a model to predict age based on individual-subject amygdala connectivity, and identified the connections that were most predictive of age. Finally, we segmented the amygdala into its four main nucleus groups, and examined the developmental changes in connectivity for each nucleus. We observed that with age, amygdalar connectivity becomes increasingly sparse and localized. Age related changes were largely localized to the subregions of the amygdala that are implicated in social inference and contextual memory (the basal and lateral nuclei). The central nucleus’ connectivity also showed differences with age but these differences affected fewer target regions than the basal and lateral nuclei. The medial nucleus did not exhibit any age related changes. These findings demonstrate increasing specificity in the connectivity patterns of amygdalar nuclei across age. PMID:25875758

Correlated gene expression and anatomical communication support synchronized brain activity in the mouse functional connectome.

PubMed

Mills, Brian D; Grayson, David S; Shunmugavel, Anandakumar; Miranda-Dominguez, Oscar; Feczko, Eric; Earl, Eric; Neve, Kim; Fair, Damien A

2018-05-22

Cognition and behavior depend on synchronized intrinsic brain activity that is organized into functional networks across the brain. Research has investigated how anatomical connectivity both shapes and is shaped by these networks, but not how anatomical connectivity interacts with intra-areal molecular properties to drive functional connectivity. Here, we present a novel linear model to explain functional connectivity by integrating systematically obtained measurements of axonal connectivity, gene expression, and resting state functional connectivity MRI in the mouse brain. The model suggests that functional connectivity arises from both anatomical links and inter-areal similarities in gene expression. By estimating these effects, we identify anatomical modules in which correlated gene expression and anatomical connectivity support functional connectivity. Along with providing evidence that not all genes equally contribute to functional connectivity, this research establishes new insights regarding the biological underpinnings of coordinated brain activity measured by BOLD fMRI. SIGNIFICANCE STATEMENT Efforts at characterizing the functional connectome with fMRI have risen exponentially over the last decade. Yet despite this rise, the biological underpinnings of these functional measurements are still largely unknown. The current report begins to fill this void by investigating the molecular underpinnings of the functional connectome through an integration of systematically obtained structural information and gene expression data throughout the rodent brain. We find that both white matter connectivity and similarity in regional gene expression relate to resting state functional connectivity. The current report furthers our understanding of the biological underpinnings of the functional connectome and provides a linear model that can be utilized to streamline preclinical animal studies of disease. Copyright © 2018 the authors.

Default mode network as a potential biomarker of chemotherapy-related brain injury

PubMed Central

Kesler, Shelli R.

2014-01-01

Chronic medical conditions and/or their treatments may interact with aging to alter or even accelerate brain senescence. Adult onset cancer, for example, is a disease associated with advanced aging and emerging evidence suggests a profile of subtle but diffuse brain injury following cancer chemotherapy. Breast cancer is currently the primary model for studying these “chemobrain” effects. Given the widespread changes to brain structure and function as well as the common impairment of integrated cognitive skills observed following breast cancer chemotherapy, it is likely that large-scale brain networks are involved. Default mode network (DMN) is a strong candidate considering its preferential vulnerability to aging and sensitivity to toxicity and disease states. Additionally, chemotherapy is associated with several physiologic effects including increased inflammation and oxidative stress that are believed to elevate toxicity in the DMN. Biomarkers of DMN connectivity could aid in the development of treatments for chemotherapy-related cognitive decline. For example, certain nutritional interventions could potentially reduce the metabolic changes (e.g. amyloid beta toxicity) associated with DMN disruption. PMID:24913897

Disrupted Small-World Networks in Schizophrenia

ERIC Educational Resources Information Center

Liu, Yong; Liang, Meng; Zhou, Yuan; He, Yong; Hao, Yihui; Song, Ming; Yu, Chunshui; Liu, Haihong; Liu, Zhening; Jiang, Tianzi

2008-01-01

The human brain has been described as a large, sparse, complex network characterized by efficient small-world properties, which assure that the brain generates and integrates information with high efficiency. Many previous neuroimaging studies have provided consistent evidence of "dysfunctional connectivity" among the brain regions in…

Challenges of microtome‐based serial block‐face scanning electron microscopy in neuroscience

PubMed Central

WANNER, A. A.; KIRSCHMANN, M. A.

2015-01-01

Summary Serial block‐face scanning electron microscopy (SBEM) is becoming increasingly popular for a wide range of applications in many disciplines from biology to material sciences. This review focuses on applications for circuit reconstruction in neuroscience, which is one of the major driving forces advancing SBEM. Neuronal circuit reconstruction poses exceptional challenges to volume EM in terms of resolution, field of view, acquisition time and sample preparation. Mapping the connections between neurons in the brain is crucial for understanding information flow and information processing in the brain. However, information on the connectivity between hundreds or even thousands of neurons densely packed in neuronal microcircuits is still largely missing. Volume EM techniques such as serial section TEM, automated tape‐collecting ultramicrotome, focused ion‐beam scanning electron microscopy and SBEM (microtome serial block‐face scanning electron microscopy) are the techniques that provide sufficient resolution to resolve ultrastructural details such as synapses and provides sufficient field of view for dense reconstruction of neuronal circuits. While volume EM techniques are advancing, they are generating large data sets on the terabyte scale that require new image processing workflows and analysis tools. In this review, we present the recent advances in SBEM for circuit reconstruction in neuroscience and an overview of existing image processing and analysis pipelines. PMID:25907464

Compensatory Motor Network Connectivity is Associated with Motor Sequence Learning after Subcortical Stroke

PubMed Central

Wadden, Katie P.; Woodward, Todd S.; Metzak, Paul D.; Lavigne, Katie M.; Lakhani, Bimal; Auriat, Angela M.; Boyd, Lara A.

2015-01-01

Following stroke, functional networks reorganize and the brain demonstrates widespread alterations in cortical activity. Implicit motor learning is preserved after stroke. However the manner in which brain reorganization occurs, and how it supports behaviour within the damaged brain remains unclear. In this functional magnetic resonance imaging (fMRI) study, we evaluated whole brain patterns of functional connectivity during the performance of an implicit tracking task at baseline and retention, following 5 days of practice. Following motor practice, a significant difference in connectivity within a motor network, consisting of bihemispheric activation of the sensory and motor cortices, parietal lobules, cerebellar and occipital lobules, was observed at retention. Healthy subjects demonstrated greater activity within this motor network during sequence learning compared to random practice. The stroke group did not show the same level of functional network integration, presumably due to the heterogeneity of functional reorganization following stroke. In a secondary analysis, a binary mask of the functional network activated from the aforementioned whole brain analyses was created to assess within-network connectivity, decreasing the spatial distribution and large variability of activation that exists within the lesioned brain. The stroke group demonstrated reduced clusters of connectivity within the masked brain regions as compared to the whole brain approach. Connectivity within this smaller motor network correlated with repeated sequence performance on the retention test. Increased functional integration within the motor network may be an important neurophysiological predictor of motor learning-related change in individuals with stroke. PMID:25757996

New insights in the homotopic and heterotopic connectivity of the frontal portion of the human corpus callosum revealed by microdissection and diffusion tractography.

PubMed

De Benedictis, Alessandro; Petit, Laurent; Descoteaux, Maxime; Marras, Carlo Efisio; Barbareschi, Mattia; Corsini, Francesco; Dallabona, Monica; Chioffi, Franco; Sarubbo, Silvio

2016-12-01

Extensive studies revealed that the human corpus callosum (CC) plays a crucial role in providing large-scale bi-hemispheric integration of sensory, motor and cognitive processing, especially within the frontal lobe. However, the literature lacks of conclusive data regarding the structural macroscopic connectivity of the frontal CC. In this study, a novel microdissection approach was adopted, to expose the frontal fibers of CC from the dorsum to the lateral cortex in eight hemispheres and in one entire brain. Post-mortem results were then combined with data from advanced constrained spherical deconvolution in 130 healthy subjects. We demonstrated as the frontal CC provides dense inter-hemispheric connections. In particular, we found three types of fronto-callosal fibers, having a dorso-ventral organization. First, the dorso-medial CC fibers subserve homotopic connections between the homologous medial cortices of the superior frontal gyrus. Second, the ventro-lateral CC fibers subserve homotopic connections between lateral frontal cortices, including both the middle frontal gyrus and the inferior frontal gyrus, as well as heterotopic connections between the medial and lateral frontal cortices. Third, the ventro-striatal CC fibers connect the medial and lateral frontal cortices with the contralateral putamen and caudate nucleus. We also highlighted an intricate crossing of CC fibers with the main association pathways terminating in the lateral regions of the frontal lobes. This combined approach of ex vivo microdissection and in vivo diffusion tractography allowed demonstrating a previously unappreciated three-dimensional architecture of the anterior frontal CC, thus clarifying the functional role of the CC in mediating the inter-hemispheric connectivity. Hum Brain Mapp 37:4718-4735, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Co-activation patterns in resting-state fMRI signals.

PubMed

Liu, Xiao; Zhang, Nanyin; Chang, Catie; Duyn, Jeff H

2018-02-08

The brain is a complex system that integrates and processes information across multiple time scales by dynamically coordinating activities over brain regions and circuits. Correlations in resting-state functional magnetic resonance imaging (rsfMRI) signals have been widely used to infer functional connectivity of the brain, providing a metric of functional associations that reflects a temporal average over an entire scan (typically several minutes or longer). Not until recently was the study of dynamic brain interactions at much shorter time scales (seconds to minutes) considered for inference of functional connectivity. One method proposed for this objective seeks to identify and extract recurring co-activation patterns (CAPs) that represent instantaneous brain configurations at single time points. Here, we review the development and recent advancement of CAP methodology and other closely related approaches, as well as their applications and associated findings. We also discuss the potential neural origins and behavioral relevance of CAPs, along with methodological issues and future research directions in the analysis of fMRI co-activation patterns. Copyright © 2018 Elsevier Inc. All rights reserved.

Extreme brain events: Higher-order statistics of brain resting activity and its relation with structural connectivity

NASA Astrophysics Data System (ADS)

Amor, T. A.; Russo, R.; Diez, I.; Bharath, P.; Zirovich, M.; Stramaglia, S.; Cortes, J. M.; de Arcangelis, L.; Chialvo, D. R.

2015-09-01

The brain exhibits a wide variety of spatiotemporal patterns of neuronal activity recorded using functional magnetic resonance imaging as the so-called blood-oxygenated-level-dependent (BOLD) signal. An active area of work includes efforts to best describe the plethora of these patterns evolving continuously in the brain. Here we explore the third-moment statistics of the brain BOLD signals in the resting state as a proxy to capture extreme BOLD events. We find that the brain signal exhibits typically nonzero skewness, with positive values for cortical regions and negative values for subcortical regions. Furthermore, the combined analysis of structural and functional connectivity demonstrates that relatively more connected regions exhibit activity with high negative skewness. Overall, these results highlight the relevance of recent results emphasizing that the spatiotemporal location of the relatively large-amplitude events in the BOLD time series contains relevant information to reproduce a number of features of the brain dynamics during resting state in health and disease.

Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy.

PubMed

Zhang, Daoqiang; Tu, Liyang; Zhang, Long-Jiang; Jie, Biao; Lu, Guang-Ming

2018-06-01

Hepatic encephalopathy (HE), as a complication of cirrhosis, is a serious brain disease, which may lead to death. Accurate diagnosis of HE and its intermediate stage, i.e., minimal HE (MHE), is very important for possibly early diagnosis and treatment. Brain connectivity network, as a simple representation of brain interaction, has been widely used for the brain disease (e.g., HE and MHE) analysis. However, those studies mainly focus on finding disease-related abnormal connectivity between brain regions, although a large number of studies have indicated that some brain diseases are usually related to local structure of brain connectivity network (i.e., subnetwork), rather than solely on some single brain regions or connectivities. Also, mining such disease-related subnetwork is a challenging task because of the complexity of brain network. To address this problem, we proposed a novel frequent-subnetwork-based method to mine disease-related subnetworks for MHE classification. Specifically, we first mine frequent subnetworks from both groups, i.e., MHE patients and non-HE (NHE) patients, respectively. Then we used the graph-kernel based method to select the most discriminative subnetworks for subsequent classification. We evaluate our proposed method on a MHE dataset with 77 cirrhosis patients, including 38 MHE patients and 39 NHE patients. The results demonstrate that our proposed method can not only obtain the improved classification performance in comparison with state-of-the-art network-based methods, but also identify disease-related subnetworks which can help us better understand the pathology of the brain diseases.

Organizational topology of brain and its relationship to ADHD in adolescents with d-transposition of the great arteries.

PubMed

Schmithorst, Vincent J; Panigrahy, Ashok; Gaynor, J William; Watson, Christopher G; Lee, Vince; Bellinger, David C; Rivkin, Michael J; Newburger, Jane W

2016-08-01

Little is currently known about the impact of congenital heart disease (CHD) on the organization of large-scale brain networks in relation to neurobehavioral outcome. We investigated whether CHD might impact ADHD symptoms via changes in brain structural network topology in a cohort of adolescents with d-transposition of the great arteries (d-TGA) repaired with the arterial switch operation in early infancy and referent subjects. We also explored whether these effects might be modified by apolipoprotein E (APOE) genotype, as the APOE ε2 allele has been associated with worse neurodevelopmental outcomes after repair of d-TGA in infancy. We applied graph analysis techniques to diffusion tensor imaging (DTI) data obtained from 47 d-TGA adolescents and 29 healthy referents to construct measures of structural topology at the global and regional levels. We developed statistical mediation models revealing the respective contributions of d-TGA, APOE genotype, and structural network topology on ADHD outcome as measured by the Connors ADHD/DSM-IV Scales (CADS). Changes in overall network connectivity, integration, and segregation mediated worse ADHD outcomes in d-TGA patients compared to healthy referents; these changes were predominantly in the left and right intrahemispheric regional subnetworks. Exploratory analysis revealed that network topology also mediated detrimental effects of the APOE ε4 allele but improved neurobehavioral outcomes for the APOE ε2 allele. Our results suggest that disruption of organization of large-scale networks may contribute to neurobehavioral dysfunction in adolescents with CHD and that this effect may interact with APOE genotype.

Investigating Focal Connectivity Deficits in Alzheimer's Disease Using Directional Brain Networks Derived from Resting-State fMRI

PubMed Central

Zhao, Sinan; Rangaprakash, D; Venkataraman, Archana; Liang, Peipeng; Deshpande, Gopikrishna

2017-01-01

Connectivity analysis of resting-state fMRI has been widely used to identify biomarkers of Alzheimer's disease (AD) based on brain network aberrations. However, it is not straightforward to interpret such connectivity results since our understanding of brain functioning relies on regional properties (activations and morphometric changes) more than connections. Further, from an interventional standpoint, it is easier to modulate the activity of regions (using brain stimulation, neurofeedback, etc.) rather than connections. Therefore, we employed a novel approach for identifying focal directed connectivity deficits in AD compared to healthy controls. In brief, we present a model of directed connectivity (using Granger causality) that characterizes the coupling among different regions in healthy controls and Alzheimer's disease. We then characterized group differences using a (between-subject) generative model of pathology, which generates latent connectivity variables that best explain the (within-subject) directed connectivity. Crucially, our generative model at the second (between-subject) level explains connectivity in terms of local or regionally specific abnormalities. This allows one to explain disconnections among multiple regions in terms of regionally specific pathology; thereby offering a target for therapeutic intervention. Two foci were identified, locus coeruleus in the brain stem and right orbitofrontal cortex. Corresponding disrupted connectivity network associated with the foci showed that the brainstem is the critical focus of disruption in AD. We further partitioned the aberrant connectomic network into four unique sub-networks, which likely leads to symptoms commonly observed in AD. Our findings suggest that fMRI studies of AD, which have been largely cortico-centric, could in future investigate the role of brain stem in AD. PMID:28729831

Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation.

PubMed

Pesaran, Bijan; Vinck, Martin; Einevoll, Gaute T; Sirota, Anton; Fries, Pascal; Siegel, Markus; Truccolo, Wilson; Schroeder, Charles E; Srinivasan, Ramesh

2018-06-25

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (electroencephalograms, magnetoencephalograms, electrocorticograms and local field potentials) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide recommendations for interpreting the data using forward and inverse models. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.

Dynamic functional connectivity and individual differences in emotions during social stress.

PubMed

Tobia, Michael J; Hayashi, Koby; Ballard, Grey; Gotlib, Ian H; Waugh, Christian E

2017-12-01

Exposure to acute stress induces multiple emotional responses, each with their own unique temporal dynamics. Dynamic functional connectivity (dFC) measures the temporal variability of network synchrony and captures individual differences in network neurodynamics. This study investigated the relationship between dFC and individual differences in emotions induced by an acute psychosocial stressor. Sixteen healthy adult women underwent fMRI scanning during a social evaluative threat (SET) task, and retrospectively completed questionnaires that assessed individual differences in subjectively experienced positive and negative emotions about stress and stress relief during the task. Group dFC was decomposed with parallel factor analysis (PARAFAC) into 10 components, each with a temporal signature, spatial network of functionally connected regions, and vector of participant loadings that captures individual differences in dFC. Participant loadings of two networks were positively correlated with stress-related emotions, indicating the existence of networks for positive and negative emotions. The emotion-related networks involved the ventromedial prefrontal cortex, cingulate cortex, anterior insula, and amygdala, among other distributed brain regions, and time signatures for these emotion-related networks were uncorrelated. These findings demonstrate that individual differences in stress-induced positive and negative emotions are each uniquely associated with large-scale brain networks, and suggest that dFC is a mechanism that generates individual differences in the emotional components of the stress response. Hum Brain Mapp 38:6185-6205, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Tau burden and the functional connectome in Alzheimer's disease and progressive supranuclear palsy.

