The maturation of cortical sleep rhythms and networks over early development.

PubMed

Chu, C J; Leahy, J; Pathmanathan, J; Kramer, M A; Cash, S S

2014-07-01

Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

The maturation of cortical sleep rhythms and networks over early development

PubMed Central

Chu, CJ; Leahy, J; Pathmanathan, J; Kramer, MA; Cash, SS

2014-01-01

Objective Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. Methods We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. Results We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. Conclusion Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. Significance This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development. PMID:24418219

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.

Topological Organization of Functional Brain Networks in Healthy Children: Differences in Relation to Age, Sex, and Intelligence

PubMed Central

Wu, Kai; Taki, Yasuyuki; Sato, Kazunori; Hashizume, Hiroshi; Sassa, Yuko; Takeuchi, Hikaru; Thyreau, Benjamin; He, Yong; Evans, Alan C.; Li, Xiaobo; Kawashima, Ryuta; Fukuda, Hiroshi

2013-01-01

Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI) was used to map the functional brain networks in 51 healthy children. We then investigated the effects of age, sex, and IQ on economic small-world properties and regional nodal properties of the functional brain networks. At a global level of whole networks, we found significant age-related increases in the small-worldness and local efficiency, significant higher values of the global efficiency in boys compared with girls, and no significant IQ-related difference. Age-related increases in the regional nodal properties were found predominately in the frontal brain regions, whereas the parietal, temporal, and occipital brain regions showed age-related decreases. Significant sex-related differences in the regional nodal properties were found in various brain regions, primarily related to the default mode, language, and vision systems. Positive correlations between IQ and the regional nodal properties were found in several brain regions related to the attention system, whereas negative correlations were found in various brain regions primarily involved in the default mode, emotion, and language systems. Together, our findings of the network topology of the functional brain networks in healthy children and its relationship with age, sex, and IQ bring new insights into the understanding of brain maturation and cognitive development during childhood and adolescence. PMID:23390528

Topological organization of functional brain networks in healthy children: differences in relation to age, sex, and intelligence.

PubMed

Wu, Kai; Taki, Yasuyuki; Sato, Kazunori; Hashizume, Hiroshi; Sassa, Yuko; Takeuchi, Hikaru; Thyreau, Benjamin; He, Yong; Evans, Alan C; Li, Xiaobo; Kawashima, Ryuta; Fukuda, Hiroshi

2013-01-01

Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI) was used to map the functional brain networks in 51 healthy children. We then investigated the effects of age, sex, and IQ on economic small-world properties and regional nodal properties of the functional brain networks. At a global level of whole networks, we found significant age-related increases in the small-worldness and local efficiency, significant higher values of the global efficiency in boys compared with girls, and no significant IQ-related difference. Age-related increases in the regional nodal properties were found predominately in the frontal brain regions, whereas the parietal, temporal, and occipital brain regions showed age-related decreases. Significant sex-related differences in the regional nodal properties were found in various brain regions, primarily related to the default mode, language, and vision systems. Positive correlations between IQ and the regional nodal properties were found in several brain regions related to the attention system, whereas negative correlations were found in various brain regions primarily involved in the default mode, emotion, and language systems. Together, our findings of the network topology of the functional brain networks in healthy children and its relationship with age, sex, and IQ bring new insights into the understanding of brain maturation and cognitive development during childhood and adolescence.

Development of large-scale functional brain networks in children.

PubMed

Supekar, Kaustubh; Musen, Mark; Menon, Vinod

2009-07-01

The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y) and 22 young-adults (ages 19-22 y). Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.

Development of Large-Scale Functional Brain Networks in Children

PubMed Central

Supekar, Kaustubh; Musen, Mark; Menon, Vinod

2009-01-01

The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7–9 y) and 22 young-adults (ages 19–22 y). Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar “small-world” organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism. PMID:19621066

Human Fetal Brain Connectome: Structural Network Development from Middle Fetal Stage to Birth

PubMed Central

Song, Limei; Mishra, Virendra; Ouyang, Minhui; Peng, Qinmu; Slinger, Michelle; Liu, Shuwei; Huang, Hao

2017-01-01

Complicated molecular and cellular processes take place in a spatiotemporally heterogeneous and precisely regulated pattern in the human fetal brain, yielding not only dramatic morphological and microstructural changes, but also macroscale connectomic transitions. As the underlying substrate of the fetal brain structural network, both dynamic neuronal migration pathways and rapid developing fetal white matter (WM) fibers could fundamentally reshape early fetal brain connectome. Quantifying structural connectome development can not only shed light on the brain reconfiguration in this critical yet rarely studied developmental period, but also reveal alterations of the connectome under neuropathological conditions. However, transition of the structural connectome from the mid-fetal stage to birth is not yet known. The contribution of different types of neural fibers to the structural network in the mid-fetal brain is not known, either. In this study, diffusion tensor magnetic resonance imaging (DT-MRI or DTI) of 10 fetal brain specimens at the age of 20 postmenstrual weeks (PMW), 12 in vivo brains at 35 PMW, and 12 in vivo brains at term (40 PMW) were acquired. The structural connectome of each brain was established with evenly parcellated cortical regions as network nodes and traced fiber pathways based on DTI tractography as network edges. Two groups of fibers were categorized based on the fiber terminal locations in the cerebral wall in the 20 PMW fetal brains. We found that fetal brain networks become stronger and more efficient during 20–40 PMW. Furthermore, network strength and global efficiency increase more rapidly during 20–35 PMW than during 35–40 PMW. Visualization of the whole brain fiber distribution by the lengths suggested that the network reconfiguration in this developmental period could be associated with a significant increase of major long association WM fibers. In addition, non-WM neural fibers could be a major contributor to the structural network configuration at 20 PMW and small-world network organization could exist as early as 20 PMW. These findings offer a preliminary record of the fetal brain structural connectome maturation from the middle fetal stage to birth and reveal the critical role of non-WM neural fibers in structural network configuration in the middle fetal stage. PMID:29081731

Development of human brain structural networks through infancy and childhood.

PubMed

Huang, Hao; Shu, Ni; Mishra, Virendra; Jeon, Tina; Chalak, Lina; Wang, Zhiyue J; Rollins, Nancy; Gong, Gaolang; Cheng, Hua; Peng, Yun; Dong, Qi; He, Yong

2015-05-01

During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Developing brain networks of attention.

PubMed

Posner, Michael I; Rothbart, Mary K; Voelker, Pascale

2016-12-01

Attention is a primary cognitive function critical for perception, language, and memory. We provide an update on brain networks related to attention, their development, training, and pathologies. An executive attention network, also called the cingulo-opercular network, allows voluntary control of behavior in accordance with goals. Individual differences among children in self-regulation have been measured by a higher order factor called effortful control, which is related to the executive network and to the size of the anterior cingulate cortex. Brain networks of attention arise in infancy and are related to individual differences, including pathology during childhood. Methods of training attention may improve performance and ameliorate pathology.

Early alterations of social brain networks in young children with autism

PubMed Central

Kojovic, Nada; Rihs, Tonia Anahi; Jan, Reem Kais; Franchini, Martina; Plomp, Gijs; Vulliemoz, Serge; Eliez, Stephan; Michel, Christoph Martin; Schaer, Marie

2018-01-01

Social impairments are a hallmark of Autism Spectrum Disorders (ASD), but empirical evidence for early brain network alterations in response to social stimuli is scant in ASD. We recorded the gaze patterns and brain activity of toddlers with ASD and their typically developing peers while they explored dynamic social scenes. Directed functional connectivity analyses based on electrical source imaging revealed frequency specific network atypicalities in the theta and alpha frequency bands, manifesting as alterations in both the driving and the connections from key nodes of the social brain associated with autism. Analyses of brain-behavioural relationships within the ASD group suggested that compensatory mechanisms from dorsomedial frontal, inferior temporal and insular cortical regions were associated with less atypical gaze patterns and lower clinical impairment. Our results provide strong evidence that directed functional connectivity alterations of social brain networks is a core component of atypical brain development at early stages of ASD. PMID:29482718

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

The impact of poverty on the development of brain networks

PubMed Central

Lipina, Sebastián J.; Posner, Michael I.

2012-01-01

Although the study of brain development in non-human animals is an old one, recent imaging methods have allowed non-invasive studies of the gray and white matter of the human brain over the lifespan. Classic animal studies show clearly that impoverished environments reduce cortical gray matter in relation to complex environments and cognitive and imaging studies in humans suggest which networks may be most influenced by poverty. Studies have been clear in showing the plasticity of many brain systems, but whether sensitivity to learning differs over the lifespan and for which networks is still unclear. A major task for current research is a successful integration of these methods to understand how development and learning shape the neural networks underlying achievements in literacy, numeracy, and attention. This paper seeks to foster further integration by reviewing the current state of knowledge relating brain changes to behavior and indicating possible future directions. PMID:22912613

The maturing architecture of the brain's default network

PubMed Central

Fair, Damien A.; Cohen, Alexander L.; Dosenbach, Nico U. F.; Church, Jessica A.; Miezin, Francis M.; Barch, Deanna M.; Raichle, Marcus E.; Petersen, Steven E.; Schlaggar, Bradley L.

2008-01-01

In recent years, the brain's “default network,” a set of regions characterized by decreased neural activity during goal-oriented tasks, has generated a significant amount of interest, as well as controversy. Much of the discussion has focused on the relationship of these regions to a “default mode” of brain function. In early studies, investigators suggested that, the brain's default mode supports “self-referential” or “introspective” mental activity. Subsequently, regions of the default network have been more specifically related to the “internal narrative,” the “autobiographical self,” “stimulus independent thought,” “mentalizing,” and most recently “self-projection.” However, the extant literature on the function of the default network is limited to adults, i.e., after the system has reached maturity. We hypothesized that further insight into the network's functioning could be achieved by characterizing its development. In the current study, we used resting-state functional connectivity MRI (rs-fcMRI) to characterize the development of the brain's default network. We found that the default regions are only sparsely functionally connected at early school age (7–9 years old); over development, these regions integrate into a cohesive, interconnected network. PMID:18322013

Functional Network Development During the First Year: Relative Sequence and Socioeconomic Correlations

PubMed Central

Gao, Wei; Alcauter, Sarael; Elton, Amanda; Hernandez-Castillo, Carlos R.; Smith, J. Keith; Ramirez, Juanita; Lin, Weili

2015-01-01

The first postnatal year is characterized by the most dramatic functional network development of the human lifespan. Yet, the relative sequence of the maturation of different networks and the impact of socioeconomic status (SES) on their development during this critical period remains poorly characterized. Leveraging a large, normally developing infant sample with multiple longitudinal resting-state functional magnetic resonance imaging scans during the first year (N = 65, scanned every 3 months), we aimed to delineate the relative maturation sequence of 9 key brain functional networks and examine their SES correlations. Our results revealed a maturation sequence from primary sensorimotor/auditory to visual to attention/default-mode, and finally to executive control networks. Network-specific critical growth periods were also identified. Finally, marginally significant positive SES–brain correlations were observed at 6 months of age for both the sensorimotor and default-mode networks, indicating interesting SES effects on functional brain maturation. To the best of our knowledge, this is the first study delineating detailed longitudinal growth trajectories of all major functional networks during the first year of life and their SES correlations. Insights from this study not only improve our understanding of early brain development, but may also inform the critical periods for SES expression during infancy. PMID:24812084

BrainNet Viewer: a network visualization tool for human brain connectomics.

PubMed

Xia, Mingrui; Wang, Jinhui; He, Yong

2013-01-01

The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

Growing trees in child brains: graph theoretical analysis of electroencephalography-derived minimum spanning tree in 5- and 7-year-old children reflects brain maturation.

PubMed

Boersma, Maria; Smit, Dirk J A; Boomsma, Dorret I; De Geus, Eco J C; Delemarre-van de Waal, Henriette A; Stam, Cornelis J

2013-01-01

The child brain is a small-world network, which is hypothesized to change toward more ordered configurations with development. In graph theoretical studies, comparing network topologies under different conditions remains a critical point. Constructing a minimum spanning tree (MST) might present a solution, since it does not require setting a threshold and uses a fixed number of nodes and edges. In this study, the MST method is introduced to examine developmental changes in functional brain network topology in young children. Resting-state electroencephalography was recorded from 227 children twice at 5 and 7 years of age. Synchronization likelihood (SL) weighted matrices were calculated in three different frequency bands from which MSTs were constructed, which represent constructs of the most important routes for information flow in a network. From these trees, several parameters were calculated to characterize developmental change in network organization. The MST diameter and eccentricity significantly increased, while the leaf number and hierarchy significantly decreased in the alpha band with development. Boys showed significant higher leaf number, betweenness, degree and hierarchy and significant lower SL, diameter, and eccentricity than girls in the theta band. The developmental changes indicate a shift toward more decentralized line-like trees, which supports the previously hypothesized increase toward regularity of brain networks with development. Additionally, girls showed more line-like decentralized configurations, which is consistent with the view that girls are ahead of boys in brain development. MST provides an elegant method sensitive to capture subtle developmental changes in network organization without the bias of network comparison.

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.

Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics.

PubMed

Bruno, Jennifer Lynn; Hosseini, S M Hadi; Saggar, Manish; Quintin, Eve-Marie; Raman, Mira Michelle; Reiss, Allan L

2017-03-01

Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism spectrum disorder, is associated with significant behavioral, social, and neurocognitive deficits. Understanding structural brain network topology in FXS provides an important link between neurobiological and behavioral/cognitive symptoms of this disorder. We investigated the connectome via whole-brain structural networks created from group-level morphological correlations. Participants included 100 individuals: 50 with FXS and 50 with typical development, age 11-23 years. Results indicated alterations in topological properties of structural brain networks in individuals with FXS. Significantly reduced small-world index indicates a shift in the balance between network segregation and integration and significantly reduced clustering coefficient suggests that reduced local segregation shifted this balance. Caudate and amygdala were less interactive in the FXS network further highlighting the importance of subcortical region alterations in the neurobiological signature of FXS. Modularity analysis indicates that FXS and typically developing groups' networks decompose into different sets of interconnected sub networks, potentially indicative of aberrant local interconnectivity in individuals with FXS. These findings advance our understanding of the effects of fragile X mental retardation protein on large-scale brain networks and could be used to develop a connectome-level biological signature for FXS. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Recent developments of functional magnetic resonance imaging research for drug development in Alzheimer's disease.

PubMed

Hampel, Harald; Prvulovic, David; Teipel, Stefan J; Bokde, Arun L W

2011-12-01

The objective of this review is to evaluate recent advances in functional magnetic resonance imaging (fMRI) research in Alzheimer's disease for the development of therapeutic agents. The basic building block underpinning cognition is a brain network. The measured brain activity serves as an integrator of the various components, from genes to structural integrity, that impact the function of networks underpinning cognition. Specific networks can be interrogated using cognitive paradigms such as a learning task or a working memory task. In addition, recent advances in our understanding of neural networks allow one to investigate the function of a brain network by investigating the inherent coherency of the brain networks that can be measured during resting state. The coherent resting state networks allow testing in cognitively impaired patients that may not be possible with the use of cognitive paradigms. In particular the default mode network (DMN) includes the medial temporal lobe and posterior cingulate, two key regions that support episodic memory function and are impaired in the earliest stages of Alzheimer's disease (AD). By investigating the effects of a prospective drug compound on this network, it could illuminate the specificity of the compound with a network supporting memory function. This could provide valuable information on the methods of action at physiological and behaviourally relevant levels. Utilizing fMRI opens up new areas of research and a new approach for drug development, as it is an integrative tool to investigate entire networks within the brain. The network based approach provides a new independent method from previous ones to translate preclinical knowledge into the clinical domain. Copyright © 2011 Elsevier Ltd. All rights reserved.

Default mode network, motor network, dorsal and ventral basal ganglia networks in the rat brain: comparison to human networks using resting state-fMRI.

PubMed

Sierakowiak, Adam; Monnot, Cyril; Aski, Sahar Nikkhou; Uppman, Martin; Li, Tie-Qiang; Damberg, Peter; Brené, Stefan

2015-01-01

Rodent models are developed to enhance understanding of the underlying biology of different brain disorders. However, before interpreting findings from animal models in a translational aspect to understand human disease, a fundamental step is to first have knowledge of similarities and differences of the biological systems studied. In this study, we analyzed and verified four known networks termed: default mode network, motor network, dorsal basal ganglia network, and ventral basal ganglia network using resting state functional MRI (rsfMRI) in humans and rats. Our work supports the notion that humans and rats have common robust resting state brain networks and that rsfMRI can be used as a translational tool when validating animal models of brain disorders. In the future, rsfMRI may be used, in addition to short-term interventions, to characterize longitudinal effects on functional brain networks after long-term intervention in humans and rats.

Default Mode Network, Motor Network, Dorsal and Ventral Basal Ganglia Networks in the Rat Brain: Comparison to Human Networks Using Resting State-fMRI

PubMed Central

Sierakowiak, Adam; Monnot, Cyril; Aski, Sahar Nikkhou; Uppman, Martin; Li, Tie-Qiang; Damberg, Peter; Brené, Stefan

2015-01-01

Rodent models are developed to enhance understanding of the underlying biology of different brain disorders. However, before interpreting findings from animal models in a translational aspect to understand human disease, a fundamental step is to first have knowledge of similarities and differences of the biological systems studied. In this study, we analyzed and verified four known networks termed: default mode network, motor network, dorsal basal ganglia network, and ventral basal ganglia network using resting state functional MRI (rsfMRI) in humans and rats. Our work supports the notion that humans and rats have common robust resting state brain networks and that rsfMRI can be used as a translational tool when validating animal models of brain disorders. In the future, rsfMRI may be used, in addition to short-term interventions, to characterize longitudinal effects on functional brain networks after long-term intervention in humans and rats. PMID:25789862

BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

PubMed

Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

2017-02-01

We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

PubMed

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Structure and function of complex brain networks

PubMed Central

Sporns, Olaf

2013-01-01

An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a “rich club,” centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed. PMID:24174898

The Neonatal Connectome During Preterm Brain Development

PubMed Central

van den Heuvel, Martijn P.; Kersbergen, Karina J.; de Reus, Marcel A.; Keunen, Kristin; Kahn, René S.; Groenendaal, Floris; de Vries, Linda S.; Benders, Manon J.N.L.

2015-01-01

The human connectome is the result of an elaborate developmental trajectory. Acquiring diffusion-weighted imaging and resting-state fMRI, we studied connectome formation during the preterm phase of macroscopic connectome genesis. In total, 27 neonates were scanned at week 30 and/or week 40 gestational age (GA). Examining the architecture of the neonatal anatomical brain network revealed a clear presence of a small-world modular organization before term birth. Analysis of neonatal functional connectivity (FC) showed the early formation of resting-state networks, suggesting that functional networks are present in the preterm brain, albeit being in an immature state. Moreover, structural and FC patterns of the neonatal brain network showed strong overlap with connectome architecture of the adult brain (85 and 81%, respectively). Analysis of brain development between week 30 and week 40 GA revealed clear developmental effects in neonatal connectome architecture, including a significant increase in white matter microstructure (P < 0.01), small-world topology (P < 0.01) and interhemispheric FC (P < 0.01). Computational analysis further showed that developmental changes involved an increase in integration capacity of the connectivity network as a whole. Taken together, we conclude that hallmark organizational structures of the human connectome are present before term birth and subject to early development. PMID:24833018

GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.

PubMed

Hosseini, S M Hadi; Hoeft, Fumiko; Kesler, Shelli R

2012-01-01

In recent years, graph theoretical analyses of neuroimaging data have increased our understanding of the organization of large-scale structural and functional brain networks. However, tools for pipeline application of graph theory for analyzing topology of brain networks is still lacking. In this report, we describe the development of a graph-analysis toolbox (GAT) that facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process. We demonstrated the capabilities of GAT by investigating the differences in the organization of regional gray-matter correlation networks in survivors of acute lymphoblastic leukemia (ALL) and healthy matched Controls (CON). The results revealed an alteration in small-world characteristics of the brain networks in the ALL survivors; an observation that confirm our hypothesis suggesting widespread neurobiological injury in ALL survivors. Along with demonstration of the capabilities of the GAT, this is the first report of altered large-scale structural brain networks in ALL survivors.

Salience network-based classification and prediction of symptom severity in children with autism.

PubMed

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

2013-08-01

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. 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. 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. 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. 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. 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.

Variations of the Functional Brain Network Efficiency in a Young Clinical Sample within the Autism Spectrum: A fNIRS Investigation.

PubMed

Li, Yanwei; Yu, Dongchuan

2018-01-01

Autism is a neurodevelopmental disorder with dimensional behavioral symptoms and various damages in the structural and functional brain. Previous neuroimaging studies focused on exploring the differences of brain development between individuals with and without autism spectrum disorders (ASD). However, few of them have attempted to investigate the individual differences of the brain features among subjects within the Autism spectrum. Our main goal was to explore the individual differences of neurodevelopment in young children with Autism by testing for the association between the functional network efficiency and levels of autistic behaviors, as well as the association between the functional network efficiency and age. Forty-six children with Autism (ages 2.0-8.9 years old) participated in the current study, with levels of autistic behaviors evaluated by their parents. The network efficiency (global and local network efficiency) were obtained from the functional networks based on the oxy-, deoxy-, and total-Hemoglobin series, respectively. Results indicated that the network efficiency decreased with age in young children with Autism in the deoxy- and total-Hemoglobin-based-networks, and children with a relatively higher level of autistic behaviors showed decreased network efficiency in the oxy-hemoglobin-based network. Results suggest individual differences of brain development in young children within the Autism spectrum, providing new insights into the psychopathology of ASD.

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

Development of the social brain from age three to twelve years.

PubMed

Richardson, Hilary; Lisandrelli, Grace; Riobueno-Naylor, Alexa; Saxe, Rebecca

2018-03-12

Human adults recruit distinct networks of brain regions to think about the bodies and minds of others. This study characterizes the development of these networks, and tests for relationships between neural development and behavioral changes in reasoning about others' minds ('theory of mind', ToM). A large sample of children (n = 122, 3-12 years), and adults (n = 33), watched a short movie while undergoing fMRI. The movie highlights the characters' bodily sensations (often pain) and mental states (beliefs, desires, emotions), and is a feasible experiment for young children. Here we report three main findings: (1) ToM and pain networks are functionally distinct by age 3 years, (2) functional specialization increases throughout childhood, and (3) functional maturity of each network is related to increasingly anti-correlated responses between the networks. Furthermore, the most studied milestone in ToM development, passing explicit false-belief tasks, does not correspond to discontinuities in the development of the social brain.

Altered resting-state whole-brain functional networks of neonates with intrauterine growth restriction.

PubMed

Batalle, Dafnis; Muñoz-Moreno, Emma; Tornador, Cristian; Bargallo, Nuria; Deco, Gustavo; Eixarch, Elisenda; Gratacos, Eduard

2016-04-01

The feasibility to use functional MRI (fMRI) during natural sleep to assess low-frequency basal brain activity fluctuations in human neonates has been demonstrated, although its potential to characterise pathologies of prenatal origin has not yet been exploited. In the present study, we used intrauterine growth restriction (IUGR) as a model of altered neurodevelopment due to prenatal condition to show the suitability of brain networks to characterise functional brain organisation at neonatal age. Particularly, we analysed resting-state fMRI signal of 20 neonates with IUGR and 13 controls, obtaining whole-brain functional networks based on correlations of blood oxygen level-dependent (BOLD) signal in 90 grey matter regions of an anatomical atlas (AAL). Characterisation of the networks obtained with graph theoretical features showed increased network infrastructure and raw efficiencies but reduced efficiency after normalisation, demonstrating hyper-connected but sub-optimally organised IUGR functional brain networks. Significant association of network features with neurobehavioral scores was also found. Further assessment of spatiotemporal dynamics displayed alterations into features associated to frontal, cingulate and lingual cortices. These findings show the capacity of functional brain networks to characterise brain reorganisation from an early age, and their potential to develop biomarkers of altered neurodevelopment. Copyright © 2016 Elsevier Ltd. All rights reserved.

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 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.

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

Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease

NASA Astrophysics Data System (ADS)

Kabbara, A.; Eid, H.; El Falou, W.; Khalil, M.; Wendling, F.; Hassan, M.

2018-04-01

Objective. Emerging evidence shows that cognitive deficits in Alzheimer’s disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. Approach. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Main results. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients’ functional brain networks and their cognitive scores. Significance. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.

Reduced integration and improved segregation of functional brain networks in Alzheimer's disease.

PubMed

Kabbara, A; Eid, H; El Falou, W; Khalil, M; Wendling, F; Hassan, M

2018-04-01

Emerging evidence shows that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients' functional brain networks and their cognitive scores. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.

Disruption to functional networks in neonates with perinatal brain injury predicts motor skills at 8 months.

PubMed

Linke, Annika C; Wild, Conor; Zubiaurre-Elorza, Leire; Herzmann, Charlotte; Duffy, Hester; Han, Victor K; Lee, David S C; Cusack, Rhodri

2018-01-01

Functional connectivity magnetic resonance imaging (fcMRI) of neonates with perinatal brain injury could improve prediction of motor impairment before symptoms manifest, and establish how early brain organization relates to subsequent development. This cohort study is the first to describe and quantitatively assess functional brain networks and their relation to later motor skills in neonates with a diverse range of perinatal brain injuries. Infants ( n  = 65, included in final analyses: n  = 53) were recruited from the neonatal intensive care unit (NICU) and were stratified based on their age at birth (premature vs. term), and on whether neuropathology was diagnosed from structural MRI. Functional brain networks and a measure of disruption to functional connectivity were obtained from 14 min of fcMRI acquired during natural sleep at term-equivalent age. Disruption to connectivity of the somatomotor and frontoparietal executive networks predicted motor impairment at 4 and 8 months. This disruption in functional connectivity was not found to be driven by differences between clinical groups, or by any of the specific measures we captured to describe the clinical course. fcMRI was predictive over and above other clinical measures available at discharge from the NICU, including structural MRI. Motor learning was affected by disruption to somatomotor networks, but also frontoparietal executive networks, which supports the functional importance of these networks in early development. Disruption to these two networks might be best addressed by distinct intervention strategies.

Altered Network Oscillations and Functional Connectivity Dynamics in Children Born Very Preterm.

PubMed

Moiseev, Alexander; Doesburg, Sam M; Herdman, Anthony T; Ribary, Urs; Grunau, Ruth E

2015-09-01

Structural brain connections develop atypically in very preterm children, and altered functional connectivity is also evident in fMRI studies. Such alterations in brain network connectivity are associated with cognitive difficulties in this population. Little is known, however, about electrophysiological interactions among specific brain networks in children born very preterm. In the present study, we recorded magnetoencephalography while very preterm children and full-term controls performed a visual short-term memory task. Regions expressing task-dependent activity changes were identified using beamformer analysis, and inter-regional phase synchrony was calculated. Very preterm children expressed altered regional recruitment in distributed networks of brain areas, across standard physiological frequency ranges including the theta, alpha, beta and gamma bands. Reduced oscillatory synchrony was observed among task-activated brain regions in very preterm children, particularly for connections involving areas critical for executive abilities, including middle frontal gyrus. These findings suggest that inability to recruit neurophysiological activity and interactions in distributed networks including frontal regions may contribute to difficulties in cognitive development in children born very preterm.

Graph-based network analysis of resting-state functional MRI.

PubMed

Wang, Jinhui; Zuo, Xinian; He, Yong

2010-01-01

In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future.

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

Modern network science of neurological disorders.

PubMed

Stam, Cornelis J

2014-10-01

Modern network science has revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, hubs and rich clubs. The next challenge is to use this knowledge to gain a better understanding of brain disease. Recent developments in the application of network science to conditions such as Alzheimer's disease, multiple sclerosis, traumatic brain injury and epilepsy have challenged the classical concept of neurological disorders being either 'local' or 'global', and have pointed to the overload and failure of hubs as a possible final common pathway in neurological disorders.

The development of functional network organization in early childhood and early adolescence: A resting-state fNIRS study.

PubMed

Cai, Lin; Dong, Qi; Niu, Haijing

2018-04-01

Early childhood (7-8 years old) and early adolescence (11-12 years old) constitute two landmark developmental stages that comprise considerable changes in neural cognition. However, very limited information from functional neuroimaging studies exists on the functional topological configuration of the human brain during specific developmental periods. In the present study, we utilized continuous resting-state functional near-infrared spectroscopy (rs-fNIRS) imaging data to examine topological changes in network organization during development from early childhood and early adolescence to adulthood. Our results showed that the properties of small-worldness and modularity were not significantly different across development, demonstrating the developmental maturity of important functional brain organization in early childhood. Intriguingly, young children had a significantly lower global efficiency than early adolescents and adults, which revealed that the integration of the distributed networks strengthens across the developmental stages underlying cognitive development. Moreover, local efficiency of young children and adolescents was significantly lower than that of adults, while there was no difference between these two younger groups. This finding demonstrated that functional segregation remained relatively steady from early childhood to early adolescence, and the brain in these developmental periods possesses no optimal network configuration. Furthermore, we found heterogeneous developmental patterns in the regional nodal properties in various brain regions, such as linear increased nodal properties in the frontal cortex, indicating increasing cognitive capacity over development. Collectively, our results demonstrated that significant topological changes in functional network organization occurred during these two critical developmental stages, and provided a novel insight into elucidating subtle changes in brain functional networks across development. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Multilayer motif analysis of brain networks

NASA Astrophysics Data System (ADS)

Battiston, Federico; Nicosia, Vincenzo; Chavez, Mario; Latora, Vito

2017-04-01

In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.

Genetic and Diagnostic Biomarker Development in ASD Toddlers Using Resting State Functional MRI

DTIC Science & Technology

2015-09-01

for public release; distribution unlimited Autism spectrum disorder (ASD); biomarker; early brain development; intrinsic functional brain networks...three large neuroimaging/neurobehavioral datasets to identify brain-imaging based biomarkers for Autism Spectrum Disorders (ASD). At Yale, we focus...neurobehavioral!datasets!in!order!to!identify! brainFimaging!based!biomarkers!for! Autism ! Spectrum ! Disorders !(ASD),!including!1)!BrainMap,! developed!and

Development of the brain's functional network architecture.

PubMed

Vogel, Alecia C; Power, Jonathan D; Petersen, Steven E; Schlaggar, Bradley L

2010-12-01

A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks.

Development of the Brain's Functional Network Architecture

PubMed Central

Power, Jonathan D.; Petersen, Steven E.; Schlaggar, Bradley L.

2013-01-01

A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks. PMID:20976563

Educating the Human Brain. Human Brain Development Series

ERIC Educational Resources Information Center

Posner, Michael I.; Rothbart, Mary K.

2006-01-01

"Educating the Human Brain" is the product of a quarter century of research. This book provides an empirical account of the early development of attention and self regulation in infants and young children. It examines the brain areas involved in regulatory networks, their connectivity, and how their development is influenced by genes and…

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.

Control Networks and Neuromodulators of Early Development

ERIC Educational Resources Information Center

Posner, Michael I.; Rothbart, Mary K.; Sheese, Brad E.; Voelker, Pascale

2012-01-01

In adults, most cognitive and emotional self-regulation is carried out by a network of brain regions, including the anterior cingulate, insula, and areas of the basal ganglia, related to executive attention. We propose that during infancy, control systems depend primarily upon a brain network involved in orienting to sensory events that includes…

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.

Progressive gender differences of structural brain networks in healthy adults: a longitudinal, diffusion tensor imaging study.

PubMed

Sun, Yu; Lee, Renick; Chen, Yu; Collinson, Simon; Thakor, Nitish; Bezerianos, Anastasios; Sim, Kang

2015-01-01

Sexual dimorphism in the brain maturation during childhood and adolescence has been repeatedly documented, which may underlie the differences in behaviors and cognitive performance. However, our understanding of how gender modulates the development of structural connectome in healthy adults is still not entirely clear. Here we utilized graph theoretical analysis of longitudinal diffusion tensor imaging data over a five-year period to investigate the progressive gender differences of brain network topology. The brain networks of both genders showed prominent economical "small-world" architecture (high local clustering and short paths between nodes). Additional analysis revealed a more economical "small-world" architecture in females as well as a greater global efficiency in males regardless of scan time point. At the regional level, both increased and decreased efficiency were found across the cerebral cortex for both males and females, indicating a compensation mechanism of cortical network reorganization over time. Furthermore, we found that weighted clustering coefficient exhibited significant gender-time interactions, implying different development trends between males and females. Moreover, several specific brain regions (e.g., insula, superior temporal gyrus, cuneus, putamen, and parahippocampal gyrus) exhibited different development trajectories between males and females. Our findings further prove the presence of sexual dimorphism in brain structures that may underlie gender differences in behavioral and cognitive functioning. The sex-specific progress trajectories in brain connectome revealed in this work provide an important foundation to delineate the gender related pathophysiological mechanisms in various neuropsychiatric disorders, which may potentially guide the development of sex-specific treatments for these devastating brain disorders.

Developmental Changes in Topological Asymmetry Between Hemispheric Brain White Matter Networks from Adolescence to Young Adulthood.