PubMed

Cope, Thomas E; Rittman, Timothy; Borchert, Robin J; Jones, P Simon; Vatansever, Deniz; Allinson, Kieren; Passamonti, Luca; Vazquez Rodriguez, Patricia; Bevan-Jones, W Richard; O'Brien, John T; Rowe, James B

2018-02-01

Alzheimer's disease and progressive supranuclear palsy (PSP) represent neurodegenerative tauopathies with predominantly cortical versus subcortical disease burden. In Alzheimer's disease, neuropathology and atrophy preferentially affect 'hub' brain regions that are densely connected. It was unclear whether hubs are differentially affected by neurodegeneration because they are more likely to receive pathological proteins that propagate trans-neuronally, in a prion-like manner, or whether they are selectively vulnerable due to a lack of local trophic factors, higher metabolic demands, or differential gene expression. We assessed the relationship between tau burden and brain functional connectivity, by combining in vivo PET imaging using the ligand AV-1451, and graph theoretic measures of resting state functional MRI in 17 patients with Alzheimer's disease, 17 patients with PSP, and 12 controls. Strongly connected nodes displayed more tau pathology in Alzheimer's disease, independently of intrinsic connectivity network, validating the predictions of theories of trans-neuronal spread but not supporting a role for metabolic demands or deficient trophic support in tau accumulation. This was not a compensatory phenomenon, as the functional consequence of increasing tau burden in Alzheimer's disease was a progressive weakening of the connectivity of these same nodes, reducing weighted degree and local efficiency and resulting in weaker 'small-world' properties. Conversely, in PSP, unlike in Alzheimer's disease, those nodes that accrued pathological tau were those that displayed graph metric properties associated with increased metabolic demand and a lack of trophic support rather than strong functional connectivity. Together, these findings go some way towards explaining why Alzheimer's disease affects large scale connectivity networks throughout cortex while neuropathology in PSP is concentrated in a small number of subcortical structures. Further, we demonstrate that in PSP increasing tau burden in midbrain and deep nuclei was associated with strengthened cortico-cortical functional connectivity. Disrupted cortico-subcortical and cortico-brainstem interactions meant that information transfer took less direct paths, passing through a larger number of cortical nodes, reducing closeness centrality and eigenvector centrality in PSP, while increasing weighted degree, clustering, betweenness centrality and local efficiency. Our results have wide-ranging implications, from the validation of models of tau trafficking in humans to understanding the relationship between regional tau burden and brain functional reorganization. © The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain.

Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks?

PubMed

Varoquaux, G; Gramfort, A; Poline, J B; Thirion, B

2012-01-01

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems. Copyright © 2012 Elsevier Ltd. All rights reserved.

The Interface between Neuroscience and Neuro-Psychoanalysis: Focus on Brain Connectivity

PubMed Central

Salone, Anatolia; Di Giacinto, Alessandra; Lai, Carlo; De Berardis, Domenico; Iasevoli, Felice; Fornaro, Michele; De Risio, Luisa; Santacroce, Rita; Martinotti, Giovanni; Giannantonio, Massimo Di

2016-01-01

Over the past 20 years, the advent of advanced techniques has significantly enhanced our knowledge on the brain. Yet, our understanding of the physiological and pathological functioning of the mind is still far from being exhaustive. Both the localizationist and the reductionist neuroscientific approaches to psychiatric disorders have proven to be largely unsatisfactory and are outdated. Accruing evidence suggests that psychoanalysis can engage the neurosciences in a productive and mutually enriching dialogue that may further our understanding of psychiatric disorders. In particular, advances in brain connectivity research have provided evidence supporting the convergence of neuroscientific findings and psychoanalysis and helped characterize the circuitry and mechanisms that underlie higher brain functions. In the present paper we discuss how knowledge on brain connectivity can impact neuropsychoanalysis, with a particular focus on schizophrenia. Brain connectivity studies in schizophrenic patients indicate complex alterations in brain functioning and circuitry, with particular emphasis on the role of cortical midline structures (CMS) and the default mode network (DMN). These networks seem to represent neural correlates of psychodynamic concepts central to the understanding of schizophrenia and of core psychopathological alterations of this disorder (i.e., ego disturbances and impaired primary process thinking). PMID:26869904

Gorilla and Orangutan Brains Conform to the Primate Cellular Scaling Rules: Implications for Human Evolution

PubMed Central

Herculano-Houzel, Suzana; Kaas, Jon H.

2011-01-01

Gorillas and orangutans are primates at least as large as humans, but their brains amount to about one third of the size of the human brain. This discrepancy has been used as evidence that the human brain is about 3 times larger than it should be for a primate species of its body size. In contrast to the view that the human brain is special in its size, we have suggested that it is the great apes that might have evolved bodies that are unusually large, on the basis of our recent finding that the cellular composition of the human brain matches that expected for a primate brain of its size, making the human brain a linearly scaled-up primate brain in its number of cells. To investigate whether the brain of great apes also conforms to the primate cellular scaling rules identified previously, we determine the numbers of neuronal and other cells that compose the orangutan and gorilla cerebella, use these numbers to calculate the size of the brain and of the cerebral cortex expected for these species, and show that these match the sizes described in the literature. Our results suggest that the brains of great apes also scale linearly in their numbers of neurons like other primate brains, including humans. The conformity of great apes and humans to the linear cellular scaling rules that apply to other primates that diverged earlier in primate evolution indicates that prehistoric Homo species as well as other hominins must have had brains that conformed to the same scaling rules, irrespective of their body size. We then used those scaling rules and published estimated brain volumes for various hominin species to predict the numbers of neurons that composed their brains. We predict that Homo heidelbergensis and Homo neanderthalensis had brains with approximately 80 billion neurons, within the range of variation found in modern Homo sapiens. We propose that while the cellular scaling rules that apply to the primate brain have remained stable in hominin evolution (since they apply to simians, great apes and modern humans alike), the Colobinae and Pongidae lineages favored marked increases in body size rather than brain size from the common ancestor with the Homo lineage, while the Homo lineage seems to have favored a large brain instead of a large body, possibly due to the metabolic limitations to having both. PMID:21228547

Gorilla and orangutan brains conform to the primate cellular scaling rules: implications for human evolution.

PubMed

Herculano-Houzel, Suzana; Kaas, Jon H

2011-01-01

Gorillas and orangutans are primates at least as large as humans, but their brains amount to about one third of the size of the human brain. This discrepancy has been used as evidence that the human brain is about 3 times larger than it should be for a primate species of its body size. In contrast to the view that the human brain is special in its size, we have suggested that it is the great apes that might have evolved bodies that are unusually large, on the basis of our recent finding that the cellular composition of the human brain matches that expected for a primate brain of its size, making the human brain a linearly scaled-up primate brain in its number of cells. To investigate whether the brain of great apes also conforms to the primate cellular scaling rules identified previously, we determine the numbers of neuronal and other cells that compose the orangutan and gorilla cerebella, use these numbers to calculate the size of the brain and of the cerebral cortex expected for these species, and show that these match the sizes described in the literature. Our results suggest that the brains of great apes also scale linearly in their numbers of neurons like other primate brains, including humans. The conformity of great apes and humans to the linear cellular scaling rules that apply to other primates that diverged earlier in primate evolution indicates that prehistoric Homo species as well as other hominins must have had brains that conformed to the same scaling rules, irrespective of their body size. We then used those scaling rules and published estimated brain volumes for various hominin species to predict the numbers of neurons that composed their brains. We predict that Homo heidelbergensis and Homo neanderthalensis had brains with approximately 80 billion neurons, within the range of variation found in modern Homo sapiens. We propose that while the cellular scaling rules that apply to the primate brain have remained stable in hominin evolution (since they apply to simians, great apes and modern humans alike), the Colobinae and Pongidae lineages favored marked increases in body size rather than brain size from the common ancestor with the Homo lineage, while the Homo lineage seems to have favored a large brain instead of a large body, possibly due to the metabolic limitations to having both. Copyright © 2011 S. Karger AG, Basel.

Counting on β-Diversity to Safeguard the Resilience of Estuaries

PubMed Central

de Juan, Silvia; Thrush, Simon F.; Hewitt, Judi E.

2013-01-01

Coastal ecosystems are often stressed by non-point source and cumulative effects that can lead to local-scale community homogenisation and a concomitant loss of large-scale ecological connectivity. Here we investigate the use of β-diversity as a measure of both community heterogeneity and ecological connectivity. To understand the consequences of different environmental scenarios on heterogeneity and connectivity, it is necessary to understand the scale at which different environmental factors affect β-diversity. We sampled macrofauna from intertidal sites in nine estuaries from New Zealand’s North Island that represented different degrees of stress derived from land-use. We used multiple regression models to identify relationships between β-diversity and local sediment variables, factors related to the estuarine and catchment hydrodynamics and morphology and land-based stressors. At local scales, we found higher β-diversity at sites with a relatively high total richness. At larger scales, β-diversity was positively related to γ-diversity, suggesting that a large regional species pool was linked with large-scale heterogeneity in these systems. Local environmental heterogeneity influenced β-diversity at both local and regional scales, although variables at the estuarine and catchment scales were both needed to explain large scale connectivity. The estuaries expected a priori to be the most stressed exhibited higher variance in community dissimilarity between sites and connectivity to the estuary species pool. This suggests that connectivity and heterogeneity metrics could be used to generate early warning signals of cumulative stress. PMID:23755252

How the amygdala affects emotional memory by altering brain network properties.

PubMed

Hermans, Erno J; Battaglia, Francesco P; Atsak, Piray; de Voogd, Lycia D; Fernández, Guillén; Roozendaal, Benno

2014-07-01

The amygdala has long been known to play a key role in supporting memory for emotionally arousing experiences. For example, classical fear conditioning depends on neural plasticity within this anterior medial temporal lobe region. Beneficial effects of emotional arousal on memory, however, are not restricted to simple associative learning. Our recollection of emotional experiences often includes rich representations of, e.g., spatiotemporal context, visceral states, and stimulus-response associations. Critically, such memory features are known to bear heavily on regions elsewhere in the brain. These observations led to the modulation account of amygdala function, which postulates that amygdala activation enhances memory consolidation by facilitating neural plasticity and information storage processes in its target regions. Rodent work in past decades has identified the most important brain regions and neurochemical processes involved in these modulatory actions, and neuropsychological and neuroimaging work in humans has produced a large body of convergent data. Importantly, recent methodological developments make it increasingly realistic to monitor neural interactions underlying such modulatory effects as they unfold. For instance, functional connectivity network modeling in humans has demonstrated how information exchanges between the amygdala and specific target regions occur within the context of large-scale neural network interactions. Furthermore, electrophysiological and optogenetic techniques in rodents are beginning to make it possible to quantify and even manipulate such interactions with millisecond precision. In this paper we will discuss that these developments will likely lead to an updated view of the amygdala as a critical nexus within large-scale networks supporting different aspects of memory processing for emotionally arousing experiences. Copyright © 2014 Elsevier Inc. All rights reserved.

Individual brain structure and modelling predict seizure propagation

PubMed Central

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K.

2017-01-01

Abstract See Lytton (doi:10.1093/awx018) for a scientific commentary on this article. Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. PMID:28364550

Connectivity Predicts Deep Brain Stimulation Outcome in Parkinson Disease

PubMed Central

Horn, Andreas; Reich, Martin; Vorwerk, Johannes; Li, Ningfei; Wenzel, Gregor; Fang, Qianqian; Schmitz-Hübsch, Tanja; Nickl, Robert; Kupsch, Andreas; Volkmann, Jens; Kühn, Andrea A.; Fox, Michael D.

2018-01-01

Objective The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort. Methods A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center. Results In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p<0.001). This same connectivity profile predicted response in an independent patient cohort (p<0.01). Structural and functional connectivity were independent predictors of clinical improvement (p<0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients. Interpretation Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves. PMID:28586141

A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.

PubMed

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2013-06-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph--a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.

A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data

PubMed Central

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2014-01-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph (DAG)—a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer’s disease (AD) and reveal findings that could lead to advancements in AD research. PMID:22665720

Frontoparietal Structural Connectivity in Childhood Predicts Development of Functional Connectivity and Reasoning Ability: A Large-Scale Longitudinal Investigation.

PubMed

Wendelken, Carter; Ferrer, Emilio; Ghetti, Simona; Bailey, Stephen K; Cutting, Laurie; Bunge, Silvia A

2017-08-30

Prior research points to a positive concurrent relationship between reasoning ability and both frontoparietal structural connectivity (SC) as measured by diffusion tensor imaging (Tamnes et al., 2010) and frontoparietal functional connectivity (FC) as measured by fMRI (Cocchi et al., 2014). Further, recent research demonstrates a link between reasoning ability and FC of two brain regions in particular: rostrolateral prefrontal cortex (RLPFC) and the inferior parietal lobe (IPL) (Wendelken et al., 2016). Here, we sought to investigate the concurrent and dynamic, lead-lag relationships among frontoparietal SC, FC, and reasoning ability in humans. To this end, we combined three longitudinal developmental datasets with behavioral and neuroimaging data from 523 male and female participants between 6 and 22 years of age. Cross-sectionally, reasoning ability was most strongly related to FC between RLPFC and IPL in adolescents and adults, but to frontoparietal SC in children. Longitudinal analysis revealed that RLPFC-IPL SC, but not FC, was a positive predictor of future changes in reasoning ability. Moreover, we found that RLPFC-IPL SC at one time point positively predicted future changes in RLPFC-IPL FC, whereas, in contrast, FC did not predict future changes in SC. Our results demonstrate the importance of strong white matter connectivity between RLPFC and IPL during middle childhood for the subsequent development of both robust FC and good reasoning ability. SIGNIFICANCE STATEMENT The human capacity for reasoning develops substantially during childhood and has a profound impact on achievement in school and in cognitively challenging careers. Reasoning ability depends on communication between lateral prefrontal and parietal cortices. Therefore, to understand how this capacity develops, we examined the dynamic relationships over time among white matter tracts connecting frontoparietal cortices (i.e., structural connectivity, SC), coordinated frontoparietal activation (functional connectivity, FC), and reasoning ability in a large longitudinal sample of subjects 6-22 years of age. We found that greater frontoparietal SC in childhood predicts future increases in both FC and reasoning ability, demonstrating the importance of white matter development during childhood for subsequent brain and cognitive functioning. Copyright © 2017 the authors 0270-6474/17/378549-10$15.00/0.

Frontoparietal Structural Connectivity in Childhood Predicts Development of Functional Connectivity and Reasoning Ability: A Large-Scale Longitudinal Investigation

PubMed Central

Ferrer, Emilio; Cutting, Laurie

2017-01-01

Prior research points to a positive concurrent relationship between reasoning ability and both frontoparietal structural connectivity (SC) as measured by diffusion tensor imaging (Tamnes et al., 2010) and frontoparietal functional connectivity (FC) as measured by fMRI (Cocchi et al., 2014). Further, recent research demonstrates a link between reasoning ability and FC of two brain regions in particular: rostrolateral prefrontal cortex (RLPFC) and the inferior parietal lobe (IPL) (Wendelken et al., 2016). Here, we sought to investigate the concurrent and dynamic, lead–lag relationships among frontoparietal SC, FC, and reasoning ability in humans. To this end, we combined three longitudinal developmental datasets with behavioral and neuroimaging data from 523 male and female participants between 6 and 22 years of age. Cross-sectionally, reasoning ability was most strongly related to FC between RLPFC and IPL in adolescents and adults, but to frontoparietal SC in children. Longitudinal analysis revealed that RLPFC–IPL SC, but not FC, was a positive predictor of future changes in reasoning ability. Moreover, we found that RLPFC–IPL SC at one time point positively predicted future changes in RLPFC–IPL FC, whereas, in contrast, FC did not predict future changes in SC. Our results demonstrate the importance of strong white matter connectivity between RLPFC and IPL during middle childhood for the subsequent development of both robust FC and good reasoning ability. SIGNIFICANCE STATEMENT The human capacity for reasoning develops substantially during childhood and has a profound impact on achievement in school and in cognitively challenging careers. Reasoning ability depends on communication between lateral prefrontal and parietal cortices. Therefore, to understand how this capacity develops, we examined the dynamic relationships over time among white matter tracts connecting frontoparietal cortices (i.e., structural connectivity, SC), coordinated frontoparietal activation (functional connectivity, FC), and reasoning ability in a large longitudinal sample of subjects 6–22 years of age. We found that greater frontoparietal SC in childhood predicts future increases in both FC and reasoning ability, demonstrating the importance of white matter development during childhood for subsequent brain and cognitive functioning. PMID:28821657

Healthy brain connectivity predicts atrophy progression in non-fluent variant of primary progressive aphasia.

PubMed

Mandelli, Maria Luisa; Vilaplana, Eduard; Brown, Jesse A; Hubbard, H Isabel; Binney, Richard J; Attygalle, Suneth; Santos-Santos, Miguel A; Miller, Zachary A; Pakvasa, Mikhail; Henry, Maya L; Rosen, Howard J; Henry, Roland G; Rabinovici, Gil D; Miller, Bruce L; Seeley, William W; Gorno-Tempini, Maria Luisa

2016-10-01

Neurodegeneration has been hypothesized to follow predetermined large-scale networks through the trans-synaptic spread of toxic proteins from a syndrome-specific epicentre. To date, no longitudinal neuroimaging study has tested this hypothesis in vivo in frontotemporal dementia spectrum disorders. The aim of this study was to demonstrate that longitudinal progression of atrophy in non-fluent/agrammatic variant primary progressive aphasia spreads over time from a syndrome-specific epicentre to additional regions, based on their connectivity to the epicentre in healthy control subjects. The syndrome-specific epicentre of the non-fluent/agrammatic variant of primary progressive aphasia was derived in a group of 10 mildly affected patients (clinical dementia rating equal to 0) using voxel-based morphometry. From this region, the inferior frontal gyrus (pars opercularis), we derived functional and structural connectivity maps in healthy controls (n = 30) using functional magnetic resonance imaging at rest and diffusion-weighted imaging tractography. Graph theory analysis was applied to derive functional network features. Atrophy progression was calculated using voxel-based morphometry longitudinal analysis on 34 non-fluent/agrammatic patients. Correlation analyses were performed to compare volume changes in patients with connectivity measures of the healthy functional and structural speech/language network. The default mode network was used as a control network. From the epicentre, the healthy functional connectivity network included the left supplementary motor area and the prefrontal, inferior parietal and temporal regions, which were connected through the aslant, superior longitudinal and arcuate fasciculi. Longitudinal grey and white matter changes were found in the left language-related regions and in the right inferior frontal gyrus. Functional connectivity strength in the healthy speech/language network, but not in the default network, correlated with longitudinal grey matter changes in the non-fluent/agrammatic variant of primary progressive aphasia. Graph theoretical analysis of the speech/language network showed that regions with shorter functional paths to the epicentre exhibited greater longitudinal atrophy. The network contained three modules, including a left inferior frontal gyrus/supplementary motor area, which was most strongly connected with the epicentre. The aslant tract was the white matter pathway connecting these two regions and showed the most significant correlation between fractional anisotropy and white matter longitudinal atrophy changes. This study showed that the pattern of longitudinal atrophy progression in the non-fluent/agrammatic variant of primary progressive aphasia relates to the strength of connectivity in pre-determined functional and structural large-scale speech production networks. These findings support the hypothesis that the spread of neurodegeneration occurs by following specific anatomical and functional neuronal network architectures. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Mindfulness-based treatments for posttraumatic stress disorder: a review of the treatment literature and neurobiological evidence

PubMed Central

Boyd, Jenna E.; Lanius, Ruth A.; McKinnon, Margaret C.

2018-01-01

Mindfulness-based treatments for posttraumatic stress disorder (PTSD) have emerged as promising adjunctive or alternative intervention approaches. A scoping review of the literature on PTSD treatment studies, including approaches such as mindfulness-based stress reduction, mindfulness-based cognitive therapy and metta mindfulness, reveals low attrition with medium to large effect sizes. We review the convergence between neurobiological models of PTSD and neuroimaging findings in the mindfulness literature, where mindfulness interventions may target emotional under- and overmodulation, both of which are critical features of PTSD symptomatology. Recent emerging work indicates that mindfulness-based treatments may also be effective in restoring connectivity between large-scale brain networks among individuals with PTSD, including connectivity between the default mode network and the central executive and salience networks. Future directions, including further identification of the neurobiological mechanisms of mindfulness interventions in patients with PTSD and direct comparison of these interventions to first-line treatments for PTSD are discussed. PMID:29252162

Altered cortical and subcortical connectivity due to infrasound administered near the hearing threshold - Evidence from fMRI.

PubMed

Weichenberger, Markus; Bauer, Martin; Kühler, Robert; Hensel, Johannes; Forlim, Caroline Garcia; Ihlenfeld, Albrecht; Ittermann, Bernd; Gallinat, Jürgen; Koch, Christian; Kühn, Simone

2017-01-01

In the present study, the brain's response towards near- and supra-threshold infrasound (IS) stimulation (sound frequency < 20 Hz) was investigated under resting-state fMRI conditions. The study involved two consecutive sessions. In the first session, 14 healthy participants underwent a hearing threshold-as well as a categorical loudness scaling measurement in which the individual loudness perception for IS was assessed across different sound pressure levels (SPL). In the second session, these participants underwent three resting-state acquisitions, one without auditory stimulation (no-tone), one with a monaurally presented 12-Hz IS tone (near-threshold) and one with a similar tone above the individual hearing threshold corresponding to a 'medium loud' hearing sensation (supra-threshold). Data analysis mainly focused on local connectivity measures by means of regional homogeneity (ReHo), but also involved independent component analysis (ICA) to investigate inter-regional connectivity. ReHo analysis revealed significantly higher local connectivity in right superior temporal gyrus (STG) adjacent to primary auditory cortex, in anterior cingulate cortex (ACC) and, when allowing smaller cluster sizes, also in the right amygdala (rAmyg) during the near-threshold, compared to both the supra-threshold and the no-tone condition. Additional independent component analysis (ICA) revealed large-scale changes of functional connectivity, reflected in a stronger activation of the right amygdala (rAmyg) in the opposite contrast (no-tone > near-threshold) as well as the right superior frontal gyrus (rSFG) during the near-threshold condition. In summary, this study is the first to demonstrate that infrasound near the hearing threshold may induce changes of neural activity across several brain regions, some of which are known to be involved in auditory processing, while others are regarded as keyplayers in emotional and autonomic control. These findings thus allow us to speculate on how continuous exposure to (sub-)liminal IS could exert a pathogenic influence on the organism, yet further (especially longitudinal) studies are required in order to substantialize these findings.

Resting-state functional connectivity imaging of the mouse brain using photoacoustic tomography

NASA Astrophysics Data System (ADS)

Nasiriavanaki, Mohammadreza; Xia, Jun; Wan, Hanlin; Bauer, Adam Q.; Culver, Joseph P.; Wang, Lihong V.

2014-03-01

Resting-state functional connectivity (RSFC) imaging is an emerging neuroimaging approach that aims to identify spontaneous cerebral hemodynamic fluctuations and their associated functional connections. Clinical studies have demonstrated that RSFC is altered in brain disorders such as stroke, Alzheimer's, autism, and epilepsy. However, conventional neuroimaging modalities cannot easily be applied to mice, the most widely used model species for human brain disease studies. For instance, functional magnetic resonance imaging (fMRI) of mice requires a very high magnetic field to obtain a sufficient signal-to-noise ratio and spatial resolution. Functional connectivity mapping with optical intrinsic signal imaging (fcOIS) is an alternative method. Due to the diffusion of light in tissue, the spatial resolution of fcOIS is limited, and experiments have been performed using an exposed skull preparation. In this study, we show for the first time, the use of photoacoustic computed tomography (PACT) to noninvasively image resting-state functional connectivity in the mouse brain, with a large field of view and a high spatial resolution. Bilateral correlations were observed in eight regions, as well as several subregions. These findings agreed well with the Paxinos mouse brain atlas. This study showed that PACT is a promising, non-invasive modality for small-animal functional brain imaging.

Radiation-induced brain structural and functional abnormalities in presymptomatic phase and outcome prediction.

PubMed

Ding, Zhongxiang; Zhang, Han; Lv, Xiao-Fei; Xie, Fei; Liu, Lizhi; Qiu, Shijun; Li, Li; Shen, Dinggang

2018-01-01

Radiation therapy, a major method of treatment for brain cancer, may cause severe brain injuries after many years. We used a rare and unique cohort of nasopharyngeal carcinoma patients with normal-appearing brains to study possible early irradiation injury in its presymptomatic phase before severe, irreversible necrosis happens. The aim is to detect any structural or functional imaging biomarker that is sensitive to early irradiation injury, and to understand the recovery and progression of irradiation injury that can shed light on outcome prediction for early clinical intervention. We found an acute increase in local brain activity that is followed by extensive reductions in such activity in the temporal lobe and significant loss of functional connectivity in a distributed, large-scale, high-level cognitive function-related brain network. Intriguingly, these radiosensitive functional alterations were found to be fully or partially recoverable. In contrast, progressive late disruptions to the integrity of the related far-end white matter structure began to be significant after one year. Importantly, early increased local brain functional activity was predictive of severe later temporal lobe necrosis. Based on these findings, we proposed a dynamic, multifactorial model for radiation injury and another preventive model for timely clinical intervention. Hum Brain Mapp 39:407-427, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Human brain distinctiveness based on EEG spectral coherence connectivity.

PubMed

Rocca, D La; Campisi, P; Vegso, B; Cserti, P; Kozmann, G; Babiloni, F; Fallani, F De Vico

2014-09-01

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of the current analyses rely on the extraction of features characterizing the activity of single brain regions, like power spectrum estimation, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N = 108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performance shows that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that the functional connectivity patterns represent effective features for improving EEG-based biometric systems.

Impact of real-time fMRI working memory feedback training on the interactions between three core brain networks.

PubMed

Zhang, Qiushi; Zhang, Gaoyan; Yao, Li; Zhao, Xiaojie

2015-01-01

Working memory (WM) refers to the temporary holding and manipulation of information during the performance of a range of cognitive tasks, and WM training is a promising method for improving an individual's cognitive functions. Our previous work demonstrated that WM performance can be improved through self-regulation of dorsal lateral prefrontal cortex (PFC) activation using real-time functional magnetic resonance imaging (rtfMRI), which enables individuals to control local brain activities volitionally according to the neurofeedback. Furthermore, research concerning large-scale brain networks has demonstrated that WM training requires the engagement of several networks, including the central executive network (CEN), the default mode network (DMN) and the salience network (SN), and functional connectivity within the CEN and DMN can be changed by WM training. Although a switching role of the SN between the CEN and DMN has been demonstrated, it remains unclear whether WM training can affect the interactions between the three networks and whether a similar mechanism also exists during the training process. In this study, we investigated the dynamic functional connectivity between the three networks during the rtfMRI feedback training using independent component analysis (ICA) and correlation analysis. The results indicated that functional connectivity within and between the three networks were significantly enhanced by feedback training, and most of the changes were associated with the insula and correlated with behavioral improvements. These findings suggest that the insula plays a critical role in the reorganization of functional connectivity among the three networks induced by rtfMRI training and in WM performance, thus providing new insights into the mechanisms of high-level functions and the clinical treatment of related functional impairments.

Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

PubMed

Wang, Yi-Feng; Long, Zhiliang; Cui, Qian; Liu, Feng; Jing, Xiu-Juan; Chen, Heng; Guo, Xiao-Nan; Yan, Jin H; Chen, Hua-Fu

2016-01-01

Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities. © 2015 Wiley Periodicals, Inc.

Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

NASA Astrophysics Data System (ADS)

Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

2017-05-01

Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

From structure to function, via dynamics

NASA Astrophysics Data System (ADS)

Stetter, O.; Soriano, J.; Geisel, T.; Battaglia, D.

2013-01-01

Neurons in the brain are wired into a synaptic network that spans multiple scales, from local circuits within cortical columns to fiber tracts interconnecting distant areas. However, brain function require the dynamic control of inter-circuit interactions on time-scales faster than synaptic changes. In particular, strength and direction of causal influences between neural populations (described by the so-called directed functional connectivity) must be reconfigurable even when the underlying structural connectivity is fixed. Such directed functional influences can be quantified resorting to causal analysis of time-series based on tools like Granger Causality or Transfer Entropy. The ability to quickly reorganize inter-areal interactions is a chief requirement for performance in a changing natural environment. But how can manifold functional networks stem "on demand" from an essentially fixed structure? We explore the hypothesis that the self-organization of neuronal synchronous activity underlies the control of brain functional connectivity. Based on simulated and real recordings of critical neuronal cultures in vitro, as well as on mean-field and spiking network models of interacting brain areas, we have found that "function follows dynamics", rather than structure. Different dynamic states of a same structural network, characterized by different synchronization properties, are indeed associated to different functional digraphs (functional multiplicity). We also highlight the crucial role of dynamics in establishing a structure-to-function link, by showing that whenever different structural topologies lead to similar dynamical states, than the associated functional connectivities are also very similar (structural degeneracy).

Direct modulation of aberrant brain network connectivity through real-time NeuroFeedback

PubMed Central

Kimmich, Sara; Gonzalez-Castillo, Javier; Roopchansingh, Vinai; Popal, Haroon; White, Emily; Gotts, Stephen J; Martin, Alex

2017-01-01

The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants’ awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns. PMID:28917059

Mapping the changed hubs and corresponding functional connectivity in idiopathic restless legs syndrome.

PubMed

Liu, Chunyan; Wang, Jiaojian; Hou, Yue; Qi, Zhigang; Wang, Li; Zhan, Shuqin; Wang, Rong; Wang, Yuping

2018-05-01

The hubs of the brain network play a key role in integrating and transferring information between different functional modules. However, whether the changed pattern in functional network hubs contributes to the onset of leg discomfort symptoms in restless legs syndrome (RLS) patients remains unclear. Using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory methods, we investigated whether alterations of hubs can be detected in RLS. First, we constructed the whole-brain voxelwise functional connectivity and calculated a functional connectivity strength (FCS) map in each of 16 drug-naive idiopathic RLS patients and 26 gender- and age-matched healthy control (HC) subjects. Next, a two-sample t test was applied to compare the FCS maps between HC and RLS patients, and to identify significant changes in FCS in RLS patients. To further elucidate the corresponding changes in the functional connectivity patterns of the aberrant hubs in RLS patients, whole-brain resting-state functional connectivity analyses for the hub areas were performed. The hub analysis revealed decreased FCS in the cuneus, fusiform gyrus, paracentral lobe, and precuneus, and increased FCS in the superior frontal gyrus and thalamus in idiopathic drug-naive RLS patients. Subsequent functional connectivity analyses revealed decreased functional connectivity in sensorimotor and visual processing networks and increased functional connectivity in the affective cognitive network and cerebellar-thalamic circuit. Furthermore, the mean FCS value in the superior frontal gyrus was significantly correlated with Hamilton Anxiety Rating Scale scores in RLS patients, and the mean FCS value in the fusiform gyrus was significantly correlated with Hamilton Depression Rating Scale scores. These findings may provide novel insight into the pathophysiology of RLS. Copyright © 2018 Elsevier B.V. All rights reserved.

Relation between cognition and neural connection from injured cingulum to brainstem cholinergic nuclei in chronic patients with traumatic brain injury.

PubMed

Yoo, Jin-Sun; Kim, Oh Lyong; Kim, Seong Ho; Kim, Min Su; Jang, Sung Ho

2014-01-01

This study investigated the relation between cognition and the neural connection from injured cingulum to brainstem cholinergic nuclei in patients with traumatic brain injury (TBI), using diffusion tensor tractography (DTT). Among 353 patients with TBI, 20 chronic patients who showed discontinuation of both anterior cingulums from the basal forebrain on DTT were recruited for this study. The Wechsler Intelligence Scale and the Memory Assessment Scale (MAS; short-term, verbal, visual and total memory) were used for assessment of cognition. Patients were divided into two groups according to the presence of a neural connection between injured cingulum and brainstem cholinergic nuclei. Eight patients who had a neural connection between injured cingulum and brainstem cholinergic nuclei showed better short-term memory on MAS than 12 patients who did not (p < 0.05). However, other results of neuropsychological testing showed no significant difference (p > 0.05). Better short-term memory in patients who had the neural connection between injured cingulum and brainstem cholinergic nuclei appears to have been attributed to the presence of cholinergic innervation to the cerebral cortex through the neural connection instead of the injured anterior cingulum. The neural connection appears to compensate for the injured anterior cingulum in obtaining cholinergic innervation.

Compensatory Hyperconnectivity in Developing Brains of Young Children With Type 1 Diabetes.

PubMed

Saggar, Manish; Tsalikian, Eva; Mauras, Nelly; Mazaika, Paul; White, Neil H; Weinzimer, Stuart; Buckingham, Bruce; Hershey, Tamara; Reiss, Allan L

2017-03-01

Sustained dysregulation of blood glucose (hyper- or hypoglycemia) associated with type 1 diabetes (T1D) has been linked to cognitive deficits and altered brain anatomy and connectivity. However, a significant gap remains with respect to how T1D affects spontaneous at-rest connectivity in young developing brains. Here, using a large multisite study, resting-state functional MRI data were examined in young children with T1D ( n = 57; mean age = 7.88 years; 27 females) as compared with age-matched control subjects without diabetes ( n = 26; mean age = 7.43 years; 14 females). Using both model-driven seed-based analysis and model-free independent component analysis and controlling for age, data acquisition site, and sex, converging results were obtained, suggesting increased connectivity in young children with T1D as compared with control subjects without diabetes. Further, increased connectivity in children with T1D was observed to be positively associated with cognitive functioning. The observed positive association of connectivity with cognitive functioning in T1D, without overall group differences in cognitive function, suggests a putative compensatory role of hyperintrinsic connectivity in the brain in children with this condition. Altogether, our study attempts to fill a critical gap in knowledge regarding how dysglycemia in T1D might affect the brain's intrinsic connectivity at very young ages. © 2017 by the American Diabetes Association.

TVB-EduPack—An Interactive Learning and Scripting Platform for The Virtual Brain

PubMed Central

Matzke, Henrik; Schirner, Michael; Vollbrecht, Daniel; Rothmeier, Simon; Llarena, Adalberto; Rojas, Raúl; Triebkorn, Paul; Domide, Lia; Mersmann, Jochen; Solodkin, Ana; Jirsa, Viktor K.; McIntosh, Anthony Randal; Ritter, Petra

2015-01-01

The Virtual Brain (TVB; thevirtualbrain.org) is a neuroinformatics platform for full brain network simulation based on individual anatomical connectivity data. The framework addresses clinical and neuroscientific questions by simulating multi-scale neural dynamics that range from local population activity to large-scale brain function and related macroscopic signals like electroencephalography and functional magnetic resonance imaging. TVB is equipped with a graphical and a command-line interface to create models that capture the characteristic biological variability to predict the brain activity of individual subjects. To enable researchers from various backgrounds a quick start into TVB and brain network modeling in general, we developed an educational module: TVB-EduPack. EduPack offers two educational functionalities that seamlessly integrate into TVB's graphical user interface (GUI): (i) interactive tutorials introduce GUI elements, guide through the basic mechanics of software usage and develop complex use-case scenarios; animations, videos and textual descriptions transport essential principles of computational neuroscience and brain modeling; (ii) an automatic script generator records model parameters and produces input files for TVB's Python programming interface; thereby, simulation configurations can be exported as scripts that allow flexible customization of the modeling process and self-defined batch- and post-processing applications while benefitting from the full power of the Python language and its toolboxes. This article covers the implementation of TVB-EduPack and its integration into TVB architecture. Like TVB, EduPack is an open source community project that lives from the participation and contribution of its users. TVB-EduPack can be obtained as part of TVB from thevirtualbrain.org. PMID:26635597

Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications

PubMed Central

Tadić, Bosiljka; Andjelković, Miroslav; Boshkoska, Biljana Mileva; Levnajić, Zoran

2016-01-01

Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener’s concentration to the story, confirmed by self-rating, and closeness to the speaker’s brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener’s group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener’s rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli. PMID:27880802

Gap junctions mediate large-scale Turing structures in a mean-field cortex driven by subcortical noise

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Wilson, M. T.; Sleigh, J. W.

2007-07-01

One of the grand puzzles in neuroscience is establishing the link between cognition and the disparate patterns of spontaneous and task-induced brain activity that can be measured clinically using a wide range of detection modalities such as scalp electrodes and imaging tomography. High-level brain function is not a single-neuron property, yet emerges as a cooperative phenomenon of multiply-interacting populations of neurons. Therefore a fruitful modeling approach is to picture the cerebral cortex as a continuum characterized by parameters that have been averaged over a small volume of cortical tissue. Such mean-field cortical models have been used to investigate gross patterns of brain behavior such as anesthesia, the cycles of natural sleep, memory and erasure in slow-wave sleep, and epilepsy. There is persuasive and accumulating evidence that direct gap-junction connections between inhibitory neurons promote synchronous oscillatory behavior both locally and across distances of some centimeters, but, to date, continuum models have ignored gap-junction connectivity. In this paper we employ simple mean-field arguments to derive an expression for D2 , the diffusive coupling strength arising from gap-junction connections between inhibitory neurons. Using recent neurophysiological measurements reported by Fukuda [J. Neurosci. 26, 3434 (2006)], we estimate an upper limit of D2≈0.6cm2 . We apply a linear stability analysis to a standard mean-field cortical model, augmented with gap-junction diffusion, and find this value for the diffusive coupling strength to be close to the critical value required to destabilize the homogeneous steady state. Computer simulations demonstrate that larger values of D2 cause the noise-driven model cortex to spontaneously crystalize into random mazelike Turing structures: centimeter-scale spatial patterns in which regions of high-firing activity are intermixed with regions of low-firing activity. These structures are consistent with the spatial variations in brain activity patterns detected with the BOLD (blood oxygen-level-dependent) signal detected with magnetic resonance imaging, and may provide a natural substrate for synchronous gamma-band rhythms observed across separated EEG (electroencephalogram) electrodes.