PubMed

Zhong, Suyu; He, Yong; Shu, Hua; Gong, Gaolang

2017-04-01

Human brain asymmetries have been well described. Intriguingly, a number of asymmetries in brain phenotypes have been shown to change throughout the lifespan. Recent studies have revealed topological asymmetries between hemispheric white matter networks in the human brain. However, it remains unknown whether and how these topological asymmetries evolve from adolescence to young adulthood, a critical period that constitutes the second peak of human brain and cognitive development. To address this question, the present study included a large cohort of healthy adolescents and young adults. Diffusion and structural magnetic resonance imaging were acquired to construct hemispheric white matter networks, and graph-theory was applied to quantify topological parameters of the hemispheric networks. In both adolescents and young adults, rightward asymmetry in both global and local network efficiencies was consistently observed between the 2 hemispheres, but the degree of the asymmetry was significantly decreased in young adults. At the nodal level, the young adults exhibited less rightward asymmetry of nodal efficiency mainly around the parasylvian area, posterior tempo-parietal cortex, and fusiform gyrus. These developmental patterns of network asymmetry provide novel insight into the human brain structural development from adolescence to young adulthood and also likely relate to the maturation of language and social cognition that takes place during this period. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Disrupted Structural Brain Network in AD and aMCI: A Finding of Long Fiber Degeneration.

PubMed

Fang, Rong; Yan, Xiao-Xiao; Wu, Zhi-Yuan; Sun, Yu; Yin, Qi-Hua; Wang, Ying; Tang, Hui-Dong; Sun, Jun-Feng; Miao, Fei; Chen, Sheng-Di

2015-01-01

Although recent evidence has emerged that Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) patients show both regional brain abnormalities and topological degeneration in brain networks, our understanding of the effects of white matter fiber aberrations on brain network topology in AD and aMCI is still rudimentary. In this study, we investigated the regional volumetric aberrations and the global topological abnormalities in AD and aMCI patients. The results showed a widely distributed atrophy in both gray and white matters in the AD and aMCI groups. In particular, AD patients had weaker connectivity with long fiber length than aMCI and normal control (NC) groups, as assessed by fractional anisotropy (FA). Furthermore, the brain networks of all three groups exhibited prominent economical small-world properties. Interestingly, the topological characteristics estimated from binary brain networks showed no significant group effect, indicating a tendency of preserving an optimal topological architecture in AD and aMCI during degeneration. However, significantly longer characteristic path length was observed in the FA weighted brain networks of AD and aMCI patients, suggesting dysfunctional global integration. Moreover, the abnormality of the characteristic path length was negatively correlated with the clinical ratings of cognitive impairment. Thus, the results therefore suggested that the topological alterations in weighted brain networks of AD are induced by the loss of connectivity with long fiber lengths. Our findings provide new insights into the alterations of the brain network in AD and may indicate the predictive value of the network metrics as biomarkers of disease development.

Detecting brain dynamics during resting state: a tensor based evolutionary clustering approach

NASA Astrophysics Data System (ADS)

Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

2017-08-01

Human brain is a complex network with connections across different regions. Understanding the functional connectivity (FC) of the brain is important both during resting state and task; as disruptions in connectivity patterns are indicators of different psychopathological and neurological diseases. In this work, we study the resting state functional connectivity networks (FCNs) of the brain from fMRI BOLD signals. Recent studies have shown that FCNs are dynamic even during resting state and understanding the temporal dynamics of FCNs is important for differentiating between different conditions. Therefore, it is important to develop algorithms to track the dynamic formation and dissociation of FCNs of the brain during resting state. In this paper, we propose a two step tensor based community detection algorithm to identify and track the brain network community structure across time. First, we introduce an information-theoretic function to reduce the dynamic FCN and identify the time points that are similar topologically to combine them into a tensor. These time points will be used to identify the different FC states. Second, a tensor based spectral clustering approach is developed to identify the community structure of the constructed tensors. The proposed algorithm applies Tucker decomposition to the constructed tensors and extract the orthogonal factor matrices along the connectivity mode to determine the common subspace within each FC state. The detected community structure is summarized and described as FC states. The results illustrate the dynamic structure of resting state networks (RSNs), including the default mode network, somatomotor network, subcortical network and visual network.

Cross-population myelination covariance of human cerebral cortex.

PubMed

Ma, Zhiwei; Zhang, Nanyin

2017-09-01

Cross-population covariance of brain morphometric quantities provides a measure of interareal connectivity, as it is believed to be determined by the coordinated neurodevelopment of connected brain regions. Although useful, structural covariance analysis predominantly employed bulky morphological measures with mixed compartments, whereas studies of the structural covariance of any specific subdivisions such as myelin are rare. Characterizing myelination covariance is of interest, as it will reveal connectivity patterns determined by coordinated development of myeloarchitecture between brain regions. Using myelin content MRI maps from the Human Connectome Project, here we showed that the cortical myelination covariance was highly reproducible, and exhibited a brain organization similar to that previously revealed by other connectivity measures. Additionally, the myelination covariance network shared common topological features of human brain networks such as small-worldness. Furthermore, we found that the correlation between myelination covariance and resting-state functional connectivity (RSFC) was uniform within each resting-state network (RSN), but could considerably vary across RSNs. Interestingly, this myelination covariance-RSFC correlation was appreciably stronger in sensory and motor networks than cognitive and polymodal association networks, possibly due to their different circuitry structures. This study has established a new brain connectivity measure specifically related to axons, and this measure can be valuable to investigating coordinated myeloarchitecture development. Hum Brain Mapp 38:4730-4743, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Topological Distances Between Brain Networks

PubMed Central

Lee, Hyekyoung; Solo, Victor; Davidson, Richard J.; Pollak, Seth D.

2018-01-01

Many existing brain network distances are based on matrix norms. The element-wise differences may fail to capture underlying topological differences. Further, matrix norms are sensitive to outliers. A few extreme edge weights may severely affect the distance. Thus it is necessary to develop network distances that recognize topology. In this paper, we introduce Gromov-Hausdorff (GH) and Kolmogorov-Smirnov (KS) distances. GH-distance is often used in persistent homology based brain network models. The superior performance of KS-distance is contrasted against matrix norms and GH-distance in random network simulations with the ground truths. The KS-distance is then applied in characterizing the multimodal MRI and DTI study of maltreated children.

Differentiation and Cell-Cell Interactions of Neural Progenitor Cells Transplanted into Intact Adult Brain.

PubMed

Sukhinich, K K; Kosykh, A V; Aleksandrova, M A

2015-11-01

We studied the behavior and cell-cell interactions of embryonic brain cell from GFP-reporter mice after their transplantation into the intact adult brain. Fragments or cell suspensions of fetal neocortical cells at different stages of development were transplanted into the neocortex and striatum of adult recipients. Even in intact brain, the processes of transplanted neurons formed extensive networks in the striatum and neocortical layers I and V-VI. Processes of transplanted cells at different stages of development attained the rostral areas of the frontal cortex and some of them reached the internal capsule. However, the cells transplanted in suspension had lower process growth potency than cells from tissue fragments. Tyrosine hydroxylase fibers penetrated from the recipient brain into grafts at both early and late stages of development. Our experiments demonstrated the formation of extensive reciprocal networks between the transplanted fetal neural cells and recipient brain neurons even in intact brain.

Increasingly diverse brain dynamics in the developmental arc: using Pareto-optimization to infer a mechanism

NASA Astrophysics Data System (ADS)

Tang, Evelyn; Giusti, Chad; Baum, Graham; Gu, Shi; Pollock, Eli; Kahn, Ari; Roalf, David; Moore, Tyler; Ruparel, Kosha; Gur, Ruben; Gur, Raquel; Satterthwaite, Theodore; Bassett, Danielle

Motivated by a recent demonstration that the network architecture of white matter supports emerging control of diverse neural dynamics as children mature into adults, we seek to investigate structural mechanisms that support these changes. Beginning from a network representation of diffusion imaging data, we simulate network evolution with a set of simple growth rules built on principles of network control. Notably, the optimal evolutionary trajectory displays a striking correspondence to the progression of child to adult brain, suggesting that network control is a driver of development. More generally, and in comparison to the complete set of available models, we demonstrate that all brain networks from child to adult are structured in a manner highly optimized for the control of diverse neural dynamics. Within this near-optimality, we observe differences in the predicted control mechanisms of the child and adult brains, suggesting that the white matter architecture in children has a greater potential to increasingly support brain state transitions, potentially underlying cognitive switching.

Tuning the developing brain to social signals of emotions

PubMed Central

Leppänen, Jukka M.; Nelson, Charles A.

2010-01-01

PREFACE Humans in diverse cultures develop a similar capacity to recognize the emotional signals of different facial expressions. This capacity is mediated by a brain network that involves emotion-related brain circuits and higher-level visual representation areas. Recent studies suggest that the key components of this network begin to emerge early in life. The studies also suggest that initial biases in emotion-related brain circuits and the early coupling of these circuits and cortical perceptual areas provides a foundation for a rapid acquisition of representations of those facial features that denote specific emotions. PMID:19050711

On characterizing population commonalities and subject variations in brain networks.

PubMed

Ghanbari, Yasser; Bloy, Luke; Tunc, Birkan; Shankar, Varsha; Roberts, Timothy P L; Edgar, J Christopher; Schultz, Robert T; Verma, Ragini

2017-05-01

Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs. Copyright © 2015 Elsevier B.V. All rights reserved.

Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy.

PubMed

Niu, Haijing; Wang, Jinhui; Zhao, Tengda; Shu, Ni; He, Yong

2012-01-01

The human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics. We utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques. Our results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.

INVESTIGATING DIFFERENCES IN BRAIN FUNCTIONAL NETWORKS USING HIERARCHICAL COVARIATE-ADJUSTED INDEPENDENT COMPONENT ANALYSIS.

PubMed

Shi, Ran; Guo, Ying

2016-12-01

Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).

Visual cortical areas of the mouse: comparison of parcellation and network structure with primates

PubMed Central

Laramée, Marie-Eve; Boire, Denis

2015-01-01

Brains have evolved to optimize sensory processing. In primates, complex cognitive tasks must be executed and evolution led to the development of large brains with many cortical areas. Rodents do not accomplish cognitive tasks of the same level of complexity as primates and remain with small brains both in relative and absolute terms. But is a small brain necessarily a simple brain? In this review, several aspects of the visual cortical networks have been compared between rodents and primates. The visual system has been used as a model to evaluate the level of complexity of the cortical circuits at the anatomical and functional levels. The evolutionary constraints are first presented in order to appreciate the rules for the development of the brain and its underlying circuits. The organization of sensory pathways, with their parallel and cross-modal circuits, is also examined. Other features of brain networks, often considered as imposing constraints on the development of underlying circuitry, are also discussed and their effect on the complexity of the mouse and primate brain are inspected. In this review, we discuss the common features of cortical circuits in mice and primates and see how these can be useful in understanding visual processing in these animals. PMID:25620914

Visual cortical areas of the mouse: comparison of parcellation and network structure with primates.

PubMed

Laramée, Marie-Eve; Boire, Denis

2014-01-01

Brains have evolved to optimize sensory processing. In primates, complex cognitive tasks must be executed and evolution led to the development of large brains with many cortical areas. Rodents do not accomplish cognitive tasks of the same level of complexity as primates and remain with small brains both in relative and absolute terms. But is a small brain necessarily a simple brain? In this review, several aspects of the visual cortical networks have been compared between rodents and primates. The visual system has been used as a model to evaluate the level of complexity of the cortical circuits at the anatomical and functional levels. The evolutionary constraints are first presented in order to appreciate the rules for the development of the brain and its underlying circuits. The organization of sensory pathways, with their parallel and cross-modal circuits, is also examined. Other features of brain networks, often considered as imposing constraints on the development of underlying circuitry, are also discussed and their effect on the complexity of the mouse and primate brain are inspected. In this review, we discuss the common features of cortical circuits in mice and primates and see how these can be useful in understanding visual processing in these animals.

Structural Connectivity Relates to Perinatal Factors and Functional Impairment at 7 Years in Children Born Very Preterm

PubMed Central

Thompson, Deanne K.; Chen, Jian; Beare, Richard; Adamson, Christopher L.; Ellis, Rachel; Ahmadzai, Zohra M.; Kelly, Claire E.; Lee, Katherine J.; Zalesky, Andrew; Yang, Joseph Y.M.; Hunt, Rodney W.; Cheong, Jeanie L.Y.; Inder, Terrie E.; Doyle, Lex W.; Seal, Marc L.; Anderson, Peter J.

2016-01-01

Objective To use structural connectivity to (1) compare brain networks between typically and atypically developing (very preterm) children, (2) explore associations between potential perinatal developmental disturbances and brain networks, and (3) describe associations between brain networks and functional impairments in very preterm children. Methods 26 full-term and 107 very preterm 7-year-old children (born <30 weeks’ gestational age and/or <1250 g) underwent T1- and diffusion-weighted imaging. Global white matter fiber networks were produced using 80 cortical and subcortical nodes, and edges created using constrained spherical deconvolution-based tractography. Global graph theory metrics were analysed, and regional networks were identified using network-based statistics. Cognitive and motor function were assessed at 7 years of age. Results Compared with full-term children, very preterm children had reduced density, lower global efficiency and higher local efficiency. Those with lower gestational age at birth, infection or higher neonatal brain abnormality score had reduced connectivity. Reduced connectivity within a widespread network was predictive of impaired IQ, while reduced connectivity within the right parietal and temporal lobes was associated with motor impairment in very preterm children. Conclusions This study utilized an innovative structural connectivity pipeline to reveal that children born very preterm have less connected and less complex brain networks compared with typically developing term-born children. Adverse perinatal factors led to disturbances in white matter connectivity, which in turn are associated with impaired functional outcomes, highlighting novel structure-function relationships. PMID:27046108

The development of hub architecture in the human functional brain network.

PubMed

Hwang, Kai; Hallquist, Michael N; Luna, Beatriz

2013-10-01

Functional hubs are brain regions that play a crucial role in facilitating communication among parallel, distributed brain networks. The developmental emergence and stability of hubs, however, is not well understood. The current study used measures of network topology drawn from graph theory to investigate the development of functional hubs in 99 participants, 10-20 years of age. We found that hub architecture was evident in late childhood and was stable from adolescence to early adulthood. Connectivity between hub and non-hub ("spoke") regions, however, changed with development. From childhood to adolescence, the strength of connections between frontal hubs and cortical and subcortical spoke regions increased. From adolescence to adulthood, hub-spoke connections with frontal hubs were stable, whereas connectivity between cerebellar hubs and cortical spoke regions increased. Our findings suggest that a developmentally stable functional hub architecture provides the foundation of information flow in the brain, whereas connections between hubs and spokes continue to develop, possibly supporting mature cognitive function.

Fetal brain extracellular matrix boosts neuronal network formation in 3D bioengineered model of cortical brain tissue.

PubMed

Sood, Disha; Chwalek, Karolina; Stuntz, Emily; Pouli, Dimitra; Du, Chuang; Tang-Schomer, Min; Georgakoudi, Irene; Black, Lauren D; Kaplan, David L

2016-01-01

The extracellular matrix (ECM) constituting up to 20% of the organ volume is a significant component of the brain due to its instructive role in the compartmentalization of functional microdomains in every brain structure. The composition, quantity and structure of ECM changes dramatically during the development of an organism greatly contributing to the remarkably sophisticated architecture and function of the brain. Since fetal brain is highly plastic, we hypothesize that the fetal brain ECM may contain cues promoting neural growth and differentiation, highly desired in regenerative medicine. Thus, we studied the effect of brain-derived fetal and adult ECM complemented with matricellular proteins on cortical neurons using in vitro 3D bioengineered model of cortical brain tissue. The tested parameters included neuronal network density, cell viability, calcium signaling and electrophysiology. Both, adult and fetal brain ECM as well as matricellular proteins significantly improved neural network formation as compared to single component, collagen I matrix. Additionally, the brain ECM improved cell viability and lowered glutamate release. The fetal brain ECM induced superior neural network formation, calcium signaling and spontaneous spiking activity over adult brain ECM. This study highlights the difference in the neuroinductive properties of fetal and adult brain ECM and suggests that delineating the basis for this divergence may have implications for regenerative medicine.

Progressively Disrupted Brain Functional Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment Patients.

PubMed

Sang, Linqiong; Chen, Lin; Wang, Li; Zhang, Jingna; Zhang, Ye; Li, Pengyue; Li, Chuanming; Qiu, Mingguo

2018-01-01

Cognitive impairment caused by subcortical ischemic vascular disease (SIVD) has been elucidated by many neuroimaging studies. However, little is known regarding the changes in brain functional connectivity networks in relation to the severity of cognitive impairment in SIVD. In the present study, 20 subcortical ischemic vascular cognitive impairment no dementia patients (SIVCIND) and 20 dementia patients (SIVaD) were enrolled; additionally, 19 normal controls were recruited. Each participant underwent a resting-state functional MRI scan. Whole-brain functional networks were analyzed with graph theory and network-based statistics (NBS) to study the functional organization of networks and find alterations in functional connectivity among brain regions. After adjustments for age, gender, and duration of formal education, there were significant group differences for two network functional organization indices, global efficiency and local efficiency, which decreased (NC > SIVCIND > SIVaD) as cognitive impairment worsened. Between-group differences in functional connectivity (NBS corrected, p  < 0.01) mainly involved the orbitofrontal, parietal, and temporal cortices, as well as the basal ganglia. The brain connectivity network was progressively disrupted as cognitive impairment worsened, with an increased number of decreased connections between brain regions. We also observed more reductions in nodal efficiency in the prefrontal and temporal cortices for SIVaD than for SIVCIND. These findings indicated a progressively disrupted pattern of the brain functional connectivity network with increased cognitive impairment and showed promise for the development of reliable biomarkers of network metric changes related to cognitive impairment caused by SIVD.

An extensive assessment of network alignment algorithms for comparison of brain connectomes.

PubMed

Milano, Marianna; Guzzi, Pietro Hiram; Tymofieva, Olga; Xu, Duan; Hess, Christofer; Veltri, Pierangelo; Cannataro, Mario

2017-06-06

Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.

Frequency specific patterns of resting-state networks development from childhood to adolescence: A magnetoencephalography study.

PubMed

Meng, Lu; Xiang, Jing

2016-11-01

The present study investigated frequency dependent developmental patterns of the brain resting-state networks from childhood to adolescence. Magnetoencephalography (MEG) data were recorded from 20 healthy subjects at resting-state with eyes-open. The resting-state networks (RSNs) was analyzed at source-level. Brain network organization was characterized by mean clustering coefficient and average path length. The correlations between brain network measures and subjects' age during development from childhood to adolescence were statistically analyzed in delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), and beta (12-30Hz) frequency bands. A significant positive correlation between functional connectivity with age was found in alpha and beta frequency bands. A significant negative correlation between average path lengths with age was found in beta frequency band. The results suggest that there are significant developmental changes of resting-state networks from childhood to adolescence, which matures from a lattice network to a small-world network. Copyright © 2016 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Investigating the Microstructural Correlation of White Matter in Autism Spectrum Disorder.

PubMed

Dean, Douglas C; Travers, Brittany G; Adluru, Nagesh; Tromp, Do P M; Destiche, Daniel J; Samsin, Danica; Prigge, Molly B; Zielinski, Brandon A; Fletcher, P Thomas; Anderson, Jeffrey S; Froehlich, Alyson L; Bigler, Erin D; Lange, Nicholas; Lainhart, Janet E; Alexander, Andrew L

2016-06-01

White matter microstructure forms a complex and dynamical system that is critical for efficient and synchronized brain function. Neuroimaging findings in children with autism spectrum disorder (ASD) suggest this condition is associated with altered white matter microstructure, which may lead to atypical macroscale brain connectivity. In this study, we used diffusion tensor imaging measures to examine the extent that white matter tracts are interrelated within ASD and typical development. We assessed the strength of inter-regional white matter correlations between typically developing and ASD diagnosed individuals. Using hierarchical clustering analysis, clustering patterns of the pairwise white matter correlations were constructed and revealed to be different between the two groups. Additionally, we explored the use of graph theory analysis to examine the characteristics of the patterns formed by inter-regional white matter correlations and compared these properties between ASD and typical development. We demonstrate that the ASD sample has significantly less coherence in white matter microstructure across the brain compared to that in the typical development sample. The ASD group also presented altered topological characteristics, which may implicate less efficient brain networking in ASD. These findings highlight the potential of graph theory based network characteristics to describe the underlying networks as measured by diffusion magnetic resonance imaging and furthermore indicates that ASD may be associated with altered brain network characteristics. Our findings are consistent with those of a growing number of studies and hypotheses that have suggested disrupted brain connectivity in ASD.

Investigating the Microstructural Correlation of White Matter in Autism Spectrum Disorder

PubMed Central

Travers, Brittany G.; Adluru, Nagesh; Tromp, Do P.M.; Destiche, Daniel J.; Samsin, Danica; Prigge, Molly B.; Zielinski, Brandon A.; Fletcher, P. Thomas; Anderson, Jeffrey S.; Froehlich, Alyson L.; Bigler, Erin D.; Lange, Nicholas; Lainhart, Janet E.; Alexander, Andrew L.

2016-01-01

Abstract White matter microstructure forms a complex and dynamical system that is critical for efficient and synchronized brain function. Neuroimaging findings in children with autism spectrum disorder (ASD) suggest this condition is associated with altered white matter microstructure, which may lead to atypical macroscale brain connectivity. In this study, we used diffusion tensor imaging measures to examine the extent that white matter tracts are interrelated within ASD and typical development. We assessed the strength of inter-regional white matter correlations between typically developing and ASD diagnosed individuals. Using hierarchical clustering analysis, clustering patterns of the pairwise white matter correlations were constructed and revealed to be different between the two groups. Additionally, we explored the use of graph theory analysis to examine the characteristics of the patterns formed by inter-regional white matter correlations and compared these properties between ASD and typical development. We demonstrate that the ASD sample has significantly less coherence in white matter microstructure across the brain compared to that in the typical development sample. The ASD group also presented altered topological characteristics, which may implicate less efficient brain networking in ASD. These findings highlight the potential of graph theory based network characteristics to describe the underlying networks as measured by diffusion magnetic resonance imaging and furthermore indicates that ASD may be associated with altered brain network characteristics. Our findings are consistent with those of a growing number of studies and hypotheses that have suggested disrupted brain connectivity in ASD. PMID:27021440

Three-dimensional neural cultures produce networks that mimic native brain activity.

PubMed

Bourke, Justin L; Quigley, Anita F; Duchi, Serena; O'Connell, Cathal D; Crook, Jeremy M; Wallace, Gordon G; Cook, Mark J; Kapsa, Robert M I

2018-02-01

Development of brain function is critically dependent on neuronal networks organized through three dimensions. Culture of central nervous system neurons has traditionally been limited to two dimensions, restricting growth patterns and network formation to a single plane. Here, with the use of multichannel extracellular microelectrode arrays, we demonstrate that neurons cultured in a true three-dimensional environment recapitulate native neuronal network formation and produce functional outcomes more akin to in vivo neuronal network activity. Copyright © 2017 John Wiley & Sons, Ltd.

Long-term variability of importance of brain regions in evolving epileptic brain networks

NASA Astrophysics Data System (ADS)

Geier, Christian; Lehnertz, Klaus

2017-04-01

We investigate the temporal and spatial variability of the importance of brain regions in evolving epileptic brain networks. We construct these networks from multiday, multichannel electroencephalographic data recorded from 17 epilepsy patients and use centrality indices to assess the importance of brain regions. Time-resolved indications of highest importance fluctuate over time to a greater or lesser extent, however, with some periodic temporal structure that can mostly be attributed to phenomena unrelated to the disease. In contrast, relevant aspects of the epileptic process contribute only marginally. Indications of highest importance also exhibit pronounced alternations between various brain regions that are of relevance for studies aiming at an improved understanding of the epileptic process with graph-theoretical approaches. Nonetheless, these findings may guide new developments for individualized diagnosis, treatment, and control.

Cell diversity and network dynamics in photosensitive human brain organoids

PubMed Central

Quadrato, Giorgia; Nguyen, Tuan; Macosko, Evan Z.; Sherwood, John L.; Yang, Sung Min; Berger, Daniel; Maria, Natalie; Scholvin, Jorg; Goldman, Melissa; Kinney, Justin; Boyden, Edward S.; Lichtman, Jeff; Williams, Ziv M.; McCarroll, Steven A.; Arlotta, Paola

2017-01-01

In vitro models of the developing brain such as 3D brain organoids offer an unprecedented opportunity to study aspects of human brain development and disease. However, it remains undefined what cells are generated within organoids and to what extent they recapitulate the regional complexity, cellular diversity, and circuit functionality of the brain. Here, we analyzed gene expression in over 80,000 individual cells isolated from 31 human brain organoids. We find that organoids can generate a broad diversity of cells, which are related to endogenous classes, including cells from the cerebral cortex and the retina. Organoids could be developed over extended periods (over 9 months) enabling unprecedented levels of maturity including the formation of dendritic spines and of spontaneously-active neuronal networks. Finally, neuronal activity within organoids could be controlled using light stimulation of photoreceptor-like cells, which may offer ways to probe the functionality of human neuronal circuits using physiological sensory stimuli. PMID:28445462

Cell diversity and network dynamics in photosensitive human brain organoids.

PubMed

Quadrato, Giorgia; Nguyen, Tuan; Macosko, Evan Z; Sherwood, John L; Min Yang, Sung; Berger, Daniel R; Maria, Natalie; Scholvin, Jorg; Goldman, Melissa; Kinney, Justin P; Boyden, Edward S; Lichtman, Jeff W; Williams, Ziv M; McCarroll, Steven A; Arlotta, Paola

2017-05-04

In vitro models of the developing brain such as three-dimensional brain organoids offer an unprecedented opportunity to study aspects of human brain development and disease. However, the cells generated within organoids and the extent to which they recapitulate the regional complexity, cellular diversity and circuit functionality of the brain remain undefined. Here we analyse gene expression in over 80,000 individual cells isolated from 31 human brain organoids. We find that organoids can generate a broad diversity of cells, which are related to endogenous classes, including cells from the cerebral cortex and the retina. Organoids could be developed over extended periods (more than 9 months), allowing for the establishment of relatively mature features, including the formation of dendritic spines and spontaneously active neuronal networks. Finally, neuronal activity within organoids could be controlled using light stimulation of photosensitive cells, which may offer a way to probe the functionality of human neuronal circuits using physiological sensory stimuli.

Increased Small-World Network Topology Following Deployment-Acquired Traumatic Brain Injury Associated with the Development of Post-Traumatic Stress Disorder.

PubMed

Rowland, Jared A; Stapleton-Kotloski, Jennifer R; Dobbins, Dorothy L; Rogers, Emily; Godwin, Dwayne W; Taber, Katherine H

2018-05-01

Cross-sectional and longitudinal studies in active duty and veteran cohorts have both demonstrated that deployment-acquired traumatic brain injury (TBI) is an independent risk factor for developing post-traumatic stress disorder (PTSD), beyond confounds such as combat exposure, physical injury, predeployment TBI, and pre-deployment psychiatric symptoms. This study investigated how resting-state brain networks differ between individuals who developed PTSD and those who did not following deployment-acquired TBI. Participants included postdeployment veterans with deployment-acquired TBI history both with and without current PTSD diagnosis. Graph metrics, including small-worldness, clustering coefficient, and modularity, were calculated from individually constructed whole-brain networks based on 5-min eyes-open resting-state magnetoencephalography (MEG) recordings. Analyses were adjusted for age and premorbid IQ. Results demonstrated that participants with current PTSD displayed higher levels of small-worldness, F(1,12) = 5.364, p < 0.039, partial eta squared = 0.309, and Cohen's d = 0.972, and clustering coefficient, F(1, 12) = 12.204, p < 0.004, partial eta squared = 0.504, and Cohen's d = 0.905, than participants without current PTSD. There were no between-group differences in modularity or the number of modules present. These findings are consistent with a hyperconnectivity hypothesis of the effect of TBI history on functional networks rather than a disconnection hypothesis, demonstrating increased levels of clustering coefficient rather than a decrease as might be expected; however, these results do not account for potential changes in brain structure. These results demonstrate the potential pathological sequelae of changes in functional brain networks following deployment-acquired TBI and represent potential neurobiological changes associated with deployment-acquired TBI that may increase the risk of subsequently developing PTSD.

Mapping Epileptic Activity: Sources or Networks for the Clinicians?

PubMed Central

Pittau, Francesca; Mégevand, Pierre; Sheybani, Laurent; Abela, Eugenio; Grouiller, Frédéric; Spinelli, Laurent; Michel, Christoph M.; Seeck, Margitta; Vulliemoz, Serge

2014-01-01

Epileptic seizures of focal origin are classically considered to arise from a focal epileptogenic zone and then spread to other brain regions. This is a key concept for semiological electro-clinical correlations, localization of relevant structural lesions, and selection of patients for epilepsy surgery. Recent development in neuro-imaging and electro-physiology and combinations, thereof, have been validated as contributory tools for focus localization. In parallel, these techniques have revealed that widespread networks of brain regions, rather than a single epileptogenic region, are implicated in focal epileptic activity. Sophisticated multimodal imaging and analysis strategies of brain connectivity patterns have been developed to characterize the spatio-temporal relationships within these networks by combining the strength of both techniques to optimize spatial and temporal resolution with whole-brain coverage and directional connectivity. In this paper, we review the potential clinical contribution of these functional mapping techniques as well as invasive electrophysiology in human beings and animal models for characterizing network connectivity. PMID:25414692

The Hierarchy of Brain Networks Is Related to Insulin Growth Factor-1 in a Large, Middle-Aged, Healthy Cohort: An Exploratory Magnetoencephalography Study.

PubMed

Sorrentino, Pierpaolo; Nieboer, Dagmar; Twisk, Jos W R; Stam, Cornelis J; Douw, Linda; Hillebrand, Arjan

2017-06-01

Recently, a large study demonstrated that lower serum levels of insulin growth factor-1 (IGF-1) relate to brain atrophy and to a greater risk for developing Alzheimer's disease in a healthy elderly population. We set out to test if functional brain networks relate to IGF-1 levels in the middle aged. Hence, we studied the association between IGF-1 and magnetoencephalography-based functional network characteristics in a middle-aged population. The functional connections between brain areas were estimated for six frequency bands (delta, theta, alpha1, alpha2, beta, gamma) using the phase lag index. Subsequently, the topology of the frequency-specific functional networks was characterized using the minimum spanning tree. Our results showed that lower levels of serum IGF-1 relate to a globally less integrated functional network in the beta and theta band. The associations remained significant when correcting for gender and systemic effects of IGF-1 that might indirectly affect the brain. The value of this exploratory study is the demonstration that lower levels of IGF-1 are associated with brain network topology in the middle aged.

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.

Small-world human brain networks: Perspectives and challenges.

PubMed

Liao, Xuhong; Vasilakos, Athanasios V; He, Yong

2017-06-01

Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Early development of structural networks and the impact of prematurity on brain connectivity.

PubMed

Batalle, Dafnis; Hughes, Emer J; Zhang, Hui; Tournier, J-Donald; Tusor, Nora; Aljabar, Paul; Wali, Luqman; Alexander, Daniel C; Hajnal, Joseph V; Nosarti, Chiara; Edwards, A David; Counsell, Serena J

2017-04-01

Preterm infants are at high risk of neurodevelopmental impairment, which may be due to altered development of brain connectivity. We aimed to (i) assess structural brain development from 25 to 45 weeks gestational age (GA) using graph theoretical approaches and (ii) test the hypothesis that preterm birth results in altered white matter network topology. Sixty-five infants underwent MRI between 25 +3 and 45 +6 weeks GA. Structural networks were constructed using constrained spherical deconvolution tractography and were weighted by measures of white matter microstructure (fractional anisotropy, neurite density and orientation dispersion index). We observed regional differences in brain maturation, with connections to and from deep grey matter showing most rapid developmental changes during this period. Intra-frontal, frontal to cingulate, frontal to caudate and inter-hemispheric connections matured more slowly. We demonstrated a core of key connections that was not affected by GA at birth. However, local connectivity involving thalamus, cerebellum, superior frontal lobe, cingulate gyrus and short range cortico-cortical connections was related to the degree of prematurity and contributed to altered global topology of the structural brain network. The relative preservation of core connections at the expense of local connections may support more effective use of impaired white matter reserve following preterm birth. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Plasticity during Early Brain Development Is Determined by Ontogenetic Potential.

PubMed

Krägeloh-Mann, Ingeborg; Lidzba, Karen; Pavlova, Marina A; Wilke, Marko; Staudt, Martin

2017-04-01

Two competing hypotheses address neuroplasticity during early brain development: the "Kennard principle" describes the compensatory capacities of the immature developing CNS as superior to those of the adult brain, whereas the "Hebb principle" argues that the young brain is especially sensitive to insults. We provide evidence that these principles are not mutually exclusive. Following early brain lesions that are unilateral, the brain can refer to homotopic areas of the healthy hemisphere. This potential for reorganization is unique to the young brain but available only when, during ontogenesis of brain development, these areas have been used for the functions addressed. With respect to motor function, ipsilateral motor tracts can be recruited, which are only available during early brain development. Language can be reorganized to the right after early left hemispheric lesions, as the representation of the language network is initially bilateral. However, even in these situations, compensatory capacities of the developing brain are found to have limitations, probably defined by early determinants. Thus, plasticity and adaptivity are seen only within ontogenetic potential; that is, axonal or cortical structures cannot be recruited beyond early developmental possibilities. The young brain is probably more sensitive and vulnerable to lesions when these are bilateral. This is shown here for bilateral periventricular white matter lesions that clearly have an impact on cortical architecture and function, thus probably interfering with early network building. Georg Thieme Verlag KG Stuttgart · New York.