Responses of Cloud Type Distributions to the Large-Scale Dynamical Circulation: Water Budget-Related Dynamical Phase Space and Dynamical Regimes

NASA Technical Reports Server (NTRS)

Wong, Sun; Del Genio, Anthony; Wang, Tao; Kahn, Brian; Fetzer, Eric J.; L'Ecuyer, Tristan S.

2015-01-01

Goals: Water budget-related dynamical phase space; Connect large-scale dynamical conditions to atmospheric water budget (including precipitation); Connect atmospheric water budget to cloud type distributions.

Dynamic multi-coil technique (DYNAMITE) shimming for echo-planar imaging of the human brain at 7 Tesla.

PubMed

Juchem, Christoph; Umesh Rudrapatna, S; Nixon, Terence W; de Graaf, Robin A

2015-01-15

Gradient-echo echo-planar imaging (EPI) is the primary method of choice in functional MRI and other methods relying on fast MRI to image brain activation and connectivity. However, the high susceptibility of EPI towards B0 magnetic field inhomogeneity poses serious challenges. Conventional magnetic field shimming with low-order spherical harmonic (SH) functions is capable of compensating shallow field distortions, but performs poorly for global brain shimming or on specific areas with strong susceptibility-induced B0 distortions such as the prefrontal cortex (PFC). Excellent B0 homogeneity has been demonstrated recently in the human brain at 7 Tesla with the DYNAmic Multi-coIl TEchnique (DYNAMITE) for magnetic field shimming (J Magn Reson (2011) 212:280-288). Here, we report the benefits of DYNAMITE shimming for multi-slice EPI and T2* mapping. A standard deviation of 13Hz was achieved for the residual B0 distribution in the human brain at 7 Tesla with DYNAMITE shimming and was 60% lower compared to conventional shimming that employs static zero through third order SH shapes. The residual field inhomogeneity with SH shimming led to an average 8mm shift at acquisition parameters commonly used for fMRI and was reduced to 1.5-3mm with DYNAMITE shimming. T2* values obtained from the prefrontal and temporal cortices with DYNAMITE shimming were 10-50% longer than those measured with SH shimming. The reduction of the confounding macroscopic B0 field gradients with DYNAMITE shimming thereby promises improved access to the relevant microscopic T2* effects. The combination of high spatial resolution and DYNAMITE shimming allows largely artifact-free EPI and T2* mapping throughout the brain, including prefrontal and temporal lobe areas. DYNAMITE shimming is expected to critically benefit a wide range of MRI applications that rely on excellent B0 magnetic field conditions including EPI-based fMRI to study various cognitive processes and assessing large-scale brain connectivity in vivo. As such, DYNAMITE shimming has the potential to replace conventional SH shim systems in human MR scanners. Copyright © 2014 Elsevier Inc. All rights reserved.

Dynamic Multi-Coil Technique (DYNAMITE) Shimming for Echo-Planar Imaging of the Human Brain at 7 Tesla

PubMed Central

Juchem, Christoph; Rudrapatna, S. Umesh; Nixon, Terence W.; de Graaf, Robin A.

2014-01-01

Gradient-echo echo-planar imaging (EPI) is the primary method of choice in functional MRI and other methods relying on fast MRI to image brain activation and connectivity. However, the high susceptibility of EPI towards B0 magnetic field inhomogeneity poses serious challenges. Conventional magnetic field shimming with low-order spherical harmonic (SH) functions is capable of compensating shallow field distortions, but performs poorly for global brain shimming or on specific areas with strong susceptibility-induced B0 distortions such as the prefrontal cortex (PFC). Excellent B0 homogeneity has been demonstrated recently in the human brain at 7 Tesla with the DYNAmic Multi-coIl TEchnique (DYNAMITE) for magnetic field shimming (Juchem et al., J Magn Reson (2011) 212:280-288). Here, we report the benefits of DYNAMITE shimming for multi-slice EPI and T2* mapping. A standard deviation of 13 Hz was achieved for the residual B0 distribution in the human brain at 7 Tesla with DYNAMITE shimming and was 60% lower compared to conventional shimming that employs static zero through third order SH shapes. The residual field inhomogeneity with SH shimming led to an average 8 mm shift at acquisition parameters commonly used for fMRI and was reduced to 1.5-3 mm with DYNAMITE shimming. T2* values obtained from the prefrontal and temporal cortices with DYNAMITE shimming were 10-50% longer than those measured with SH shimming. The reduction of the confounding macroscopic B0 field gradients with DYNAMITE shimming thereby promises improved access to the relevant microscopic T2* effects. The combination of high spatial resolution and DYNAMITE shimming allows largely artifact-free EPI and T2* mapping throughout the brain, including prefrontal and temporal lobe areas. DYNAMITE shimming is expected to critically benefit a wide range of MRI applications that rely on excellent B0 magnetic field conditions including EPI-based fMRI to study various cognitive processes and assessing large-scale brain connectivity in vivo. As such, DYNAMITE shimming has the potential to replace conventional SH shim systems in human MR scanners. PMID:25462795

Altered Brain Functional Connectivity in Betel Quid-Dependent Chewers.

PubMed

Huang, Xiaojun; Pu, Weidan; Liu, Haihong; Li, Xinmin; Greenshaw, Andrew J; Dursun, Serdar M; Xue, Zhimin; Liu, Zhening

2017-01-01

Betel quid (BQ) is a common psychoactive substance worldwide with particularly high usage in many Asian countries. This study aimed to explore the effect of BQ use on functional connectivity by comparing global functional brain networks and their subset between BQ chewers and healthy controls (HCs). Resting-state functional magnetic resonance imaging (fMRI) was obtained from 24 betel quid-dependent (BQD) male chewers and 27 healthy male individuals on a 3.0T scanner. We used independent component analysis (ICA) to determine components that represent the brain's functional networks and their spatial aspects of functional connectivity. Two sample t -tests were used to identify the functional connectivity differences in each network between these two groups. Seventeen networks were identified by ICA. Nine of them showed connectivity differences between BQD and HCs (two sample t -tests, p  < 0.001 uncorrected). We found increased functional connectivity in the orbitofrontal, bilateral frontoparietal, frontotemporal, occipital/parietal, frontotemporal/cerebellum, and temporal/limbic networks, and decreased connectivity in the parietal and medial frontal/anterior cingulate networks in the BQD compared to the HCs. The betel quid dependence scale scores were positively related to the increased functional connectivity in the orbitofrontal ( r  = 0.39, p  = 0.03) while negatively related to the decreased functional connectivity in medial frontal/anterior cingulate networks ( r  = -0.35, p  = 0.02). Our findings provide further evidence that BQ chewing may lead to brain functional connectivity changes, which may play a key role in the psychological and physiological effects of BQ.

Attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest.

PubMed

Spreng, R Nathan; Stevens, W Dale; Viviano, Joseph D; Schacter, Daniel L

2016-09-01

Anticorrelation between the default and dorsal attention networks is a central feature of human functional brain organization. Hallmarks of aging include impaired default network modulation and declining medial temporal lobe (MTL) function. However, it remains unclear if this anticorrelation is preserved into older adulthood during task performance, or how this is related to the intrinsic architecture of the brain. We hypothesized that older adults would show reduced within- and increased between-network functional connectivity (FC) across the default and dorsal attention networks. To test this hypothesis, we examined the effects of aging on task-related and intrinsic FC using functional magnetic resonance imaging during an autobiographical planning task known to engage the default network and during rest, respectively, with young (n = 72) and older (n = 79) participants. The task-related FC analysis revealed reduced anticorrelation with aging. At rest, there was a robust double dissociation, with older adults showing a pattern of reduced within-network FC, but increased between-network FC, across both networks, relative to young adults. Moreover, older adults showed reduced intrinsic resting-state FC of the MTL with both networks suggesting a fractionation of the MTL memory system in healthy aging. These findings demonstrate age-related dedifferentiation among these competitive large-scale networks during both task and rest, consistent with the idea that age-related changes are associated with a breakdown in the intrinsic functional architecture within and among large-scale brain networks. Copyright © 2016 Elsevier Inc. All rights reserved.

Multiple cortical thickness sub-networks and cognitive impairments in first episode, drug naïve patients with late life depression: A graph theory analysis.

PubMed

Shin, Jeong-Hyeon; Um, Yu Hyun; Lee, Chang Uk; Lim, Hyun Kook; Seong, Joon-Kyung

2018-03-15

Coordinated and pattern-wise changes in large scale gray matter structural networks reflect neural circuitry dysfunction in late life depression (LLD), which in turn is associated with emotional dysregulation and cognitive impairments. However, due to methodological limitations, there have been few attempts made to identify individual-level structural network properties or sub-networks that are involved in important brain functions in LLD. In this study, we sought to construct individual-level gray matter structural networks using average cortical thicknesses of several brain areas to investigate the characteristics of the gray matter structural networks in normal controls and LLD patients. Additionally, we investigated the structural sub-networks correlated with several clinical measurements including cognitive impairment and depression severity. We observed that small worldness, clustering coefficients, global and local efficiency, and hub structures in the brains of LLD patients were significantly different from healthy controls. We further found that a sub-network including the anterior cingulate, dorsolateral prefrontal cortex and superior prefrontal cortex is significantly associated with attention control and executive function. The severity of depression was associated with the sub-networks comprising the salience network, including the anterior cingulate and insula. We investigated cortico-cortical connectivity, but omitted the subcortical structures such as the striatum and thalamus. We report differences in patterns between several clinical measurements and sub-networks from large-scale and individual-level cortical thickness networks in LLD. Copyright © 2018 Elsevier B.V. All rights reserved.

Cortical network architecture for context processing in primate brain

PubMed Central

Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka

2015-01-01

Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition. DOI: http://dx.doi.org/10.7554/eLife.06121.001 PMID:26416139

Altered functional connectivity links in neuroleptic-naïve and neuroleptic-treated patients with schizophrenia, and their relation to symptoms including volition

PubMed Central

Pu, Weidan; Rolls, Edmund T.; Guo, Shuixia; Liu, Haihong; Yu, Yun; Xue, Zhimin; Feng, Jianfeng; Liu, Zhening

2014-01-01

In order to analyze functional connectivity in untreated and treated patients with schizophrenia, resting-state fMRI data were obtained for whole-brain functional connectivity analysis from 22 first-episode neuroleptic-naïve schizophrenia (NNS), 61 first-episode neuroleptic-treated schizophrenia (NTS) patients, and 60 healthy controls (HC). Reductions were found in untreated and treated patients in the functional connectivity between the posterior cingulate gyrus and precuneus, and this was correlated with the reduction in volition from the Positive and Negative Symptoms Scale (PANSS), that is in the willful initiation, sustenance, and control of thoughts, behavior, movements, and speech, and with the general and negative symptoms. In addition in both patient groups interhemispheric functional connectivity was weaker between the orbitofrontal cortex, amygdala and temporal pole. These functional connectivity changes and the related symptoms were not treated by the neuroleptics. Differences between the patient groups were that there were more strong functional connectivity links in the NNS patients (including in hippocampal, frontal, and striatal circuits) than in the NTS patients. These findings with a whole brain analysis in untreated and treated patients with schizophrenia provide evidence on some of the brain regions implicated in the volitional, other general, and negative symptoms, of schizophrenia that are not treated by neuroleptics so have implications for the development of other treatments; and provide evidence on some brain systems in which neuroleptics do alter the functional connectivity. PMID:25389520

Multimodal neuroimaging investigations of alterations to consciousness: the relationship between absence epilepsy and sleep.

PubMed

Bagshaw, Andrew P; Rollings, David T; Khalsa, Sakh; Cavanna, Andrea E

2014-01-01

The link between epilepsy and sleep is well established on many levels. The focus of the current review is on recent neuroimaging investigations into the alterations of consciousness that are observed during absence seizures and the descent into sleep. Functional neuroimaging provides simultaneous cortical and subcortical recording of activity throughout the brain, allowing a detailed definition and characterization of large-scale brain networks and the interactions between them. This has led to the identification of a set of regions which collectively form the consciousness system, which includes contributions from the default mode network (DMN), ascending arousal systems, and the thalamus. Electrophysiological and neuroimaging investigations have also clearly demonstrated the importance of thalamocortical and corticothalamic networks in the evolution of sleep and absence epilepsy, two phenomena in which the subject experiences an alteration to the conscious state and a disconnection from external input. However, the precise relationship between the consciousness system, thalamocortical networks, and consciousness itself remains to be clarified. One of the fundamental challenges is to understand how distributed brain networks coordinate their activity in order to maintain and implement complex behaviors such as consciousness and how modifications to this network activity lead to alterations in consciousness. By taking into account not only the level of activation of individual brain regions but also their connectivity within specific networks and the activity and connectivity of other relevant networks, a more specific quantification of brain states can be achieved. This, in turn, may provide a more fundamental understanding of the alterations to consciousness experienced in sleep and epilepsy. © 2013.

Brain network dysregulation, emotion, and complaints after mild traumatic brain injury.

PubMed

van der Horn, Harm J; Liemburg, Edith J; Scheenen, Myrthe E; de Koning, Myrthe E; Marsman, Jan-Bernard C; Spikman, Jacoba M; van der Naalt, Joukje

2016-04-01

To assess the role of brain networks in emotion regulation and post-traumatic complaints in the sub-acute phase after non-complicated mild traumatic brain injury (mTBI). Fifty-four patients with mTBI (34 with and 20 without complaints) and 20 healthy controls (group-matched for age, sex, education, and handedness) were included. Resting-state fMRI was performed at four weeks post-injury. Static and dynamic functional connectivity were studied within and between the default mode, executive (frontoparietal and bilateral frontal network), and salience network. The hospital anxiety and depression scale (HADS) was used to measure anxiety (HADS-A) and depression (HADS-D). Regarding within-network functional connectivity, none of the selected brain networks were different between groups. Regarding between-network interactions, patients with complaints exhibited lower functional connectivity between the bilateral frontal and salience network compared to patients without complaints. In the total patient group, higher HADS-D scores were related to lower functional connectivity between the bilateral frontal network and both the right frontoparietal and salience network, and to higher connectivity between the right frontoparietal and salience network. Furthermore, whereas higher HADS-D scores were associated with lower connectivity within the parietal midline areas of the bilateral frontal network, higher HADS-A scores were related to lower connectivity within medial prefrontal areas of the bilateral frontal network. Functional interactions of the executive and salience networks were related to emotion regulation and complaints after mTBI, with a key role for the bilateral frontal network. These findings may have implications for future studies on the effect of psychological interventions. © 2016 Wiley Periodicals, Inc.

Immediate and Longitudinal Alterations of Functional Networks after Thalamotomy in Essential Tremor

PubMed Central

Jang, Changwon; Park, Hae-Jeong; Chang, Won Seok; Pae, Chongwon; Chang, Jin Woo

2016-01-01

Thalamotomy at the ventralis intermedius nucleus has been an effective treatment method for essential tremor, but how the brain network changes immediately responding to this deliberate lesion and then reorganizes afterwards are not clear. Taking advantage of a non-cranium-opening MRI-guided focused ultrasound ablation technique, we investigated functional network changes due to a focal lesion. To classify the diverse time courses of those network changes with respect to symptom-related long-lasting treatment effects and symptom-unrelated transient effects, we applied graph-theoretic analyses to longitudinal resting-state functional magnetic resonance imaging data before and 1 day, 7 days, and 3 months after thalamotomy with essential tremor. We found reduced average connections among the motor-related areas, reduced connectivity between substantia nigra and external globus pallidum and reduced total connection in the thalamus after thalamotomy, which are all associated with clinical rating scales. The average connectivity among whole brain regions and inter-hemispheric network asymmetry show symptom-unrelated transient increases, indicating temporary reconfiguration of the whole brain network. In summary, thalamotomy regulates interactions over the motor network via symptom-related connectivity changes but accompanies transient, symptom-unrelated diaschisis in the global brain network. This study suggests the significance of longitudinal network analysis, combined with minimal-invasive treatment techniques, in understanding time-dependent diaschisis in the brain network due to a focal lesion. PMID:27822200

The brainstem reticular formation is a small-world, not scale-free, network

PubMed Central

Humphries, M.D; Gurney, K; Prescott, T.J

2005-01-01

Recently, it has been demonstrated that several complex systems may have simple graph-theoretic characterizations as so-called ‘small-world’ and ‘scale-free’ networks. These networks have also been applied to the gross neural connectivity between primate cortical areas and the nervous system of Caenorhabditis elegans. Here, we extend this work to a specific neural circuit of the vertebrate brain—the medial reticular formation (RF) of the brainstem—and, in doing so, we have made three key contributions. First, this work constitutes the first model (and quantitative review) of this important brain structure for over three decades. Second, we have developed the first graph-theoretic analysis of vertebrate brain connectivity at the neural network level. Third, we propose simple metrics to quantitatively assess the extent to which the networks studied are small-world or scale-free. We conclude that the medial RF is configured to create small-world (implying coherent rapid-processing capabilities), but not scale-free, type networks under assumptions which are amenable to quantitative measurement. PMID:16615219

Planum Temporale Asymmetries Correlate with Corpus Callosum Axon Fiber Density in Chimpanzees (Pan troglodytes)

PubMed Central

Hopkins, William D.; Pilger, John F.; Storz, Rachel; Ambrose, Alex; Hof, Patrick R.; Sherwood, Chet C.

2012-01-01

The corpus callosum (CC) is the major white matter tract that connects the two cerebral hemispheres. Some have theorized that individual differences in behavioral and brain asymmetries are linked to variation in the density of axon fibers that traverse different sections of the CC. In this study, we examined whether variation in axon fiber density in the CC was associated with variation in asymmetries in the planum temporale (PT) in a sample of 20 post-mortem chimpanzee brains. We further tested for sex differences in small and large CC fiber proportions and density in the chimpanzees. We found that the distribution of small and large fibers within the CC of chimpanzees follows a similar pattern to those reported in humans. We also found that chimpanzees with larger asymmetries in the PT had fewer large fibers in the posterior portion of the CC, particularly among females. As has been reported in human brains, the findings reported here indicate that individual differences in brain asymmetries are associated with variation in interhemispheric connectivity as manifest in axon fiber density and size. PMID:22766214

Prediction of brain maturity in infants using machine-learning algorithms.

PubMed

Smyser, Christopher D; Dosenbach, Nico U F; Smyser, Tara A; Snyder, Abraham Z; Rogers, Cynthia E; Inder, Terrie E; Schlaggar, Bradley L; Neil, Jeffrey J

2016-08-01

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p<0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants. Copyright © 2016 Elsevier Inc. All rights reserved.

Prediction of brain maturity in infants using machine-learning algorithms

PubMed Central

Smyser, Christopher D.; Dosenbach, Nico U.F.; Smyser, Tara A.; Snyder, Abraham Z.; Rogers, Cynthia E.; Inder, Terrie E.; Schlaggar, Bradley L.; Neil, Jeffrey J.

2016-01-01

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23–29 weeks of gestation and without moderate–severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p < 0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants. PMID:27179605

EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome.

PubMed

Hassan, Mahmoud; Shamas, Mohamad; Khalil, Mohamad; El Falou, Wassim; Wendling, Fabrice

2015-01-01

The brain is a large-scale complex network often referred to as the "connectome". Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/.

EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome

PubMed Central

Hassan, Mahmoud; Shamas, Mohamad; Khalil, Mohamad; El Falou, Wassim; Wendling, Fabrice

2015-01-01

The brain is a large-scale complex network often referred to as the “connectome”. Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/. PMID:26379232

Disconnecting Consciousness: Is There a Common Anesthetic End Point?

PubMed

Hudetz, Anthony G; Mashour, George A

2016-11-01

A quest for a systems-level neuroscientific basis of anesthetic-induced loss and return of consciousness has been in the forefront of research for the past 2 decades. Recent advances toward the discovery of underlying mechanisms have been achieved using experimental electrophysiology, multichannel electroencephalography, magnetoencephalography, and functional magnetic resonance imaging. By the careful dosing of various volatile and IV anesthetic agents to the level of behavioral unresponsiveness, both specific and common changes in functional and effective connectivity across large-scale brain networks have been discovered and interpreted in the context of how the synthesis of neural information might be affected during anesthesia. The results of most investigations to date converge toward the conclusion that a common neural correlate of anesthetic-induced unresponsiveness is a consistent depression or functional disconnection of lateral frontoparietal networks, which are thought to be critical for consciousness of the environment. A reduction in the repertoire of brain states may contribute to the anesthetic disruption of large-scale information integration leading to unconsciousness. In future investigations, a systematic delineation of connectivity changes with multiple anesthetics using the same experimental design, and the same analytical method will be desirable. The critical neural events that account for the transition between responsive and unresponsive states should be assessed at similar anesthetic doses just below and above the loss or return of responsiveness. There will also be a need to identify a robust, sensitive, and reliable measure of information transfer. Ultimately, finding a behavior-independent measure of subjective experience that can track covert cognition in unresponsive subjects and a delineation of causal factors versus correlated events will be essential to understand the neuronal basis of human consciousness and unconsciousness.

Disconnecting Consciousness: Is There a Common Anesthetic End-Point?

PubMed Central

Hudetz, Anthony G.; Mashour, George A.

2016-01-01

A quest for a systems-level neuroscientific basis of anesthetic-induced loss and return of consciousness has been in the forefront of research of the last two decades. Recent advances toward the discovery of underlying mechanisms have been achieved using experimental electrophysiology, multichannel electroencephalography, magnetoencephalography, and functional magnetic resonance imaging. By the careful dosing of various volatile and IV anesthetic agents to the level of behavioral unresponsiveness, both specific and common changes in functional and effective connectivity across large-scale brain networks have been discovered and interpreted in the context of how the synthesis of neural information might be affected during anesthesia. The results of most investigations to date converge toward the conclusion that a common neural correlate of anesthetic-induced unresponsiveness is a consistent depression or functional disconnection of lateral frontoparietal networks, which are thought to be critical for consciousness of the environment. A reduction in the repertoire of brain states may contribute to the anesthetic disruption of large-scale information integration leading to unconsciousness. In future investigations, a systematic delineation of connectivity changes with multiple anesthetics using the same experimental design and the same analytical method will be desirable. The critical neural events that account for the transition between responsive and unresponsive states should be assessed at similar anesthetic doses just below and above the loss or return of responsiveness. There will also be a need to identify a robust, sensitive, and reliable measure of information transfer. Ultimately, finding a behavior-independent measure of subjective experience that can track covert cognition in unresponsive subjects and a delineation of causal factors vs. correlated events will be essential to understand the neuronal basis of human consciousness and unconsciousness. PMID:27331780

Individual brain structure and modelling predict seizure propagation.

PubMed

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K

2017-03-01

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

[From Brownian motion to mind imaging: diffusion MRI].

PubMed

Le Bihan, Denis

2006-11-01

The success of diffusion MRI, which was introduced in the mid 1980s is deeply rooted in the powerful concept that during their random, diffusion-driven movements water molecules probe tissue structure at a microscopic scale well beyond the usual image resolution. The observation of these movements thus provides valuable information on the structure and the geometric organization of tissues. The most successful application of diffusion MRI has been in brain ischemia, following the discovery that water diffusion drops at a very early stage of the ischemic event. Diffusion MRI provides some patients with the opportunity to receive suitable treatment at a very acute stage when brain tissue might still be salvageable. On the other hand, diffusion is modulated by the spatial orientation of large bundles of myelinated axons running in parallel through in brain white matter. This feature can be exploited to map out the orientation in space of the white matter tracks and to visualize the connections between different parts of the brain on an individual basis. Furthermore, recent data suggest that diffusion MRI may also be used to visualize rapid dynamic tissue changes, such as neuronal swelling, associated with cortical activation, offering a new and direct approach to brain functional imaging.

Functional connectivity within and between intrinsic brain networks correlates with trait mind wandering.

PubMed

Godwin, Christine A; Hunter, Michael A; Bezdek, Matthew A; Lieberman, Gregory; Elkin-Frankston, Seth; Romero, Victoria L; Witkiewitz, Katie; Clark, Vincent P; Schumacher, Eric H

2017-08-01

Individual differences across a variety of cognitive processes are functionally associated with individual differences in intrinsic networks such as the default mode network (DMN). The extent to which these networks correlate or anticorrelate has been associated with performance in a variety of circumstances. Despite the established role of the DMN in mind wandering processes, little research has investigated how large-scale brain networks at rest relate to mind wandering tendencies outside the laboratory. Here we examine the extent to which the DMN, along with the dorsal attention network (DAN) and frontoparietal control network (FPCN) correlate with the tendency to mind wander in daily life. Participants completed the Mind Wandering Questionnaire and a 5-min resting state fMRI scan. In addition, participants completed measures of executive function, fluid intelligence, and creativity. We observed significant positive correlations between trait mind wandering and 1) increased DMN connectivity at rest and 2) increased connectivity between the DMN and FPCN at rest. Lastly, we found significant positive correlations between trait mind wandering and fluid intelligence (Ravens) and creativity (Remote Associates Task). We interpret these findings within the context of current theories of mind wandering and executive function and discuss the possibility that certain instances of mind wandering may not be inherently harmful. Due to the controversial nature of global signal regression (GSReg) in functional connectivity analyses, we performed our analyses with and without GSReg and contrast the results from each set of analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Hui, Hui; Wang, Shuo; Dong, Di; Liu, Xia; Yang, Xin; Tian, Jie

2017-03-01

Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.

Delayed Development of Brain Connectivity in Adolescents With Schizophrenia and Their Unaffected Siblings.

PubMed

Zalesky, Andrew; Pantelis, Christos; Cropley, Vanessa; Fornito, Alex; Cocchi, Luca; McAdams, Harrison; Clasen, Liv; Greenstein, Deanna; Rapoport, Judith L; Gogtay, Nitin

2015-09-01

Abnormalities in structural brain connectivity have been observed in patients with schizophrenia. Mapping these abnormalities longitudinally and understanding their genetic risk via sibship studies will provide crucial insight into progressive developmental changes associated with schizophrenia. To identify corticocortical connections exhibiting an altered developmental trajectory in adolescents with childhood-onset schizophrenia (COS) and to determine whether similar alterations are found in patients' unaffected siblings. Using prospective structural brain magnetic resonance imaging, large-scale corticocortical connectivity was mapped from ages 12 to 24 years in 109 patients with COS (272 images), 86 of their unaffected siblings (184 images), and 102 healthy controls (262 images) over a 20-year period beginning January 1, 1991, through April 30, 2011, as part of the ongoing COS study at the National Institute of Mental Health. Structural connectivity between pairs of cortical regions was estimated using a validated technique based on across-subject covariation in magnetic resonance imaging-derived cortical thickness measurements. Compared with normally developing controls, significant left-hemisphere occipitotemporal deficits in cortical thickness correlations were found in patients with COS as well as their healthy siblings (P < .05). Deficits in siblings normalized by mid-adolescence, whereas patients with COS showed significantly longer maturational delays, with cortical thickness correlations between the left temporal lobe and left occipital cortex not showing evidence of development until early adulthood. The normalization of deficits with age in patients with COS correlated with improvement in symptoms. Compared with controls, left-hemisphere occipitotemporal thickness correlations in a subgroup of patients with high positive symptoms were significantly reduced from age 14 to 18 years (P < .05); however, other patients with low positive symptoms showed no significant deficits. Delayed maturation of occipitotemporal connectivity appears to be a trait marker in patients with COS, with a milder endophenotype in unaffected siblings associated with resilience to developing schizophrenia. These findings indicate genetically influenced and connection-specific developmental abnormalities in the schizophrenia connectome, and lead to the hypothesis that visual hallucinations in patients with COS may be because of delayed development of the inferior longitudinal fasciculus, a prominent occipitotemporal fiber.

Herbivorous fishes, ecosystem function and mobile links on coral reefs

NASA Astrophysics Data System (ADS)

Welsh, J. Q.; Bellwood, D. R.

2014-06-01

Understanding large-scale movement of ecologically important taxa is key to both species and ecosystem management. Those species responsible for maintaining functional connectivity between habitats are often called mobile links and are regarded as essential elements of resilience. By providing connectivity, they support resilience across spatial scales. Most marine organisms, including fishes, have long-term, biogeographic-scale connectivity through larval movement. Although most reef species are highly site attached after larval settlement, some taxa may also be able to provide rapid, reef-scale connectivity as adults. On coral reefs, the identity of such taxa and the extent of their mobility are not yet known. We use acoustic telemetry to monitor the movements of Kyphosus vaigiensis, one of the few reef fishes that feeds on adult brown macroalgae. Unlike other benthic herbivorous fish species, it also exhibits large-scale (>2 km) movements. Individual K. vaigiensis cover, on average, a 2.5 km length of reef (11 km maximum) each day. These large-scale movements suggest that this species may act as a mobile link, providing functional connectivity, should the need arise, and helping to support functional processes across habitats and spatial scales. An analysis of published studies of home ranges in reef fishes found a consistent relationship between home range size and body length. K. vaigiensis is the sole herbivore to depart significantly from the expected home range-body size relationship, with home range sizes more comparable to exceptionally mobile large pelagic predators rather than other reef herbivores. While the large-scale movements of K. vaigiensis reveal its potential capacity to enhance resilience over large areas, it also emphasizes the potential limitations of small marine reserves to protect some herbivore populations.

Dissociable effects of local inhibitory and excitatory theta-burst stimulation on large-scale brain dynamics

PubMed Central

Sale, Martin V.; Lord, Anton; Zalesky, Andrew; Breakspear, Michael; Mattingley, Jason B.

2015-01-01

Normal brain function depends on a dynamic balance between local specialization and large-scale integration. It remains unclear, however, how local changes in functionally specialized areas can influence integrated activity across larger brain networks. By combining transcranial magnetic stimulation with resting-state functional magnetic resonance imaging, we tested for changes in large-scale integration following the application of excitatory or inhibitory stimulation on the human motor cortex. After local inhibitory stimulation, regions encompassing the sensorimotor module concurrently increased their internal integration and decreased their communication with other modules of the brain. There were no such changes in modular dynamics following excitatory stimulation of the same area of motor cortex nor were there changes in the configuration and interactions between core brain hubs after excitatory or inhibitory stimulation of the same area. These results suggest the existence of selective mechanisms that integrate local changes in neural activity, while preserving ongoing communication between brain hubs. PMID:25717162

Functional connectivity patterns reflect individual differences in conflict adaptation.

PubMed

Wang, Xiangpeng; Wang, Ting; Chen, Zhencai; Hitchman, Glenn; Liu, Yijun; Chen, Antao

2015-04-01

Individuals differ in the ability to utilize previous conflict information to optimize current conflict resolution, which is termed the conflict adaptation effect. Previous studies have linked individual differences in conflict adaptation to distinct brain regions. However, the network-based neural mechanisms subserving the individual differences of the conflict adaptation effect have not been studied. The present study employed a psychophysiological interaction (PPI) analysis with a color-naming Stroop task to examine this issue. The main results were as follows: (1) the anterior cingulate cortex (ACC)-seeded PPI revealed the involvement of the salience network (SN) in conflict adaptation, while the posterior parietal cortex (PPC)-seeded PPI revealed the engagement of the central executive network (CEN). (2) Participants with high conflict adaptation effect showed higher intra-CEN connectivity and lower intra-SN connectivity; while those with low conflict adaptation effect showed higher intra-SN connectivity and lower intra-CEN connectivity. (3) The PPC-centered intra-CEN connectivity positively predicted the conflict adaptation effect; while the ACC-centered intra-SN connectivity had a negative correlation with this effect. In conclusion, our data demonstrated that conflict adaptation is likely supported by the CEN and the SN, providing a new perspective on studying individual differences in conflict adaptation on the basis of large-scale networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

Grid cells form a global representation of connected environments.

PubMed

Carpenter, Francis; Manson, Daniel; Jeffery, Kate; Burgess, Neil; Barry, Caswell

2015-05-04

The firing patterns of grid cells in medial entorhinal cortex (mEC) and associated brain areas form triangular arrays that tessellate the environment [1, 2] and maintain constant spatial offsets to each other between environments [3, 4]. These cells are thought to provide an efficient metric for navigation in large-scale space [5-8]. However, an accurate and universal metric requires grid cell firing patterns to uniformly cover the space to be navigated, in contrast to recent demonstrations that environmental features such as boundaries can distort [9-11] and fragment [12] grid patterns. To establish whether grid firing is determined by local environmental cues, or provides a coherent global representation, we recorded mEC grid cells in rats foraging in an environment containing two perceptually identical compartments connected via a corridor. During initial exposures to the multicompartment environment, grid firing patterns were dominated by local environmental cues, replicating between the two compartments. However, with prolonged experience, grid cell firing patterns formed a single, continuous representation that spanned both compartments. Thus, we provide the first evidence that in a complex environment, grid cell firing can form the coherent global pattern necessary for them to act as a metric capable of supporting large-scale spatial navigation. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Grid Cells Form a Global Representation of Connected Environments

PubMed Central

Carpenter, Francis; Manson, Daniel; Jeffery, Kate; Burgess, Neil; Barry, Caswell

2015-01-01

Summary The firing patterns of grid cells in medial entorhinal cortex (mEC) and associated brain areas form triangular arrays that tessellate the environment [1, 2] and maintain constant spatial offsets to each other between environments [3, 4]. These cells are thought to provide an efficient metric for navigation in large-scale space [5–8]. However, an accurate and universal metric requires grid cell firing patterns to uniformly cover the space to be navigated, in contrast to recent demonstrations that environmental features such as boundaries can distort [9–11] and fragment [12] grid patterns. To establish whether grid firing is determined by local environmental cues, or provides a coherent global representation, we recorded mEC grid cells in rats foraging in an environment containing two perceptually identical compartments connected via a corridor. During initial exposures to the multicompartment environment, grid firing patterns were dominated by local environmental cues, replicating between the two compartments. However, with prolonged experience, grid cell firing patterns formed a single, continuous representation that spanned both compartments. Thus, we provide the first evidence that in a complex environment, grid cell firing can form the coherent global pattern necessary for them to act as a metric capable of supporting large-scale spatial navigation. PMID:25913404

Joint Attention and Brain Functional Connectivity in Infants and Toddlers.

PubMed

Eggebrecht, Adam T; Elison, Jed T; Feczko, Eric; Todorov, Alexandre; Wolff, Jason J; Kandala, Sridhar; Adams, Chloe M; Snyder, Abraham Z; Lewis, John D; Estes, Annette M; Zwaigenbaum, Lonnie; Botteron, Kelly N; McKinstry, Robert C; Constantino, John N; Evans, Alan; Hazlett, Heather C; Dager, Stephen; Paterson, Sarah J; Schultz, Robert T; Styner, Martin A; Gerig, Guido; Das, Samir; Kostopoulos, Penelope; Schlaggar, Bradley L; Petersen, Steven E; Piven, Joseph; Pruett, John R

2017-03-01

Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social-communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. Herein, we show that the functional organization of the brain is intimately related to the emergence of IJA using functional connectivity magnetic resonance imaging and dimensional behavioral assessments in a large semilongitudinal cohort of infants and toddlers. In particular, though functional connections spanning the brain are involved in IJA, the strongest brain-behavior associations cluster within connections between a small subset of functional brain networks; namely between the visual network and dorsal attention network and between the visual network and posterior cingulate aspects of the default mode network. These observations mark the earliest known description of how functional brain systems underlie a burgeoning fundamental social behavior, may help improve the design of targeted therapies for neurodevelopmental disorders, and, more generally, elucidate physiological mechanisms essential to healthy social behavior development. © The Author 2017. Published by Oxford University Press.