Riemannian multi-manifold modeling and clustering in brain networks

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Stimulation-Based Control of Dynamic Brain Networks

PubMed Central

Pasqualetti, Fabio; Gu, Shi; Cieslak, Matthew

2016-01-01

The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement. PMID:27611328

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

PubMed Central

Ghanbari, Yasser; Smith, Alex R.; Schultz, Robert T.; Verma, Ragini

2014-01-01

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain’s traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations. PMID:25037933

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

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.

Neural connections foster social connections: a diffusion-weighted imaging study of social networks

PubMed Central

Hampton, William H.; Unger, Ashley; Von Der Heide, Rebecca J.

2016-01-01

Although we know the transition from childhood to adulthood is marked by important social and neural development, little is known about how social network size might affect neurocognitive development or vice versa. Neuroimaging research has identified several brain regions, such as the amygdala, as key to this affiliative behavior. However, white matter connectivity among these regions, and its behavioral correlates, remain unclear. Here we tested two hypotheses: that an amygdalocentric structural white matter network governs social affiliative behavior and that this network changes during adolescence and young adulthood. We measured social network size behaviorally, and white matter microstructure using probabilistic diffusion tensor imaging in a sample of neurologically normal adolescents and young adults. Our results suggest amygdala white matter microstructure is key to understanding individual differences in social network size, with connectivity to other social brain regions such as the orbitofrontal cortex and anterior temporal lobe predicting much variation. In addition, participant age correlated with both network size and white matter variation in this network. These findings suggest the transition to adulthood may constitute a critical period for the optimization of structural brain networks underlying affiliative behavior. PMID:26755769

The autistic brain in the context of normal neurodevelopment.

PubMed

Ziats, Mark N; Edmonson, Catherine; Rennert, Owen M

2015-01-01

The etiology of autism spectrum disorders (ASDs) is complex and largely unclear. Among various lines of inquiry, many have suggested convergence onto disruptions in both neural circuitry and immune regulation/glial cell function pathways. However, the interpretation of the relationship between these two putative mechanisms has largely focused on the role of exogenous factors and insults, such as maternal infection, in activating immune pathways that in turn result in neural network abnormalities. Yet, given recent insights into our understanding of human neurodevelopment, and in particular the critical role of glia and the immune system in normal brain development, it is important to consider these putative pathological processes in their appropriate normal neurodevelopmental context. In this review, we explore the hypothesis that the autistic brain cellular phenotype likely represents intrinsic abnormalities of glial/immune processes constitutively operant in normal brain development that result in the observed neural network dysfunction. We review recent studies demonstrating the intercalated role of neural circuit development, the immune system, and glial cells in the normal developing brain, and integrate them with studies demonstrating pathological alterations in these processes in autism. By discussing known abnormalities in the autistic brain in the context of normal brain development, we explore the hypothesis that the glial/immune component of ASD may instead be related to intrinsic exaggerated/abnormal constitutive neurodevelopmental processes such as network pruning. Moreover, this hypothesis may be relevant to other neurodevelopmental disorders that share genetic, pathologic, and clinical features with autism.

Resting state cerebral blood flow with arterial spin labeling MRI in developing human brains.

PubMed

Liu, Feng; Duan, Yunsuo; Peterson, Bradley S; Asllani, Iris; Zelaya, Fernando; Lythgoe, David; Kangarlu, Alayar

2018-07-01

The development of brain circuits is coupled with changes in neurovascular coupling, which refers to the close relationship between neural activity and cerebral blood flow (CBF). Studying the characteristics of CBF during resting state in developing brain can be a complementary way to understand the functional connectivity of the developing brain. Arterial spin labeling (ASL), as a noninvasive MR technique, is particularly attractive for studying cerebral perfusion in children and even newborns. We have collected pulsed ASL data in resting state for 47 healthy subjects from young children to adolescence (aged from 6 to 20 years old). In addition to studying the developmental change of static CBF maps during resting state, we also analyzed the CBF time series to reveal the dynamic characteristics of CBF in differing age groups. We used the seed-based correlation analysis to examine the temporal relationship of CBF time series between the selected ROIs and other brain regions. We have shown the developmental patterns in both static CBF maps and dynamic characteristics of CBF. While higher CBF of default mode network (DMN) in all age groups supports that DMN is the prominent active network during the resting state, the CBF connectivity patterns of some typical resting state networks show distinct patterns of metabolic activity during the resting state in the developing brains. Copyright © 2018 European Paediatric Neurology Society. All rights reserved.

Signal or noise: brain network interactions underlying the experience and training of mindfulness.

PubMed

Mooneyham, Benjamin W; Mrazek, Michael D; Mrazek, Alissa J; Schooler, Jonathan W

2016-04-01

A broad set of brain regions has been associated with the experience and training of mindfulness. Many of these regions lie within key intrinsic brain networks, including the executive control, salience, and default networks. In this paper, we review the existing literature on the cognitive neuroscience of mindfulness through the lens of network science. We describe the characteristics of the intrinsic brain networks implicated in mindfulness and summarize the relevant findings pertaining to changes in functional connectivity (FC) within and between these networks. Convergence across these findings suggests that mindfulness may be associated with increased FC between two regions within the default network: the posterior cingulate cortex and the ventromedial prefrontal cortex. Additionally, extensive meditation experience may be associated with increased FC between the insula and the dorsolateral prefrontal cortex. However, little consensus has emerged within the existing literature owing to the diversity of operational definitions of mindfulness, neuroimaging methods, and network characterizations. We describe several challenges to develop a coherent cognitive neuroscience of mindfulness and to provide detailed recommendations for future research. © 2016 New York Academy of Sciences.

Brain functional connectivity changes in children that differ in impulsivity temperamental trait

PubMed Central

Inuggi, Alberto; Sanz-Arigita, Ernesto; González-Salinas, Carmen; Valero-García, Ana V.; García-Santos, Jose M.; Fuentes, Luis J.

2014-01-01

Impulsivity is a core personality trait forming part of normal behavior and contributing to adaptive functioning. However, in typically developing children, altered patterns of impulsivity constitute a risk factor for the development of behavioral problems. Since both pathological and non-pathological states are commonly characterized by continuous transitions, we used a correlative approach to investigate the potential link between personality and brain dynamics. We related brain functional connectivity of typically developing children, measured with magnetic resonance imaging at rest, with their impulsivity scores obtained from a questionnaire completed by their parents. We first looked for areas within the default mode network (DMN) whose functional connectivity might be modulated by trait impulsivity. Then, we calculated the functional connectivity among these regions and the rest of the brain in order to assess if impulsivity trait altered their relationships. We found two DMN clusters located at the posterior cingulate cortex and the right angular gyrus which were negatively correlated with impulsivity scores. The whole-brain correlation analysis revealed the classic network of correlating and anti-correlating areas with respect to the DMN. The impulsivity trait modulated such pattern showing that the canonical anti-phasic relation between DMN and action-related network was reduced in high impulsive children. These results represent the first evidence that the impulsivity, measured as personality trait assessed through parents' report, exerts a modulatory influence over the functional connectivity of resting state brain networks in typically developing children. The present study goes further to connect developmental approaches, mainly based on data collected through the use of questionnaires, and behavioral neuroscience, interested in how differences in brain structure and functions reflect in differences in behavior. PMID:24834038

Brain functional connectivity changes in children that differ in impulsivity temperamental trait.

PubMed

Inuggi, Alberto; Sanz-Arigita, Ernesto; González-Salinas, Carmen; Valero-García, Ana V; García-Santos, Jose M; Fuentes, Luis J

2014-01-01

Impulsivity is a core personality trait forming part of normal behavior and contributing to adaptive functioning. However, in typically developing children, altered patterns of impulsivity constitute a risk factor for the development of behavioral problems. Since both pathological and non-pathological states are commonly characterized by continuous transitions, we used a correlative approach to investigate the potential link between personality and brain dynamics. We related brain functional connectivity of typically developing children, measured with magnetic resonance imaging at rest, with their impulsivity scores obtained from a questionnaire completed by their parents. We first looked for areas within the default mode network (DMN) whose functional connectivity might be modulated by trait impulsivity. Then, we calculated the functional connectivity among these regions and the rest of the brain in order to assess if impulsivity trait altered their relationships. We found two DMN clusters located at the posterior cingulate cortex and the right angular gyrus which were negatively correlated with impulsivity scores. The whole-brain correlation analysis revealed the classic network of correlating and anti-correlating areas with respect to the DMN. The impulsivity trait modulated such pattern showing that the canonical anti-phasic relation between DMN and action-related network was reduced in high impulsive children. These results represent the first evidence that the impulsivity, measured as personality trait assessed through parents' report, exerts a modulatory influence over the functional connectivity of resting state brain networks in typically developing children. The present study goes further to connect developmental approaches, mainly based on data collected through the use of questionnaires, and behavioral neuroscience, interested in how differences in brain structure and functions reflect in differences in behavior.

The development of brain network architecture.

PubMed

Wierenga, Lara M; van den Heuvel, Martijn P; van Dijk, Sarai; Rijks, Yvonne; de Reus, Marcel A; Durston, Sarah

2016-02-01

Brain connectivity shows protracted development throughout childhood and adolescence, and, as such, the topology of brain networks changes during this period. The complexity of these changes with development is reflected by regional differences in maturation. This study explored age-related changes in network topology and regional developmental patterns during childhood and adolescence. We acquired two sets of Diffusion Weighted Imaging-scans and anatomical T1-weighted scans. The first dataset included 85 typically developing individuals (53 males; 32 females), aged between 7 and 23 years and was acquired on a Philips Achieva 1.5 Tesla scanner. A second dataset (N = 38) was acquired on a different (but identical) 1.5 T scanner and was used for independent replication of our results. We reconstructed whole brain networks using tractography. We operationalized fiber tract development as changes in mean diffusivity and radial diffusivity with age. Most fibers showed maturational changes in mean and radial diffusivity values throughout childhood and adolescence, likely reflecting increasing white matter integrity. The largest age-related changes were observed in association fibers within and between the frontal and parietal lobes. Furthermore, there was a simultaneous age-related decrease in average path length (P < 0.0001), increase in node strength (P < 0.0001) as well as network clustering (P = 0.001), which may reflect fine-tuning of topological organization. These results suggest a sequential maturational model where connections between unimodal regions strengthen in childhood, followed by connections from these unimodal regions to association regions, while adolescence is characterized by the strengthening of connections between association regions within the frontal and parietal cortex. Hum Brain Mapp 37:717-729, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Stimulus-Elicited Connectivity Influences Resting-State Connectivity Years Later in Human Development: A Prospective Study.

PubMed

Gabard-Durnam, Laurel Joy; Gee, Dylan Grace; Goff, Bonnie; Flannery, Jessica; Telzer, Eva; Humphreys, Kathryn Leigh; Lumian, Daniel Stephen; Fareri, Dominic Stephen; Caldera, Christina; Tottenham, Nim

2016-04-27

Although the functional architecture of the brain is indexed by resting-state connectivity networks, little is currently known about the mechanisms through which these networks assemble into stable mature patterns. The current study posits and tests the long-term phasic molding hypothesis that resting-state networks are gradually shaped by recurring stimulus-elicited connectivity across development by examining how both stimulus-elicited and resting-state functional connections of the human brain emerge over development at the systems level. Using a sequential design following 4- to 18-year-olds over a 2 year period, we examined the predictive associations between stimulus-elicited and resting-state connectivity in amygdala-cortical circuitry as an exemplar case (given this network's protracted development across these ages). Age-related changes in amygdala functional connectivity converged on the same regions of medial prefrontal cortex (mPFC) and inferior frontal gyrus when elicited by emotional stimuli and when measured at rest. Consistent with the long-term phasic molding hypothesis, prospective analyses for both connections showed that the magnitude of an individual's stimulus-elicited connectivity unidirectionally predicted resting-state functional connectivity 2 years later. For the amygdala-mPFC connection, only stimulus-elicited connectivity during childhood and the transition to adolescence shaped future resting-state connectivity, consistent with a sensitive period ending with adolescence for the amygdala-mPFC circuit. Together, these findings suggest that resting-state functional architecture may arise from phasic patterns of functional connectivity elicited by environmental stimuli over the course of development on the order of years. A fundamental issue in understanding the ontogeny of brain function is how resting-state (intrinsic) functional networks emerge and relate to stimulus-elicited functional connectivity. Here, we posit and test the long-term phasic molding hypothesis that resting-state network development is influenced by recurring stimulus-elicited connectivity through prospective examination of the developing human amygdala-cortical functional connections. Our results provide critical insight into how early environmental events sculpt functional network architecture across development and highlight childhood as a potential developmental period of heightened malleability for the amygdala-medial prefrontal cortex circuit. These findings have implications for how both positive and adverse experiences influence the developing brain and motivate future investigations of whether this molding mechanism reflects a general phenomenon of brain development. Copyright © 2016 the authors 0270-6474/16/364772-14$15.00/0.

Stimulus-Elicited Connectivity Influences Resting-State Connectivity Years Later in Human Development: A Prospective Study

PubMed Central

Gee, Dylan Grace; Goff, Bonnie; Flannery, Jessica; Telzer, Eva; Humphreys, Kathryn Leigh; Lumian, Daniel Stephen; Fareri, Dominic Stephen; Caldera, Christina; Tottenham, Nim

2016-01-01

Although the functional architecture of the brain is indexed by resting-state connectivity networks, little is currently known about the mechanisms through which these networks assemble into stable mature patterns. The current study posits and tests the long-term phasic molding hypothesis that resting-state networks are gradually shaped by recurring stimulus-elicited connectivity across development by examining how both stimulus-elicited and resting-state functional connections of the human brain emerge over development at the systems level. Using a sequential design following 4- to 18-year-olds over a 2 year period, we examined the predictive associations between stimulus-elicited and resting-state connectivity in amygdala-cortical circuitry as an exemplar case (given this network's protracted development across these ages). Age-related changes in amygdala functional connectivity converged on the same regions of medial prefrontal cortex (mPFC) and inferior frontal gyrus when elicited by emotional stimuli and when measured at rest. Consistent with the long-term phasic molding hypothesis, prospective analyses for both connections showed that the magnitude of an individual's stimulus-elicited connectivity unidirectionally predicted resting-state functional connectivity 2 years later. For the amygdala-mPFC connection, only stimulus-elicited connectivity during childhood and the transition to adolescence shaped future resting-state connectivity, consistent with a sensitive period ending with adolescence for the amygdala-mPFC circuit. Together, these findings suggest that resting-state functional architecture may arise from phasic patterns of functional connectivity elicited by environmental stimuli over the course of development on the order of years. SIGNIFICANCE STATEMENT A fundamental issue in understanding the ontogeny of brain function is how resting-state (intrinsic) functional networks emerge and relate to stimulus-elicited functional connectivity. Here, we posit and test the long-term phasic molding hypothesis that resting-state network development is influenced by recurring stimulus-elicited connectivity through prospective examination of the developing human amygdala-cortical functional connections. Our results provide critical insight into how early environmental events sculpt functional network architecture across development and highlight childhood as a potential developmental period of heightened malleability for the amygdala-medial prefrontal cortex circuit. These findings have implications for how both positive and adverse experiences influence the developing brain and motivate future investigations of whether this molding mechanism reflects a general phenomenon of brain development. PMID:27122035

Development of brain-wide connectivity architecture in awake rats.

PubMed

Ma, Zilu; Ma, Yuncong; Zhang, Nanyin

2018-08-01

Childhood and adolescence are both critical developmental periods, evidenced by complex neurophysiological changes the brain undergoes and high occurrence rates of neuropsychiatric disorders during these periods. Despite substantial progress in elucidating the developmental trajectories of individual neural circuits, our knowledge of developmental changes of whole-brain connectivity architecture in animals is sparse. To fill this gap, here we longitudinally acquired rsfMRI data in awake rats during five developmental stages from juvenile to adulthood. We found that the maturation timelines of brain circuits were heterogeneous and system specific. Functional connectivity (FC) tended to decrease in subcortical circuits, but increase in cortical circuits during development. In addition, the developing brain exhibited hemispheric functional specialization, evidenced by reduced inter-hemispheric FC between homotopic regions, and lower similarity of region-to-region FC patterns between the two hemispheres. Finally, we showed that whole-brain network development was characterized by reduced clustering (i.e. local communication) but increased integration (distant communication). Taken together, the present study has systematically characterized the development of brain-wide connectivity architecture from juvenile to adulthood in awake rats. It also serves as a critical reference point for understanding circuit- and network-level changes in animal models of brain development-related disorders. Furthermore, FC data during brain development in awake rodents contain high translational value and can shed light onto comparative neuroanatomy. Copyright © 2018 Elsevier Inc. All rights reserved.

Abnormal brain synchrony in Down Syndrome☆

PubMed Central

Anderson, Jeffrey S.; Nielsen, Jared A.; Ferguson, Michael A.; Burback, Melissa C.; Cox, Elizabeth T.; Dai, Li; Gerig, Guido; Edgin, Jamie O.; Korenberg, Julie R.

2013-01-01

Down Syndrome is the most common genetic cause for intellectual disability, yet the pathophysiology of cognitive impairment in Down Syndrome is unknown. We compared fMRI scans of 15 individuals with Down Syndrome to 14 typically developing control subjects while they viewed 50 min of cartoon video clips. There was widespread increased synchrony between brain regions, with only a small subset of strong, distant connections showing underconnectivity in Down Syndrome. Brain regions showing negative correlations were less anticorrelated and were among the most strongly affected connections in the brain. Increased correlation was observed between all of the distributed brain networks studied, with the strongest internetwork correlation in subjects with the lowest performance IQ. A functional parcellation of the brain showed simplified network structure in Down Syndrome organized by local connectivity. Despite increased interregional synchrony, intersubject correlation to the cartoon stimuli was lower in Down Syndrome, indicating that increased synchrony had a temporal pattern that was not in response to environmental stimuli, but idiosyncratic to each Down Syndrome subject. Short-range, increased synchrony was not observed in a comparison sample of 447 autism vs. 517 control subjects from the Autism Brain Imaging Exchange (ABIDE) collection of resting state fMRI data, and increased internetwork synchrony was only observed between the default mode and attentional networks in autism. These findings suggest immature development of connectivity in Down Syndrome with impaired ability to integrate information from distant brain regions into coherent distributed networks. PMID:24179822

Network integrity of the parental brain in infancy supports the development of children's social competencies.

PubMed

Abraham, Eyal; Hendler, Talma; Zagoory-Sharon, Orna; Feldman, Ruth

2016-11-01

The cross-generational transmission of mammalian sociality, initiated by the parent's postpartum brain plasticity and species-typical behavior that buttress offspring's socialization, has not been studied in humans. In this longitudinal study, we measured brain response of 45 primary-caregiving parents to their infant's stimuli, observed parent-infant interactions, and assayed parental oxytocin (OT). Intra- and inter-network connectivity were computed in three main networks of the human parental brain: core limbic, embodied simulation and mentalizing. During preschool, two key child social competencies were observed: emotion regulation and socialization. Parent's network integrity in infancy predicted preschoolers' social outcomes, with subcortical and cortical network integrity foreshadowing simple evolutionary-based regulatory tactics vs complex self-regulatory strategies and advanced socialization. Parent-infant synchrony mediated the links between connectivity of the parent's embodied simulation network and preschoolers' ability to use cognitive/executive emotion regulation strategies, highlighting the inherently dyadic nature of this network and its long-term effects on tuning young to social life. Parent's inter-network core limbic-embodied simulation connectivity predicted children's OT as moderated by parental OT. Findings challenge solipsistic neuroscience perspectives by demonstrating how the parent-offspring interface enables the brain of one human to profoundly impact long-term adaptation of another. © The Author (2016). Published by Oxford University Press.

Network integrity of the parental brain in infancy supports the development of children’s social competencies

PubMed Central

Abraham, Eyal; Hendler, Talma; Zagoory-Sharon, Orna

2016-01-01

The cross-generational transmission of mammalian sociality, initiated by the parent’s postpartum brain plasticity and species-typical behavior that buttress offspring’s socialization, has not been studied in humans. In this longitudinal study, we measured brain response of 45 primary-caregiving parents to their infant’s stimuli, observed parent–infant interactions, and assayed parental oxytocin (OT). Intra- and inter-network connectivity were computed in three main networks of the human parental brain: core limbic, embodied simulation and mentalizing. During preschool, two key child social competencies were observed: emotion regulation and socialization. Parent’s network integrity in infancy predicted preschoolers’ social outcomes, with subcortical and cortical network integrity foreshadowing simple evolutionary-based regulatory tactics vs complex self-regulatory strategies and advanced socialization. Parent–infant synchrony mediated the links between connectivity of the parent’s embodied simulation network and preschoolers' ability to use cognitive/executive emotion regulation strategies, highlighting the inherently dyadic nature of this network and its long-term effects on tuning young to social life. Parent’s inter-network core limbic-embodied simulation connectivity predicted children’s OT as moderated by parental OT. Findings challenge solipsistic neuroscience perspectives by demonstrating how the parent–offspring interface enables the brain of one human to profoundly impact long-term adaptation of another. PMID:27369068

Plasticity in the Developing Brain: Implications for Rehabilitation

ERIC Educational Resources Information Center

Johnston, Michael V.

2009-01-01

Neuronal plasticity allows the central nervous system to learn skills and remember information, to reorganize neuronal networks in response to environmental stimulation, and to recover from brain and spinal cord injuries. Neuronal plasticity is enhanced in the developing brain and it is usually adaptive and beneficial but can also be maladaptive…

Brain network informed subject community detection in early-onset schizophrenia.

PubMed

Yang, Zhi; Xu, Yong; Xu, Ting; Hoy, Colin W; Handwerker, Daniel A; Chen, Gang; Northoff, Georg; Zuo, Xi-Nian; Bandettini, Peter A

2014-07-03

Early-onset schizophrenia (EOS) offers a unique opportunity to study pathophysiological mechanisms and development of schizophrenia. Using 26 drug-naïve, first-episode EOS patients and 25 age- and gender-matched control subjects, we examined intrinsic connectivity network (ICN) deficits underlying EOS. Due to the emerging inconsistency between behavior-based psychiatric disease classification system and the underlying brain dysfunctions, we applied a fully data-driven approach to investigate whether the subjects can be grouped into highly homogeneous communities according to the characteristics of their ICNs. The resultant subject communities and the representative characteristics of ICNs were then associated with the clinical diagnosis and multivariate symptom patterns. A default mode ICN was statistically absent in EOS patients. Another frontotemporal ICN further distinguished EOS patients with predominantly negative symptoms. Connectivity patterns of this second network for the EOS patients with predominantly positive symptom were highly similar to typically developing controls. Our post-hoc functional connectivity modeling confirmed that connectivity strength in this frontotemporal circuit was significantly modulated by relative severity of positive and negative syndromes in EOS. This study presents a novel subtype discovery approach based on brain networks and proposes complex links between brain networks and symptom patterns in EOS.

Mapping Functional Brain Development: Building a Social Brain through Interactive Specialization

ERIC Educational Resources Information Center

Johnson, Mark H.; Grossmann, Tobias; Kadosh, Kathrin Cohen

2009-01-01

The authors review a viewpoint on human functional brain development, interactive specialization (IS), and its application to the emerging network of cortical regions referred to as the "social brain." They advance the IS view in 2 new ways. First, they extend IS into a domain to which it has not previously been applied--the emergence of social…

Altered Integration of Structural Covariance Networks in Young Children With Type 1 Diabetes.

PubMed

Hosseini, S M Hadi; Mazaika, Paul; Mauras, Nelly; Buckingham, Bruce; Weinzimer, Stuart A; Tsalikian, Eva; White, Neil H; Reiss, Allan L

2016-11-01

Type 1 diabetes mellitus (T1D), one of the most frequent chronic diseases in children, is associated with glucose dysregulation that contributes to an increased risk for neurocognitive deficits. While there is a bulk of evidence regarding neurocognitive deficits in adults with T1D, little is known about how early-onset T1D affects neural networks in young children. Recent data demonstrated widespread alterations in regional gray matter and white matter associated with T1D in young children. These widespread neuroanatomical changes might impact the organization of large-scale brain networks. In the present study, we applied graph-theoretical analysis to test whether the organization of structural covariance networks in the brain for a cohort of young children with T1D (N = 141) is altered compared to healthy controls (HC; N = 69). While the networks in both groups followed a small world organization-an architecture that is simultaneously highly segregated and integrated-the T1D network showed significantly longer path length compared with HC, suggesting reduced global integration of brain networks in young children with T1D. In addition, network robustness analysis revealed that the T1D network model showed more vulnerability to neural insult compared with HC. These results suggest that early-onset T1D negatively impacts the global organization of structural covariance networks and influences the trajectory of brain development in childhood. This is the first study to examine structural covariance networks in young children with T1D. Improving glycemic control for young children with T1D might help prevent alterations in brain networks in this population. Hum Brain Mapp 37:4034-4046, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

NASA Astrophysics Data System (ADS)

Rosário, R. S.; Cardoso, P. T.; Muñoz, M. A.; Montoya, P.; Miranda, J. G. V.

2015-12-01

The major aim of this work was to propose a new association method known as Motif-Synchronization. This method was developed to provide information about the synchronization degree and direction between two nodes of a network by counting the number of occurrences of some patterns between any two time series. The second objective of this work was to present a new methodology for the analysis of dynamic brain networks, by combining the Time-Varying Graph (TVG) method with a directional association method. We further applied the new algorithms to a set of human electroencephalogram (EEG) signals to perform a dynamic analysis of the brain functional networks (BFN).

Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness

PubMed Central

Kim, Minkyung; Mashour, George A.; Moraes, Stefanie-Blain; Vanini, Giancarlo; Tarnal, Vijay; Janke, Ellen; Hudetz, Anthony G.; Lee, Uncheol

2016-01-01

Sleep, anesthesia, and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four variables that are involved in facilitating ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states. PMID:26834616

Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness.

PubMed

Kim, Minkyung; Mashour, George A; Moraes, Stefanie-Blain; Vanini, Giancarlo; Tarnal, Vijay; Janke, Ellen; Hudetz, Anthony G; Lee, Uncheol

2016-01-01

Sleep, anesthesia, and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four variables that are involved in facilitating ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states.

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.

Atypical cross talk between mentalizing and mirror neuron networks in autism spectrum disorder.

PubMed

Fishman, Inna; Keown, Christopher L; Lincoln, Alan J; Pineda, Jaime A; Müller, Ralph-Axel

2014-07-01

Converging evidence indicates that brain abnormalities in autism spectrum disorder (ASD) involve atypical network connectivity, but it is unclear whether altered connectivity is especially prominent in brain networks that participate in social cognition. To investigate whether adolescents with ASD show altered functional connectivity in 2 brain networks putatively impaired in ASD and involved in social processing, theory of mind (ToM) and mirror neuron system (MNS). Cross-sectional study using resting-state functional magnetic resonance imaging involving 25 adolescents with ASD between the ages of 11 and 18 years and 25 typically developing adolescents matched for age, handedness, and nonverbal IQ. Statistical parametric maps testing the degree of whole-brain functional connectivity and social functioning measures. Relative to typically developing controls, participants with ASD showed a mixed pattern of both over- and underconnectivity in the ToM network, which was associated with greater social impairment. Increased connectivity in the ASD group was detected primarily between the regions of the MNS and ToM, and was correlated with sociocommunicative measures, suggesting that excessive ToM-MNS cross talk might be associated with social impairment. In a secondary analysis comparing a subset of the 15 participants with ASD with the most severe symptomology and a tightly matched subset of 15 typically developing controls, participants with ASD showed exclusive overconnectivity effects in both ToM and MNS networks, which were also associated with greater social dysfunction. Adolescents with ASD showed atypically increased functional connectivity involving the mentalizing and mirror neuron systems, largely reflecting greater cross talk between the 2. This finding is consistent with emerging evidence of reduced network segregation in ASD and challenges the prevailing theory of general long-distance underconnectivity in ASD. This excess ToM-MNS connectivity may reflect immature or aberrant developmental processes in 2 brain networks involved in understanding of others, a domain of impairment in ASD. Further, robust links with sociocommunicative symptoms of ASD implicate atypically increased ToM-MNS connectivity in social deficits observed in ASD.

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.

Big words, halved brains and small worlds: complex brain networks of figurative language comprehension.

PubMed

Arzouan, Yossi; Solomon, Sorin; Faust, Miriam; Goldstein, Abraham

2011-04-27

Language comprehension is a complex task that involves a wide network of brain regions. We used topological measures to qualify and quantify the functional connectivity of the networks used under various comprehension conditions. To that aim we developed a technique to represent functional networks based on EEG recordings, taking advantage of their excellent time resolution in order to capture the fast processes that occur during language comprehension. Networks were created by searching for a specific causal relation between areas, the negative feedback loop, which is ubiquitous in many systems. This method is a simple way to construct directed graphs using event-related activity, which can then be analyzed topologically. Brain activity was recorded while subjects read expressions of various types and indicated whether they found them meaningful. Slightly different functional networks were obtained for event-related activity evoked by each expression type. The differences reflect the special contribution of specific regions in each condition and the balance of hemispheric activity involved in comprehending different types of expressions and are consistent with the literature in the field. Our results indicate that representing event-related brain activity as a network using a simple temporal relation, such as the negative feedback loop, to indicate directional connectivity is a viable option for investigation which also derives new information about aspects not reflected in the classical methods for investigating brain activity.

Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network.

PubMed

Charron, Odelin; Lallement, Alex; Jarnet, Delphine; Noblet, Vincent; Clavier, Jean-Baptiste; Meyer, Philippe

2018-04-01

Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter <1 cm). As part of these treatments, effective detection and precise segmentation of lesions are imperative. Many methods based on deep-learning approaches have been developed for the automatic segmentation of gliomas, but very little for that of brain metastases. We adapted an existing 3D convolutional neural network (DeepMedic) to detect and segment brain metastases on MRI. At first, we sought to adapt the network parameters to brain metastases. We then explored the single or combined use of different MRI modalities, by evaluating network performance in terms of detection and segmentation. We also studied the interest of increasing the database with virtual patients or of using an additional database in which the active parts of the metastases are separated from the necrotic parts. Our results indicated that a deep network approach is promising for the detection and the segmentation of brain metastases on multimodal MRI. Copyright © 2018 Elsevier Ltd. All rights reserved.

Development of the brain's structural network efficiency in early adolescence: A longitudinal DTI twin study.

PubMed

Koenis, Marinka M G; Brouwer, Rachel M; van den Heuvel, Martijn P; Mandl, René C W; van Soelen, Inge L C; Kahn, René S; Boomsma, Dorret I; Hulshoff Pol, Hilleke E

2015-12-01

The brain is a network and our intelligence depends in part on the efficiency of this network. The network of adolescents differs from that of adults suggesting developmental changes. However, whether the network changes over time at the individual level and, if so, how this relates to intelligence, is unresolved in adolescence. In addition, the influence of genetic factors in the developing network is not known. Therefore, in a longitudinal study of 162 healthy adolescent twins and their siblings (mean age at baseline 9.9 [range 9.0-15.0] years), we mapped local and global structural network efficiency of cerebral fiber pathways (weighted with mean FA and streamline count) and assessed intelligence over a three-year interval. We find that the efficiency of the brain's structural network is highly heritable (locally up to 74%). FA-based local and global efficiency increases during early adolescence. Streamline count based local efficiency both increases and decreases, and global efficiency reorganizes to a net decrease. Local FA-based efficiency was correlated to IQ. Moreover, increases in FA-based network efficiency (global and local) and decreases in streamline count based local efficiency are related to increases in intellectual functioning. Individual changes in intelligence and local FA-based efficiency appear to go hand in hand in frontal and temporal areas. More widespread local decreases in streamline count based efficiency (frontal cingulate and occipital) are correlated with increases in intelligence. We conclude that the teenage brain is a network in progress in which individual differences in maturation relate to level of intellectual functioning. © 2015 Wiley Periodicals, Inc.

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

Development of global cortical networks in early infancy.

PubMed

Homae, Fumitaka; Watanabe, Hama; Otobe, Takayuki; Nakano, Tamami; Go, Tohshin; Konishi, Yukuo; Taga, Gentaro

2010-04-07

Human cognition and behaviors are subserved by global networks of neural mechanisms. Although the organization of the brain is a subject of interest, the process of development of global cortical networks in early infancy has not yet been clarified. In the present study, we explored developmental changes in these networks from several days to 6 months after birth by examining spontaneous fluctuations in brain activity, using multichannel near-infrared spectroscopy. We set up 94 measurement channels over the frontal, temporal, parietal, and occipital regions of the infant brain. The obtained signals showed complex time-series properties, which were characterized as 1/f fluctuations. To reveal the functional connectivity of the cortical networks, we calculated the temporal correlations of continuous signals between all the pairs of measurement channels. We found that the cortical network organization showed regional dependency and dynamic changes in the course of development. In the temporal, parietal, and occipital regions, connectivity increased between homologous regions in the two hemispheres and within hemispheres; in the frontal regions, it decreased progressively. Frontoposterior connectivity changed to a "U-shaped" pattern within 6 months: it decreases from the neonatal period to the age of 3 months and increases from the age of 3 months to the age of 6 months. We applied cluster analyses to the correlation coefficients and showed that the bilateral organization of the networks begins to emerge during the first 3 months of life. Our findings suggest that these developing networks, which form multiple clusters, are precursors of the functional cerebral architecture.