Joint Attention and Brain Functional Connectivity in Infants and Toddlers

PubMed Central

Eggebrecht, Adam T.; Elison, Jed T.; Feczko, Eric; Todorov, Alexandre; Wolff, Jason J.; Kandala, Sridhar; Adams, Chloe M.; Snyder, Abraham Z.; Lewis, John D.; Estes, Annette M.; Zwaigenbaum, Lonnie; Botteron, Kelly N.; McKinstry, Robert C.; Constantino, John N.; Evans, Alan; Hazlett, Heather C.; Dager, Stephen; Paterson, Sarah J.; Schultz, Robert T.; Styner, Martin A.; Gerig, Guido; Das, Samir; Kostopoulos, Penelope; Schlaggar, Bradley L.; Petersen, Steven E.; Piven, Joseph; Pruett, John R.

2017-01-01

Abstract Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social-communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. Herein, we show that the functional organization of the brain is intimately related to the emergence of IJA using functional connectivity magnetic resonance imaging and dimensional behavioral assessments in a large semilongitudinal cohort of infants and toddlers. In particular, though functional connections spanning the brain are involved in IJA, the strongest brain-behavior associations cluster within connections between a small subset of functional brain networks; namely between the visual network and dorsal attention network and between the visual network and posterior cingulate aspects of the default mode network. These observations mark the earliest known description of how functional brain systems underlie a burgeoning fundamental social behavior, may help improve the design of targeted therapies for neurodevelopmental disorders, and, more generally, elucidate physiological mechanisms essential to healthy social behavior development. PMID:28062515

Novelty seeking and reward dependence-related large-scale brain networks functional connectivity variation during salience expectancy.

PubMed

Li, Shijia; Demenescu, Liliana Ramona; Sweeney-Reed, Catherine M; Krause, Anna Linda; Metzger, Coraline D; Walter, Martin

2017-08-01

A salience network (SN) anchored in the anterior insula (AI) and dorsal anterior cingulate cortex (dACC) plays a key role in switching between brain networks during salience detection and attention regulation. Previous fMRI studies have associated expectancy behaviors and SN activation with novelty seeking (NS) and reward dependence (RD) personality traits. To address the question of how functional connectivity (FC) in the SN is modulated by internal (expectancy-related) salience assignment and different personality traits, 68 healthy participants performed a salience expectancy task using functional magnetic resonance imaging, and psychophysiological interaction analysis (PPI) was conducted to determine salience-related connectivity changes during these anticipation periods. Correlation was then evaluated between PPI and personality traits, assessed using the temperament and character inventory of 32 male participants. During high salience expectancy, SN-seed regions showed reduced FC to visual areas and parts of the default mode network, but increased FC to the central executive network. With increasing NS, participants showed significantly increasing disconnection between right AI and middle cingulate cortex when expecting high-salience pictures as compared to low-salience pictures, while increased RD also predicted decreased right dACC and caudate FC for high salience expectancy. Our findings suggest a direct link between personality traits and internal salience processing mediated by differential network integration of the SN. SN activity and coordination may therefore be moderated by novelty seeking and reward dependency personality traits, which are associated with risk of addiction. Hum Brain Mapp 38:4064-4077, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A Composite Network Approach for Assessing Multi-Species Connectivity: An Application to Road Defragmentation Prioritisation

PubMed Central

Saura, Santiago; Rondinini, Carlo

2016-01-01

One of the biggest challenges in large-scale conservation is quantifying connectivity at broad geographic scales and for a large set of species. Because connectivity analyses can be computationally intensive, and the planning process quite complex when multiple taxa are involved, assessing connectivity at large spatial extents for many species turns to be often intractable. Such limitation results in that conducted assessments are often partial by focusing on a few key species only, or are generic by considering a range of dispersal distances and a fixed set of areas to connect that are not directly linked to the actual spatial distribution or mobility of particular species. By using a graph theory framework, here we propose an approach to reduce computational effort and effectively consider large assemblages of species in obtaining multi-species connectivity priorities. We demonstrate the potential of the approach by identifying defragmentation priorities in the Italian road network focusing on medium and large terrestrial mammals. We show that by combining probabilistic species graphs prior to conducting the network analysis (i) it is possible to analyse connectivity once for all species simultaneously, obtaining conservation or restoration priorities that apply for the entire species assemblage; and that (ii) those priorities are well aligned with the ones that would be obtained by aggregating the results of separate connectivity analysis for each of the individual species. This approach offers great opportunities to extend connectivity assessments to large assemblages of species and broad geographic scales. PMID:27768718

Decreased functional connectivity in schizophrenia: The relationship between social functioning, social cognition and graph theoretical network measures.

PubMed

Erdeniz, Burak; Serin, Emin; İbadi, Yelda; Taş, Cumhur

2017-12-30

Schizophrenia is a complex disorder in which abnormalities in brain connectivity and social functioning play a central role. The aim of this study is to explore small-world network properties, and understand their relationship with social functioning and social cognition in the context of schizophrenia, by testing functional connectivity differences in network properties and its relation to clinical behavioral measures. Resting-state fMRI time series data were acquired from 23 patients diagnosed with schizophrenia and 23 healthy volunteers. The results revealed that patients with schizophrenia show significantly decreased connectivity between a range of brain regions, particularly involving connections among the right orbitofrontal cortex, bilateral putamen and left amygdala. Furthermore, topological properties of functional brain networks in patients with schizophrenia were characterized by reduced path length compared to healthy controls; however, no significant difference was found for clustering coefficient, local efficiency or global efficiency. Additionally, we found that nodal efficiency of the amygdala and the putamen were significantly correlated with the independence-performance subscale of social functioning scale (SFC), and Reading the Mind in the Eyes test; however, the correlations do not survive correction for multiple comparison. The current results help to clarify the relationship between social functioning deficits and topological brain measures in schizophrenia. Copyright © 2017 Elsevier B.V. All rights reserved.

Large-scale brain networks in affective and social neuroscience: Towards an integrative functional architecture of the brain

PubMed Central

Barrett, Lisa Feldman; Satpute, Ajay

2013-01-01

Understanding how a human brain creates a human mind ultimately depends on mapping psychological categories and concepts to physical measurements of neural response. Although it has long been assumed that emotional, social, and cognitive phenomena are realized in the operations of separate brain regions or brain networks, we demonstrate that it is possible to understand the body of neuroimaging evidence using a framework that relies on domain general, distributed structure-function mappings. We review current research in affective and social neuroscience and argue that the emerging science of large-scale intrinsic brain networks provides a coherent framework for a domain-general functional architecture of the human brain. PMID:23352202

What We Know About the Brain Structure-Function Relationship.

PubMed

Batista-García-Ramó, Karla; Fernández-Verdecia, Caridad Ivette

2018-04-18

How the human brain works is still a question, as is its implication with brain architecture: the non-trivial structure–function relationship. The main hypothesis is that the anatomic architecture conditions, but does not determine, the neural network dynamic. The functional connectivity cannot be explained only considering the anatomical substrate. This involves complex and controversial aspects of the neuroscience field and that the methods and methodologies to obtain structural and functional connectivity are not always rigorously applied. The goal of the present article is to discuss about the progress made to elucidate the structure–function relationship of the Central Nervous System, particularly at the brain level, based on results from human and animal studies. The current novel systems and neuroimaging techniques with high resolutive physio-structural capacity have brought about the development of an integral framework of different structural and morphometric tools such as image processing, computational modeling and graph theory. Different laboratories have contributed with in vivo, in vitro and computational/mathematical models to study the intrinsic neural activity patterns based on anatomical connections. We conclude that multi-modal techniques of neuroimaging are required such as an improvement on methodologies for obtaining structural and functional connectivity. Even though simulations of the intrinsic neural activity based on anatomical connectivity can reproduce much of the observed patterns of empirical functional connectivity, future models should be multifactorial to elucidate multi-scale relationships and to infer disorder mechanisms.

Modes of Large-Scale Brain Network Organization during Threat Processing and Posttraumatic Stress Disorder Symptom Reduction during TF-CBT among Adolescent Girls.

PubMed

Cisler, Josh M; Sigel, Benjamin A; Kramer, Teresa L; Smitherman, Sonet; Vanderzee, Karin; Pemberton, Joy; Kilts, Clinton D

2016-01-01

Posttraumatic stress disorder (PTSD) is often chronic and disabling across the lifespan. The gold standard treatment for adolescent PTSD is Trauma-Focused Cognitive-Behavioral Therapy (TF-CBT), though treatment response is variable and mediating neural mechanisms are not well understood. Here, we test whether PTSD symptom reduction during TF-CBT is associated with individual differences in large-scale brain network organization during emotion processing. Twenty adolescent girls, aged 11-16, with PTSD related to assaultive violence completed a 12-session protocol of TF-CBT. Participants completed an emotion processing task, in which neutral and fearful facial expressions were presented either overtly or covertly during 3T fMRI, before and after treatment. Analyses focused on characterizing network properties of modularity, assortativity, and global efficiency within an 824 region-of-interest brain parcellation separately during each of the task blocks using weighted functional connectivity matrices. We similarly analyzed an existing dataset of healthy adolescent girls undergoing an identical emotion processing task to characterize normative network organization. Pre-treatment individual differences in modularity, assortativity, and global efficiency during covert fear vs neutral blocks predicted PTSD symptom reduction. Patients who responded better to treatment had greater network modularity and assortativity but lesser efficiency, a pattern that closely resembled the control participants. At a group level, greater symptom reduction was associated with greater pre-to-post-treatment increases in network assortativity and modularity, but this was more pronounced among participants with less symptom improvement. The results support the hypothesis that modularized and resilient brain organization during emotion processing operate as mechanisms enabling symptom reduction during TF-CBT.

Role of physical and mental training in brain network configuration

PubMed Central

Foster, Philip P.

2015-01-01

It is hypothesized that the topology of brain networks is constructed by connecting nodes which may be continuously remodeled by appropriate training. Efficiency of physical and/or mental training on the brain relies on the flexibility of networks' architecture molded by local remodeling of proteins and synapses of excitatory neurons producing transformations in network topology. Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of “energy cost-driven small-world network disorder” with dysfunction of high-energy cost wiring as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement, presumably via reconfiguration of brain networks into greater small-worldness, appears essential in learning, memory, and executive functions. A macroscopic cartography of creation-removal of synaptic connections in a macro-network, and at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF). The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.). However, mental training, meditation or virtual reality (films, games) require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brain ↔ brain com.) molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g., amyotrophic lateral sclerosis, traumatism) also achieve successful cognitive enhancement albeit they may only elicit mental practice. PMID:26157387

Role of physical and mental training in brain network configuration.

PubMed

Foster, Philip P

2015-01-01

It is hypothesized that the topology of brain networks is constructed by connecting nodes which may be continuously remodeled by appropriate training. Efficiency of physical and/or mental training on the brain relies on the flexibility of networks' architecture molded by local remodeling of proteins and synapses of excitatory neurons producing transformations in network topology. Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of "energy cost-driven small-world network disorder" with dysfunction of high-energy cost wiring as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement, presumably via reconfiguration of brain networks into greater small-worldness, appears essential in learning, memory, and executive functions. A macroscopic cartography of creation-removal of synaptic connections in a macro-network, and at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF). The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.). However, mental training, meditation or virtual reality (films, games) require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brain ↔ brain com.) molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g., amyotrophic lateral sclerosis, traumatism) also achieve successful cognitive enhancement albeit they may only elicit mental practice.

Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences.

PubMed

Raz, Gal; Touroutoglou, Alexandra; Wilson-Mendenhall, Christine; Gilam, Gadi; Lin, Tamar; Gonen, Tal; Jacob, Yael; Atzil, Shir; Admon, Roee; Bleich-Cohen, Maya; Maron-Katz, Adi; Hendler, Talma; Barrett, Lisa Feldman

2016-08-01

Recent theoretical and empirical work has highlighted the role of domain-general, large-scale brain networks in generating emotional experiences. These networks are hypothesized to process aspects of emotional experiences that are not unique to a specific emotional category (e.g., "sadness," "happiness"), but rather that generalize across categories. In this article, we examined the dynamic interactions (i.e., changing cohesiveness) between specific domain-general networks across time while participants experienced various instances of sadness, fear, and anger. We used a novel method for probing the network connectivity dynamics between two salience networks and three amygdala-based networks. We hypothesized, and found, that the functional connectivity between these networks covaried with the intensity of different emotional experiences. Stronger connectivity between the dorsal salience network and the medial amygdala network was associated with more intense ratings of emotional experience across six different instances of the three emotion categories examined. Also, stronger connectivity between the dorsal salience network and the ventrolateral amygdala network was associated with more intense ratings of emotional experience across five out of the six different instances. Our findings demonstrate that a variety of emotional experiences are associated with dynamic interactions of domain-general neural systems.

High-cost, high-capacity backbone for global brain communication.

PubMed

van den Heuvel, Martijn P; Kahn, René S; Goñi, Joaquín; Sporns, Olaf

2012-07-10

Network studies of human brain structural connectivity have identified a specific set of brain regions that are both highly connected and highly central. Recent analyses have shown that these putative hub regions are mutually and densely interconnected, forming a "rich club" within the human brain. Here we show that the set of pathways linking rich club regions forms a central high-cost, high-capacity backbone for global brain communication. Diffusion tensor imaging (DTI) data of two sets of 40 healthy subjects were used to map structural brain networks. The contributions to network cost and communication capacity of global cortico-cortical connections were assessed through measures of their topology and spatial embedding. Rich club connections were found to be more costly than predicted by their density alone and accounted for 40% of the total communication cost. Furthermore, 69% of all minimally short paths between node pairs were found to travel through the rich club and a large proportion of these communication paths consisted of ordered sequences of edges ("path motifs") that first fed into, then traversed, and finally exited the rich club, while passing through nodes of increasing and then decreasing degree. The prevalence of short paths that follow such ordered degree sequences suggests that neural communication might take advantage of strategies for dynamic routing of information between brain regions, with an important role for a highly central rich club. Taken together, our results show that rich club connections make an important contribution to interregional signal traffic, forming a central high-cost, high-capacity backbone for global brain communication.

On nodes and modes in resting state fMRI

PubMed Central

Friston, Karl J.; Kahan, Joshua; Razi, Adeel; Stephan, Klaas Enno; Sporns, Olaf

2014-01-01

This paper examines intrinsic brain networks in light of recent developments in the characterisation of resting state fMRI timeseries — and simulations of neuronal fluctuations based upon the connectome. Its particular focus is on patterns or modes of distributed activity that underlie functional connectivity. We first demonstrate that the eigenmodes of functional connectivity – or covariance among regions or nodes – are the same as the eigenmodes of the underlying effective connectivity, provided we limit ourselves to symmetrical connections. This symmetry constraint is motivated by appealing to proximity graphs based upon multidimensional scaling. Crucially, the principal modes of functional connectivity correspond to the dynamically unstable modes of effective connectivity that decay slowly and show long term memory. Technically, these modes have small negative Lyapunov exponents that approach zero from below. Interestingly, the superposition of modes – whose exponents are sampled from a power law distribution – produces classical 1/f (scale free) spectra. We conjecture that the emergence of dynamical instability – that underlies intrinsic brain networks – is inevitable in any system that is separated from external states by a Markov blanket. This conjecture appeals to a free energy formulation of nonequilibrium steady-state dynamics. The common theme that emerges from these theoretical considerations is that endogenous fluctuations are dominated by a small number of dynamically unstable modes. We use this as the basis of a dynamic causal model (DCM) of resting state fluctuations — as measured in terms of their complex cross spectra. In this model, effective connectivity is parameterised in terms of eigenmodes and their Lyapunov exponents — that can also be interpreted as locations in a multidimensional scaling space. Model inversion provides not only estimates of edges or connectivity but also the topography and dimensionality of the underlying scaling space. Here, we focus on conceptual issues with simulated fMRI data and provide an illustrative application using an empirical multi-region timeseries. PMID:24862075

Developmental process emerges from extended brain-body-behavior networks

PubMed Central

Byrge, Lisa; Sporns, Olaf; Smith, Linda B.

2014-01-01

Studies of brain connectivity have focused on two modes of networks: structural networks describing neuroanatomy and the intrinsic and evoked dependencies of functional networks at rest and during tasks. Each mode constrains and shapes the other across multiple time scales, and each also shows age-related changes. Here we argue that understanding how brains change across development requires understanding the interplay between behavior and brain networks: changing bodies and activities modify the statistics of inputs to the brain; these changing inputs mold brain networks; these networks, in turn, promote further change in behavior and input. PMID:24862251

Beyond localized and distributed accounts of brain functions. Comment on “Understanding brain networks and brain organization” by Pessoa

NASA Astrophysics Data System (ADS)

Cauda, Franco; Costa, Tommaso; Tamietto, Marco

2014-09-01

Recent evidence in cognitive neuroscience lends support to the idea that network models of brain architecture provide a privileged access to the understanding of the relation between brain organization and cognitive processes [1]. The core perspective holds that cognitive processes depend on the interactions among distributed neuronal populations and brain structures, and that the impact of a given region on behavior largely depends on its pattern of anatomical and functional connectivity [2,3].

Mapping population-based structural connectomes.

PubMed

Zhang, Zhengwu; Descoteaux, Maxime; Zhang, Jingwen; Girard, Gabriel; Chamberland, Maxime; Dunson, David; Srivastava, Anuj; Zhu, Hongtu

2018-05-15

Advances in understanding the structural connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects' brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural connectomes in healthy subjects. Copyright © 2018 Elsevier Inc. All rights reserved.

Will big data yield new mathematics? An evolving synergy with neuroscience

PubMed Central

Feng, S.; Holmes, P.

2016-01-01

New mathematics has often been inspired by new insights into the natural world. Here we describe some ongoing and possible future interactions among the massive data sets being collected in neuroscience, methods for their analysis and mathematical models of the underlying, still largely uncharted neural substrates that generate these data. We start by recalling events that occurred in turbulence modelling when substantial space-time velocity field measurements and numerical simulations allowed a new perspective on the governing equations of fluid mechanics. While no analogous global mathematical model of neural processes exists, we argue that big data may enable validation or at least rejection of models at cellular to brain area scales and may illuminate connections among models. We give examples of such models and survey some relatively new experimental technologies, including optogenetics and functional imaging, that can report neural activity in live animals performing complex tasks. The search for analytical techniques for these data is already yielding new mathematics, and we believe their multi-scale nature may help relate well-established models, such as the Hodgkin–Huxley equations for single neurons, to more abstract models of neural circuits, brain areas and larger networks within the brain. In brief, we envisage a closer liaison, if not a marriage, between neuroscience and mathematics. PMID:27516705

Will big data yield new mathematics? An evolving synergy with neuroscience.