Changes in event-related potential functional networks predict traumatic brain injury in piglets.

PubMed

Atlan, Lorre S; Lan, Ingrid S; Smith, Colin; Margulies, Susan S

2018-06-01

Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species. Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured. Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy. This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome.

PubMed

Batalle, Dafnis; Eixarch, Elisenda; Figueras, Francesc; Muñoz-Moreno, Emma; Bargallo, Nuria; Illa, Miriam; Acosta-Rojas, Ruthy; Amat-Roldan, Ivan; Gratacos, Eduard

2012-04-02

Intrauterine growth restriction (IUGR) due to placental insufficiency affects 5-10% of all pregnancies and it is associated with a wide range of short- and long-term neurodevelopmental disorders. Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. In recent years several studies have used magnetic resonance imaging (MRI) to demonstrate differences in brain structure in IUGR subjects, but the ability to use MRI for individual predictive purposes in IUGR is limited. Recent research suggests that MRI in vivo access to brain connectivity might have the potential to help understanding cognitive and neurodevelopment processes. Specifically, MRI based connectomics is an emerging approach to extract information from MRI data that exhaustively maps inter-regional connectivity within the brain to build a graph model of its neural circuitry known as brain network. In the present study we used diffusion MRI based connectomics to obtain structural brain networks of a prospective cohort of one year old infants (32 controls and 24 IUGR) and analyze the existence of quantifiable brain reorganization of white matter circuitry in IUGR group by means of global and regional graph theory features of brain networks. Based on global and regional analyses of the brain network topology we demonstrated brain reorganization in IUGR infants at one year of age. Specifically, IUGR infants presented decreased global and local weighted efficiency, and a pattern of altered regional graph theory features. By means of binomial logistic regression, we also demonstrated that connectivity measures were associated with abnormal performance in later neurodevelopmental outcome as measured by Bayley Scale for Infant and Toddler Development, Third edition (BSID-III) at two years of age. These findings show the potential of diffusion MRI based connectomics and graph theory based network characteristics for estimating differences in the architecture of neural circuitry and developing imaging biomarkers of poor neurodevelopment outcome in infants with prenatal diseases. Copyright © 2012 Elsevier Inc. All rights reserved.

Neural Network Development Tool (NETS)

NASA Technical Reports Server (NTRS)

Baffes, Paul T.

1990-01-01

Artificial neural networks formed from hundreds or thousands of simulated neurons, connected in manner similar to that in human brain. Such network models learning behavior. Using NETS involves translating problem to be solved into input/output pairs, designing network configuration, and training network. Written in C.

Software for Brain Network Simulations: A Comparative Study

PubMed Central

Tikidji-Hamburyan, Ruben A.; Narayana, Vikram; Bozkus, Zeki; El-Ghazawi, Tarek A.

2017-01-01

Numerical simulations of brain networks are a critical part of our efforts in understanding brain functions under pathological and normal conditions. For several decades, the community has developed many software packages and simulators to accelerate research in computational neuroscience. In this article, we select the three most popular simulators, as determined by the number of models in the ModelDB database, such as NEURON, GENESIS, and BRIAN, and perform an independent evaluation of these simulators. In addition, we study NEST, one of the lead simulators of the Human Brain Project. First, we study them based on one of the most important characteristics, the range of supported models. Our investigation reveals that brain network simulators may be biased toward supporting a specific set of models. However, all simulators tend to expand the supported range of models by providing a universal environment for the computational study of individual neurons and brain networks. Next, our investigations on the characteristics of computational architecture and efficiency indicate that all simulators compile the most computationally intensive procedures into binary code, with the aim of maximizing their computational performance. However, not all simulators provide the simplest method for module development and/or guarantee efficient binary code. Third, a study of their amenability for high-performance computing reveals that NEST can almost transparently map an existing model on a cluster or multicore computer, while NEURON requires code modification if the model developed for a single computer has to be mapped on a computational cluster. Interestingly, parallelization is the weakest characteristic of BRIAN, which provides no support for cluster computations and limited support for multicore computers. Fourth, we identify the level of user support and frequency of usage for all simulators. Finally, we carry out an evaluation using two case studies: a large network with simplified neural and synaptic models and a small network with detailed models. These two case studies allow us to avoid any bias toward a particular software package. The results indicate that BRIAN provides the most concise language for both cases considered. Furthermore, as expected, NEST mostly favors large network models, while NEURON is better suited for detailed models. Overall, the case studies reinforce our general observation that simulators have a bias in the computational performance toward specific types of the brain network models. PMID:28775687

Intrinsic protective mechanisms of the neuron-glia network against glioma invasion.

PubMed

Iwadate, Yasuo; Fukuda, Kazumasa; Matsutani, Tomoo; Saeki, Naokatsu

2016-04-01

Gliomas arising in the brain parenchyma infiltrate into the surrounding brain and break down established complex neuron-glia networks. However, mounting evidence suggests that initially the network microenvironment of the adult central nervous system (CNS) is innately non-permissive to glioma cell invasion. The main players are inhibitory molecules in CNS myelin, as well as proteoglycans associated with astrocytes. Neural stem cells, and neurons themselves, possess inhibitory functions against neighboring tumor cells. These mechanisms have evolved to protect the established neuron-glia network, which is necessary for brain function. Greater insight into the interaction between glioma cells and the surrounding neuron-glia network is crucial for developing new therapies for treating these devastating tumors while preserving the important and complex neural functions of patients. Copyright © 2015 Elsevier Ltd. All rights reserved.

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

Time Course of Brain Network Reconfiguration Supporting Inhibitory Control.

PubMed

Popov, Tzvetan; Westner, Britta U; Silton, Rebecca L; Sass, Sarah M; Spielberg, Jeffrey M; Rockstroh, Brigitte; Heller, Wendy; Miller, Gregory A

2018-05-02

Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color-word Stroop task. Time-frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal-parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation. SIGNIFICANCE STATEMENT Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to inherent methodological constraints, much of this research has been unable to characterize the temporal dynamics of such networks (e.g., direction of information flow between nodes). Guided by fMRI research identifying the structure of brain networks supporting inhibitory control, results of EEG source analysis in a test sample ( n = 96) and replication sample ( n = 237) using effective connectivity and predictive analytics strategies advance a model of inhibitory control by characterizing the precise temporal dynamics by which this network operates and exemplify an approach by which mechanistic models can be developed for other key psychological processes. Copyright © 2018 the authors 0270-6474/18/384348-09$15.00/0.

Genetic and Diagnostic Biomarker Development in ASD Toddlers Using Resting State Functional MRI

DTIC Science & Technology

2016-09-01

networks during resting states. Autism spectrum disorder (ASD) begins prenatal, and early maldevelopment is present in many sites and systems that mediate...molecular and genomic evidence indicates autism spectrum disorder (ASD) begins prenatally, most likely by or before the late second trimester 10-15 as...ages 3 to 4 years. 2. KEYWORDS Autism spectrum disorder, ASD, early brain development, intrinsic functional brain networks, fMRI, infants, toddlers

Rich-club organization of the newborn human brain

PubMed Central

Ball, Gareth; Aljabar, Paul; Zebari, Sally; Tusor, Nora; Arichi, Tomoki; Merchant, Nazakat; Robinson, Emma C.; Ogundipe, Enitan; Rueckert, Daniel; Edwards, A. David; Counsell, Serena J.

2014-01-01

Combining diffusion magnetic resonance imaging and network analysis in the adult human brain has identified a set of highly connected cortical hubs that form a “rich club”—a high-cost, high-capacity backbone thought to enable efficient network communication. Rich-club architecture appears to be a persistent feature of the mature mammalian brain, but it is not known when this structure emerges during human development. In this longitudinal study we chart the emergence of structural organization in mid to late gestation. We demonstrate that a rich club of interconnected cortical hubs is already present by 30 wk gestation. Subsequently, until the time of normal birth, the principal development is a proliferation of connections between core hubs and the rest of the brain. We also consider the impact of environmental factors on early network development, and compare term-born neonates to preterm infants at term-equivalent age. Though rich-club organization remains intact following premature birth, we reveal significant disruptions in both in cortical–subcortical connectivity and short-distance corticocortical connections. Rich club organization is present well before the normal time of birth and may provide the fundamental structural architecture for the subsequent emergence of complex neurological functions. Premature exposure to the extrauterine environment is associated with altered network architecture and reduced network capacity, which may in part account for the high prevalence of cognitive problems in preterm infants. PMID:24799693

Specialization and Universals in the Development of Reading Skill: How Chinese Research Informs a Universal Science of Reading

PubMed Central

Perfetti, Charles; Cao, Fan; Booth, James

2014-01-01

Understanding Chinese reading is important for identifying the universal aspects of reading, separated from those aspects that are specific to alphabetic writing or to English in particular. Chinese and alphabetic writing make different demands on reading and learning to read, despite reading procedures and their supporting brain networks that are partly universal. Learning to read accommodates the demands of a writing system through the specialization of brain networks that support word identification. This specialization increases with reading development, leading to differences in the brain networks for alphabetic and Chinese reading. We suggest that beyond reading procedures that are partly universal and partly writing-system specific, functional reading universals arise across writing systems in their adaptation to human cognitive abilities. PMID:24744605

Development of distinct control networks through segregation and integration

PubMed Central

Fair, Damien A.; Dosenbach, Nico U. F.; Church, Jessica A.; Cohen, Alexander L.; Brahmbhatt, Shefali; Miezin, Francis M.; Barch, Deanna M.; Raichle, Marcus E.; Petersen, Steven E.; Schlaggar, Bradley L.

2007-01-01

Human attentional control is unrivaled. We recently proposed that adults depend on distinct frontoparietal and cinguloopercular networks for adaptive online task control versus more stable set control, respectively. During development, both experience-dependent evoked activity and spontaneous waves of synchronized cortical activity are thought to support the formation and maintenance of neural networks. Such mechanisms may encourage tighter “integration” of some regions into networks over time while “segregating” other sets of regions into separate networks. Here we use resting state functional connectivity MRI, which measures correlations in spontaneous blood oxygenation level-dependent signal fluctuations between brain regions to compare previously identified control networks between children and adults. We find that development of the proposed adult control networks involves both segregation (i.e., decreased short-range connections) and integration (i.e., increased long-range connections) of the brain regions that comprise them. Delay/disruption in the developmental processes of segregation and integration may play a role in disorders of control, such as autism, attention deficit hyperactivity disorder, and Tourette's syndrome. PMID:17679691

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

Molecular networks and the evolution of human cognitive specializations.

PubMed

Fontenot, Miles; Konopka, Genevieve

2014-12-01

Inroads into elucidating the origins of human cognitive specializations have taken many forms, including genetic, genomic, anatomical, and behavioral assays that typically compare humans to non-human primates. While the integration of all of these approaches is essential for ultimately understanding human cognition, here, we review the usefulness of coexpression network analysis for specifically addressing this question. An increasing number of studies have incorporated coexpression networks into brain expression studies comparing species, disease versus control tissue, brain regions, or developmental time periods. A clearer picture has emerged of the key genes driving brain evolution, as well as the developmental and regional contributions of gene expression patterns important for normal brain development and those misregulated in cognitive diseases. Copyright © 2014 Elsevier Ltd. All rights reserved.

Tracing Activity Across the Whole Brain Neural Network with Optogenetic Functional Magnetic Resonance Imaging

PubMed Central

Lee, Jin Hyung

2011-01-01

Despite the overwhelming need, there has been a relatively large gap in our ability to trace network level activity across the brain. The complex dense wiring of the brain makes it extremely challenging to understand cell-type specific activity and their communication beyond a few synapses. Recent development of the optogenetic functional magnetic resonance imaging (ofMRI) provides a new impetus for the study of brain circuits by enabling causal tracing of activities arising from defined cell types and firing patterns across the whole brain. Brain circuit elements can be selectively triggered based on their genetic identity, cell body location, and/or their axonal projection target with temporal precision while the resulting network response is monitored non-invasively with unprecedented spatial and temporal accuracy. With further studies including technological innovations to bring ofMRI to its full potential, ofMRI is expected to play an important role in our system-level understanding of the brain circuit mechanism. PMID:22046160

Structural and Maturational Covariance in Early Childhood Brain Development.

PubMed

Geng, Xiujuan; Li, Gang; Lu, Zhaohua; Gao, Wei; Wang, Li; Shen, Dinggang; Zhu, Hongtu; Gilmore, John H

2017-03-01

Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and sensorimotor networks in a longitudinal population of 118 children after birth to 2 years old and compared them with intrinsic functional connectivity networks. We found that structural covariance of all networks exhibit strong correlations mostly limited to their seed regions. By Age 2, default and dorsal attention structural networks are much less distributed compared with their functional maps. The maturational covariance maps, however, revealed significant couplings in rates of change between distributed regions, which partially recapitulate their functional networks. The structural and maturational covariance of the primary visual and sensorimotor networks shows similar patterns to the corresponding functional networks. Results indicate that functional networks are in place prior to structural networks, that correlated structural patterns in adult may arise in part from coordinated cortical maturation, and that regional co-activation in functional networks may guide and refine the maturation of SCNs over childhood development. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture

PubMed Central

Ferguson, Michael A.; Anderson, Jeffrey S.; Spreng, R. Nathan

2017-01-01

Human intelligence has been conceptualized as a complex system of dissociable cognitive processes, yet studies investigating the neural basis of intelligence have typically emphasized the contributions of discrete brain regions or, more recently, of specific networks of functionally connected regions. Here we take a broader, systems perspective in order to investigate whether intelligence is an emergent property of synchrony within the brain’s intrinsic network architecture. Using a large sample of resting-state fMRI and cognitive data (n = 830), we report that the synchrony of functional interactions within and across distributed brain networks reliably predicts fluid and flexible intellectual functioning. By adopting a whole-brain, systems-level approach, we were able to reliably predict individual differences in human intelligence by characterizing features of the brain’s intrinsic network architecture. These findings hold promise for the eventual development of neural markers to predict changes in intellectual function that are associated with neurodevelopment, normal aging, and brain disease.

Brain State Differentiation and Behavioral Inflexibility in Autism†

PubMed Central

Uddin, Lucina Q.; Supekar, Kaustubh; Lynch, Charles J.; Cheng, Katherine M.; Odriozola, Paola; Barth, Maria E.; Phillips, Jennifer; Feinstein, Carl; Abrams, Daniel A.; Menon, Vinod

2015-01-01

Autism spectrum disorders (ASDs) are characterized by social impairments alongside cognitive and behavioral inflexibility. While social deficits in ASDs have extensively been characterized, the neurobiological basis of inflexibility and its relation to core clinical symptoms of the disorder are unknown. We acquired functional neuroimaging data from 2 cohorts, each consisting of 17 children with ASDs and 17 age- and IQ-matched typically developing (TD) children, during stimulus-evoked brain states involving performance of social attention and numerical problem solving tasks, as well as during intrinsic, resting brain states. Effective connectivity between key nodes of the salience network, default mode network, and central executive network was used to obtain indices of functional organization across evoked and intrinsic brain states. In both cohorts examined, a machine learning algorithm was able to discriminate intrinsic (resting) and evoked (task) functional brain network configurations more accurately in TD children than in children with ASD. Brain state discriminability was related to severity of restricted and repetitive behaviors, indicating that weak modulation of brain states may contribute to behavioral inflexibility in ASD. These findings provide novel evidence for a potential link between neurophysiological inflexibility and core symptoms of this complex neurodevelopmental disorder. PMID:25073720

Brain tumour cells interconnect to a functional and resistant network.

PubMed

Osswald, Matthias; Jung, Erik; Sahm, Felix; Solecki, Gergely; Venkataramani, Varun; Blaes, Jonas; Weil, Sophie; Horstmann, Heinz; Wiestler, Benedikt; Syed, Mustafa; Huang, Lulu; Ratliff, Miriam; Karimian Jazi, Kianush; Kurz, Felix T; Schmenger, Torsten; Lemke, Dieter; Gömmel, Miriam; Pauli, Martin; Liao, Yunxiang; Häring, Peter; Pusch, Stefan; Herl, Verena; Steinhäuser, Christian; Krunic, Damir; Jarahian, Mostafa; Miletic, Hrvoje; Berghoff, Anna S; Griesbeck, Oliver; Kalamakis, Georgios; Garaschuk, Olga; Preusser, Matthias; Weiss, Samuel; Liu, Haikun; Heiland, Sabine; Platten, Michael; Huber, Peter E; Kuner, Thomas; von Deimling, Andreas; Wick, Wolfgang; Winkler, Frank

2015-12-03

Astrocytic brain tumours, including glioblastomas, are incurable neoplasms characterized by diffusely infiltrative growth. Here we show that many tumour cells in astrocytomas extend ultra-long membrane protrusions, and use these distinct tumour microtubes as routes for brain invasion, proliferation, and to interconnect over long distances. The resulting network allows multicellular communication through microtube-associated gap junctions. When damage to the network occurred, tumour microtubes were used for repair. Moreover, the microtube-connected astrocytoma cells, but not those remaining unconnected throughout tumour progression, were protected from cell death inflicted by radiotherapy. The neuronal growth-associated protein 43 was important for microtube formation and function, and drove microtube-dependent tumour cell invasion, proliferation, interconnection, and radioresistance. Oligodendroglial brain tumours were deficient in this mechanism. In summary, astrocytomas can develop functional multicellular network structures. Disconnection of astrocytoma cells by targeting their tumour microtubes emerges as a new principle to reduce the treatment resistance of this disease.

Toward the Development of an Artificial Brain on a Micropatterned and Material-Regulated Biochip by Guiding and Promoting the Differentiation and Neurite Outgrowth of Neural Stem/Progenitor Cells.

PubMed

Liu, Yung-Chiang; Lee, I-Chi; Lei, Kin Fong

2018-02-14

An in vitro model mimicking the in vivo environment of the brain must be developed to study neural communication and regeneration and to obtain an understanding of cellular and molecular responses. In this work, a multilayered neural network was successfully constructed on a biochip by guiding and promoting neural stem/progenitor cell differentiation and network formation. The biochip consisted of 3 × 3 arrays of cultured wells connected with channels. Neurospheroids were cultured on polyelectrolyte multilayer (PEM) films in the culture wells. Neurite outgrowth and neural differentiation were guided and promoted by the micropatterns and the PEM films. After 5 days in culture, a 3 × 3 neural network was constructed on the biochip. The function and the connections of the network were evaluated by immunocytochemistry and impedance measurements. Neurons were generated and produced functional and recyclable synaptic vesicles. Moreover, the electrical connections of the neural network were confirmed by measuring the impedance across the neurospheroids. The current work facilitates the development of an artificial brain on a chip for investigations of electrical stimulations and recordings of multilayered neural communication and regeneration.

Visible rodent brain-wide networks at single-neuron resolution

PubMed Central

Yuan, Jing; Gong, Hui; Li, Anan; Li, Xiangning; Chen, Shangbin; Zeng, Shaoqun; Luo, Qingming

2015-01-01

There are some unsolvable fundamental questions, such as cell type classification, neural circuit tracing and neurovascular coupling, though great progresses are being made in neuroscience. Because of the structural features of neurons and neural circuits, the solution of these questions needs us to break through the current technology of neuroanatomy for acquiring the exactly fine morphology of neuron and vessels and tracing long-distant circuit at axonal resolution in the whole brain of mammals. Combined with fast-developing labeling techniques, efficient whole-brain optical imaging technology emerging at the right moment presents a huge potential in the structure and function research of specific-function neuron and neural circuit. In this review, we summarize brain-wide optical tomography techniques, review the progress on visible brain neuronal/vascular networks benefit from these novel techniques, and prospect the future technical development. PMID:26074784

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.

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.

Developmental changes in organization of structural brain networks.

PubMed

Khundrakpam, Budhachandra S; Reid, Andrew; Brauer, Jens; Carbonell, Felix; Lewis, John; Ameis, Stephanie; Karama, Sherif; Lee, Junki; Chen, Zhang; Das, Samir; Evans, Alan C

2013-09-01

Recent findings from developmental neuroimaging studies suggest that the enhancement of cognitive processes during development may be the result of a fine-tuning of the structural and functional organization of brain with maturation. However, the details regarding the developmental trajectory of large-scale structural brain networks are not yet understood. Here, we used graph theory to examine developmental changes in the organization of structural brain networks in 203 normally growing children and adolescents. Structural brain networks were constructed using interregional correlations in cortical thickness for 4 age groups (early childhood: 4.8-8.4 year; late childhood: 8.5-11.3 year; early adolescence: 11.4-14.7 year; late adolescence: 14.8-18.3 year). Late childhood showed prominent changes in topological properties, specifically a significant reduction in local efficiency, modularity, and increased global efficiency, suggesting a shift of topological organization toward a more random configuration. An increase in number and span of distribution of connector hubs was found in this age group. Finally, inter-regional connectivity analysis and graph-theoretic measures indicated early maturation of primary sensorimotor regions and protracted development of higher order association and paralimbic regions. Our finding reveals a time window of plasticity occurring during late childhood which may accommodate crucial changes during puberty and the new developmental tasks that an adolescent faces.

The impacts of pesticide and nicotine exposures on functional brain networks in Latino immigrant workers.

PubMed

Bahrami, Mohsen; Laurienti, Paul J; Quandt, Sara A; Talton, Jennifer; Pope, Carey N; Summers, Phillip; Burdette, Jonathan H; Chen, Haiying; Liu, Jing; Howard, Timothy D; Arcury, Thomas A; Simpson, Sean L

2017-09-01

Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and strength, brain networks in farmworkers had more clustered and modular structures when compared to non-farmworkers. Our findings suggest increased functional specificity and decreased functional integration in farmworkers when compared to non-farmworkers. Cholinesterase activity was associated with population differences in community structure and the strength of brain network functional connections. Urinary cotinine, a marker of nicotine exposure, was associated with the differences in network community structure. Brain network differences between farmworkers and non-farmworkers, as well as pesticide and nicotine exposure effects on brain functional connections in this study, may illuminate underlying mechanisms that cause neurological implications in later life. Copyright © 2017 Elsevier B.V. All rights reserved.

Gestational Age is Dimensionally Associated with Structural Brain Network Abnormalities Across Development.

PubMed

Nassar, Rula; Kaczkurkin, Antonia N; Xia, Cedric Huchuan; Sotiras, Aristeidis; Pehlivanova, Marieta; Moore, Tyler M; Garcia de La Garza, Angel; Roalf, David R; Rosen, Adon F G; Lorch, Scott A; Ruparel, Kosha; Shinohara, Russell T; Davatzikos, Christos; Gur, Ruben C; Gur, Raquel E; Satterthwaite, Theodore D

2018-04-21

Prematurity is associated with diverse developmental abnormalities, yet few studies relate cognitive and neurostructural deficits to a dimensional measure of prematurity. Leveraging a large sample of children, adolescents, and young adults (age 8-22 years) studied as part of the Philadelphia Neurodevelopmental Cohort, we examined how variation in gestational age impacted cognition and brain structure later in development. Participants included 72 preterm youth born before 37 weeks' gestation and 206 youth who were born at term (37 weeks or later). Using a previously-validated factor analysis, cognitive performance was assessed in three domains: (1) executive function and complex reasoning, (2) social cognition, and (3) episodic memory. All participants completed T1-weighted neuroimaging at 3 T to measure brain volume. Structural covariance networks were delineated using non-negative matrix factorization, an advanced multivariate analysis technique. Lower gestational age was associated with both deficits in executive function and reduced volume within 11 of 26 structural covariance networks, which included orbitofrontal, temporal, and parietal cortices as well as subcortical regions including the hippocampus. Notably, the relationship between lower gestational age and executive dysfunction was accounted for in part by structural network deficits. Together, these findings emphasize the durable impact of prematurity on cognition and brain structure, which persists across development.

Coordinated Gene Expression of Neuroinflammatory and Cell Signaling Markers in Dorsolateral Prefrontal Cortex during Human Brain Development and Aging

PubMed Central

Primiani, Christopher T.; Ryan, Veronica H.; Rao, Jagadeesh S.; Cam, Margaret C.; Ahn, Kwangmi; Modi, Hiren R.; Rapoport, Stanley I.

2014-01-01

Background Age changes in expression of inflammatory, synaptic, and neurotrophic genes are not well characterized during human brain development and senescence. Knowing these changes may elucidate structural, metabolic, and functional brain processes over the lifespan, as well vulnerability to neurodevelopmental or neurodegenerative diseases. Hypothesis Expression levels of inflammatory, synaptic, and neurotrophic genes in the human brain are coordinated over the lifespan and underlie changes in phenotypic networks or cascades. Methods We used a large-scale microarray dataset from human prefrontal cortex, BrainCloud, to quantify age changes over the lifespan, divided into Development (0 to 21 years, 87 brains) and Aging (22 to 78 years, 144 brains) intervals, in transcription levels of 39 genes. Results Gene expression levels followed different trajectories over the lifespan. Many changes were intercorrelated within three similar groups or clusters of genes during both Development and Aging, despite different roles of the gene products in the two intervals. During Development, changes were related to reported neuronal loss, dendritic growth and pruning, and microglial events; TLR4, IL1R1, NFKB1, MOBP, PLA2G4A, and PTGS2 expression increased in the first years of life, while expression of synaptic genes GAP43 and DBN1 decreased, before reaching plateaus. During Aging, expression was upregulated for potentially pro-inflammatory genes such as NFKB1, TRAF6, TLR4, IL1R1, TSPO, and GFAP, but downregulated for neurotrophic and synaptic integrity genes such as BDNF, NGF, PDGFA, SYN, and DBN1. Conclusions Coordinated changes in gene transcription cascades underlie changes in synaptic, neurotrophic, and inflammatory phenotypic networks during brain Development and Aging. Early postnatal expression changes relate to neuronal, glial, and myelin growth and synaptic pruning events, while late Aging is associated with pro-inflammatory and synaptic loss changes. Thus, comparable transcriptional regulatory networks that operate throughout the lifespan underlie different phenotypic processes during Aging compared to Development. PMID:25329999

Coordinated gene expression of neuroinflammatory and cell signaling markers in dorsolateral prefrontal cortex during human brain development and aging.

PubMed

Primiani, Christopher T; Ryan, Veronica H; Rao, Jagadeesh S; Cam, Margaret C; Ahn, Kwangmi; Modi, Hiren R; Rapoport, Stanley I

2014-01-01

Age changes in expression of inflammatory, synaptic, and neurotrophic genes are not well characterized during human brain development and senescence. Knowing these changes may elucidate structural, metabolic, and functional brain processes over the lifespan, as well vulnerability to neurodevelopmental or neurodegenerative diseases. Expression levels of inflammatory, synaptic, and neurotrophic genes in the human brain are coordinated over the lifespan and underlie changes in phenotypic networks or cascades. We used a large-scale microarray dataset from human prefrontal cortex, BrainCloud, to quantify age changes over the lifespan, divided into Development (0 to 21 years, 87 brains) and Aging (22 to 78 years, 144 brains) intervals, in transcription levels of 39 genes. Gene expression levels followed different trajectories over the lifespan. Many changes were intercorrelated within three similar groups or clusters of genes during both Development and Aging, despite different roles of the gene products in the two intervals. During Development, changes were related to reported neuronal loss, dendritic growth and pruning, and microglial events; TLR4, IL1R1, NFKB1, MOBP, PLA2G4A, and PTGS2 expression increased in the first years of life, while expression of synaptic genes GAP43 and DBN1 decreased, before reaching plateaus. During Aging, expression was upregulated for potentially pro-inflammatory genes such as NFKB1, TRAF6, TLR4, IL1R1, TSPO, and GFAP, but downregulated for neurotrophic and synaptic integrity genes such as BDNF, NGF, PDGFA, SYN, and DBN1. Coordinated changes in gene transcription cascades underlie changes in synaptic, neurotrophic, and inflammatory phenotypic networks during brain Development and Aging. Early postnatal expression changes relate to neuronal, glial, and myelin growth and synaptic pruning events, while late Aging is associated with pro-inflammatory and synaptic loss changes. Thus, comparable transcriptional regulatory networks that operate throughout the lifespan underlie different phenotypic processes during Aging compared to Development.

In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses.

PubMed

Bonifazi, Paolo; Difato, Francesco; Massobrio, Paolo; Breschi, Gian L; Pasquale, Valentina; Levi, Timothée; Goldin, Miri; Bornat, Yannick; Tedesco, Mariateresa; Bisio, Marta; Kanner, Sivan; Galron, Ronit; Tessadori, Jacopo; Taverna, Stefano; Chiappalone, Michela

2013-01-01

Brain-machine interfaces (BMI) were born to control "actions from thoughts" in order to recover motor capability of patients with impaired functional connectivity between the central and peripheral nervous system. The final goal of our studies is the development of a new proof-of-concept BMI-a neuromorphic chip for brain repair-to reproduce the functional organization of a damaged part of the central nervous system. To reach this ambitious goal, we implemented a multidisciplinary "bottom-up" approach in which in vitro networks are the paradigm for the development of an in silico model to be incorporated into a neuromorphic device. In this paper we present the overall strategy and focus on the different building blocks of our studies: (i) the experimental characterization and modeling of "finite size networks" which represent the smallest and most general self-organized circuits capable of generating spontaneous collective dynamics; (ii) the induction of lesions in neuronal networks and the whole brain preparation with special attention on the impact on the functional organization of the circuits; (iii) the first production of a neuromorphic chip able to implement a real-time model of neuronal networks. A dynamical characterization of the finite size circuits with single cell resolution is provided. A neural network model based on Izhikevich neurons was able to replicate the experimental observations. Changes in the dynamics of the neuronal circuits induced by optical and ischemic lesions are presented respectively for in vitro neuronal networks and for a whole brain preparation. Finally the implementation of a neuromorphic chip reproducing the network dynamics in quasi-real time (10 ns precision) is presented.

Normalization of similarity-based individual brain networks from gray matter MRI and its association with neurodevelopment in infants with intrauterine growth restriction.

PubMed

Batalle, Dafnis; Muñoz-Moreno, Emma; Figueras, Francesc; Bargallo, Nuria; Eixarch, Elisenda; Gratacos, Eduard

2013-12-01

Obtaining individual biomarkers for the prediction of altered neurological outcome is a challenge of modern medicine and neuroscience. Connectomics based on magnetic resonance imaging (MRI) stands as a good candidate to exhaustively extract information from MRI by integrating the information obtained in a few network features that can be used as individual biomarkers of neurological outcome. However, this approach typically requires the use of diffusion and/or functional MRI to extract individual brain networks, which require high acquisition times and present an extreme sensitivity to motion artifacts, critical problems when scanning fetuses and infants. Extraction of individual networks based on morphological similarity from gray matter is a new approach that benefits from the power of graph theory analysis to describe gray matter morphology as a large-scale morphological network from a typical clinical anatomic acquisition such as T1-weighted MRI. In the present paper we propose a methodology to normalize these large-scale morphological networks to a brain network with standardized size based on a parcellation scheme. The proposed methodology was applied to reconstruct individual brain networks of 63 one-year-old infants, 41 infants with intrauterine growth restriction (IUGR) and 22 controls, showing altered network features in the IUGR group, and their association with neurodevelopmental outcome at two years of age by means of ordinal regression analysis of the network features obtained with Bayley Scale for Infant and Toddler Development, third edition. Although it must be more widely assessed, this methodology stands as a good candidate for the development of biomarkers for altered neurodevelopment in the pediatric population. © 2013 Elsevier Inc. All rights reserved.

Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis.

PubMed

Goch, Caspar J; Stieltjes, Bram; Henze, Romy; Hering, Jan; Poustka, Luise; Meinzer, Hans-Peter; Maier-Hein, Klaus H

2014-05-01

Diagnosis of autism spectrum disorders (ASD) is difficult, as symptoms vary greatly and are difficult to quantify objectively. Recent work has focused on the assessment of non-invasive diffusion tensor imaging-based biomarkers that reflect the microstructural characteristics of neuronal pathways in the brain. While tractography-based approaches typically analyze specific structures of interest, a graph-based large-scale network analysis of the connectome can yield comprehensive measures of larger-scale architectural patterns in the brain. Commonly applied global network indices, however, do not provide any specificity with respect to functional areas or anatomical structures. Aim of this work was to assess the concept of network centrality as a tool to perform locally specific analysis without disregarding the global network architecture and compare it to other popular network indices. We create connectome networks from fiber tractographies and parcellations of the human brain and compute global network indices as well as local indices for Wernicke's Area, Broca's Area and the Motor Cortex. Our approach was evaluated on 18 children suffering from ASD and 18 typically developed controls using magnetic resonance imaging-based cortical parcellations in combination with diffusion tensor imaging tractography. We show that the network centrality of Wernicke's area is significantly (p<0.001) reduced in ASD, while the motor cortex, which was used as a control region, did not show significant alterations. This could reflect the reduced capacity for comprehension of language in ASD. The betweenness centrality could potentially be an important metric in the development of future diagnostic tools in the clinical context of ASD diagnosis. Our results further demonstrate the applicability of large-scale network analysis tools in the domain of region-specific analysis with a potential application in many different psychological disorders.

Age-related functional brain changes in young children.

PubMed

Long, Xiangyu; Benischek, Alina; Dewey, Deborah; Lebel, Catherine

2017-07-15

Brain function and structure change significantly during the toddler and preschool years. However, most studies focus on older or younger children, so the specific nature of these changes is unclear. In the present study, we analyzed 77 functional magnetic resonance imaging datasets from 44 children aged 2-6 years. We extracted measures of both local (amplitude of low frequency fluctuation and regional homogeneity) and global (eigenvector centrality mapping) activity and connectivity, and examined their relationships with age using robust linear correlation analysis and strict control for head motion. Brain areas within the default mode network and the frontoparietal network, such as the middle frontal gyrus, the inferior parietal lobule and the posterior cingulate cortex, showed increases in local and global functional features with age. Several brain areas such as the superior parietal lobule and superior temporal gyrus presented opposite development trajectories of local and global functional features, suggesting a shifting connectivity framework in early childhood. This development of functional connectivity in early childhood likely underlies major advances in cognitive abilities, including language and development of theory of mind. These findings provide important insight into the development patterns of brain function during the preschool years, and lay the foundation for future studies of altered brain development in young children with brain disorders or injury. Copyright © 2017 Elsevier Inc. All rights reserved.

Multimodal Approaches to Define Network Oscillations in Depression

PubMed Central

Smart, Otis Lkuwamy; Tiruvadi, Vineet Ravi; Mayberg, Helen S.

2018-01-01

The renaissance in the use of encephalography-based research methods to probe the pathophysiology of neuropsychiatric disorders is well afoot and continues to advance. Building on the platform of neuroimaging evidence on brain circuit models, magnetoencephalography, scalp electroencephalography, and even invasive electroencephalography are now being used to characterize brain network dysfunctions that underlie major depressive disorder using brain oscillation measurements and associated treatment responses. Such multiple encephalography modalities provide avenues to study pathologic network dynamics with high temporal resolution and over long time courses, opportunities to complement neuroimaging methods and findings, and new approaches to identify quantitative biomarkers that indicate critical targets for brain therapy. Such goals have been facilitated by the ongoing testing of novel invasive neuromodulation therapies, notably, deep brain stimulation, where clinically relevant treatment effects can be monitored at multiple brain sites in a time-locked causal manner. We review key brain rhythms identified in major depressive disorder as foundation for development of putative biomarkers for objectively evaluating neuromodulation success and for guiding deep brain stimulation or other target-based neuromodulation strategies for treatment-resistant depression patients. PMID:25681871

Electrophysiological signatures of atypical intrinsic brain connectivity networks in autism

NASA Astrophysics Data System (ADS)

Shou, Guofa; Mosconi, Matthew W.; Wang, Jun; Ethridge, Lauren E.; Sweeney, John A.; Ding, Lei

2017-08-01

Objective. Abnormal local and long-range brain connectivity have been widely reported in autism spectrum disorder (ASD), yet the nature of these abnormalities and their functional relevance at distinct cortical rhythms remains unknown. Investigations of intrinsic connectivity networks (ICNs) and their coherence across whole brain networks hold promise for determining whether patterns of functional connectivity abnormalities vary across frequencies and networks in ASD. In the present study, we aimed to probe atypical intrinsic brain connectivity networks in ASD from resting-state electroencephalography (EEG) data via characterizing the whole brain network. Approach. Connectivity within individual ICNs (measured by spectral power) and between ICNs (measured by coherence) were examined at four canonical frequency bands via a time-frequency independent component analysis on high-density EEG, which were recorded from 20 ASD and 20 typical developing (TD) subjects during an eyes-closed resting state. Main results. Among twelve identified electrophysiological ICNs, individuals with ASD showed hyper-connectivity in individual ICNs and hypo-connectivity between ICNs. Functional connectivity alterations in ASD were more severe in the frontal lobe and the default mode network (DMN) and at low frequency bands. These functional connectivity measures also showed abnormal age-related associations in ICNs related to frontal, temporal and motor regions in ASD. Significance. Our findings suggest that ASD is characterized by the opposite directions of abnormalities (i.e. hypo- and hyper-connectivity) in the hierarchical structure of the whole brain network, with more impairments in the frontal lobe and the DMN at low frequency bands, which are critical for top-down control of sensory systems, as well as for both cognition and social skills.

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

PubMed Central

Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.

2016-01-01

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. SIGNIFICANCE STATEMENT Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention. PMID:27629707

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention.

PubMed

Rosenberg, Monica D; Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S; Shen, Xilin; Constable, R Todd; Li, Chiang-Shan R; Chun, Marvin M

2016-09-14

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention. Copyright © 2016 the authors 0270-6474/16/369547-11$15.00/0.

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.

Socioeconomic status moderates age-related differences in the brain's functional network organization and anatomy across the adult lifespan.

PubMed

Chan, Micaela Y; Na, Jinkyung; Agres, Phillip F; Savalia, Neil K; Park, Denise C; Wig, Gagan S

2018-05-14

An individual's environmental surroundings interact with the development and maturation of their brain. An important aspect of an individual's environment is his or her socioeconomic status (SES), which estimates access to material resources and social prestige. Previous characterizations of the relation between SES and the brain have primarily focused on earlier or later epochs of the lifespan (i.e., childhood, older age). We broaden this work to examine the relationship between SES and the brain across a wide range of human adulthood (20-89 years), including individuals from the less studied middle-age range. SES, defined by education attainment and occupational socioeconomic characteristics, moderates previously reported age-related differences in the brain's functional network organization and whole-brain cortical structure. Across middle age (35-64 years), lower SES is associated with reduced resting-state system segregation (a measure of effective functional network organization). A similar but less robust relationship exists between SES and age with respect to brain anatomy: Lower SES is associated with reduced cortical gray matter thickness in middle age. Conversely, younger and older adulthood do not exhibit consistent SES-related difference in the brain measures. The SES-brain relationships persist after controlling for measures of physical and mental health, cognitive ability, and participant demographics. Critically, an individual's childhood SES cannot account for the relationship between their current SES and functional network organization. These findings provide evidence that SES relates to the brain's functional network organization and anatomy across adult middle age, and that higher SES may be a protective factor against age-related brain decline. Copyright © 2018 the Author(s). Published by PNAS.

Boosting Classification Accuracy of Diffusion MRI Derived Brain Networks for the Subtypes of Mild Cognitive Impairment Using Higher Order Singular Value Decomposition

PubMed Central

Zhan, L.; Liu, Y.; Zhou, J.; Ye, J.; Thompson, P.M.

2015-01-01

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's disease (AD), and around 10-15% of people with MCI develop AD each year. More recently, MCI has been further subdivided into early and late stages, and there is interest in identifying sensitive brain imaging biomarkers that help to differentiate stages of MCI. Here, we focused on anatomical brain networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying early versus late MCI. PMID:26413202

Brain tumor segmentation using holistically nested neural networks in MRI images.

PubMed

Zhuge, Ying; Krauze, Andra V; Ning, Holly; Cheng, Jason Y; Arora, Barbara C; Camphausen, Kevin; Miller, Robert W

2017-10-01

Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of 0.83 and 0.85 were achieved. A quantitative comparison indicated that the proposed method outperforms the popular fully convolutional network (FCN) method. In terms of efficiency, the proposed method took around 10 h for training with 50,000 iterations, and approximately 30 s for testing of a typical MRI image in the BRATS 2013 dataset with a size of 160 × 216 × 176, using a DELL PRECISION workstation T7400, with an NVIDIA Tesla K20c GPU. An effective brain tumor segmentation method for MRI images based on a HNN has been developed. The high level of accuracy and efficiency make this method practical in brain tumor segmentation. It may play a crucial role in both brain tumor diagnostic analysis and in the treatment planning of radiation therapy. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

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.

Resting state neural networks for visual Chinese word processing in Chinese adults and children.

PubMed

Li, Ling; Liu, Jiangang; Chen, Feiyan; Feng, Lu; Li, Hong; Tian, Jie; Lee, Kang

2013-07-01

This study examined the resting state neural networks for visual Chinese word processing in Chinese children and adults. Both the functional connectivity (FC) and amplitude of low frequency fluctuation (ALFF) approaches were used to analyze the fMRI data collected when Chinese participants were not engaged in any specific explicit tasks. We correlated time series extracted from the visual word form area (VWFA) with those in other regions in the brain. We also performed ALFF analysis in the resting state FC networks. The FC results revealed that, regarding the functionally connected brain regions, there exist similar intrinsically organized resting state networks for visual Chinese word processing in adults and children, suggesting that such networks may already be functional after 3-4 years of informal exposure to reading plus 3-4 years formal schooling. The ALFF results revealed that children appear to recruit more neural resources than adults in generally reading-irrelevant brain regions. Differences between child and adult ALFF results suggest that children's intrinsic word processing network during the resting state, though similar in functional connectivity, is still undergoing development. Further exposure to visual words and experience with reading are needed for children to develop a mature intrinsic network for word processing. The developmental course of the intrinsically organized word processing network may parallel that of the explicit word processing network. Copyright © 2013 Elsevier Ltd. All rights reserved.

Face processing in autism spectrum disorders: from brain regions to brain networks

PubMed Central

Nomi, Jason S.; Uddin, Lucina Q.

2015-01-01

Autism spectrum disorder (ASD) is characterized by reduced attention to social stimuli including the human face. This hypo-responsiveness to stimuli that are engaging to typically developing individuals may result from dysfunctioning motivation, reward, and attention systems in the brain. Here we review an emerging neuroimaging literature that emphasizes a shift from focusing on hypo-activation of isolated brain regions such as the fusiform gyrus, amygdala, and superior temporal sulcus in ASD to a more holistic approach to understanding face perception as a process supported by distributed cortical and subcortical brain networks. We summarize evidence for atypical activation patterns within brain networks that may contribute to social deficits characteristic of the disorder. We conclude by pointing to gaps in the literature and future directions that will continue to shed light on aspects of face processing in autism that are still under-examined. In particular, we highlight the need for more developmental studies and studies examining ecologically valid and naturalistic social stimuli. PMID:25829246

MR connectomics: a conceptual framework for studying the developing brain

PubMed Central

Hagmann, Patric; Grant, Patricia E.; Fair, Damien A.

2012-01-01

The combination of advanced neuroimaging techniques and major developments in complex network science, have given birth to a new framework for studying the brain: “connectomics.” This framework provides the ability to describe and study the brain as a dynamic network and to explore how the coordination and integration of information processing may occur. In recent years this framework has been used to investigate the developing brain and has shed light on many dynamic changes occurring from infancy through adulthood. The aim of this article is to review this work and to discuss what we have learned from it. We will also use this body of work to highlight key technical aspects that are necessary in general for successful connectome analysis using today's advanced neuroimaging techniques. We look to identify current limitations of such approaches, what can be improved, and how these points generalize to other topics in connectome research. PMID:22707934

Differences between child and adult large-scale functional brain networks for reading tasks.

PubMed

Liu, Xin; Gao, Yue; Di, Qiqi; Hu, Jiali; Lu, Chunming; Nan, Yun; Booth, James R; Liu, Li

2018-02-01

Reading is an important high-level cognitive function of the human brain, requiring interaction among multiple brain regions. Revealing differences between children's large-scale functional brain networks for reading tasks and those of adults helps us to understand how the functional network changes over reading development. Here we used functional magnetic resonance imaging data of 17 adults (19-28 years old) and 16 children (11-13 years old), and graph theoretical analyses to investigate age-related changes in large-scale functional networks during rhyming and meaning judgment tasks on pairs of visually presented Chinese characters. We found that: (1) adults had stronger inter-regional connectivity and nodal degree in occipital regions, while children had stronger inter-regional connectivity in temporal regions, suggesting that adults rely more on visual orthographic processing whereas children rely more on auditory phonological processing during reading. (2) Only adults showed between-task differences in inter-regional connectivity and nodal degree, whereas children showed no task differences, suggesting the topological organization of adults' reading network is more specialized. (3) Children showed greater inter-regional connectivity and nodal degree than adults in multiple subcortical regions; the hubs in children were more distributed in subcortical regions while the hubs in adults were more distributed in cortical regions. These findings suggest that reading development is manifested by a shift from reliance on subcortical to cortical regions. Taken together, our study suggests that Chinese reading development is supported by developmental changes in brain connectivity properties, and some of these changes may be domain-general while others may be specific to the reading domain. © 2017 Wiley Periodicals, Inc.

Congenital blindness is associated with large-scale reorganization of anatomical networks.

PubMed

Hasson, Uri; Andric, Michael; Atilgan, Hicret; Collignon, Olivier

2016-03-01

Blindness is a unique model for understanding the role of experience in the development of the brain's functional and anatomical architecture. Documenting changes in the structure of anatomical networks for this population would substantiate the notion that the brain's core network-level organization may undergo neuroplasticity as a result of life-long experience. To examine this issue, we compared whole-brain networks of regional cortical-thickness covariance in early blind and matched sighted individuals. This covariance is thought to reflect signatures of integration between systems involved in similar perceptual/cognitive functions. Using graph-theoretic metrics, we identified a unique mode of anatomical reorganization in the blind that differed from that found for sighted. This was seen in that network partition structures derived from subgroups of blind were more similar to each other than they were to partitions derived from sighted. Notably, after deriving network partitions, we found that language and visual regions tended to reside within separate modules in sighted but showed a pattern of merging into shared modules in the blind. Our study demonstrates that early visual deprivation triggers a systematic large-scale reorganization of whole-brain cortical-thickness networks, suggesting changes in how occipital regions interface with other functional networks in the congenitally blind. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Graph theoretical analysis of complex networks in the brain

PubMed Central

Stam, Cornelis J; Reijneveld, Jaap C

2007-01-01

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern. PMID:17908336

Temporal Lobe and “Default” Hemodynamic Brain Modes Discriminate Between Schizophrenia and Bipolar Disorder

PubMed Central

Calhoun, Vince D.; Maciejewski, Paul K.; Pearlson, Godfrey D.; Kiehl, Kent A.

2009-01-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or “spatial modes” exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder. PMID:17894392

Temporal lobe and "default" hemodynamic brain modes discriminate between schizophrenia and bipolar disorder.

PubMed

Calhoun, Vince D; Maciejewski, Paul K; Pearlson, Godfrey D; Kiehl, Kent A

2008-11-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or "spatial modes" exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder.

Patterns recognition of electric brain activity using artificial neural networks

NASA Astrophysics Data System (ADS)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

Resting-State Functional Connectivity in Individuals with Down Syndrome and Williams Syndrome Compared with Typically Developing Controls.

PubMed

Vega, Jennifer N; Hohman, Timothy J; Pryweller, Jennifer R; Dykens, Elisabeth M; Thornton-Wells, Tricia A

2015-10-01

The emergence of resting-state functional connectivity (rsFC) analysis, which examines temporal correlations of low-frequency (<0.1 Hz) blood oxygen level-dependent signal fluctuations between brain regions, has dramatically improved our understanding of the functional architecture of the typically developing (TD) human brain. This study examined rsFC in Down syndrome (DS) compared with another neurodevelopmental disorder, Williams syndrome (WS), and TD. Ten subjects with DS, 18 subjects with WS, and 40 subjects with TD each participated in a 3-Tesla MRI scan. We tested for group differences (DS vs. TD, DS vs. WS, and WS vs. TD) in between- and within-network rsFC connectivity for seven functional networks. For the DS group, we also examined associations between rsFC and other cognitive and genetic risk factors. In DS compared with TD, we observed higher levels of between-network connectivity in 6 out 21 network pairs but no differences in within-network connectivity. Participants with WS showed lower levels of within-network connectivity and no significant differences in between-network connectivity relative to DS. Finally, our comparison between WS and TD controls revealed lower within-network connectivity in multiple networks and higher between-network connectivity in one network pair relative to TD controls. While preliminary due to modest sample sizes, our findings suggest a global difference in between-network connectivity in individuals with neurodevelopmental disorders compared with controls and that such a difference is exacerbated across many brain regions in DS. However, this alteration in DS does not appear to extend to within-network connections, and therefore, the altered between-network connectivity must be interpreted within the framework of an intact intra-network pattern of activity. In contrast, WS shows markedly lower levels of within-network connectivity in the default mode network and somatomotor network relative to controls. These findings warrant further investigation using a task-based procedure that may help disentangle the relationship between brain function and cognitive performance across the spectrum of neurodevelopmental disorders.

Segregated Systems of Human Brain Networks.

PubMed

Wig, Gagan S

2017-12-01

The organization of the brain network enables its function. Evaluation of this organization has revealed that large-scale brain networks consist of multiple segregated subnetworks of interacting brain areas. Descriptions of resting-state network architecture have provided clues for understanding the functional significance of these segregated subnetworks, many of which correspond to distinct brain systems. The present report synthesizes accumulating evidence to reveal how maintaining segregated brain systems renders the human brain network functionally specialized, adaptable to task demands, and largely resilient following focal brain damage. The organizational properties that support system segregation are harmonious with the properties that promote integration across the network, but confer unique and important features to the brain network that are central to its function and behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

Extraversion and neuroticism relate to topological properties of resting-state brain networks.

PubMed

Gao, Qing; Xu, Qiang; Duan, Xujun; Liao, Wei; Ding, Jurong; Zhang, Zhiqiang; Li, Yuan; Lu, Guangming; Chen, Huafu

2013-01-01

With the advent and development of modern neuroimaging techniques, there is an increasing interest in linking extraversion and neuroticism to anatomical and functional brain markers. Here, we aimed to test the theoretically derived biological personality model as proposed by Eysenck using graph theoretical analyses. Specifically, the association between the topological organization of whole-brain functional networks and extraversion/neuroticism was explored. To construct functional brain networks, functional connectivity among 90 brain regions was measured by temporal correlation using resting-state functional magnetic resonance imaging (fMRI) data of 71 healthy subjects. Graph theoretical analysis revealed a positive association of extraversion scores and normalized clustering coefficient values. These results suggested a more clustered configuration in brain networks of individuals high in extraversion, which could imply a higher arousal threshold and higher levels of arousal tolerance in the cortex of extraverts. On a local network level, we observed that a specific nodal measure, i.e., betweenness centrality (BC), was positively associated with neuroticism scores in the right precentral gyrus (PreCG), right caudate nucleus, right olfactory cortex, and bilateral amygdala. For individuals high in neuroticism, these results suggested a more frequent participation of these specific regions in information transition within the brain network and, in turn, may partly explain greater regional activation levels and lower arousal thresholds in these regions. In contrast, extraversion scores were positively correlated with BC in the right insula, while negatively correlated with BC in the bilateral middle temporal gyrus (MTG), indicating that the relationship between extraversion and regional arousal is not as simple as proposed by Eysenck.

The sleeping brain as a complex system.

PubMed

Olbrich, Eckehard; Achermann, Peter; Wennekers, Thomas

2011-10-13

'Complexity science' is a rapidly developing research direction with applications in a multitude of fields that study complex systems consisting of a number of nonlinear elements with interesting dynamics and mutual interactions. This Theme Issue 'The complexity of sleep' aims at fostering the application of complexity science to sleep research, because the brain in its different sleep stages adopts different global states that express distinct activity patterns in large and complex networks of neural circuits. This introduction discusses the contributions collected in the present Theme Issue. We highlight the potential and challenges of a complex systems approach to develop an understanding of the brain in general and the sleeping brain in particular. Basically, we focus on two topics: the complex networks approach to understand the changes in the functional connectivity of the brain during sleep, and the complex dynamics of sleep, including sleep regulation. We hope that this Theme Issue will stimulate and intensify the interdisciplinary communication to advance our understanding of the complex dynamics of the brain that underlies sleep and consciousness.

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

PubMed

Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien; Gu, Xuejun

2017-01-01

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

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

Possible Brain Mechanisms of Creativity.

PubMed

Heilman, Kenneth M

2016-06-01

Creativity is the new discovery, understanding, development and expression of orderly and meaningful relationships. Creativity has three major stages: preparation, the development (nature and nurture) of critical knowledge and skills; innovation, the development of a creative solution; and creative production. Successful preparation requires a basic level of general intelligence and domain specific knowledge and skills and highly creative people may have anatomic alterations of specific neocortical regions. Innovation requires disengagement and divergent thinking primarily mediated by frontal networks. Creative people are often risk-takers and novelty seekers, behaviors that activate their ventral striatal reward system. Innovation also requires associative and convergent thinking, activities that are dependent on the integration of highly distributed networks. People are often most creative when they are in mental states associated with reduced levels of brain norepinephrine, which may enhance the communication between distributed networks. We, however, need to learn more about the brain mechanisms of creativity. Published by Oxford University Press 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Origin of hyperbolicity in brain-to-brain coordination networks

NASA Astrophysics Data System (ADS)

Tadić, Bosiljka; Andjelković, Miroslav; Šuvakov, Milovan

2018-02-01

Hyperbolicity or negative curvature of complex networks is the intrinsic geometric proximity of nodes in the graph metric space, which implies an improved network function. Here, we investigate hidden combinatorial geometries in brain-to-brain coordination networks arising through social communications. The networks originate from correlations among EEG signals previously recorded during spoken communications comprising of 14 individuals with 24 speaker-listener pairs. We find that the corresponding networks are delta-hyperbolic with delta_max=1 and the graph diameter D=3 in each brain. While the emergent hyperbolicity in the two-brain networks satisfies delta_max/D/2 < 1 and can be attributed to the topology of the subgraph formed around the cross-brains linking channels. We identify these subgraphs in each studied two-brain network and decompose their structure into simple geometric descriptors (triangles, tetrahedra and cliques of higher orders) that contribute to hyperbolicity. Considering topologies that exceed two separate brain networks as a measure of coordination synergy between the brains, we identify different neuronal correlation patterns ranging from weak coordination to super-brain structure. These topology features are in qualitative agreement with the listener’s self-reported ratings of own experience and quality of the speaker, suggesting that studies of the cross-brain connector networks can reveal new insight into the neural mechanisms underlying human social behavior.

Analysing Local Sparseness in the Macaque Brain Network

PubMed Central

Singh, Raghavendra; Nagar, Seema; Nanavati, Amit A.

2015-01-01

Understanding the network structure of long distance pathways in the brain is a necessary step towards developing an insight into the brain’s function, organization and evolution. Dense global subnetworks of these pathways have often been studied, primarily due to their functional implications. Instead we study sparse local subnetworks of the pathways to establish the role of a brain area in enabling shortest path communication between its non-adjacent topological neighbours. We propose a novel metric to measure the topological communication load on a vertex due to its immediate neighbourhood, and show that in terms of distribution of this local communication load, a network of Macaque long distance pathways is substantially different from other real world networks and random graph models. Macaque network contains the entire range of local subnetworks, from star-like networks to clique-like networks, while other networks tend to contain a relatively small range of subnetworks. Further, sparse local subnetworks in the Macaque network are not only found across topographical super-areas, e.g., lobes, but also within a super-area, arguing that there is conservation of even relatively short-distance pathways. To establish the communication role of a vertex we borrow the concept of brokerage from social science, and present the different types of brokerage roles that brain areas play, highlighting that not only the thalamus, but also cingulate gyrus and insula often act as “relays” for areas in the neocortex. These and other analysis of communication load and roles of the sparse subnetworks of the Macaque brain provide new insights into the organisation of its pathways. PMID:26437077

Developmental Changes in Organization of Structural Brain Networks

PubMed Central

Khundrakpam, Budhachandra S.; Reid, Andrew; Brauer, Jens; Carbonell, Felix; Lewis, John; Ameis, Stephanie; Karama, Sherif; Lee, Junki; Chen, Zhang; Das, Samir; Evans, Alan C.; Ball, William S.; Byars, Anna Weber; Schapiro, Mark; Bommer, Wendy; Carr, April; German, April; Dunn, Scott; Rivkin, Michael J.; Waber, Deborah; Mulkern, Robert; Vajapeyam, Sridhar; Chiverton, Abigail; Davis, Peter; Koo, Julie; Marmor, Jacki; Mrakotsky, Christine; Robertson, Richard; McAnulty, Gloria; Brandt, Michael E.; Fletcher, Jack M.; Kramer, Larry A.; Yang, Grace; McCormack, Cara; Hebert, Kathleen M.; Volero, Hilda; Botteron, Kelly; McKinstry, Robert C.; Warren, William; Nishino, Tomoyuki; Robert Almli, C.; Todd, Richard; Constantino, John; McCracken, James T.; Levitt, Jennifer; Alger, Jeffrey; O'Neil, Joseph; Toga, Arthur; Asarnow, Robert; Fadale, David; Heinichen, Laura; Ireland, Cedric; Wang, Dah-Jyuu; Moss, Edward; Zimmerman, Robert A.; Bintliff, Brooke; Bradford, Ruth; Newman, Janice; Evans, Alan C.; Arnaoutelis, Rozalia; Bruce Pike, G.; Louis Collins, D.; Leonard, Gabriel; Paus, Tomas; Zijdenbos, Alex; Das, Samir; Fonov, Vladimir; Fu, Luke; Harlap, Jonathan; Leppert, Ilana; Milovan, Denise; Vins, Dario; Zeffiro, Thomas; Van Meter, John; Lange, Nicholas; Froimowitz, Michael P.; Botteron, Kelly; Robert Almli, C.; Rainey, Cheryl; Henderson, Stan; Nishino, Tomoyuki; Warren, William; Edwards, Jennifer L.; Dubois, Diane; Smith, Karla; Singer, Tish; Wilber, Aaron A.; Pierpaoli, Carlo; Basser, Peter J.; Chang, Lin-Ching; Koay, Chen Guan; Walker, Lindsay; Freund, Lisa; Rumsey, Judith; Baskir, Lauren; Stanford, Laurence; Sirocco, Karen; Gwinn-Hardy, Katrina; Spinella, Giovanna; McCracken, James T.; Alger, Jeffry R.; Levitt, Jennifer; O'Neill, Joseph

2013-01-01

Recent findings from developmental neuroimaging studies suggest that the enhancement of cognitive processes during development may be the result of a fine-tuning of the structural and functional organization of brain with maturation. However, the details regarding the developmental trajectory of large-scale structural brain networks are not yet understood. Here, we used graph theory to examine developmental changes in the organization of structural brain networks in 203 normally growing children and adolescents. Structural brain networks were constructed using interregional correlations in cortical thickness for 4 age groups (early childhood: 4.8–8.4 year; late childhood: 8.5–11.3 year; early adolescence: 11.4–14.7 year; late adolescence: 14.8–18.3 year). Late childhood showed prominent changes in topological properties, specifically a significant reduction in local efficiency, modularity, and increased global efficiency, suggesting a shift of topological organization toward a more random configuration. An increase in number and span of distribution of connector hubs was found in this age group. Finally, inter-regional connectivity analysis and graph-theoretic measures indicated early maturation of primary sensorimotor regions and protracted development of higher order association and paralimbic regions. Our finding reveals a time window of plasticity occurring during late childhood which may accommodate crucial changes during puberty and the new developmental tasks that an adolescent faces. PMID:22784607

Plasticity of brain wave network interactions and evolution across physiologic states

PubMed Central

Liu, Kang K. L.; Bartsch, Ronny P.; Lin, Aijing; Mantegna, Rosario N.; Ivanov, Plamen Ch.

2015-01-01

Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability (TDS) to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very different degree of network connectivity and link strength, while at the same time each frequency-specific network is characterized by a different signature pattern of sleep-stage stratification, reflecting a remarkable flexibility in response to change in physiologic state. These new aspects of neural plasticity demonstrate that in addition to dominant brain waves, the network of brain wave interactions is a previously unrecognized hallmark of physiologic state and function. PMID:26578891

Disrupted Structural and Functional Networks and Their Correlation with Alertness in Right Temporal Lobe Epilepsy: A Graph Theory Study.

PubMed

Jiang, Wenyu; Li, Jianping; Chen, Xuemei; Ye, Wei; Zheng, Jinou

2017-01-01

Previous studies have shown that temporal lobe epilepsy (TLE) involves abnormal structural or functional connectivity in specific brain areas. However, limited comprehensive studies have been conducted on TLE associated changes in the topological organization of structural and functional networks. Additionally, epilepsy is associated with impairment in alertness, a fundamental component of attention. In this study, structural networks were constructed using diffusion tensor imaging tractography, and functional networks were obtained from resting-state functional MRI temporal series correlations in 20 right temporal lobe epilepsy (rTLE) patients and 19 healthy controls. Global network properties were computed by graph theoretical analysis, and correlations were assessed between global network properties and alertness. The results from these analyses showed that rTLE patients exhibit abnormal small-world attributes in structural and functional networks. Structural networks shifted toward more regular attributes, but functional networks trended toward more random attributes. After controlling for the influence of the disease duration, negative correlations were found between alertness, small-worldness, and the cluster coefficient. However, alertness did not correlate with either the characteristic path length or global efficiency in rTLE patients. Our findings show that disruptions of the topological construction of brain structural and functional networks as well as small-world property bias are associated with deficits in alertness in rTLE patients. These data suggest that reorganization of brain networks develops as a mechanism to compensate for altered structural and functional brain function during disease progression.

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.

Development and application of an optogenetic platform for controlling and imaging a large number of individual neurons

NASA Astrophysics Data System (ADS)

Mohammed, Ali Ibrahim Ali

The understanding and treatment of brain disorders as well as the development of intelligent machines is hampered by the lack of knowledge of how the brain fundamentally functions. Over the past century, we have learned much about how individual neurons and neural networks behave, however new tools are critically needed to interrogate how neural networks give rise to complex brain processes and disease conditions. Recent innovations in molecular techniques, such as optogenetics, have enabled neuroscientists unprecedented precision to excite, inhibit and record defined neurons. The impressive sensitivity of currently available optogenetic sensors and actuators has now enabled the possibility of analyzing a large number of individual neurons in the brains of behaving animals. To promote the use of these optogenetic tools, this thesis integrates cutting edge optogenetic molecular sensors which is ultrasensitive for imaging neuronal activity with custom wide field optical microscope to analyze a large number of individual neurons in living brains. Wide-field microscopy provides a large field of view and better spatial resolution approaching the Abbe diffraction limit of fluorescent microscope. To demonstrate the advantages of this optical platform, we imaged a deep brain structure, the Hippocampus, and tracked hundreds of neurons over time while mouse was performing a memory task to investigate how those individual neurons related to behavior. In addition, we tested our optical platform in investigating transient neural network changes upon mechanical perturbation related to blast injuries. In this experiment, all blasted mice show a consistent change in neural network. A small portion of neurons showed a sustained calcium increase for an extended period of time, whereas the majority lost their activities. Finally, using optogenetic silencer to control selective motor cortex neurons, we examined their contributions to the network pathology of basal ganglia related to Parkinson's disease. We found that inhibition of motor cortex does not alter exaggerated beta oscillations in the striatum that are associated with parkinsonianism. Together, these results demonstrate the potential of developing integrated optogenetic system to advance our understanding of the principles underlying neural network computation, which would have broad applications from advancing artificial intelligence to disease diagnosis and treatment.

Increased Sleep Depth in Developing Neural Networks: New Insights from Sleep Restriction in Children

PubMed Central

Kurth, Salome; Dean, Douglas C.; Achermann, Peter; O’Muircheartaigh, Jonathan; Huber, Reto; Deoni, Sean C. L.; LeBourgeois, Monique K.

2016-01-01

Brain networks respond to sleep deprivation or restriction with increased sleep depth, which is quantified as slow-wave activity (SWA) in the sleep electroencephalogram (EEG). When adults are sleep deprived, this homeostatic response is most pronounced over prefrontal brain regions. However, it is unknown how children’s developing brain networks respond to acute sleep restriction, and whether this response is linked to myelination, an ongoing process in childhood that is critical for brain development and cortical integration. We implemented a bedtime delay protocol in 5- to 12-year-old children to obtain partial sleep restriction (1-night; 50% of their habitual sleep). High-density sleep EEG was assessed during habitual and restricted sleep and brain myelin content was obtained using mcDESPOT magnetic resonance imaging. The effect of sleep restriction was analyzed using statistical non-parametric mapping with supra-threshold cluster analysis. We observed a localized homeostatic SWA response following sleep restriction in a specific parieto-occipital region. The restricted/habitual SWA ratio was negatively associated with myelin water fraction in the optic radiation, a developing fiber bundle. This relationship occurred bilaterally over parieto-temporal areas and was adjacent to, but did not overlap with the parieto-occipital region showing the most pronounced homeostatic SWA response. These results provide evidence for increased sleep need in posterior neural networks in children. Sleep need in parieto-temporal areas is related to myelin content, yet it remains speculative whether age-related myelin growth drives the fading of the posterior homeostatic SWA response during the transition to adulthood. Whether chronic insufficient sleep in the sensitive period of early life alters the anatomical generators of deep sleep slow-waves is an important unanswered question. PMID:27708567

Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species

NASA Astrophysics Data System (ADS)

Moon, Joon-Young; Kim, Junhyeok; Ko, Tae-Wook; Kim, Minkyung; Iturria-Medina, Yasser; Choi, Jee-Hyun; Lee, Joseph; Mashour, George A.; Lee, Uncheol

2017-04-01

Identifying how spatially distributed information becomes integrated in the brain is essential to understanding higher cognitive functions. Previous computational and empirical studies suggest a significant influence of brain network structure on brain network function. However, there have been few analytical approaches to explain the role of network structure in shaping regional activities and directionality patterns. In this study, analytical methods are applied to a coupled oscillator model implemented in inhomogeneous networks. We first derive a mathematical principle that explains the emergence of directionality from the underlying brain network structure. We then apply the analytical methods to the anatomical brain networks of human, macaque, and mouse, successfully predicting simulation and empirical electroencephalographic data. The results demonstrate that the global directionality patterns in resting state brain networks can be predicted solely by their unique network structures. This study forms a foundation for a more comprehensive understanding of how neural information is directed and integrated in complex brain networks.