PubMed

Feng, S; Holmes, P

2016-06-01

New mathematics has often been inspired by new insights into the natural world. Here we describe some ongoing and possible future interactions among the massive data sets being collected in neuroscience, methods for their analysis and mathematical models of the underlying, still largely uncharted neural substrates that generate these data. We start by recalling events that occurred in turbulence modelling when substantial space-time velocity field measurements and numerical simulations allowed a new perspective on the governing equations of fluid mechanics. While no analogous global mathematical model of neural processes exists, we argue that big data may enable validation or at least rejection of models at cellular to brain area scales and may illuminate connections among models. We give examples of such models and survey some relatively new experimental technologies, including optogenetics and functional imaging, that can report neural activity in live animals performing complex tasks. The search for analytical techniques for these data is already yielding new mathematics, and we believe their multi-scale nature may help relate well-established models, such as the Hodgkin-Huxley equations for single neurons, to more abstract models of neural circuits, brain areas and larger networks within the brain. In brief, we envisage a closer liaison, if not a marriage, between neuroscience and mathematics.

Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation

PubMed Central

Williams, Leanne M

2016-01-01

Complex emotional, cognitive and self-reflective functions rely on the activation and connectivity of large-scale neural circuits. These circuits offer a relevant scale of focus for conceptualizing a taxonomy for depression and anxiety based on specific profiles (or biotypes) of neural circuit dysfunction. Here, the theoretical review first outlined the current consensus as to what constitutes the organization of large-scale circuits in the human brain identified using parcellation and meta-analysis. The focus is on neural circuits implicated in resting reflection (“default mode”), detection of “salience”, affective processing (“threat” and “reward”), “attention” and “cognitive control”. Next, the current evidence regarding which type of dysfunctions in these circuits characterize depression and anxiety disorders was reviewed, with an emphasis on published meta-analyses and reviews of circuit dysfunctions that have been identified in at least two well-powered case:control studies. Grounded in the review of these topics, a conceptual framework is proposed for considering neural circuit-defined “biotypes”. In this framework, biotypes are defined by profiles of extent of dysfunction on each large-scale circuit. The clinical implications of a biotype approach for guiding classification and treatment of depression and anxiety is considered. Future research directions will develop the validity and clinical utility of a neural circuit biotype model that spans diagnostic categories and helps to translate neuroscience into clinical practice in the real world. PMID:27653321

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach.

PubMed

Xu, Nan; Spreng, R Nathan; Doerschuk, Peter C

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the "common driver" problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.

Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance.

PubMed

Violante, Ines R; Li, Lucia M; Carmichael, David W; Lorenz, Romy; Leech, Robert; Hampshire, Adam; Rothwell, John C; Sharp, David J

2017-03-14

Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization.

Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance

PubMed Central

Violante, Ines R; Li, Lucia M; Carmichael, David W; Lorenz, Romy; Leech, Robert; Hampshire, Adam; Rothwell, John C; Sharp, David J

2017-01-01

Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization. DOI: http://dx.doi.org/10.7554/eLife.22001.001 PMID:28288700

Structural connectivity of right frontal hyperactive areas scales with stuttering severity.

PubMed

Neef, Nicole E; Anwander, Alfred; Bütfering, Christoph; Schmidt-Samoa, Carsten; Friederici, Angela D; Paulus, Walter; Sommer, Martin

2018-01-01

A neuronal sign of persistent developmental stuttering is the magnified coactivation of right frontal brain regions during speech production. Whether and how stuttering severity relates to the connection strength of these hyperactive right frontal areas to other brain areas is an open question. Scrutinizing such brain-behaviour and structure-function relationships aims at disentangling suspected underlying neuronal mechanisms of stuttering. Here, we acquired diffusion-weighted and functional images from 31 adults who stutter and 34 matched control participants. Using a newly developed structural connectivity measure, we calculated voxel-wise correlations between connection strength and stuttering severity within tract volumes that originated from functionally hyperactive right frontal regions. Correlation analyses revealed that with increasing speech motor deficits the connection strength increased in the right frontal aslant tract, the right anterior thalamic radiation, and in U-shaped projections underneath the right precentral sulcus. In contrast, with decreasing speech motor deficits connection strength increased in the right uncinate fasciculus. Additional group comparisons of whole-brain white matter skeletons replicated the previously reported reduction of fractional anisotropy in the left and right superior longitudinal fasciculus as well as at the junction of right frontal aslant tract and right superior longitudinal fasciculus in adults who stutter compared to control participants. Overall, our investigation suggests that right fronto-temporal networks play a compensatory role as a fluency enhancing mechanism. In contrast, the increased connection strength within subcortical-cortical pathways may be implied in an overly active global response suppression mechanism in stuttering. Altogether, this combined functional MRI-diffusion tensor imaging study disentangles different networks involved in the neuronal underpinnings of the speech motor deficit in persistent developmental stuttering. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

A large-scale photonic node architecture that utilizes interconnected OXC subsystems.

PubMed

Iwai, Yuto; Hasegawa, Hiroshi; Sato, Ken-ichi

2013-01-14

We propose a novel photonic node architecture that is composed of interconnected small-scale optical cross-connect subsystems. We also developed an efficient dynamic network control algorithm that complies with a restriction on the number of intra-node fibers used for subsystem interconnection. Numerical evaluations verify that the proposed architecture offers almost the same performance as the equivalent single large-scale cross-connect switch, while enabling substantial hardware scale reductions.

Effect of Resting-State fNIRS Scanning Duration on Functional Brain Connectivity and Graph Theory Metrics of Brain Network.

PubMed

Geng, Shujie; Liu, Xiangyu; Biswal, Bharat B; Niu, Haijing

2017-01-01

As an emerging brain imaging technique, functional near infrared spectroscopy (fNIRS) has attracted widespread attention for advancing resting-state functional connectivity (FC) and graph theoretical analyses of brain networks. However, it remains largely unknown how the duration of the fNIRS signal scanning is related to stable and reproducible functional brain network features. To answer this question, we collected resting-state fNIRS signals (10-min duration, two runs) from 18 participants and then truncated the hemodynamic time series into 30-s time bins that ranged from 1 to 10 min. Measures of nodal efficiency, nodal betweenness, network local efficiency, global efficiency, and clustering coefficient were computed for each subject at each fNIRS signal acquisition duration. Analyses of the stability and between-run reproducibility were performed to identify optimal time length for each measure. We found that the FC, nodal efficiency and nodal betweenness stabilized and were reproducible after 1 min of fNIRS signal acquisition, whereas network clustering coefficient, local and global efficiencies stabilized after 1 min and were reproducible after 5 min of fNIRS signal acquisition for only local and global efficiencies. These quantitative results provide direct evidence regarding the choice of the resting-state fNIRS scanning duration for functional brain connectivity and topological metric stability of brain network connectivity.

Individual diversity of functional brain network economy.

PubMed

Hahn, Andreas; Kranz, Georg S; Sladky, Ronald; Ganger, Sebastian; Windischberger, Christian; Kasper, Siegfried; Lanzenberger, Rupert

2015-04-01

On average, brain network economy represents a trade-off between communication efficiency, robustness, and connection cost, although an analogous understanding on an individual level is largely missing. Evaluating resting-state networks of 42 healthy participants with seven Tesla functional magnetic resonance imaging and graph theory revealed that not even half of all possible connections were common across subjects. The strongest similarities among individuals were observed for interhemispheric and/or short-range connections, which may relate to the essential feature of the human brain to develop specialized systems within each hemisphere. Despite this marked variability in individual network architecture, all subjects exhibited equal small-world properties. Furthermore, interdependency between four major network economy metrics was observed across healthy individuals. The characteristic path length was associated with the clustering coefficient (peak correlation r=0.93), the response to network attacks (r=-0.97), and the physical connection cost in three-dimensional space (r=-0.62). On the other hand, clustering was negatively related to attack response (r=-0.75) and connection cost (r=-0.59). Finally, increased connection cost was associated with better response to attacks (r=0.65). This indicates that functional brain networks with high global information transfer also exhibit strong network resilience. However, it seems that these advantages come at the cost of decreased local communication efficiency and increased physical connection cost. Except for wiring length, the results were replicated on a subsample at three Tesla (n=20). These findings highlight the finely tuned interrelationships between different parameters of brain network economy. Moreover, the understanding of the individual diversity of functional brain network economy may provide further insights in the vulnerability to mental and neurological disorders.

Resting state brain network function in major depression - Depression symptomatology, antidepressant treatment effects, future research.

PubMed

Brakowski, Janis; Spinelli, Simona; Dörig, Nadja; Bosch, Oliver Gero; Manoliu, Andrei; Holtforth, Martin Grosse; Seifritz, Erich

2017-09-01

The alterations of functional connectivity brain networks in major depressive disorder (MDD) have been subject of a large number of studies. Using different methodologies and focusing on diverse aspects of the disease, research shows heterogeneous results lacking integration. Disrupted network connectivity has been found in core MDD networks like the default mode network (DMN), the central executive network (CEN), and the salience network, but also in cerebellar and thalamic circuitries. Here we review literature published on resting state brain network function in MDD focusing on methodology, and clinical characteristics including symptomatology and antidepressant treatment related findings. There are relatively few investigations concerning the qualitative aspects of symptomatology of MDD, whereas most studies associate quantitative aspects with distinct resting state functional connectivity alterations. Such depression severity associated alterations are found in the DMN, frontal, cerebellar and thalamic brain regions as well as the insula and the subgenual anterior cingulate cortex. Similarly, different therapeutical options in MDD and their effects on brain function showed patchy results. Herein, pharmaceutical treatments reveal functional connectivity alterations throughout multiple brain regions notably the DMN, fronto-limbic, and parieto-temporal regions. Psychotherapeutical interventions show significant functional connectivity alterations in fronto-limbic networks, whereas electroconvulsive therapy and repetitive transcranial magnetic stimulation result in alterations of the subgenual anterior cingulate cortex, the DMN, the CEN and the dorsal lateral prefrontal cortex. While it appears clear that functional connectivity alterations are associated with the pathophysiology and treatment of MDD, future research should also generate a common strategy for data acquisition and analysis, as a least common denominator, to set the basis for comparability across studies and implementation of functional connectivity as a scientifically and clinically useful biomarker. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structure-Function Network Mapping and Its Assessment via Persistent Homology

PubMed Central

2017-01-01

Understanding the relationship between brain structure and function is a fundamental problem in network neuroscience. This work deals with the general method of structure-function mapping at the whole-brain level. We formulate the problem as a topological mapping of structure-function connectivity via matrix function, and find a stable solution by exploiting a regularization procedure to cope with large matrices. We introduce a novel measure of network similarity based on persistent homology for assessing the quality of the network mapping, which enables a detailed comparison of network topological changes across all possible thresholds, rather than just at a single, arbitrary threshold that may not be optimal. We demonstrate that our approach can uncover the direct and indirect structural paths for predicting functional connectivity, and our network similarity measure outperforms other currently available methods. We systematically validate our approach with (1) a comparison of regularized vs. non-regularized procedures, (2) a null model of the degree-preserving random rewired structural matrix, (3) different network types (binary vs. weighted matrices), and (4) different brain parcellation schemes (low vs. high resolutions). Finally, we evaluate the scalability of our method with relatively large matrices (2514x2514) of structural and functional connectivity obtained from 12 healthy human subjects measured non-invasively while at rest. Our results reveal a nonlinear structure-function relationship, suggesting that the resting-state functional connectivity depends on direct structural connections, as well as relatively parsimonious indirect connections via polysynaptic pathways. PMID:28046127

Gamma Oscillations and Visual Binding

NASA Astrophysics Data System (ADS)

Robinson, Peter A.; Kim, Jong Won

2006-03-01

At the root of visual perception is the mechanism the brain uses to analyze features in a scene and bind related ones together. Experiments show this process is linked to oscillations of brain activity in the 30-100 Hz gamma band. Oscillations at different sites have correlation functions (CFs) that often peak at zero lag, implying simultaneous firing, even when conduction delays are large. CFs are strongest between cells stimulated by related features. Gamma oscillations are studied here by modeling mm-scale patchy interconnections in the visual cortex. Resulting predictions for gamma responses to stimuli account for numerous experimental findings, including why oscillations and zero-lag synchrony are associated, observed connections with feature preferences, the shape of the zero-lag peak, and variations of CFs with attention. Gamma waves are found to obey the Schroedinger equation, opening the possibility of cortical analogs of quantum phenomena. Gamma instabilities are tied to observations of gamma activity linked to seizures and hallucinations.

On the Reliability of Individual Brain Activity Networks.

PubMed

Cassidy, Ben; Bowman, F DuBois; Rae, Caroline; Solo, Victor

2018-02-01

There is intense interest in fMRI research on whole-brain functional connectivity, and however, two fundamental issues are still unresolved: the impact of spatiotemporal data resolution (spatial parcellation and temporal sampling) and the impact of the network construction method on the reliability of functional brain networks. In particular, the impact of spatiotemporal data resolution on the resulting connectivity findings has not been sufficiently investigated. In fact, a number of studies have already observed that functional networks often give different conclusions across different parcellation scales. If the interpretations from functional networks are inconsistent across spatiotemporal scales, then the whole validity of the functional network paradigm is called into question. This paper investigates the consistency of resting state network structure when using different temporal sampling or spatial parcellation, or different methods for constructing the networks. To pursue this, we develop a novel network comparison framework based on persistent homology from a topological data analysis. We use the new network comparison tools to characterize the spatial and temporal scales under which consistent functional networks can be constructed. The methods are illustrated on Human Connectome Project data, showing that the DISCOH 2 network construction method outperforms other approaches at most data spatiotemporal resolutions.

Driving and driven architectures of directed small-world human brain functional networks.

PubMed

Yan, Chaogan; He, Yong

2011-01-01

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome.

Differences between brain mass and body weight scaling to height: potential mechanism of reduced mass-specific resting energy expenditure of taller adults.

PubMed

Heymsfield, Steven B; Chirachariyavej, Thamrong; Rhyu, Im Joo; Roongpisuthipong, Chulaporn; Heo, Moonseong; Pietrobelli, Angelo

2009-01-01

Adult resting energy expenditure (REE) scales as height( approximately 1.5), whereas body weight (BW) scales as height( approximately 2). Mass-specific REE (i.e., REE/BW) is thus lower in tall subjects compared with their shorter counterparts, the mechanism of which is unknown. We evaluated the hypothesis that high-metabolic-rate brain mass scales to height with a power significantly less than that of BW, a theory that if valid would provide a potential mechanism for height-related REE effects. The hypothesis was tested by measuring brain mass on a large (n = 372) postmortem sample of Thai men. Since brain mass-body size relations may be influenced by age, the hypothesis was secondarily explored in Thai men age < or =45 yr (n = 299) and with brain magnetic resonance imaging (MRI) studies in Korean men (n = 30) age > or =20<30 yr. The scaling of large body compartments was examined in a third group of Asian men living in New York (NY, n = 28) with MRI and dual-energy X-ray absorptiometry. Brain mass scaled to height with a power (mean +/- SEE; 0.46 +/- 0.13) significantly smaller (P < 0.001) than that of BW scaled to height (2.36 +/- 0.19) in the whole group of Thai men; brain mass/BW scaled negatively to height (-1.94 +/- 0.20, P < 0.001). Similar results were observed in younger Thai men, and results for brain mass/BW vs. height were directionally the same (P = 0.09) in Korean men. Skeletal muscle and bone scaled to height with powers similar to that of BW (i.e., approximately 2-3) in the NY Asian men. Models developed using REE estimates in Thai men suggest that brain accounts for most of the REE/BW height dependency. Tall and short men thus differ in relative brain mass, but the proportions of BW as large compartments appear independent of height, observations that provide a potential mechanistic basis for related differences in REE and that have implications for the study of adult energy requirements.

Altered contralateral sensorimotor system organization after experimental hemispherectomy: a structural and functional connectivity study.

PubMed

Otte, Willem M; van der Marel, Kajo; van Meer, Maurits P A; van Rijen, Peter C; Gosselaar, Peter H; Braun, Kees P J; Dijkhuizen, Rick M

2015-08-01

Hemispherectomy is often followed by remarkable recovery of cognitive and motor functions. This reflects plastic capacities of the remaining hemisphere, involving large-scale structural and functional adaptations. Better understanding of these adaptations may (1) provide new insights in the neuronal configuration and rewiring that underlies sensorimotor outcome restoration, and (2) guide development of rehabilitation strategies to enhance recovery after hemispheric lesioning. We assessed brain structure and function in a hemispherectomy model. With MRI we mapped changes in white matter structural integrity and gray matter functional connectivity in eight hemispherectomized rats, compared with 12 controls. Behavioral testing involved sensorimotor performance scoring. Diffusion tensor imaging and resting-state functional magnetic resonance imaging were acquired 7 and 49 days post surgery. Hemispherectomy caused significant sensorimotor deficits that largely recovered within 2 weeks. During the recovery period, fractional anisotropy was maintained and white matter volume and axial diffusivity increased in the contralateral cerebral peduncle, suggestive of preserved or improved white matter integrity despite overall reduced white matter volume. This was accompanied by functional adaptations in the contralateral sensorimotor network. The observed white matter modifications and reorganization of functional network regions may provide handles for rehabilitation strategies improving functional recovery following large lesions.

Evoked effective connectivity of the human neocortex.

PubMed

Entz, László; Tóth, Emília; Keller, Corey J; Bickel, Stephan; Groppe, David M; Fabó, Dániel; Kozák, Lajos R; Erőss, Loránd; Ulbert, István; Mehta, Ashesh D

2014-12-01

The role of cortical connectivity in brain function and pathology is increasingly being recognized. While in vivo magnetic resonance imaging studies have provided important insights into anatomical and functional connectivity, these methodologies are limited in their ability to detect electrophysiological activity and the causal relationships that underlie effective connectivity. Here, we describe results of cortico-cortical evoked potential (CCEP) mapping using single pulse electrical stimulation in 25 patients undergoing seizure monitoring with subdural electrode arrays. Mapping was performed by stimulating adjacent electrode pairs and recording CCEPs from the remainder of the electrode array. CCEPs reliably revealed functional networks and showed an inverse relationship to distance between sites. Coregistration to Brodmann areas (BA) permitted group analysis. Connections were frequently directional with 43% of early responses and 50% of late responses of connections reflecting relative dominance of incoming or outgoing connections. The most consistent connections were seen as outgoing from motor cortex, BA6-BA9, somatosensory (SS) cortex, anterior cingulate cortex, and Broca's area. Network topology revealed motor, SS, and premotor cortices along with BA9 and BA10 and language areas to serve as hubs for cortical connections. BA20 and BA39 demonstrated the most consistent dominance of outdegree connections, while BA5, BA7, auditory cortex, and anterior cingulum demonstrated relatively greater indegree. This multicenter, large-scale, directional study of local and long-range cortical connectivity using direct recordings from awake, humans will aid the interpretation of noninvasive functional connectome studies. © 2014 Wiley Periodicals, Inc.