Broad Integration of Expression Maps and Co-Expression Networks Compassing Novel Gene Functions in the Brain

PubMed Central

Okamura-Oho, Yuko; Shimokawa, Kazuro; Nishimura, Masaomi; Takemoto, Satoko; Sato, Akira; Furuichi, Teiichi; Yokota, Hideo

2014-01-01

Using a recently invented technique for gene expression mapping in the whole-anatomy context, termed transcriptome tomography, we have generated a dataset of 36,000 maps of overall gene expression in the adult-mouse brain. Here, using an informatics approach, we identified a broad co-expression network that follows an inverse power law and is rich in functional interaction and gene-ontology terms. Our framework for the integrated analysis of expression maps and graphs of co-expression networks revealed that groups of combinatorially expressed genes, which regulate cell differentiation during development, were present in the adult brain and each of these groups was associated with a discrete cell types. These groups included non-coding genes of unknown function. We found that these genes specifically linked developmentally conserved groups in the network. A previously unrecognized robust expression pattern covering the whole brain was related to the molecular anatomy of key biological processes occurring in particular areas. PMID:25382412

Neural stem cells and neuro/gliogenesis in the central nervous system: understanding the structural and functional plasticity of the developing, mature, and diseased brain.

PubMed

Yamaguchi, Masahiro; Seki, Tatsunori; Imayoshi, Itaru; Tamamaki, Nobuaki; Hayashi, Yoshitaka; Tatebayashi, Yoshitaka; Hitoshi, Seiji

2016-05-01

Neurons and glia in the central nervous system (CNS) originate from neural stem cells (NSCs). Knowledge of the mechanisms of neuro/gliogenesis from NSCs is fundamental to our understanding of how complex brain architecture and function develop. NSCs are present not only in the developing brain but also in the mature brain in adults. Adult neurogenesis likely provides remarkable plasticity to the mature brain. In addition, recent progress in basic research in mental disorders suggests an etiological link with impaired neuro/gliogenesis in particular brain regions. Here, we review the recent progress and discuss future directions in stem cell and neuro/gliogenesis biology by introducing several topics presented at a joint meeting of the Japanese Association of Anatomists and the Physiological Society of Japan in 2015. Collectively, these topics indicated that neuro/gliogenesis from NSCs is a common event occurring in many brain regions at various ages in animals. Given that significant structural and functional changes in cells and neural networks are accompanied by neuro/gliogenesis from NSCs and the integration of newly generated cells into the network, stem cell and neuro/gliogenesis biology provides a good platform from which to develop an integrated understanding of the structural and functional plasticity that underlies the development of the CNS, its remodeling in adulthood, and the recovery from diseases that affect it.

Autism BrainNet: A network of postmortem brain banks established to facilitate autism research.

PubMed

Amaral, David G; Anderson, Matthew P; Ansorge, Olaf; Chance, Steven; Hare, Carolyn; Hof, Patrick R; Miller, Melissa; Nagakura, Ikue; Pickett, Jane; Schumann, Cynthia; Tamminga, Carol

2018-01-01

Autism spectrum disorder (ASD or autism) is a neurodevelopmental condition that affects over 1% of the population worldwide. Developing effective preventions and treatments for autism will depend on understanding the genetic perturbations and underlying neuropathology of the disorder. While evidence from magnetic resonance imaging and other noninvasive techniques points to altered development and organization of the autistic brain, these tools lack the resolution for identifying the cellular and molecular underpinnings of the disorder. Postmortem studies of high-quality human brain tissue currently represent the only viable option to pursuing these types of studies. However, the availability of high-quality ASD brain tissue has been extremely limited. Here we describe the establishment of a privately funded tissue bank, Autism BrainNet, a network of brain collection sites that work in a coordinated fashion to develop an adequate library of human postmortem brain tissues. Autism BrainNet was initiated as a collaboration between the Simons Foundation and Autism Speaks, and is currently funded by the Simons Foundation Autism Research Initiative. Autism BrainNet has collection sites (nodes) in California, Texas, New York, and Massachusetts; an affiliated, international node is located in Oxford, England. All donations to this network become part of a consolidated pool of tissue that is distributed to qualified investigators worldwide to carry out autism research. An essential component of this program is a widespread outreach program that highlights the need for postmortem brain donations to families affected by autism, led by the Autism Science Foundation. Challenges include an outreach campaign that deals with a disorder beginning in early childhood, collecting an adequate number of donations to deal with the high level of biologic heterogeneity of autism, and preparing this limited resource for optimal distribution to the greatest number of investigators. Copyright © 2018 Elsevier B.V. All rights reserved.

Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain.

PubMed

Cao, Miao; He, Yong; Dai, Zhengjia; Liao, Xuhong; Jeon, Tina; Ouyang, Minhui; Chalak, Lina; Bi, Yanchao; Rollins, Nancy; Dong, Qi; Huang, Hao

2017-03-01

Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small-worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting-state functional MRI and voxel-based graph theory analysis to systematically investigate the topological organization of whole-brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high-order default-mode and executive-control regions. The hub and rich-club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder

PubMed Central

Chen, Jian-Huai; Yao, Zhi-Jian; Qin, Jiao-Long; Yan, Rui; Hua, Ling-Ling; Lu, Qing

2016-01-01

Background: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network. PMID:26960371

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.

The Contribution of Network Organization and Integration to the Development of Cognitive Control

PubMed Central

Marek, Scott; Hwang, Kai; Foran, William; Hallquist, Michael N.; Luna, Beatriz

2015-01-01

Abstract Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10–26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control. PMID:26713863

The Contribution of Network Organization and Integration to the Development of Cognitive Control.

PubMed

Marek, Scott; Hwang, Kai; Foran, William; Hallquist, Michael N; Luna, Beatriz

2015-12-01

Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10-26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control.

Diverse types of genetic variation converge on functional gene networks involved in schizophrenia.

PubMed

Gilman, Sarah R; Chang, Jonathan; Xu, Bin; Bawa, Tejdeep S; Gogos, Joseph A; Karayiorgou, Maria; Vitkup, Dennis

2012-12-01

Despite the successful identification of several relevant genomic loci, the underlying molecular mechanisms of schizophrenia remain largely unclear. We developed a computational approach (NETBAG+) that allows an integrated analysis of diverse disease-related genetic data using a unified statistical framework. The application of this approach to schizophrenia-associated genetic variations, obtained using unbiased whole-genome methods, allowed us to identify several cohesive gene networks related to axon guidance, neuronal cell mobility, synaptic function and chromosomal remodeling. The genes forming the networks are highly expressed in the brain, with higher brain expression during prenatal development. The identified networks are functionally related to genes previously implicated in schizophrenia, autism and intellectual disability. A comparative analysis of copy number variants associated with autism and schizophrenia suggests that although the molecular networks implicated in these distinct disorders may be related, the mutations associated with each disease are likely to lead, at least on average, to different functional consequences.

Altered brain network topology in left-behind children: A resting-state functional magnetic resonance imaging study.

PubMed

Zhao, Youjin; Du, Meimei; Gao, Xin; Xiao, Yuan; Shah, Chandan; Sun, Huaiqiang; Chen, Fuqin; Yang, Lili; Yan, Zhihan; Fu, Yuchuan; Lui, Su

2016-12-01

Whether a lack of direct parental care affects brain function in children is an important question, particularly in developing countries where hundreds of millions of children are left behind when their parents migrate for economic or political reasons. In this study, we investigated changes in the topological architectures of brain functional networks in left-behind children (LBC). Resting-state functional magnetic resonance imaging data were obtained from 26 LBC and 21 children living within their nuclear family (non-LBC). LBC showed a significant increase in the normalized characteristic path length (λ), suggesting a decrease in efficiency in information access, and altered nodal centralities in the fronto-limbic regions and motor and sensory systems. Moreover, a decreased nodal degree and the nodal betweenness of the right rectus gyrus were positively correlated with annual family income. The present study provides the first empirical evidence that suggests that a lack of direct parental care could affect brain functional development in children, particularly involving emotional networks. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe

PubMed Central

Berto, Stefano; Perdomo-Sabogal, Alvaro; Gerighausen, Daniel; Qin, Jing; Nowick, Katja

2016-01-01

Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies. PMID:27014338

Emergent properties of interacting populations of spiking neurons.

PubMed

Cardanobile, Stefano; Rotter, Stefan

2011-01-01

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations.

Emergent Properties of Interacting Populations of Spiking Neurons

PubMed Central

Cardanobile, Stefano; Rotter, Stefan

2011-01-01

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations. PMID:22207844

To cut or not to cut? Assessing the modular structure of brain networks.

PubMed

Chang, Yu-Teng; Pantazis, Dimitrios; Leahy, Richard M

2014-05-01

A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks. Copyright © 2014 Elsevier Inc. All rights reserved.

Resting state brain networks in the prairie vole.

PubMed

Ortiz, Juan J; Portillo, Wendy; Paredes, Raul G; Young, Larry J; Alcauter, Sarael

2018-01-19

Resting state functional magnetic resonance imaging (rsfMRI) has shown the hierarchical organization of the human brain into large-scale complex networks, referred as resting state networks. This technique has turned into a promising translational research tool after the finding of similar resting state networks in non-human primates, rodents and other animal models of great value for neuroscience. Here, we demonstrate and characterize the presence of resting states networks in Microtus ochrogaster, the prairie vole, an extraordinary animal model to study complex human-like social behavior, with potential implications for the research of normal social development, addiction and neuropsychiatric disorders. Independent component analysis of rsfMRI data from isoflurane-anestethized prairie voles resulted in cortical and subcortical networks, including primary motor and sensory networks, but also included putative salience and default mode networks. We further discuss how future research could help to close the gap between the properties of the large scale functional organization and the underlying neurobiology of several aspects of social cognition. These results contribute to the evidence of preserved resting state brain networks across species and provide the foundations to explore the use of rsfMRI in the prairie vole for basic and translational research.

Working memory brain activity and capacity link MAOA polymorphism to aggressive behavior during development.

PubMed

Ziermans, T; Dumontheil, I; Roggeman, C; Peyrard-Janvid, M; Matsson, H; Kere, J; Klingberg, T

2012-02-28

A developmental increase in working memory capacity is an important part of cognitive development, and low working memory (WM) capacity is a risk factor for developing psychopathology. Brain activity represents a promising endophenotype for linking genes to behavior and for improving our understanding of the neurobiology of WM development. We investigated gene-brain-behavior relationships by focusing on 18 single-nucleotide polymorphisms (SNPs) located in six dopaminergic candidate genes (COMT, SLC6A3/DAT1, DBH, DRD4, DRD5, MAOA). Visuospatial WM (VSWM) brain activity, measured with functional magnetic resonance imaging, and VSWM capacity were assessed in a longitudinal study of typically developing children and adolescents. Behavioral problems were evaluated using the Child Behavior Checklist (CBCL). One SNP (rs6609257), located ~6.6 kb downstream of the monoamine oxidase A gene (MAOA) on human chromosome X, significantly affected brain activity in a network of frontal, parietal and occipital regions. Increased activity in this network, but not in caudate nucleus or anterior prefrontal regions, was correlated with VSWM capacity, which in turn predicted externalizing (aggressive/oppositional) symptoms, with higher WM capacity associated with fewer externalizing symptoms. There were no direct significant correlations between rs6609257 and behavioral symptoms. These results suggest a mediating role of WM brain activity and capacity in linking the MAOA gene to aggressive behavior during development.

An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation

PubMed Central

Wang, Yikai; Kang, Jian; Kemmer, Phebe B.; Guo, Ying

2016-01-01

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package “DensParcorr” can be downloaded from CRAN for implementing the proposed statistical methods. PMID:27242395

An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation.

PubMed

Wang, Yikai; Kang, Jian; Kemmer, Phebe B; Guo, Ying

2016-01-01

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package "DensParcorr" can be downloaded from CRAN for implementing the proposed statistical methods.

Analysis of structure-function network decoupling in the brain systems of spastic diplegic cerebral palsy.

PubMed

Lee, Dongha; Pae, Chongwon; Lee, Jong Doo; Park, Eun Sook; Cho, Sung-Rae; Um, Min-Hee; Lee, Seung-Koo; Oh, Maeng-Keun; Park, Hae-Jeong

2017-10-01

Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure-function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting-state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole-brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural-functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure-function coupling than the control group. This reduced coupling but reverse directionality in the whole-brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation-based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure-function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292-5306, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Infants and adults have similar regional functional brain organization for the perception of emotions.

PubMed

Rotem-Kohavi, N; Oberlander, T F; Virji-Babul, N

2017-05-22

An infant's ability to perceive emotional facial expressions is critical for developing social skills. Infants are tuned to faces from early in life, however the functional organization of the brain that supports the processing of emotional faces in infants is still not well understood. We recorded electroencephalography (EEG) brain responses in 8-10 month old infants and adults and applied graph theory analysis on the functional connections to compare the network organization at the global and the regional levels underlying the perception of negative and positive dynamic facial expressions (happiness and sadness). We first show that processing of dynamic emotional faces occurs across multiple brain regions in both infants and adults. Across all brain regions, at the global level, network density was higher in the infant group in comparison with adults suggesting that the overall brain organization in relation to emotion perception is still immature in infancy. In contrast, at the regional levels, the functional characteristics of the frontal and parietal nodes were similar between infants and adults, suggesting that functional regional specialization for emotion perception is already established at this age. In addition, in both groups the occipital, parietal and temporal nodes appear to have the strongest influence on information flow within the network. These results suggest that while the global organization for the emotion perception of sad and happy emotions is still under development, the basic functional network organization at the regional level is already in place early in infancy. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

PubMed Central

2017-01-01

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson's disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about 0.729 ± 0.16 for decoding movement from the resting state and about 0.671 ± 0.14 for decoding left and right visually cued movements. PMID:29201041

Topological relationships between brain and social networks.

PubMed

Sakata, Shuzo; Yamamori, Tetsuo

2007-01-01

Brains are complex networks. Previously, we revealed that specific connected structures are either significantly abundant or rare in cortical networks. However, it remains unknown whether systems from other disciplines have similar architectures to brains. By applying network-theoretical methods, here we show topological similarities between brain and social networks. We found that the statistical relevance of specific tied structures differs between social "friendship" and "disliking" networks, suggesting relation-type-specific topology of social networks. Surprisingly, overrepresented connected structures in brain networks are more similar to those in the friendship networks than to those in other networks. We found that balanced and imbalanced reciprocal connections between nodes are significantly abundant and rare, respectively, whereas these results are unpredictable by simply counting mutual connections. We interpret these results as evidence of positive selection of balanced mutuality between nodes. These results also imply the existence of underlying common principles behind the organization of brain and social networks.

Developmental Reorganization of the Core and Extended Face Networks Revealed by Global Functional Connectivity.

PubMed

Wang, Xu; Zhu, Qi; Song, Yiying; Liu, Jia

2017-08-28

Prior studies on development of functional specialization in human brain mainly focus on age-related increases in regional activation and connectivity among regions. However, a few recent studies on the face network demonstrate age-related decrease in face-specialized activation in the extended face network (EFN), in addition to increase in activation in the core face network (CFN). Here we used a voxel-based global brain connectivity approach to investigate whether development of the face network exhibited both increase and decrease in network connectivity. We found the voxel-wise resting-state functional connectivity (FC) within the CFN increased with age in bilateral posterior superior temporal sulcus, suggesting the integration of the CFN during development. Interestingly, the FC of the voxels in the EFN to the right fusiform face area and occipital face area decreased with age, suggesting that the CFN segregated from the EFN during development. Moreover, the age-related connectivity in the CFN was related to behavioral performance in face processing. Overall, our study demonstrated developmental reorganization of the face network achieved by both integration within the CFN and segregation of the CFN from the EFN, which may account for the simultaneous increases and decreases in neural activation during the development of the face network. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Effect of Transcranial Magnetic Stimulation on Neuronal Networks

NASA Astrophysics Data System (ADS)

Unsal, Ahmet; Hadimani, Ravi; Jiles, David

2013-03-01

The human brain contains around 100 billion nerve cells controlling our day to day activities. Consequently, brain disorders often result in impairments such as paralysis, loss of coordination and seizure. It has been said that 1 in 5 Americans suffer some diagnosable mental disorder. There is an urgent need to understand the disorders, prevent them and if possible, develop permanent cure for them. As a result, a significant amount of research activities is being directed towards brain research. Transcranial Magnetic Stimulation (TMS) is a promising tool for diagnosing and treating brain disorders. It is a non-invasive treatment method that produces a current flow in the brain which excites the neurons. Even though TMS has been verified to have advantageous effects on various brain related disorders, there have not been enough studies on the impact of TMS on cells. In this study, we are investigating the electrophysiological effects of TMS on one dimensional neuronal culture grown in a circular pathway. Electrical currents are produced on the neuronal networks depending on the directionality of the applied field. This aids in understanding how neuronal networks react under TMS treatment.

Functional brain imaging across development.

PubMed

Rubia, Katya

2013-12-01

The developmental cognitive neuroscience literature has grown exponentially over the last decade. This paper reviews the functional magnetic resonance imaging (fMRI) literature on brain function development of typically late developing functions of cognitive and motivation control, timing and attention as well as of resting state neural networks. Evidence shows that between childhood and adulthood, concomitant with cognitive maturation, there is progressively increased functional activation in task-relevant lateral and medial frontal, striatal and parieto-temporal brain regions that mediate these higher level control functions. This is accompanied by progressively stronger functional inter-regional connectivity within task-relevant fronto-striatal and fronto-parieto-temporal networks. Negative age associations are observed in earlier developing posterior and limbic regions, suggesting a shift with age from the recruitment of "bottom-up" processing regions towards "top-down" fronto-cortical and fronto-subcortical connections, leading to a more mature, supervised cognition. The resting state fMRI literature further complements this evidence by showing progressively stronger deactivation with age in anti-correlated task-negative resting state networks, which is associated with better task performance. Furthermore, connectivity analyses during the resting state show that with development increasingly stronger long-range connections are being formed, for example, between fronto-parietal and fronto-cerebellar connections, in both task-positive networks and in task-negative default mode networks, together with progressively lesser short-range connections, suggesting progressive functional integration and segregation with age. Overall, evidence suggests that throughout development between childhood and adulthood, there is progressive refinement and integration of both task-positive fronto-cortical and fronto-subcortical activation and task-negative deactivation, leading to a more mature and controlled cognition.

Abnormal metabolic brain networks in Parkinson's disease from blackboard to bedside.

PubMed

Tang, Chris C; Eidelberg, David

2010-01-01

Metabolic imaging in the rest state has provided valuable information concerning the abnormalities of regional brain function that underlie idiopathic Parkinson's disease (PD). Moreover, network modeling procedures, such as spatial covariance analysis, have further allowed for the quantification of these changes at the systems level. In recent years, we have utilized this strategy to identify and validate three discrete metabolic networks in PD associated with the motor and cognitive manifestations of the disease. In this chapter, we will review and compare the specific functional topographies underlying parkinsonian akinesia/rigidity, tremor, and cognitive disturbance. While network activity progressed over time, the rate of change for each pattern was distinctive and paralleled the development of the corresponding clinical symptoms in early-stage patients. This approach is already showing great promise in identifying individuals with prodromal manifestations of PD and in assessing the rate of progression before clinical onset. Network modulation was found to correlate with the clinical effects of dopaminergic treatment and surgical interventions, such as subthalamic nucleus (STN) deep brain stimulation (DBS) and gene therapy. Abnormal metabolic networks have also been identified for atypical parkinsonian syndromes, such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Using multiple disease-related networks for PD, MSA, and PSP, we have developed a novel, fully automated algorithm for accurate classification at the single-patient level, even at early disease stages. Copyright © 2010 Elsevier B.V. All rights reserved.

Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning.

PubMed

Sato, João Ricardo; Biazoli, Claudinei Eduardo; Salum, Giovanni Abrahão; Gadelha, Ary; Crossley, Nicolas; Vieira, Gilson; Zugman, André; Picon, Felipe Almeida; Pan, Pedro Mario; Hoexter, Marcelo Queiroz; Amaro, Edson; Anés, Mauricio; Moura, Luciana Monteiro; Del'Aquilla, Marco Antonio Gomes; Mcguire, Philip; Rohde, Luis Augusto; Miguel, Euripedes Constantino; Jackowski, Andrea Parolin; Bressan, Rodrigo Affonseca

2018-03-01

One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM). We applied this approach to resting-state fMRI data from 622 children and adolescents. Eigenvector centrality (EVC) of nodes from positive- and negative-task networks were extracted from each subject and used as input to an OC-SVM to label individual brain networks as typical or atypical. We hypothesised that classification of these subjects regarding the pattern of brain connectivity would predict the level of psychopathology. Subjects with atypical brain network organisation had higher levels of psychopathology (p < 0.001). There was a greater EVC in the typical group at the bilateral posterior cingulate and bilateral posterior temporal cortices; and significant decreases in EVC at left temporal pole. The combination of graph theory methods and an OC-SVM is a promising method to characterise neurodevelopment, and may be useful to understand the deviations leading to mental disorders.

Whole-brain functional hypoconnectivity as an endophenotype of autism in adolescents

PubMed Central

Moseley, R.L.; Ypma, R.J.F.; Holt, R.J.; Floris, D.; Chura, L.R.; Spencer, M.D.; Baron-Cohen, S.; Suckling, J.; Bullmore, E.; Rubinov, M.

2015-01-01

Endophenotypes are heritable and quantifiable markers that may assist in the identification of the complex genetic underpinnings of psychiatric conditions. Here we examined global hypoconnectivity as an endophenotype of autism spectrum conditions (ASCs). We studied well-matched groups of adolescent males with autism, genetically-related siblings of individuals with autism, and typically-developing control participants. We parcellated the brain into 258 regions and used complex-network analysis to detect a robust hypoconnectivity endophenotype in our participant group. We observed that whole-brain functional connectivity was highest in controls, intermediate in siblings, and lowest in ASC, in task and rest conditions. We identified additional, local endophenotype effects in specific networks including the visual processing and default mode networks. Our analyses are the first to show that whole-brain functional hypoconnectivity is an endophenotype of autism in adolescence, and may thus underlie the heritable similarities seen in adolescents with ASC and their relatives. PMID:26413477

Spectral properties of the temporal evolution of brain network structure.

PubMed

Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

2015-12-01

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Spectral properties of the temporal evolution of brain network structure

NASA Astrophysics Data System (ADS)

Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

2015-12-01

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

NASA Astrophysics Data System (ADS)

Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2018-04-01

In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

Brain imaging research in autism spectrum disorders: in search of neuropathology and health across the lifespan.

PubMed

Lainhart, Janet E

2015-03-01

Advances in brain imaging research in autism spectrum disorders (ASD) are rapidly occurring, and the amount of neuroimaging research has dramatically increased over the past 5 years. In this review, advances during the past 12 months and longitudinal studies are highlighted. Cross-sectional neuroimaging research provides evidence that the neural underpinnings of the behavioral signs of ASD involve not only dysfunctional integration of information across distributed brain networks but also basic dysfunction in primary cortices.Longitudinal studies of ASD show abnormally enlarged brain volumes and increased rates of brain growth during early childhood in only a small minority of ASD children. There is evidence of disordered development of white matter microstructure and amygdala growth, and at 2 years of age, network inefficiencies in posterior cerebral regions.From older childhood into adulthood, atypical age-variant and age-invariant changes in the trajectories of total and regional brain volumes and cortical thickness are apparent at the group level. There is evidence of abnormalities in posterior lobes and posterior brain networks during the first 2 years of life in ASD and, even in older children and adults, dysfunction in primary cortical areas.

Modeling fluctuations in default-mode brain network using a spiking neural network.

PubMed

Yamanishi, Teruya; Liu, Jian-Qin; Nishimura, Haruhiko

2012-08-01

Recently, numerous attempts have been made to understand the dynamic behavior of complex brain systems using neural network models. The fluctuations in blood-oxygen-level-dependent (BOLD) brain signals at less than 0.1 Hz have been observed by functional magnetic resonance imaging (fMRI) for subjects in a resting state. This phenomenon is referred to as a "default-mode brain network." In this study, we model the default-mode brain network by functionally connecting neural communities composed of spiking neurons in a complex network. Through computational simulations of the model, including transmission delays and complex connectivity, the network dynamics of the neural system and its behavior are discussed. The results show that the power spectrum of the modeled fluctuations in the neuron firing patterns is consistent with the default-mode brain network's BOLD signals when transmission delays, a characteristic property of the brain, have finite values in a given range.

The application of integrated knowledge-based systems for the Biomedical Risk Assessment Intelligent Network (BRAIN)

NASA Technical Reports Server (NTRS)

Loftin, Karin C.; Ly, Bebe; Webster, Laurie; Verlander, James; Taylor, Gerald R.; Riley, Gary; Culbert, Chris

1992-01-01

One of NASA's goals for long duration space flight is to maintain acceptable levels of crew health, safety, and performance. One way of meeting this goal is through BRAIN, an integrated network of both human and computer elements. BRAIN will function as an advisor to mission managers by assessing the risk of inflight biomedical problems and recommending appropriate countermeasures. Described here is a joint effort among various NASA elements to develop BRAIN and the Infectious Disease Risk Assessment (IDRA) prototype. The implementation of this effort addresses the technological aspects of knowledge acquisition, integration of IDRA components, the use of expert systems to automate the biomedical prediction process, development of a user friendly interface, and integration of IDRA and ExerCISys systems. Because C language, CLIPS and the X-Window System are portable and easily integrated, they were chosen ss the tools for the initial IDRA prototype.

Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization

NASA Astrophysics Data System (ADS)

Wu, Huijun; Wang, Hao; Lü, Linyuan

Applying network science to investigate the complex systems has become a hot topic. In neuroscience, understanding the architectures of complex brain networks was a vital issue. An enormous amount of evidence had supported the brain was cost/efficiency trade-off with small-worldness, hubness and modular organization through the functional MRI and structural MRI investigations. However, the T1-weighted/T2-weighted (T1w/T2w) ratio brain networks were mostly unexplored. Here, we utilized a KL divergence-based method to construct large-scale individual T1w/T2w ratio brain networks and investigated the underlying topological attributes of these networks. Our results supported that the T1w/T2w ratio brain networks were comprised of small-worldness, an exponentially truncated power-law degree distribution, frontal-parietal hubs and modular organization. Besides, there were significant positive correlations between the network metrics and fluid intelligence. Thus, the T1w/T2w ratio brain networks open a new avenue to understand the human brain and are a necessary supplement for future MRI studies.

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery

PubMed Central

Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien

2017-01-01

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases. PMID:28985229

Regulation of cerebrospinal fluid (CSF) flow in neurodegenerative, neurovascular and neuroinflammatory disease

PubMed Central

Simon, Matthew J.; Iliff, Jeffrey J.

2015-01-01

Cerebrospinal fluid (CSF) circulation and turnover provides a sink for the elimination of solutes from the brain interstitium, serving an important homeostatic role for the function of the central nervous system. Disruption of normal CSF circulation and turnover is believed to contribute to the development of many diseases, including neurodegenerative conditions such as Alzheimer’s disease, ischemic and traumatic brain injury, and neuroinflammatory conditions such as multiple sclerosis. Recent insights into CSF biology suggesting that CSF and interstitial fluid exchange along a brain-wide network of perivascular spaces termed the ‘glymphatic’ system suggest that CSF circulation may interact intimately with glial and vascular function to regulate basic aspects of brain function. Dysfunction within this glial vascular network, which is a feature of the aging and injured brain, is a potentially critical link between brain injury, neuroinflammation and the development of chronic neurodegeneration. Ongoing research within this field may provide a powerful new framework for understanding the common links between neurodegenerative, neurovascular and neuroinflammatory disease, in addition to providing potentially novel therapeutic targets for these conditions. PMID:26499397

The pathophysiology of post-stroke aphasia: A network approach.

PubMed

Thiel, Alexander; Zumbansen, Anna

2016-06-13

Post-stroke aphasia syndromes as a clinical entity arise from the disruption of brain networks specialized in language production and comprehension due to permanent focal ischemia. This approach to post-stroke aphasia is based on two pathophysiological concepts: 1) Understanding language processing in terms of distributed networks rather than language centers and 2) understanding the molecular pathophysiology of ischemic brain injury as a dynamic process beyond the direct destruction of network centers and their connections. While considerable progress has been made in the past 10 years to develop such models on a systems as well as a molecular level, the influence of these approaches on understanding and treating clinical aphasia syndromes has been limited. In this article, we review current pathophysiological concepts of ischemic brain injury, their relationship to altered information processing in language networks after ischemic stroke and how these mechanisms may be influenced therapeutically to improve treatment of post-stroke aphasia. Understanding the pathophysiological mechanism of post-stroke aphasia on a neurophysiological systems level as well as on the molecular level becomes more and more important for aphasia treatment, as the field moves from standardized therapies towards more targeted individualized treatment strategies comprising behavioural therapies as well as non-invasive brain stimulation (NIBS).

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.

Dynamic interactions in neural networks

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

Arbib, M.A.; Amari, S.

The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of intelligent machines. This volume presents models and data on the dynamic interactions occurring in the brain, and exhibits the dynamic interactions between research in computational neuroscience and in neural computing. The authors present current research, future trends and open problems.

Frequency of Maternal Touch Predicts Resting Activity and Connectivity of the Developing Social Brain.

PubMed

Brauer, Jens; Xiao, Yaqiong; Poulain, Tanja; Friederici, Angela D; Schirmer, Annett

2016-08-01

Previous behavioral research points to a positive relationship between maternal touch and early social development. Here, we explored the brain correlates of this relationship. The frequency of maternal touch was recorded for 43 five-year-old children during a 10 min standardized play session. Additionally, all children completed a resting-state functional magnetic resonance imaging session. Investigating the default mode network revealed a positive relation between the frequency of maternal touch and activity in the right posterior superior temporal sulcus (pSTS) extending into the temporo-parietal junction. Using this effect as a seed in a functional connectivity analysis identified a network including extended bilateral regions along the temporal lobe, bilateral frontal cortex, and left insula. Compared with children with low maternal touch, children with high maternal touch showed additional connectivity with the right dorso-medial prefrontal cortex. Together these results support the notion that childhood tactile experiences shape the developing "social brain" with a particular emphasis on a network involved in mentalizing. © The Author 2016. Published by Oxford University Press.

The Application of Integrated Knowledge-based Systems for the Biomedical Risk Assessment Intelligent Network (BRAIN)

NASA Technical Reports Server (NTRS)

Loftin, Karin C.; Ly, Bebe; Webster, Laurie; Verlander, James; Taylor, Gerald R.; Riley, Gary; Culbert, Chris; Holden, Tina; Rudisill, Marianne

1993-01-01

One of NASA's goals for long duration space flight is to maintain acceptable levels of crew health, safety, and performance. One way of meeting this goal is through the Biomedical Risk Assessment Intelligent Network (BRAIN), an integrated network of both human and computer elements. The BRAIN will function as an advisor to flight surgeons by assessing the risk of in-flight biomedical problems and recommending appropriate countermeasures. This paper describes the joint effort among various NASA elements to develop BRAIN and an Infectious Disease Risk Assessment (IDRA) prototype. The implementation of this effort addresses the technological aspects of the following: (1) knowledge acquisition; (2) integration of IDRA components; (3) use of expert systems to automate the biomedical prediction process; (4) development of a user-friendly interface; and (5) integration of the IDRA prototype and Exercise Countermeasures Intelligent System (ExerCISys). Because the C Language, CLIPS (the C Language Integrated Production System), and the X-Window System were portable and easily integrated, they were chosen as the tools for the initial IDRA prototype. The feasibility was tested by developing an IDRA prototype that predicts the individual risk of influenza. The application of knowledge-based systems to risk assessment is of great market value to the medical technology industry.

Meeting the memory challenges of brain-scale network simulation.

PubMed

Kunkel, Susanne; Potjans, Tobias C; Eppler, Jochen M; Plesser, Hans Ekkehard; Morrison, Abigail; Diesmann, Markus

2011-01-01

The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity, and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10(5) neurons with up to 10(9) synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been investigated in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Blue Gene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of neuronal simulators as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place. As a consequence, development cycles can be shorter and less expensive. Applying the model to our freely available Neural Simulation Tool (NEST), we identify the software components dominant at different scales, and develop general strategies for reducing the memory consumption, in particular by using data structures that exploit the sparseness of the local representation of the network. We show that these adaptations enable our simulation software to scale up to the order of 10,000 processors and beyond. As memory consumption issues are likely to be relevant for any software dealing with complex connectome data on such architectures, our approach and our findings should be useful for researchers developing novel neuroinformatics solutions to the challenges posed by the connectome project.

Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [18F]FDG-PET of Rats.