Digital tissue and what it may reveal about the brain.

PubMed

Morgan, Josh L; Lichtman, Jeff W

2017-10-30

Imaging as a means of scientific data storage has evolved rapidly over the past century from hand drawings, to photography, to digital images. Only recently can sufficiently large datasets be acquired, stored, and processed such that tissue digitization can actually reveal more than direct observation of tissue. One field where this transformation is occurring is connectomics: the mapping of neural connections in large volumes of digitized brain tissue.

Structure-function relationships during segregated and integrated network states of human brain functional connectivity.

PubMed

Fukushima, Makoto; Betzel, Richard F; He, Ye; van den Heuvel, Martijn P; Zuo, Xi-Nian; Sporns, Olaf

2018-04-01

Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.

Levodopa modulates small-world architecture of functional brain networks in Parkinson's disease.

PubMed

Berman, Brian D; Smucny, Jason; Wylie, Korey P; Shelton, Erika; Kronberg, Eugene; Leehey, Maureen; Tregellas, Jason R

2016-11-01

PD is associated with disrupted connectivity to a large number of distributed brain regions. How the disease alters the functional topological organization of the brain, however, remains poorly understood. Furthermore, how levodopa modulates network topology in PD is largely unknown. The objective of this study was to use resting-state functional MRI and graph theory to determine how small-world architecture is altered in PD and affected by levodopa administration. Twenty-one PD patients and 20 controls underwent functional MRI scanning. PD patients were scanned off medication and 1 hour after 200 mg levodopa. Imaging data were analyzed using 226 nodes comprising 10 intrinsic brain networks. Correlation matrices were generated for each subject and converted into cost-thresholded, binarized adjacency matrices. Cost-integrated whole-brain global and local efficiencies were compared across groups and tested for relationships with disease duration and severity. Data from 2 patients and 4 controls were excluded because of excess motion. Patients off medication showed no significant changes in global efficiency and overall local efficiency, but in a subnetwork analysis did show increased local efficiency in executive (P = 0.006) and salience (P = 0.018) networks. Levodopa significantly decreased local efficiency (P = 0.039) in patients except within the subcortical network, in which it significantly increased local efficiency (P = 0.007). Levodopa modulates global and local efficiency measures of small-world topology in PD, suggesting that degeneration of nigrostriatal neurons in PD may be associated with a large-scale network reorganization and that levodopa tends to normalize the disrupted network topology in PD. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

Graph Lasso-Based Test for Evaluating Functional Brain Connectivity in Sickle Cell Disease.

PubMed

Coloigner, Julie; Phlypo, Ronald; Coates, Thomas D; Lepore, Natasha; Wood, John C

2017-09-01

Sickle cell disease (SCD) is a vascular disorder that is often associated with recurrent ischemia-reperfusion injury, anemia, vasculopathy, and strokes. These cerebral injuries are associated with neurological dysfunction, limiting the full developing potential of the patient. However, recent large studies of SCD have demonstrated that cognitive impairment occurs even in the absence of brain abnormalities on conventional magnetic resonance imaging (MRI). These observations support an emerging consensus that brain injury in SCD is diffuse and that conventional neuroimaging often underestimates the extent of injury. In this article, we postulated that alterations in the cerebral connectivity may constitute a sensitive biomarker of SCD severity. Using functional MRI, a connectivity study analyzing the SCD patients individually was performed. First, a robust learning scheme based on graphical lasso model and Fréchet mean was used for estimating a consistent descriptor of healthy brain connectivity. Then, we tested a statistical method that provides an individual index of similarity between this healthy connectivity model and each SCD patient's connectivity matrix. Our results demonstrated that the reference connectivity model was not appropriate to model connectivity for only 4 out of 27 patients. After controlling for the gender, two separate predictors of this individual similarity index were the anemia (p = 0.02) and white matter hyperintensities (WMH) (silent stroke) (p = 0.03), so that patients with low hemoglobin level or with WMH have the least similarity to the reference connectivity model. Further studies are required to determine whether the resting-state connectivity changes reflect pathological changes or compensatory responses to chronic anemia.

Functional Connectivity Parcellation of the Human Thalamus by Independent Component Analysis.

PubMed

Zhang, Sheng; Li, Chiang-Shan R

2017-11-01

As a key structure to relay and integrate information, the thalamus supports multiple cognitive and affective functions through the connectivity between its subnuclei and cortical and subcortical regions. Although extant studies have largely described thalamic regional functions in anatomical terms, evidence accumulates to suggest a more complex picture of subareal activities and connectivities of the thalamus. In this study, we aimed to parcellate the thalamus and examine whole-brain connectivity of its functional clusters. With resting state functional magnetic resonance imaging data from 96 adults, we used independent component analysis (ICA) to parcellate the thalamus into 10 components. On the basis of the independence assumption, ICA helps to identify how subclusters overlap spatially. Whole brain functional connectivity of each subdivision was computed for independent component's time course (ICtc), which is a unique time series to represent an IC. For comparison, we computed seed-region-based functional connectivity using the averaged time course across all voxels within a thalamic subdivision. The results showed that, at p < 10 -6 , corrected, 49% of voxels on average overlapped among subdivisions. Compared with seed-region analysis, ICtc analysis revealed patterns of connectivity that were more distinguished between thalamic clusters. ICtc analysis demonstrated thalamic connectivity to the primary motor cortex, which has eluded the analysis as well as previous studies based on averaged time series, and clarified thalamic connectivity to the hippocampus, caudate nucleus, and precuneus. The new findings elucidate functional organization of the thalamus and suggest that ICA clustering in combination with ICtc rather than seed-region analysis better distinguishes whole-brain connectivities among functional clusters of a brain region.

Degree connectivity in body dysmorphic disorder and relationships with obsessive and compulsive symptoms.

PubMed

Beucke, Jan C; Sepulcre, Jorge; Buhlmann, Ulrike; Kathmann, Norbert; Moody, Teena; Feusner, Jamie D

2016-10-01

Individuals with body dysmorphic disorder (BDD) and obsessive-compulsive disorder (OCD) are categorized within the same major diagnostic group and both show regional brain hyperactivity in the orbitofrontal cortex (OFC) and the basal ganglia during symptom provocation. While recent studies revealed that degree connectivity of these areas is abnormally high in OCD and positively correlates with symptom severity, no study has investigated degree connectivity in BDD. We used functional magnetic resonance imaging (fMRI) to compare the local and distant degree of functional connectivity in all brain areas between 28 unmedicated BDD participants and 28 demographically matched healthy controls during a face-processing task. Correlational analyses tested for associations between degree connectivity and symptom severity assessed by the BDD version of the Yale-Brown obsessive-compulsive scale (BDD-Y-BOCS). Reduced local amygdalar connectivity was found in participants with BDD. No differences in distant connectivity were found. BDD-Y-BOCS scores significantly correlated with the local connectivity of the posterior-lateral OFC, and distant connectivity of the posterior-lateral and post-central OFC, respectively. These findings represent preliminary evidence that individuals with BDD exhibit brain-behavioral associations related to obsessive thoughts and compulsive behaviors that are highly similar to correlations previously found in OCD, further underscoring their related pathophysiology. This relationship could be further elucidated through investigation of resting-state functional connectivity in BDD, ideally in direct comparison with OCD and other obsessive-compulsive and related disorders. Copyright © 2016 Elsevier B.V. and ECNP. All rights reserved.

Hemispheric Connectivity and the Visual-Spatial Divergent-Thinking Component of Creativity

ERIC Educational Resources Information Center

Moore, Dana W.; Bhadelia, Rafeeque A.; Billings, Rebecca L.; Fulwiler, Carl; Heilman, Kenneth M.; Rood, Kenneth M. J.; Gansler, David A.

2009-01-01

Background/hypothesis: Divergent thinking is an important measurable component of creativity. This study tested the postulate that divergent thinking depends on large distributed inter- and intra-hemispheric networks. Although preliminary evidence supports increased brain connectivity during divergent thinking, the neural correlates of this…

Behavioral Interpretations of Intrinsic Connectivity Networks

ERIC Educational Resources Information Center

Laird, Angela R.; Fox, P. Mickle; Eickhoff, Simon B.; Turner, Jessica A.; Ray, Kimberly L.; McKay, D. Reese; Glahn, David C.; Beckmann, Christian F.; Smith, Stephen M.; Fox, Peter T.

2011-01-01

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific…

3D plasmonic nanoantennas integrated with MEA biosensors

NASA Astrophysics Data System (ADS)

Dipalo, Michele; Messina, Gabriele C.; Amin, Hayder; La Rocca, Rosanna; Shalabaeva, Victoria; Simi, Alessandro; Maccione, Alessandro; Zilio, Pierfrancesco; Berdondini, Luca; de Angelis, Francesco

2015-02-01

Neuronal signaling in brain circuits occurs at multiple scales ranging from molecules and cells to large neuronal assemblies. However, current sensing neurotechnologies are not designed for parallel access of signals at multiple scales. With the aim of combining nanoscale molecular sensing with electrical neural activity recordings within large neuronal assemblies, in this work three-dimensional (3D) plasmonic nanoantennas are integrated with multielectrode arrays (MEA). Nanoantennas are fabricated by fast ion beam milling on optical resist; gold is deposited on the nanoantennas in order to connect them electrically to the MEA microelectrodes and to obtain plasmonic behavior. The optical properties of these 3D nanostructures are studied through finite elements method (FEM) simulations that show a high electromagnetic field enhancement. This plasmonic enhancement is confirmed by surface enhancement Raman spectroscopy of a dye performed in liquid, which presents an enhancement of almost 100 times the incident field amplitude at resonant excitation. Finally, the reported MEA devices are tested on cultured rat hippocampal neurons. Neurons develop by extending branches on the nanostructured electrodes and extracellular action potentials are recorded over multiple days in vitro. Raman spectra of living neurons cultured on the nanoantennas are also acquired. These results highlight that these nanostructures could be potential candidates for combining electrophysiological measures of large networks with simultaneous spectroscopic investigations at the molecular level.Neuronal signaling in brain circuits occurs at multiple scales ranging from molecules and cells to large neuronal assemblies. However, current sensing neurotechnologies are not designed for parallel access of signals at multiple scales. With the aim of combining nanoscale molecular sensing with electrical neural activity recordings within large neuronal assemblies, in this work three-dimensional (3D) plasmonic nanoantennas are integrated with multielectrode arrays (MEA). Nanoantennas are fabricated by fast ion beam milling on optical resist; gold is deposited on the nanoantennas in order to connect them electrically to the MEA microelectrodes and to obtain plasmonic behavior. The optical properties of these 3D nanostructures are studied through finite elements method (FEM) simulations that show a high electromagnetic field enhancement. This plasmonic enhancement is confirmed by surface enhancement Raman spectroscopy of a dye performed in liquid, which presents an enhancement of almost 100 times the incident field amplitude at resonant excitation. Finally, the reported MEA devices are tested on cultured rat hippocampal neurons. Neurons develop by extending branches on the nanostructured electrodes and extracellular action potentials are recorded over multiple days in vitro. Raman spectra of living neurons cultured on the nanoantennas are also acquired. These results highlight that these nanostructures could be potential candidates for combining electrophysiological measures of large networks with simultaneous spectroscopic investigations at the molecular level. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr05578k

The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models.

PubMed

Djurfeldt, Mikael

2012-07-01

The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

Topological Principles of Control in Dynamical Networks

NASA Astrophysics Data System (ADS)

Kim, Jason; Pasqualetti, Fabio; Bassett, Danielle

Networked biological systems, such as the brain, feature complex patterns of interactions. To predict and correct the dynamic behavior of such systems, it is imperative to understand how the underlying topological structure affects and limits the function of the system. Here, we use network control theory to extract topological features that favor or prevent network controllability, and to understand the network-wide effect of external stimuli on large-scale brain systems. Specifically, we treat each brain region as a dynamic entity with real-valued state, and model the time evolution of all interconnected regions using linear, time-invariant dynamics. We propose a simplified feed-forward scheme where the effect of upstream regions (drivers) on the connected downstream regions (non-drivers) is characterized in closed-form. Leveraging this characterization of the simplified model, we derive topological features that predict the controllability properties of non-simplified networks. We show analytically and numerically that these predictors are accurate across a large range of parameters. Among other contributions, our analysis shows that heterogeneity in the network weights facilitate controllability, and allows us to implement targeted interventions that profoundly improve controllability. By assuming an underlying dynamical mechanism, we are able to understand the complex topology of networked biological systems in a functionally meaningful way.

Asphyxia-activated corticocardiac signaling accelerates onset of cardiac arrest

PubMed Central

Li, Duan; Mabrouk, Omar S.; Liu, Tiecheng; Tian, Fangyun; Xu, Gang; Rengifo, Santiago; Choi, Sarah J.; Mathur, Abhay; Crooks, Charles P.; Kennedy, Robert T.; Wang, Michael M.; Ghanbari, Hamid; Borjigin, Jimo

2015-01-01

The mechanism by which the healthy heart and brain die rapidly in the absence of oxygen is not well understood. We performed continuous electrocardiography and electroencephalography in rats undergoing experimental asphyxia and analyzed cortical release of core neurotransmitters, changes in brain and heart electrical activity, and brain–heart connectivity. Asphyxia stimulates a robust and sustained increase of functional and effective cortical connectivity, an immediate increase in cortical release of a large set of neurotransmitters, and a delayed activation of corticocardiac functional and effective connectivity that persists until the onset of ventricular fibrillation. Blocking the brain’s autonomic outflow significantly delayed terminal ventricular fibrillation and lengthened the duration of detectable cortical activities despite the continued absence of oxygen. These results demonstrate that asphyxia activates a brainstorm, which accelerates premature death of the heart and the brain. PMID:25848007

Re-emergence of modular brain networks in stroke recovery.

PubMed

Siegel, Joshua S; Seitzman, Benjamin A; Ramsey, Lenny E; Ortega, Mario; Gordon, Evan M; Dosenbach, Nico U F; Petersen, Steven E; Shulman, Gordon L; Corbetta, Maurizio

2018-04-01

Studies of stroke have identified local reorganization in perilesional tissue. However, because the brain is highly networked, strokes also broadly alter the brain's global network organization. Here, we assess brain network structure longitudinally in adult stroke patients using resting state fMRI. The topology and boundaries of cortical regions remain grossly unchanged across recovery. In contrast, the modularity of brain systems i.e. the degree of integration within and segregation between networks, was significantly reduced sub-acutely (n = 107), but partially recovered by 3 months (n = 85), and 1 year (n = 67). Importantly, network recovery correlated with recovery from language, spatial memory, and attention deficits, but not motor or visual deficits. Finally, in-depth single subject analyses were conducted using tools for visualization of changes in brain networks over time. This exploration indicated that changes in modularity during successful recovery reflect specific alterations in the relationships between different networks. For example, in a patient with left temporo-parietal stroke and severe aphasia, sub-acute loss of modularity reflected loss of association between frontal and temporo-parietal regions bi-hemispherically across multiple modules. These long-distance connections then returned over time, paralleling aphasia recovery. This work establishes the potential importance of normalization of large-scale modular brain systems in stroke recovery. Copyright © 2017. Published by Elsevier Ltd.

Hyperedge bundling: A practical solution to spurious interactions in MEG/EEG source connectivity analyses.

PubMed

Wang, Sheng H; Lobier, Muriel; Siebenhühner, Felix; Puoliväli, Tuomas; Palva, Satu; Palva, J Matias

2018-06-01

Inter-areal functional connectivity (FC), neuronal synchronization in particular, is thought to constitute a key systems-level mechanism for coordination of neuronal processing and communication between brain regions. Evidence to support this hypothesis has been gained largely using invasive electrophysiological approaches. In humans, neuronal activity can be non-invasively recorded only with magneto- and electroencephalography (MEG/EEG), which have been used to assess FC networks with high temporal resolution and whole-scalp coverage. However, even in source-reconstructed MEG/EEG data, signal mixing, or "source leakage", is a significant confounder for FC analyses and network localization. Signal mixing leads to two distinct kinds of false-positive observations: artificial interactions (AI) caused directly by mixing and spurious interactions (SI) arising indirectly from the spread of signals from true interacting sources to nearby false loci. To date, several interaction metrics have been developed to solve the AI problem, but the SI problem has remained largely intractable in MEG/EEG all-to-all source connectivity studies. Here, we advance a novel approach for correcting SIs in FC analyses using source-reconstructed MEG/EEG data. Our approach is to bundle observed FC connections into hyperedges by their adjacency in signal mixing. Using realistic simulations, we show here that bundling yields hyperedges with good separability of true positives and little loss in the true positive rate. Hyperedge bundling thus significantly decreases graph noise by minimizing the false-positive to true-positive ratio. Finally, we demonstrate the advantage of edge bundling in the visualization of large-scale cortical networks with real MEG data. We propose that hypergraphs yielded by bundling represent well the set of true cortical interactions that are detectable and dissociable in MEG/EEG connectivity analysis. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Arithmetic and Brain Connectivity: Mental Calculation in Adolescents with Periventricular Lesions

ERIC Educational Resources Information Center

Pavlova, Marina; Sokolov, Alexander N.; Krageloh-Mann, Ingeborg

2009-01-01

The ability for mental calculation represents a fundamental prerequisite for development of intelligence, which is predictive for educational and professional success in life. Many individuals with calculation difficulties are survivors of premature birth. The brain mechanisms of these deficits are, however, largely unknown. In this work, we…

Replicability of time-varying connectivity patterns in large resting state fMRI samples.

PubMed

Abrol, Anees; Damaraju, Eswar; Miller, Robyn L; Stephen, Julia M; Claus, Eric D; Mayer, Andrew R; Calhoun, Vince D

2017-12-01

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.