PubMed

Wan, Hongkai; Tan, Ziyu; Zheng, Qiang; Yu, Jing

2018-03-12

Recent researches have demonstrated the value of using 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography (PET) imaging to reveal the hypothyroidism-related damages in local brain regions. However, the influence of hypothyroidism on the entire brain network is barely studied. This study focuses on the application of graph theory on analyzing functional brain networks of the hypothyroidism symptom. For both the hypothyroidism and the control groups of Wistar rats, the functional brain networks were constructed by thresholding the glucose metabolism correlation matrices of 58 brain regions. The network topological properties (including the small-world properties and the nodal centralities) were calculated and compared between the two groups. We found that the rat brains, like human brains, have typical properties of the small-world network in both the hypothyroidism and the control groups. However, the hypothyroidism group demonstrated lower global efficiency and decreased local cliquishness of the brain network, indicating hypothyroidism-related impairment to the brain network. The hypothyroidism group also has decreased nodal centrality in the left posterior hippocampus, the right hypothalamus, pituitary, pons, and medulla. This observation accorded with the hypothyroidism-related functional disorder of hypothalamus-pituitary-thyroid (HPT) feedback regulation mechanism. Our research quantitatively confirms that hypothyroidism hampers brain cognitive function by causing impairment to the brain network of glucose metabolism. This study reveals the feasibility and validity of applying graph theory method to preclinical [ 18 F]FDG-PET images and facilitates future study on human subjects.

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.

Transcranial light-emitting diode therapy for neuropsychological improvement after traumatic brain injury: a new perspective for diffuse axonal lesion management

PubMed Central

dos Santos, João Gustavo Rocha Peixoto; Paiva, Wellingson Silva; Teixeira, Manoel Jacobsen

2018-01-01

The cost of traumatic brain injury (TBI) for public health policies is undeniable today. Even patients who suffer from mild TBI may persist with cognitive symptoms weeks after the accident. Most of them show no lesion in computed tomography or conventional magnetic resonance imaging, but microstructural white matter abnormalities (diffuse axonal lesion) can be found in diffusion tensor imaging. Different brain networks work together to form an important part of the cognition process, and they can be affected by TBI. The default mode network (DMN) plays an important central role in normal brain activities, presenting greater relative deactivation during more cognitively demanding tasks. After deactivation, it allows a distinct network to activate. This network (the central executive network) acts mainly during tasks involving executive functions. The salience network is another network necessary for normal executive function, and its activation leads to deactivation of the DMN. The use of red or near-infrared (NIR) light to stimulate or regenerate tissue is known as photobiomodulation. It was discovered that NIR (wavelength 800–900 nm) and red (wavelength 600 nm) light-emitting diodes (LEDs) are able to penetrate through scalp and skull and have the potential to improve the subnormal, cellular activity of compromised brain tissue. Based on this, different experimental and clinical studies were done to test LED therapy for TBI, and promising results were found. It leads us to consider developing different approaches to maximize the positive effects of this therapy and improve the quality of life of TBI patients. PMID:29731669

Transcranial light-emitting diode therapy for neuropsychological improvement after traumatic brain injury: a new perspective for diffuse axonal lesion management.

PubMed

Dos Santos, João Gustavo Rocha Peixoto; Paiva, Wellingson Silva; Teixeira, Manoel Jacobsen

2018-01-01

The cost of traumatic brain injury (TBI) for public health policies is undeniable today. Even patients who suffer from mild TBI may persist with cognitive symptoms weeks after the accident. Most of them show no lesion in computed tomography or conventional magnetic resonance imaging, but microstructural white matter abnormalities (diffuse axonal lesion) can be found in diffusion tensor imaging. Different brain networks work together to form an important part of the cognition process, and they can be affected by TBI. The default mode network (DMN) plays an important central role in normal brain activities, presenting greater relative deactivation during more cognitively demanding tasks. After deactivation, it allows a distinct network to activate. This network (the central executive network) acts mainly during tasks involving executive functions. The salience network is another network necessary for normal executive function, and its activation leads to deactivation of the DMN. The use of red or near-infrared (NIR) light to stimulate or regenerate tissue is known as photobiomodulation. It was discovered that NIR (wavelength 800-900 nm) and red (wavelength 600 nm) light-emitting diodes (LEDs) are able to penetrate through scalp and skull and have the potential to improve the subnormal, cellular activity of compromised brain tissue. Based on this, different experimental and clinical studies were done to test LED therapy for TBI, and promising results were found. It leads us to consider developing different approaches to maximize the positive effects of this therapy and improve the quality of life of TBI patients.

More randomized and resilient in the topological properties of functional brain networks in patients with major depressive disorder.

PubMed

Li, Huaizhou; Zhou, Haiyan; Yang, Yang; Wang, Haiyuan; Zhong, Ning

2017-10-01

Previous studies have reported the enhanced randomization of functional brain networks in patients with major depressive disorder (MDD). However, little is known about the changes of key nodal attributes for randomization, the resilience of network, and the clinical significance of the alterations. In this study, we collected the resting-state functional MRI data from 19 MDD patients and 19 healthy control (HC) individuals. Graph theory analysis showed that decreases were found in the small-worldness, clustering coefficient, local efficiency, and characteristic path length (i.e., increase of global efficiency) in the network of MDD group compared with HC group, which was consistent with previous findings and suggested the development toward randomization in the brain network in MDD. In addition, the greater resilience under the targeted attacks was also found in the network of patients with MDD. Furthermore, the abnormal nodal properties were found, including clustering coefficients and nodal efficiencies in the left orbital superior frontal gyrus, bilateral insula, left amygdala, right supramarginal gyrus, left putamen, left posterior cingulate cortex, left angular gyrus. Meanwhile, the correlation analysis showed that most of these abnormal areas were associated with the clinical status. The observed increased randomization and resilience in MDD might be related to the abnormal hub nodes in the brain networks, which were attacked by the disease pathology. Our findings provide new evidence to indicate that the weakening of specialized regions and the enhancement of whole brain integrity could be the potential endophenotype of the depressive pathology. Copyright © 2017 Elsevier Ltd. All rights reserved.

Graph analysis of functional brain networks: practical issues in translational neuroscience

PubMed Central

De Vico Fallani, Fabrizio; Richiardi, Jonas; Chavez, Mario; Achard, Sophie

2014-01-01

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes. PMID:25180301

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.

Concurrent white matter bundles and grey matter networks using independent component analysis.

PubMed

O'Muircheartaigh, Jonathan; Jbabdi, Saad

2018-04-15

Developments in non-invasive diffusion MRI tractography techniques have permitted the investigation of both the anatomy of white matter pathways connecting grey matter regions and their structural integrity. In parallel, there has been an expansion in automated techniques aimed at parcellating grey matter into distinct regions based on functional imaging. Here we apply independent component analysis to whole-brain tractography data to automatically extract brain networks based on their associated white matter pathways. This method decomposes the tractography data into components that consist of paired grey matter 'nodes' and white matter 'edges', and automatically separates major white matter bundles, including known cortico-cortical and cortico-subcortical tracts. We show how this framework can be used to investigate individual variations in brain networks (in terms of both nodes and edges) as well as their associations with individual differences in behaviour and anatomy. Finally, we investigate correspondences between tractography-based brain components and several canonical resting-state networks derived from functional MRI. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Finding influential nodes for integration in brain networks using optimal percolation theory.

PubMed

Del Ferraro, Gino; Moreno, Andrea; Min, Byungjoon; Morone, Flaviano; Pérez-Ramírez, Úrsula; Pérez-Cervera, Laura; Parra, Lucas C; Holodny, Andrei; Canals, Santiago; Makse, Hernán A

2018-06-11

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.

Reduced integration and differentiation of the imitation network in autism: A combined functional connectivity magnetic resonance imaging and diffusion-weighted imaging study.

PubMed

Fishman, Inna; Datko, Michael; Cabrera, Yuliana; Carper, Ruth A; Müller, Ralph-Axel

2015-12-01

Converging evidence indicates that brain abnormalities in autism spectrum disorder (ASD) involve atypical network connectivity, but few studies have integrated functional with structural connectivity measures. This multimodal investigation examined functional and structural connectivity of the imitation network in children and adolescents with ASD, and its links with clinical symptoms. Resting state functional magnetic resonance imaging and diffusion-weighted imaging were performed in 35 participants with ASD and 35 typically developing controls, aged 8 to 17 years, matched for age, gender, intelligence quotient, and head motion. Within-network analyses revealed overall reduced functional connectivity (FC) between distributed imitation regions in the ASD group. Whole brain analyses showed that underconnectivity in ASD occurred exclusively in regions belonging to the imitation network, whereas overconnectivity was observed between imitation nodes and extraneous regions. Structurally, reduced fractional anisotropy and increased mean diffusivity were found in white matter tracts directly connecting key imitation regions with atypical FC in ASD. These differences in microstructural organization of white matter correlated with weaker FC and greater ASD symptomatology. Findings demonstrate atypical connectivity of the brain network supporting imitation in ASD, characterized by a highly specific pattern. This pattern of underconnectivity within, but overconnectivity outside the functional network is in contrast with typical development and suggests reduced network integration and differentiation in ASD. Our findings also indicate that atypical connectivity of the imitation network may contribute to ASD clinical symptoms, highlighting the role of this fundamental social cognition ability in the pathophysiology of ASD. © 2015 American Neurological Association.

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

Functional brain networks reconstruction using group sparsity-regularized learning.

PubMed

Zhao, Qinghua; Li, Will X Y; Jiang, Xi; Lv, Jinglei; Lu, Jianfeng; Liu, Tianming

2018-06-01

Investigating functional brain networks and patterns using sparse representation of fMRI data has received significant interests in the neuroimaging community. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. To date, most of data-driven network reconstruction approaches rarely take consideration of anatomical structures, which are the substrate of brain function. Furthermore, it has been rarely explored whether structured sparse representation with anatomical guidance could facilitate functional networks reconstruction. To address this problem, in this paper, we propose to reconstruct brain networks utilizing the structure guided group sparse regression (S2GSR) in which 116 anatomical regions from the AAL template, as prior knowledge, are employed to guide the network reconstruction when performing sparse representation of whole-brain fMRI data. Specifically, we extract fMRI signals from standard space aligned with the AAL template. Then by learning a global over-complete dictionary, with the learned dictionary as a set of features (regressors), the group structured regression employs anatomical structures as group information to regress whole brain signals. Finally, the decomposition coefficients matrix is mapped back to the brain volume to represent functional brain networks and patterns. We use the publicly available Human Connectome Project (HCP) Q1 dataset as the test bed, and the experimental results indicate that the proposed anatomically guided structure sparse representation is effective in reconstructing concurrent functional brain networks.

Correspondence of the brain's functional architecture during activation and rest.

PubMed

Smith, Stephen M; Fox, Peter T; Miller, Karla L; Glahn, David C; Fox, P Mickle; Mackay, Clare E; Filippini, Nicola; Watkins, Kate E; Toro, Roberto; Laird, Angela R; Beckmann, Christian F

2009-08-04

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion.

PubMed

Lin, Mingyan; Pedrosa, Erika; Hrabovsky, Anastasia; Chen, Jian; Puliafito, Benjamin R; Gilbert, Stephanie R; Zheng, Deyou; Lachman, Herbert M

2016-11-15

Individuals with 22q11.2 Deletion Syndrome (22q11.2 DS) are a specific high-risk group for developing schizophrenia (SZ), schizoaffective disorder (SAD) and autism spectrum disorders (ASD). Several genes in the deleted region have been implicated in the development of SZ, e.g., PRODH and DGCR8. However, the mechanistic connection between these genes and the neuropsychiatric phenotype remains unclear. To elucidate the molecular consequences of 22q11.2 deletion in early neural development, we carried out RNA-seq analysis to investigate gene expression in early differentiating human neurons derived from induced pluripotent stem cells (iPSCs) of 22q11.2 DS SZ and SAD patients. Eight cases (ten iPSC-neuron samples in total including duplicate clones) and seven controls (nine in total including duplicate clones) were subjected to RNA sequencing. Using a systems level analysis, differentially expressed genes/gene-modules and pathway of interests were identified. Lastly, we related our findings from in vitro neuronal cultures to brain development by mapping differentially expressed genes to BrainSpan transcriptomes. We observed ~2-fold reduction in expression of almost all genes in the 22q11.2 region in SZ (37 genes reached p-value < 0.05, 36 of which reached a false discovery rate < 0.05). Outside of the deleted region, 745 genes showed significant differences in expression between SZ and control neurons (p < 0.05). Function enrichment and network analysis of the differentially expressed genes uncovered converging evidence on abnormal expression in key functional pathways, such as apoptosis, cell cycle and survival, and MAPK signaling in the SZ and SAD samples. By leveraging transcriptome profiles of normal human brain tissues across human development into adulthood, we showed that the differentially expressed genes converge on a sub-network mediated by CDC45 and the cell cycle, which would be disrupted by the 22q11.2 deletion during embryonic brain development, and another sub-network modulated by PRODH, which could contribute to disruption of brain function during adolescence. This study has provided evidence for disruption of potential molecular events in SZ patient with 22q11.2 deletion and related our findings from in vitro neuronal cultures to functional perturbations that can occur during brain development in SZ.

Small-World Brain Networks Revisited

PubMed Central

Bassett, Danielle S.; Bullmore, Edward T.

2016-01-01

It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex. PMID:27655008

Disrupted functional connectome in antisocial personality disorder.

PubMed

Jiang, Weixiong; Shi, Feng; Liao, Jian; Liu, Huasheng; Wang, Tao; Shen, Celina; Shen, Hui; Hu, Dewen; Wang, Wei; Shen, Dinggang

2017-08-01

Studies on antisocial personality disorder (ASPD) subjects focus on brain functional alterations in relation to antisocial behaviors. Neuroimaging research has identified a number of focal brain regions with abnormal structures or functions in ASPD. However, little is known about the connections among brain regions in terms of inter-regional whole-brain networks in ASPD patients, as well as possible alterations of brain functional topological organization. In this study, we employ resting-state functional magnetic resonance imaging (R-fMRI) to examine functional connectome of 32 ASPD patients and 35 normal controls by using a variety of network properties, including small-worldness, modularity, and connectivity. The small-world analysis reveals that ASPD patients have increased path length and decreased network efficiency, which implies a reduced ability of global integration of whole-brain functions. Modularity analysis suggests ASPD patients have decreased overall modularity, merged network modules, and reduced intra- and inter-module connectivities related to frontal regions. Also, network-based statistics show that an internal sub-network, composed of 16 nodes and 16 edges, is significantly affected in ASPD patients, where brain regions are mostly located in the fronto-parietal control network. These results suggest that ASPD is associated with both reduced brain integration and segregation in topological organization of functional brain networks, particularly in the fronto-parietal control network. These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD.

Disrupted functional connectome in antisocial personality disorder

PubMed Central

Jiang, Weixiong; Shi, Feng; Liao, Jian; Liu, Huasheng; Wang, Tao; Shen, Celina; Shen, Hui; Hu, Dewen

2017-01-01

Studies on antisocial personality disorder (ASPD) subjects focus on brain functional alterations in relation to antisocial behaviors. Neuroimaging research has identified a number of focal brain regions with abnormal structures or functions in ASPD. However, little is known about the connections among brain regions in terms of inter-regional whole-brain networks in ASPD patients, as well as possible alterations of brain functional topological organization. In this study, we employ resting-state functional magnetic resonance imaging (R-fMRI) to examine functional connectome of 32 ASPD patients and 35 normal controls by using a variety of network properties, including small-worldness, modularity, and connectivity. The small-world analysis reveals that ASPD patients have increased path length and decreased network efficiency, which implies a reduced ability of global integration of whole-brain functions. Modularity analysis suggests ASPD patients have decreased overall modularity, merged network modules, and reduced intra- and inter-module connectivities related to frontal regions. Also, network-based statistics show that an internal sub-network, composed of 16 nodes and 16 edges, is significantly affected in ASPD patients, where brain regions are mostly located in the fronto-parietal control network. These results suggest that ASPD is associated with both reduced brain integration and segregation in topological organization of functional brain networks, particularly in the fronto-parietal control network. These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD. PMID:27541949

Foxp2 Regulates Gene Networks Implicated in Neurite Outgrowth in the Developing Brain

PubMed Central

Vernes, Sonja C.; Oliver, Peter L.; Spiteri, Elizabeth; Lockstone, Helen E.; Puliyadi, Rathi; Taylor, Jennifer M.; Ho, Joses; Mombereau, Cedric; Brewer, Ariel; Lowy, Ernesto; Nicod, Jérôme; Groszer, Matthias; Baban, Dilair; Sahgal, Natasha; Cazier, Jean-Baptiste; Ragoussis, Jiannis; Davies, Kay E.; Geschwind, Daniel H.; Fisher, Simon E.

2011-01-01

Forkhead-box protein P2 is a transcription factor that has been associated with intriguing aspects of cognitive function in humans, non-human mammals, and song-learning birds. Heterozygous mutations of the human FOXP2 gene cause a monogenic speech and language disorder. Reduced functional dosage of the mouse version (Foxp2) causes deficient cortico-striatal synaptic plasticity and impairs motor-skill learning. Moreover, the songbird orthologue appears critically important for vocal learning. Across diverse vertebrate species, this well-conserved transcription factor is highly expressed in the developing and adult central nervous system. Very little is known about the mechanisms regulated by Foxp2 during brain development. We used an integrated functional genomics strategy to robustly define Foxp2-dependent pathways, both direct and indirect targets, in the embryonic brain. Specifically, we performed genome-wide in vivo ChIP–chip screens for Foxp2-binding and thereby identified a set of 264 high-confidence neural targets under strict, empirically derived significance thresholds. The findings, coupled to expression profiling and in situ hybridization of brain tissue from wild-type and mutant mouse embryos, strongly highlighted gene networks linked to neurite development. We followed up our genomics data with functional experiments, showing that Foxp2 impacts on neurite outgrowth in primary neurons and in neuronal cell models. Our data indicate that Foxp2 modulates neuronal network formation, by directly and indirectly regulating mRNAs involved in the development and plasticity of neuronal connections. PMID:21765815

Foxp2 regulates gene networks implicated in neurite outgrowth in the developing brain.

PubMed

Vernes, Sonja C; Oliver, Peter L; Spiteri, Elizabeth; Lockstone, Helen E; Puliyadi, Rathi; Taylor, Jennifer M; Ho, Joses; Mombereau, Cedric; Brewer, Ariel; Lowy, Ernesto; Nicod, Jérôme; Groszer, Matthias; Baban, Dilair; Sahgal, Natasha; Cazier, Jean-Baptiste; Ragoussis, Jiannis; Davies, Kay E; Geschwind, Daniel H; Fisher, Simon E

2011-07-01

Forkhead-box protein P2 is a transcription factor that has been associated with intriguing aspects of cognitive function in humans, non-human mammals, and song-learning birds. Heterozygous mutations of the human FOXP2 gene cause a monogenic speech and language disorder. Reduced functional dosage of the mouse version (Foxp2) causes deficient cortico-striatal synaptic plasticity and impairs motor-skill learning. Moreover, the songbird orthologue appears critically important for vocal learning. Across diverse vertebrate species, this well-conserved transcription factor is highly expressed in the developing and adult central nervous system. Very little is known about the mechanisms regulated by Foxp2 during brain development. We used an integrated functional genomics strategy to robustly define Foxp2-dependent pathways, both direct and indirect targets, in the embryonic brain. Specifically, we performed genome-wide in vivo ChIP-chip screens for Foxp2-binding and thereby identified a set of 264 high-confidence neural targets under strict, empirically derived significance thresholds. The findings, coupled to expression profiling and in situ hybridization of brain tissue from wild-type and mutant mouse embryos, strongly highlighted gene networks linked to neurite development. We followed up our genomics data with functional experiments, showing that Foxp2 impacts on neurite outgrowth in primary neurons and in neuronal cell models. Our data indicate that Foxp2 modulates neuronal network formation, by directly and indirectly regulating mRNAs involved in the development and plasticity of neuronal connections.

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.

Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders

PubMed Central

Sun, Yu; Li, Junhua; Suckling, John; Feng, Lei

2017-01-01

Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging), we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years) community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging. PMID:29209197

Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks

PubMed Central

Meyer-Bäse, Anke; Roberts, Rodney G.; Illan, Ignacio A.; Meyer-Bäse, Uwe; Lobbes, Marc; Stadlbauer, Andreas; Pinker-Domenig, Katja

2017-01-01

Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary conditions for (1) area aggregation and time-scale modeling in brain networks and for (2) pinning observability of nodes in dynamic graph networks. Simulation examples are given to illustrate the theoretical concepts. PMID:29051730

Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks.

PubMed

Meyer-Bäse, Anke; Roberts, Rodney G; Illan, Ignacio A; Meyer-Bäse, Uwe; Lobbes, Marc; Stadlbauer, Andreas; Pinker-Domenig, Katja

2017-01-01

Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary conditions for (1) area aggregation and time-scale modeling in brain networks and for (2) pinning observability of nodes in dynamic graph networks. Simulation examples are given to illustrate the theoretical concepts.

PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data.

PubMed

Du, Haixiao; Xia, Mingrui; Zhao, Kang; Liao, Xuhong; Yang, Huazhong; Wang, Yu; He, Yong

2018-05-01

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease. © 2018 Wiley Periodicals, Inc.

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

Identification of common coexpression modules based on quantitative network comparison.

PubMed

Jo, Yousang; Kim, Sanghyeon; Lee, Doheon

2018-06-13

Finding common molecular interactions from different samples is essential work to understanding diseases and other biological processes. Coexpression networks and their modules directly reflect sample-specific interactions among genes. Therefore, identification of common coexpression network or modules may reveal the molecular mechanism of complex disease or the relationship between biological processes. However, there has been no quantitative network comparison method for coexpression networks and we examined previous methods for other networks that cannot be applied to coexpression network. Therefore, we aimed to propose quantitative comparison methods for coexpression networks and to find common biological mechanisms between Huntington's disease and brain aging by the new method. We proposed two similarity measures for quantitative comparison of coexpression networks. Then, we performed experiments using known coexpression networks. We showed the validity of two measures and evaluated threshold values for similar coexpression network pairs from experiments. Using these similarity measures and thresholds, we quantitatively measured the similarity between disease-specific and aging-related coexpression modules and found similar Huntington's disease-aging coexpression module pairs. We identified similar Huntington's disease-aging coexpression module pairs and found that these modules are related to brain development, cell death, and immune response. It suggests that up-regulated cell signalling related cell death and immune/ inflammation response may be the common molecular mechanisms in the pathophysiology of HD and normal brain aging in the frontal cortex.

Neurodevelopmental alterations of large-scale structural networks in children with new-onset epilepsy

PubMed Central

Bonilha, Leonardo; Tabesh, Ali; Dabbs, Kevin; Hsu, David A.; Stafstrom, Carl E.; Hermann, Bruce P.; Lin, Jack J.

2014-01-01

Recent neuroimaging and behavioral studies have revealed that children with new onset epilepsy already exhibit brain structural abnormalities and cognitive impairment. How the organization of large-scale brain structural networks is altered near the time of seizure onset and whether network changes are related to cognitive performances remain unclear. Recent studies also suggest that regional brain volume covariance reflects synchronized brain developmental changes. Here, we test the hypothesis that epilepsy during early-life is associated with abnormalities in brain network organization and cognition. We used graph theory to study structural brain networks based on regional volume covariance in 39 children with new-onset seizures and 28 healthy controls. Children with new-onset epilepsy showed a suboptimal topological structural organization with enhanced network segregation and reduced global integration compared to controls. At the regional level, structural reorganization was evident with redistributed nodes from the posterior to more anterior head regions. The epileptic brain network was more vulnerable to targeted but not random attacks. Finally, a subgroup of children with epilepsy, namely those with lower IQ and poorer executive function, had a reduced balance between network segregation and integration. Taken together, the findings suggest that the neurodevelopmental impact of new onset childhood epilepsies alters large-scale brain networks, resulting in greater vulnerability to network failure and cognitive impairment. PMID:24453089

Hemispheric asymmetry of electroencephalography-based functional brain networks.

PubMed

Jalili, Mahdi

2014-11-12

Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.

Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain

PubMed Central

2016-01-01

Abstract When the brain is stimulated, for example, by sensory inputs or goal-oriented tasks, the brain initially responds with activities in specific areas. The subsequent pattern formation of functional networks is constrained by the structural connectivity (SC) of the brain. The extent to which information is processed over short- or long-range SC is unclear. Whole-brain models based on long-range axonal connections, for example, can partly describe measured functional connectivity dynamics at rest. Here, we study the effect of SC on the network response to stimulation. We use a human whole-brain network model comprising long- and short-range connections. We systematically activate each cortical or thalamic area, and investigate the network response as a function of its short- and long-range SC. We show that when the brain is operating at the edge of criticality, stimulation causes a cascade of network recruitments, collapsing onto a smaller space that is partly constrained by SC. We found both short- and long-range SC essential to reproduce experimental results. In particular, the stimulation of specific areas results in the activation of one or more resting-state networks. We suggest that the stimulus-induced brain activity, which may indicate information and cognitive processing, follows specific routes imposed by structural networks explaining the emergence of functional networks. We provide a lookup table linking stimulation targets and functional network activations, which potentially can be useful in diagnostics and treatments with brain stimulation. PMID:27752540

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.

An Investigation of the Differences and Similarities between Generated Small-World Networks for Right- and Left-Hand Motor Imageries.

PubMed

Zhang, Jiang; Li, Yuyao; Chen, Huafu; Ding, Jurong; Yuan, Zhen

2016-11-04

In this study, small-world network analysis was performed to identify the similarities and differences between functional brain networks for right- and left-hand motor imageries (MIs). First, Pearson correlation coefficients among the nodes within the functional brain networks from healthy subjects were calculated. Then, small-world network indicators, including the clustering coefficient, the average path length, the global efficiency, the local efficiency, the average node degree, and the small-world index, were generated for the functional brain networks during both right- and left-hand MIs. We identified large differences in the small-world network indicators between the functional networks during MI and in the random networks. More importantly, the functional brain networks underlying the right- and left-hand MIs exhibited similar small-world properties in terms of the clustering coefficient, the average path length, the global efficiency, and the local efficiency. By contrast, the right- and left-hand MI brain networks showed differences in small-world characteristics, including indicators such as the average node degree and the small-world index. Interestingly, our findings also suggested that the differences in the activity intensity and range, the average node degree, and the small-world index of brain networks between the right- and left-hand MIs were associated with the asymmetry of brain functions.

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.

Highly adaptive tests for group differences in brain functional connectivity.

PubMed

Kim, Junghi; Pan, Wei

2015-01-01

Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have been offering evidence and insights showing that altered brain functional networks are associated with neurological illnesses such as Alzheimer's disease. Exploring brain networks of clinical populations compared to those of controls would be a key inquiry to reveal underlying neurological processes related to such illnesses. For such a purpose, group-level inference is a necessary first step in order to establish whether there are any genuinely disrupted brain subnetworks. Such an analysis is also challenging due to the high dimensionality of the parameters in a network model and high noise levels in neuroimaging data. We are still in the early stage of method development as highlighted by Varoquaux and Craddock (2013) that "there is currently no unique solution, but a spectrum of related methods and analytical strategies" to learn and compare brain connectivity. In practice the important issue of how to choose several critical parameters in estimating a network, such as what association measure to use and what is the sparsity of the estimated network, has not been carefully addressed, largely because the answers are unknown yet. For example, even though the choice of tuning parameters in model estimation has been extensively discussed in the literature, as to be shown here, an optimal choice of a parameter for network estimation may not be optimal in the current context of hypothesis testing. Arbitrarily choosing or mis-specifying such parameters may lead to extremely low-powered tests. Here we develop highly adaptive tests to detect group differences in brain connectivity while accounting for unknown optimal choices of some tuning parameters. The proposed tests combine statistical evidence against a null hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth. These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios. The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.

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

Apollo’s gift: new aspects of neurologic music therapy

PubMed Central

Altenmüller, Eckart; Schlaug, Gottfried

2015-01-01

Music listening and music making activities are powerful tools to engage multisensory and motor networks, induce changes within these networks, and foster links between distant, but functionally related brain regions with continued and life-long musical practice. These multimodal effects of music together with music’s ability to tap into the emotion and reward system in the brain can be used to facilitate and enhance therapeutic approaches geared toward rehabilitating and restoring neurological dysfunctions and impairments of an acquired or congenital brain disorder. In this article, we review plastic changes in functional networks and structural components of the brain in response to short- and long-term music listening and music making activities. The specific influence of music on the developing brain is emphasized and possible transfer effects on emotional and cognitive processes are discussed. Furthermore, we present data on the potential of using musical tools and activities to support and facilitate neurorehabilitation. We will focus on interventions such as melodic intonation therapy and music-supported motor rehabilitation to showcase the effects of neurologic music therapies and discuss their underlying neural mechanisms. PMID:25725918

Apollo's gift: new aspects of neurologic music therapy.

PubMed

Altenmüller, Eckart; Schlaug, Gottfried

2015-01-01

Music listening and music making activities are powerful tools to engage multisensory and motor networks, induce changes within these networks, and foster links between distant, but functionally related brain regions with continued and life-long musical practice. These multimodal effects of music together with music's ability to tap into the emotion and reward system in the brain can be used to facilitate and enhance therapeutic approaches geared toward rehabilitating and restoring neurological dysfunctions and impairments of an acquired or congenital brain disorder. In this article, we review plastic changes in functional networks and structural components of the brain in response to short- and long-term music listening and music making activities. The specific influence of music on the developing brain is emphasized and possible transfer effects on emotional and cognitive processes are discussed. Furthermore, we present data on the potential of using musical tools and activities to support and facilitate neurorehabilitation. We will focus on interventions such as melodic intonation therapy and music-supported motor rehabilitation to showcase the effects of neurologic music therapies and discuss their underlying neural mechanisms. © 2015 Elsevier B.V. All rights reserved.

Source space analysis of event-related dynamic reorganization of brain networks.

PubMed

Ioannides, Andreas A; Dimitriadis, Stavros I; Saridis, George A; Voultsidou, Marotesa; Poghosyan, Vahe; Liu, Lichan; Laskaris, Nikolaos A

2012-01-01

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

Working memory brain activity and capacity link MAOA polymorphism to aggressive behavior during development

PubMed Central

Ziermans, T; Dumontheil, I; Roggeman, C; Peyrard-Janvid, M; Matsson, H; Kere, J; Klingberg, T

2012-01-01

A developmental increase in working memory capacity is an important part of cognitive development, and low working memory (WM) capacity is a risk factor for developing psychopathology. Brain activity represents a promising endophenotype for linking genes to behavior and for improving our understanding of the neurobiology of WM development. We investigated gene–brain–behavior relationships by focusing on 18 single-nucleotide polymorphisms (SNPs) located in six dopaminergic candidate genes (COMT, SLC6A3/DAT1, DBH, DRD4, DRD5, MAOA). Visuospatial WM (VSWM) brain activity, measured with functional magnetic resonance imaging, and VSWM capacity were assessed in a longitudinal study of typically developing children and adolescents. Behavioral problems were evaluated using the Child Behavior Checklist (CBCL). One SNP (rs6609257), located ∼6.6 kb downstream of the monoamine oxidase A gene (MAOA) on human chromosome X, significantly affected brain activity in a network of frontal, parietal and occipital regions. Increased activity in this network, but not in caudate nucleus or anterior prefrontal regions, was correlated with VSWM capacity, which in turn predicted externalizing (aggressive/oppositional) symptoms, with higher WM capacity associated with fewer externalizing symptoms. There were no direct significant correlations between rs6609257 and behavioral symptoms. These results suggest a mediating role of WM brain activity and capacity in linking the MAOA gene to aggressive behavior during development. PMID:22832821

Neural-like growing networks

NASA Astrophysics Data System (ADS)

Yashchenko, Vitaliy A.

2000-03-01

On the basis of the analysis of scientific ideas reflecting the law in the structure and functioning the biological structures of a brain, and analysis and synthesis of knowledge, developed by various directions in Computer Science, also there were developed the bases of the theory of a new class neural-like growing networks, not having the analogue in world practice. In a base of neural-like growing networks the synthesis of knowledge developed by classical theories - semantic and neural of networks is. The first of them enable to form sense, as objects and connections between them in accordance with construction of the network. With thus each sense gets a separate a component of a network as top, connected to other tops. In common it quite corresponds to structure reflected in a brain, where each obvious concept is presented by certain structure and has designating symbol. Secondly, this network gets increased semantic clearness at the expense owing to formation not only connections between neural by elements, but also themselves of elements as such, i.e. here has a place not simply construction of a network by accommodation sense structures in environment neural of elements, and purely creation of most this environment, as of an equivalent of environment of memory. Thus neural-like growing networks are represented by the convenient apparatus for modeling of mechanisms of teleological thinking, as a fulfillment of certain psychophysiological of functions.

Task vs. rest-different network configurations between the coactivation and the resting-state brain networks.

PubMed

Di, Xin; Gohel, Suril; Kim, Eun H; Biswal, Bharat B

2013-01-01

There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest.

Task vs. rest—different network configurations between the coactivation and the resting-state brain networks

PubMed Central

Di, Xin; Gohel, Suril; Kim, Eun H.; Biswal, Bharat B.

2013-01-01

There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest. PMID:24062654

Small Worldness in Dense and Weighted Connectomes

NASA Astrophysics Data System (ADS)

Colon-Perez, Luis; Couret, Michelle; Triplett, William; Price, Catherine; Mareci, Thomas

2016-05-01

The human brain is a heterogeneous network of connected functional regions; however, most brain network studies assume that all brain connections can be described in a framework of binary connections. The brain is a complex structure of white matter tracts connected by a wide range of tract sizes, which suggests a broad range of connection strengths. Therefore, the assumption that the connections are binary yields an incomplete picture of the brain. Various thresholding methods have been used to remove spurious connections and reduce the graph density in binary networks. But these thresholds are arbitrary and make problematic the comparison of networks created at different thresholds. The heterogeneity of connection strengths can be represented in graph theory by applying weights to the network edges. Using our recently introduced edge weight parameter, we estimated the topological brain network organization using a complimentary weighted connectivity framework to the traditional framework of a binary network. To examine the reproducibility of brain networks in a controlled condition, we studied the topological network organization of a single healthy individual by acquiring 10 repeated diffusion-weighted magnetic resonance image datasets, over a one-month period on the same scanner, and analyzing these networks with deterministic tractography. We applied a threshold to both the binary and weighted networks and determined that the extra degree of freedom that comes with the framework of weighting network connectivity provides a robust result as any threshold level. The proposed weighted connectivity framework provides a stable result and is able to demonstrate the small world property of brain networks in situations where the binary framework is inadequate and unable to demonstrate this network property.

A permutation testing framework to compare groups of brain networks.

PubMed

Simpson, Sean L; Lyday, Robert G; Hayasaka, Satoru; Marsh, Anthony P; Laurienti, Paul J

2013-01-01

Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Current comparison approaches generally either rely on a summary metric or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network, yielding little power and failing to make network level comparisons. Gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Here we propose a permutation testing framework that allows comparing groups of networks while incorporating topological features inherent in each individual network. We validate our approach using simulated data with known group differences. We then apply the method to functional brain networks derived from fMRI data.

Age-associated changes in rich-club organisation in autistic and neurotypical human brains

PubMed Central

Watanabe, Takamitsu; Rees, Geraint

2015-01-01

Macroscopic structural networks in the human brain have a rich-club architecture comprising both highly inter-connected central regions and sparsely connected peripheral regions. Recent studies show that disruption of this functionally efficient organisation is associated with several psychiatric disorders. However, despite increasing attention to this network property, whether age-associated changes in rich-club organisation occur during human adolescence remains unclear. Here, analysing a publicly shared diffusion tensor imaging dataset, we found that, during adolescence, brains of typically developing (TD) individuals showed increases in rich-club organisation and inferred network functionality, whereas individuals with autism spectrum disorders (ASD) did not. These differences between TD and ASD groups were statistically significant for both structural and functional properties. Moreover, this typical age-related changes in rich-club organisation were characterised by progressive involvement of the right anterior insula. In contrast, in ASD individuals, did not show typical increases in grey matter volume, and this relative anatomical immaturity was correlated with the severity of ASD social symptoms. These results provide evidence that rich-club architecture is one of the bases of functionally efficient brain networks underpinning complex cognitive functions in adult human brains. Furthermore, our findings suggest that immature rich-club organisation might be associated with some neurodevelopmental disorders. PMID:26537477

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.

Longitudinal Dynamics of 3-Dimensional Components of Selfhood After Severe Traumatic Brain Injury: A qEEG Case Study.

PubMed

Fingelkurts, Andrew A; Fingelkurts, Alexander A

2017-09-01

In this report, we describe the case of a patient who sustained extremely severe traumatic brain damage with diffuse axonal injury in a traffic accident and whose recovery was monitored during 6 years. Specifically, we were interested in the recovery dynamics of 3-dimensional components of selfhood (a 3-dimensional construct model for the complex experiential selfhood has been recently proposed based on the empirical findings on the functional-topographical specialization of 3 operational modules of brain functional network responsible for the self-consciousness processing) derived from the electroencephalographic (EEG) signal. The analysis revealed progressive (though not monotonous) restoration of EEG functional connectivity of 3 modules of brain functional network responsible for the self-consciousness processing, which was also paralleled by the clinically significant functional recovery. We propose that restoration of normal integrity of the operational modules of the self-referential brain network may underlie the positive dynamics of 3 aspects of selfhood and provide a neurobiological mechanism for their recovery. The results are discussed in the context of recent experimental studies that support this inference. Studies of ongoing recovery after severe brain injury utilizing knowledge about each separate aspect of complex selfhood will likely help to develop more efficient and targeted rehabilitation programs for patients with brain trauma.

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks

PubMed Central

Courellis, Hristos; Mullen, Tim; Poizner, Howard; Cauwenberghs, Gert; Iversen, John R.

2017-01-01

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a “reach/saccade to spatial target” cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI. PMID:28566997

Network science and the human brain: Using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease.

PubMed

Mears, David; Pollard, Harvey B

2016-06-01

Over the past 15 years, the emerging field of network science has revealed the key features of brain networks, which include small-world topology, the presence of highly connected hubs, and hierarchical modularity. The value of network studies of the brain is underscored by the range of network alterations that have been identified in neurological and psychiatric disorders, including epilepsy, depression, Alzheimer's disease, schizophrenia, and many others. Here we briefly summarize the concepts of graph theory that are used to quantify network properties and describe common experimental approaches for analysis of brain networks of structural and functional connectivity. These range from tract tracing to functional magnetic resonance imaging, diffusion tensor imaging, electroencephalography, and magnetoencephalography. We then summarize the major findings from the application of graph theory to nervous systems ranging from Caenorhabditis elegans to more complex primate brains, including man. Focusing, then, on studies involving the amygdala, a brain region that has attracted intense interest as a center for emotional processing, fear, and motivation, we discuss the features of the amygdala in brain networks for fear conditioning and emotional perception. Finally, to highlight the utility of graph theory for studying dysfunction of the amygdala in mental illness, we review data with regard to changes in the hub properties of the amygdala in brain networks of patients with depression. We suggest that network studies of the human brain may serve to focus attention on regions and connections that act as principal drivers and controllers of brain function in health and disease. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Prediction of individual brain maturity using fMRI.

PubMed

Dosenbach, Nico U F; Nardos, Binyam; Cohen, Alexander L; Fair, Damien A; Power, Jonathan D; Church, Jessica A; Nelson, Steven M; Wig, Gagan S; Vogel, Alecia C; Lessov-Schlaggar, Christina N; Barnes, Kelly Anne; Dubis, Joseph W; Feczko, Eric; Coalson, Rebecca S; Pruett, John R; Barch, Deanna M; Petersen, Steven E; Schlaggar, Bradley L

2010-09-10

Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals' brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain's major functional networks.

Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration

PubMed Central

Cole, Michael W.

2016-01-01

The human brain is able to exceed modern computers on multiple computational demands (e.g., language, planning) using a small fraction of the energy. The mystery of how the brain can be so efficient is compounded by recent evidence that all brain regions are constantly active as they interact in so-called resting-state networks (RSNs). To investigate the brain's ability to process complex cognitive demands efficiently, we compared functional connectivity (FC) during rest and multiple highly distinct tasks. We found previously that RSNs are present during a wide variety of tasks and that tasks only minimally modify FC patterns throughout the brain. Here, we tested the hypothesis that, although subtle, these task-evoked FC updates from rest nonetheless contribute strongly to behavioral performance. One might expect that larger changes in FC reflect optimization of networks for the task at hand, improving behavioral performance. Alternatively, smaller changes in FC could reflect optimization for efficient (i.e., small) network updates, reducing processing demands to improve behavioral performance. We found across three task domains that high-performing individuals exhibited more efficient brain connectivity updates in the form of smaller changes in functional network architecture between rest and task. These smaller changes suggest that individuals with an optimized intrinsic network configuration for domain-general task performance experience more efficient network updates generally. Confirming this, network update efficiency correlated with general intelligence. The brain's reconfiguration efficiency therefore appears to be a key feature contributing to both its network dynamics and general cognitive ability. SIGNIFICANCE STATEMENT The brain's network configuration varies based on current task demands. For example, functional brain connections are organized in one way when one is resting quietly but in another way if one is asked to make a decision. We found that the efficiency of these updates in brain network organization is positively related to general intelligence, the ability to perform a wide variety of cognitively challenging tasks well. Specifically, we found that brain network configuration at rest was already closer to a wide variety of task configurations in intelligent individuals. This suggests that the ability to modify network connectivity efficiently when task demands change is a hallmark of high intelligence. PMID:27535904

Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Voos, Avery; Pelphrey, Kevin

2013-01-01

Functional magnetic resonance imaging (fMRI), with its excellent spatial resolution and ability to visualize networks of neuroanatomical structures involved in complex information processing, has become the dominant technique for the study of brain function and its development. The accessibility of in-vivo pediatric brain-imaging techniques…

Uncovering the neuroanatomical correlates of cognitive, affective and conative theory of mind in paediatric traumatic brain injury: a neural systems perspective

PubMed Central

Catroppa, Cathy; Beare, Richard; Silk, Timothy J.; Hearps, Stephen J.; Beauchamp, Miriam H.; Yeates, Keith O.; Anderson, Vicki A.

2017-01-01

Abstract Deficits in theory of mind (ToM) are common after neurological insult acquired in the first and second decade of life, however the contribution of large-scale neural networks to ToM deficits in children with brain injury is unclear. Using paediatric traumatic brain injury (TBI) as a model, this study investigated the sub-acute effect of paediatric traumatic brain injury on grey-matter volume of three large-scale, domain-general brain networks (the Default Mode Network, DMN; the Central Executive Network, CEN; and the Salience Network, SN), as well as two domain-specific neural networks implicated in social-affective processes (the Cerebro-Cerebellar Mentalizing Network, CCMN and the Mirror Neuron/Empathy Network, MNEN). We also evaluated prospective structure–function relationships between these large-scale neural networks and cognitive, affective and conative ToM. 3D T1- weighted magnetic resonance imaging sequences were acquired sub-acutely in 137 children [TBI: n = 103; typically developing (TD) children: n = 34]. All children were assessed on measures of ToM at 24-months post-injury. Children with severe TBI showed sub-acute volumetric reductions in the CCMN, SN, MNEN, CEN and DMN, as well as reduced grey-matter volumes of several hub regions of these neural networks. Volumetric reductions in the CCMN and several of its hub regions, including the cerebellum, predicted poorer cognitive ToM. In contrast, poorer affective and conative ToM were predicted by volumetric reductions in the SN and MNEN, respectively. Overall, results suggest that cognitive, affective and conative ToM may be prospectively predicted by individual differences in structure of different neural systems—the CCMN, SN and MNEN, respectively. The prospective relationship between cerebellar volume and cognitive ToM outcomes is a novel finding in our paediatric brain injury sample and suggests that the cerebellum may play a role in the neural networks important for ToM. These findings are discussed in relation to neurocognitive models of ToM. We conclude that detection of sub-acute volumetric abnormalities of large-scale neural networks and their hub regions may aid in the early identification of children at risk for chronic social-cognitive impairment. PMID:28505355

Uncovering the neuroanatomical correlates of cognitive, affective and conative theory of mind in paediatric traumatic brain injury: a neural systems perspective.

PubMed

Ryan, Nicholas P; Catroppa, Cathy; Beare, Richard; Silk, Timothy J; Hearps, Stephen J; Beauchamp, Miriam H; Yeates, Keith O; Anderson, Vicki A

2017-09-01

Deficits in theory of mind (ToM) are common after neurological insult acquired in the first and second decade of life, however the contribution of large-scale neural networks to ToM deficits in children with brain injury is unclear. Using paediatric traumatic brain injury (TBI) as a model, this study investigated the sub-acute effect of paediatric traumatic brain injury on grey-matter volume of three large-scale, domain-general brain networks (the Default Mode Network, DMN; the Central Executive Network, CEN; and the Salience Network, SN), as well as two domain-specific neural networks implicated in social-affective processes (the Cerebro-Cerebellar Mentalizing Network, CCMN and the Mirror Neuron/Empathy Network, MNEN). We also evaluated prospective structure-function relationships between these large-scale neural networks and cognitive, affective and conative ToM. 3D T1- weighted magnetic resonance imaging sequences were acquired sub-acutely in 137 children [TBI: n = 103; typically developing (TD) children: n = 34]. All children were assessed on measures of ToM at 24-months post-injury. Children with severe TBI showed sub-acute volumetric reductions in the CCMN, SN, MNEN, CEN and DMN, as well as reduced grey-matter volumes of several hub regions of these neural networks. Volumetric reductions in the CCMN and several of its hub regions, including the cerebellum, predicted poorer cognitive ToM. In contrast, poorer affective and conative ToM were predicted by volumetric reductions in the SN and MNEN, respectively. Overall, results suggest that cognitive, affective and conative ToM may be prospectively predicted by individual differences in structure of different neural systems-the CCMN, SN and MNEN, respectively. The prospective relationship between cerebellar volume and cognitive ToM outcomes is a novel finding in our paediatric brain injury sample and suggests that the cerebellum may play a role in the neural networks important for ToM. These findings are discussed in relation to neurocognitive models of ToM. We conclude that detection of sub-acute volumetric abnormalities of large-scale neural networks and their hub regions may aid in the early identification of children at risk for chronic social-cognitive impairment. © The Author (2017). Published by Oxford University Press.

Complex network inference from P300 signals: Decoding brain state under visual stimulus for able-bodied and disabled subjects

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Cai, Qing; Dong, Na; Zhang, Shan-Shan; Bo, Yun; Zhang, Jie

2016-10-01

Distinguishing brain cognitive behavior underlying disabled and able-bodied subjects constitutes a challenging problem of significant importance. Complex network has established itself as a powerful tool for exploring functional brain networks, which sheds light on the inner workings of the human brain. Most existing works in constructing brain network focus on phase-synchronization measures between regional neural activities. In contrast, we propose a novel approach for inferring functional networks from P300 event-related potentials by integrating time and frequency domain information extracted from each channel signal, which we show to be efficient in subsequent pattern recognition. In particular, we construct brain network by regarding each channel signal as a node and determining the edges in terms of correlation of the extracted feature vectors. A six-choice P300 paradigm with six different images is used in testing our new approach, involving one able-bodied subject and three disabled subjects suffering from multiple sclerosis, cerebral palsy, traumatic brain and spinal-cord injury, respectively. We then exploit global efficiency, local efficiency and small-world indices from the derived brain networks to assess the network topological structure associated with different target images. The findings suggest that our method allows identifying brain cognitive behaviors related to visual stimulus between able-bodied and disabled subjects.

The effects of alcohol on the nonhuman primate brain: a network science approach to neuroimaging.

PubMed

Telesford, Qawi K; Laurienti, Paul J; Friedman, David P; Kraft, Robert A; Daunais, James B

2013-11-01

Animal studies have long been an important tool for basic research as they offer a degree of control often lacking in clinical studies. Of particular value is the use of nonhuman primates (NHPs) for neuroimaging studies. Currently, studies have been published using functional magnetic resonance imaging (fMRI) to understand the default-mode network in the NHP brain. Network science provides an alternative approach to neuroimaging allowing for evaluation of whole-brain connectivity. In this study, we used network science to build NHP brain networks from fMRI data to understand the basic functional organization of the NHP brain. We also explored how the brain network is affected following an acute ethanol (EtOH) pharmacological challenge. Baseline resting-state fMRI was acquired in an adult male rhesus macaque (n = 1) and a cohort of vervet monkeys (n = 10). A follow-up scan was conducted in the rhesus macaque to assess network variability and to assess the effects of an acute EtOH challenge on the brain network. The most connected regions in the resting-state networks were similar across species and matched regions identified as the default-mode network in previous NHP fMRI studies. Under an acute EtOH challenge, the functional organization of the brain was significantly impacted. Network science offers a great opportunity to understand the brain as a complex system and how pharmacological conditions can affect the system globally. These models are sensitive to changes in the brain and may prove to be a valuable tool in long-term studies on alcohol exposure. Copyright © 2013 by the Research Society on Alcoholism.

Altered Whole-Brain and Network-Based Functional Connectivity in Parkinson's Disease.

PubMed

de Schipper, Laura J; Hafkemeijer, Anne; van der Grond, Jeroen; Marinus, Johan; Henselmans, Johanna M L; van Hilten, Jacobus J

2018-01-01

Background: Functional imaging methods, such as resting-state functional magnetic resonance imaging, reflect changes in neural connectivity and may help to assess the widespread consequences of disease-specific network changes in Parkinson's disease. In this study we used a relatively new graph analysis approach in functional imaging: eigenvector centrality mapping. This model-free method, applied to all voxels in the brain, identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. In other neurological disorders, eigenvector centrality mapping has been linked to changes in functional connectivity in certain nodes of brain networks. Objectives: Examining changes in functional brain connectivity architecture on a whole brain and network level in patients with Parkinson's disease. Methods: Whole brain resting-state functional architecture was studied with a recently introduced graph analysis approach (eigenvector centrality mapping). Functional connectivity was further investigated in relation to eight known resting-state networks. Cross-sectional analyses included group comparison of functional connectivity measures of Parkinson's disease patients ( n = 107) with control subjects ( n = 58) and correlations with clinical data, including motor and cognitive impairment and a composite measure of predominantly non-dopaminergic symptoms. Results: Eigenvector centrality mapping revealed that frontoparietal regions were more prominent in the whole-brain network function in patients compared to control subjects, while frontal and occipital brain areas were less prominent in patients. Using standard resting-state networks, we found predominantly increased functional connectivity, namely within sensorimotor system and visual networks in patients. Regional group differences in functional connectivity of both techniques between patients and control subjects partly overlapped for highly connected posterior brain regions, in particular in the posterior cingulate cortex and precuneus. Clinico-functional imaging relations were not found. Conclusions: Changes on the level of functional brain connectivity architecture might provide a different perspective of pathological consequences of Parkinson's disease. The involvement of specific, highly connected (hub) brain regions may influence whole brain functional network architecture in Parkinson's disease.

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.

EEG-based research on brain functional networks in cognition.

PubMed

Wang, Niannian; Zhang, Li; Liu, Guozhong

2015-01-01

Recently, exploring the cognitive functions of the brain by establishing a network model to understand the working mechanism of the brain has become a popular research topic in the field of neuroscience. In this study, electroencephalography (EEG) was used to collect data from subjects given four different mathematical cognitive tasks: recite numbers clockwise and counter-clockwise, and letters clockwise and counter-clockwise to build a complex brain function network (BFN). By studying the connectivity features and parameters of those brain functional networks, it was found that the average clustering coefficient is much larger than its corresponding random network and the average shortest path length is similar to the corresponding random networks, which clearly shows the characteristics of the small-world network. The brain regions stimulated during the experiment are consistent with traditional cognitive science regarding learning, memory, comprehension, and other rational judgment results. The new method of complex networking involves studying the mathematical cognitive process of reciting, providing an effective research foundation for exploring the relationship between brain cognition and human learning skills and memory. This could help detect memory deficits early in young and mentally handicapped children, and help scientists understand the causes of cognitive brain disorders.

Understanding brain networks and brain organization

PubMed Central

Pessoa, Luiz

2014-01-01

What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. The aim of this paper is to discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. As others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one-to-one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-finding algorithms, does not by itself reveal “true” subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different “slices” of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity profiles. The concept can also be used to describe the way different brain regions participate in networks. PMID:24819881

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.

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain.

PubMed

Higgins, Irina; Stringer, Simon; Schnupp, Jan

2017-01-01

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain

PubMed Central

Stringer, Simon

2017-01-01

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable. PMID:28797034

Future Directions for Examination of Brain Networks in Neurodevelopmental Disorders.

PubMed

Uddin, Lucina Q; Karlsgodt, Katherine H

2018-01-01

Neurodevelopmental disorders are associated with atypical development and maturation of brain networks. A recent focus on human connectomics research and the growing popularity of open science initiatives has created the ideal climate in which to make real progress toward understanding the neurobiology of disorders affecting youth. Here we outline future directions for neuroscience researchers examining brain networks in neurodevelopmental disorders, highlighting gaps in the current literature. We emphasize the importance of leveraging large neuroimaging and phenotypic data sets recently made available to the research community, and we suggest specific novel methodological approaches, including analysis of brain dynamics and structural connectivity, that have the potential to produce the greatest clinical insight. Transdiagnostic approaches will also become increasingly necessary as the Research Domain Criteria framework put forth by the National Institute of Mental Health permeates scientific discourse. During this exciting era of big data and increased computational sophistication of analytic tools, the possibilities for significant advancement in understanding neurodevelopmental disorders are limitless.

Structural covariance networks in the mouse brain.

PubMed

Pagani, Marco; Bifone, Angelo; Gozzi, Alessandro

2016-04-01

The presence of networks of correlation between regional gray matter volume as measured across subjects in a group of individuals has been consistently described in several human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain mapping modalities like functional and diffusion-weighted imaging, the approach can provide precious insights into the mutual influence of trophic and plastic processes in health and pathological states. To investigate whether analogous scMRI networks are present in lower mammal species amenable to genetic and experimental manipulation such as the laboratory mouse, we employed high resolution morphoanatomical MRI in a large cohort of genetically-homogeneous wild-type mice (C57Bl6/J) and mapped scMRI networks using a seed-based approach. We show that the mouse brain exhibits robust homotopic scMRI networks in both primary and associative cortices, a finding corroborated by independent component analyses of cortical volumes. Subcortical structures also showed highly symmetric inter-hemispheric correlations, with evidence of distributed antero-posterior networks in diencephalic regions of the thalamus and hypothalamus. Hierarchical cluster analysis revealed six identifiable clusters of cortical and sub-cortical regions corresponding to previously described neuroanatomical systems. Our work documents the presence of homotopic cortical and subcortical scMRI networks in the mouse brain, thus supporting the use of this species to investigate the elusive biological and neuroanatomical underpinnings of scMRI network development and its derangement in neuropathological states. The identification of scMRI networks in genetically homogeneous inbred mice is consistent with the emerging view of a key role of environmental factors in shaping these correlational networks. Copyright © 2016 Elsevier Inc. All rights reserved.

Complex network analysis of resting-state fMRI of the brain.

PubMed

Anwar, Abdul Rauf; Hashmy, Muhammad Yousaf; Imran, Bilal; Riaz, Muhammad Hussnain; Mehdi, Sabtain Muhammad Muntazir; Muthalib, Makii; Perrey, Stephane; Deuschl, Gunther; Groppa, Sergiu; Muthuraman, Muthuraman

2016-08-01

Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected. Regions of the brain, that are functionally connected, during rest constitutes to the resting state network. In the present study, we employed the complex network measures to estimate the presence of community structures within a network. Such estimate is named as modularity. Instead of using a traditional correlation matrix, we used a coherence matrix taken from the causality measure between different nodes. Our results show that in prolonged resting state the modularity starts to decrease. This decrease was observed in all the resting state networks and on both sides of the brain. Our study highlights the usage of coherence matrix instead of correlation matrix for complex network analysis.

Brain Anatomical Network and Intelligence

PubMed Central

Li, Jun; Qin, Wen; Li, Kuncheng; Yu, Chunshui; Jiang, Tianzi

2009-01-01

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence. PMID:19492086

Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

PubMed

Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

2015-03-01

Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing, especially in brain regions involved in working memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Large-Scale Brain Network Coupling Predicts Total Sleep Deprivation Effects on Cognitive Capacity

PubMed Central

Wang, Lubin; Zhai, Tianye; Zou, Feng; Ye, Enmao; Jin, Xiao; Li, Wuju; Qi, Jianlin; Yang, Zheng

2015-01-01

Interactions between large-scale brain networks have received most attention in the study of cognitive dysfunction of human brain. In this paper, we aimed to test the hypothesis that the coupling strength of large-scale brain networks will reflect the pressure for sleep and will predict cognitive performance, referred to as sleep pressure index (SPI). Fourteen healthy subjects underwent this within-subject functional magnetic resonance imaging (fMRI) study during rested wakefulness (RW) and after 36 h of total sleep deprivation (TSD). Self-reported scores of sleepiness were higher for TSD than for RW. A subsequent working memory (WM) task showed that WM performance was lower after 36 h of TSD. Moreover, SPI was developed based on the coupling strength of salience network (SN) and default mode network (DMN). Significant increase of SPI was observed after 36 h of TSD, suggesting stronger pressure for sleep. In addition, SPI was significantly correlated with both the visual analogue scale score of sleepiness and the WM performance. These results showed that alterations in SN-DMN coupling might be critical in cognitive alterations that underlie the lapse after TSD. Further studies may validate the SPI as a potential clinical biomarker to assess the impact of sleep deprivation. PMID:26218521

Fetal functional imaging portrays heterogeneous development of emerging human brain networks

PubMed Central

Jakab, András; Schwartz, Ernst; Kasprian, Gregor; Gruber, Gerlinde M.; Prayer, Daniela; Schöpf, Veronika; Langs, Georg

2014-01-01

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction, and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st to 38th gestational weeks (GWs) with a network-based statistical inference approach. The overall connectivity network, short range, and interhemispheric connections showed sigmoid expansion curve peaking at the 26–29 GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW), temporal (peak: 26 GW), frontal (peak: 26.4 GW), and parietal expansion (peak: 27.5 GW). We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macro connectivity. PMID:25374531

Fetal functional imaging portrays heterogeneous development of emerging human brain networks.

PubMed

Jakab, András; Schwartz, Ernst; Kasprian, Gregor; Gruber, Gerlinde M; Prayer, Daniela; Schöpf, Veronika; Langs, Georg

2014-01-01

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction, and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st to 38th gestational weeks (GWs) with a network-based statistical inference approach. The overall connectivity network, short range, and interhemispheric connections showed sigmoid expansion curve peaking at the 26-29 GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW), temporal (peak: 26 GW), frontal (peak: 26.4 GW), and parietal expansion (peak: 27.5 GW). We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macro connectivity.

Violence: heightened brain attentional network response is selectively muted in Down syndrome.

PubMed

Anderson, Jeffrey S; Treiman, Scott M; Ferguson, Michael A; Nielsen, Jared A; Edgin, Jamie O; Dai, Li; Gerig, Guido; Korenberg, Julie R

2015-01-01

The ability to recognize and respond appropriately to threat is critical to survival, and the neural substrates subserving attention to threat may be probed using depictions of media violence. Whether neural responses to potential threat differ in Down syndrome is not known. We performed functional MRI scans of 15 adolescent and adult Down syndrome and 14 typically developing individuals, group matched by age and gender, during 50 min of passive cartoon viewing. Brain activation to auditory and visual features, violence, and presence of the protagonist and antagonist were compared across cartoon segments. fMRI signal from the brain's dorsal attention network was compared to thematic and violent events within the cartoons between Down syndrome and control samples. We found that in typical development, the brain's dorsal attention network was most active during violent scenes in the cartoons and that this was significantly and specifically reduced in Down syndrome. When the antagonist was on screen, there was significantly less activation in the left medial temporal lobe of individuals with Down syndrome. As scenes represented greater relative threat, the disparity between attentional brain activation in Down syndrome and control individuals increased. There was a reduction in the temporal autocorrelation of the dorsal attention network, consistent with a shortened attention span in Down syndrome. Individuals with Down syndrome exhibited significantly reduced activation in primary sensory cortices, and such perceptual impairments may constrain their ability to respond to more complex social cues such as violence. These findings may indicate a relative deficit in emotive perception of violence in Down syndrome, possibly mediated by impaired sensory perception and hypoactivation of medial temporal structures in response to threats, with relative preservation of activity in pro-social brain regions. These findings indicate that specific genetic differences associated with Down syndrome can modulate the brain's response to violence and other complex emotive ideas.

Effect of tumor resection on the characteristics of functional brain networks.

PubMed

Wang, H; Douw, L; Hernández, J M; Reijneveld, J C; Stam, C J; Van Mieghem, P

2010-08-01

Brain functioning such as cognitive performance depends on the functional interactions between brain areas, namely, the functional brain networks. The functional brain networks of a group of patients with brain tumors are measured before and after tumor resection. In this work, we perform a weighted network analysis to understand the effect of neurosurgery on the characteristics of functional brain networks. Statistically significant changes in network features have been discovered in the beta (13-30 Hz) band after neurosurgery: the link weight correlation around nodes and within triangles increases which implies improvement in local efficiency of information transfer and robustness; the clustering of high link weights in a subgraph becomes stronger, which enhances the global transport capability; and the decrease in the synchronization or virus spreading threshold, revealed by the increase in the largest eigenvalue of the adjacency matrix, which suggests again the improvement of information dissemination.

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

Disrupted topological properties of brain white matter networks in left temporal lobe epilepsy: a diffusion tensor imaging study.

PubMed

Xu, Y; Qiu, S; Wang, J; Liu, Z; Zhang, R; Li, S; Cheng, L; Liu, Z; Wang, W; Huang, R

2014-10-24

Mesial temporal lobe epilepsy (mTLE) is the most common drug-refractory focal epilepsy in adults. Although previous functional and morphological studies have revealed abnormalities in the brain networks of mTLE, the topological organization of the brain white matter (WM) networks in mTLE patients is still ambiguous. In this study, we constructed brain WM networks for 14 left mTLE patients and 22 age- and gender-matched normal controls using diffusion tensor tractography and estimated the alterations of network properties in the mTLE brain networks using graph theoretical analysis. We found that networks for both the mTLE patients and the controls exhibited prominent small-world properties, suggesting a balanced topology of integration and segregation. However, the brain WM networks of mTLE patients showed a significant increased characteristic path length but significant decreased global efficiency, which indicate a disruption in the organization of the brain WM networks in mTLE patients. Moreover, we found significant between-group differences in the nodal properties in several brain regions, such as the left superior temporal gyrus, left hippocampus, the right occipital and right temporal cortices. The robustness analysis showed that the results were likely to be consistent for the networks constructed with different definitions of node and edge weight. Taken together, our findings may suggest an adverse effect of epileptic seizures on the organization of large-scale brain WM networks in mTLE patients. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

ORIENTING OF ATTENTION: THEN AND NOW1

PubMed Central

Posner, Michael I.

2014-01-01

It is nearly 35 years since I gave the 7th Sir Frederick Bartlett lecture at Oxford University. This was published as a paper entitled Orienting of Attention in the Quarterly Journal (1980, 32: 3–25). The topic was then primarily in Psychology, but now equally often in Neuroscience. This paper summarizes the background of the reaction time methods used in the original paper and findings that emerged later on the sensory consequences of orienting, mainly in the visual system. It then discusses the brain network which is the source of the sensory amplification and other brain networks that are involved in attention. Next, it reviews studies of the development of attentional networks in early life. Finally, it indicates how the new tools available to explore the human brain can lead to further progress. PMID:25176352

Germline Chd8 haploinsufficiency alters brain development in mouse

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

Gompers, Andrea L.; Su-Feher, Linda; Ellegood, Jacob

The chromatin remodeling gene CHD8 represents a central node in neurodevelopmental gene networks implicated in autism. In this paper, we examined the impact of germline heterozygous frameshift Chd8 mutation on neurodevelopment in mice. Chd8 +/ del5 mice displayed normal social interactions with no repetitive behaviors but exhibited cognitive impairment correlated with increased regional brain volume, validating that phenotypes of Chd8 +/ del5 mice overlap pathology reported in humans with CHD8 mutations. We applied network analysis to characterize neurodevelopmental gene expression, revealing widespread transcriptional changes in Chd8 +/ del5 mice across pathways disrupted in neurodevelopmental disorders, including neurogenesis, synaptic processes andmore » neuroimmune signaling. We identified a co-expression module with peak expression in early brain development featuring dysregulation of RNA processing, chromatin remodeling and cell-cycle genes enriched for promoter binding by Chd8, and we validated increased neuronal proliferation and developmental splicing perturbation in Chd8 +/ del5 mice. Finally, this integrative analysis offers an initial picture of the consequences of Chd8 haploinsufficiency for brain development.« less

Germline Chd8 haploinsufficiency alters brain development in mouse

DOE PAGES

Gompers, Andrea L.; Su-Feher, Linda; Ellegood, Jacob; ...

2017-06-26

The chromatin remodeling gene CHD8 represents a central node in neurodevelopmental gene networks implicated in autism. In this paper, we examined the impact of germline heterozygous frameshift Chd8 mutation on neurodevelopment in mice. Chd8 +/ del5 mice displayed normal social interactions with no repetitive behaviors but exhibited cognitive impairment correlated with increased regional brain volume, validating that phenotypes of Chd8 +/ del5 mice overlap pathology reported in humans with CHD8 mutations. We applied network analysis to characterize neurodevelopmental gene expression, revealing widespread transcriptional changes in Chd8 +/ del5 mice across pathways disrupted in neurodevelopmental disorders, including neurogenesis, synaptic processes andmore » neuroimmune signaling. We identified a co-expression module with peak expression in early brain development featuring dysregulation of RNA processing, chromatin remodeling and cell-cycle genes enriched for promoter binding by Chd8, and we validated increased neuronal proliferation and developmental splicing perturbation in Chd8 +/ del5 mice. Finally, this integrative analysis offers an initial picture of the consequences of Chd8 haploinsufficiency for brain development.« less

Correspondence of the brain's functional architecture during activation and rest

PubMed Central

Smith, Stephen M.; Fox, Peter T.; Miller, Karla L.; Glahn, David C.; Fox, P. Mickle; Mackay, Clare E.; Filippini, Nicola; Watkins, Kate E.; Toro, Roberto; Laird, Angela R.; Beckmann, Christian F.

2009-01-01

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.” PMID:19620724

Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study.

PubMed

Liang, Xia; Wang, Jinhui; Yan, Chaogan; Shu, Ni; Xu, Ke; Gong, Gaolang; He, Yong

2012-01-01

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01-0.027 Hz) versus slow-4 (0.027-0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the "best" network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027-0.073 Hz band exhibited greater reliability than those in the 0.01-0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies.

Neurodevelopmental alterations of large-scale structural networks in children with new-onset epilepsy.

PubMed

Bonilha, Leonardo; Tabesh, Ali; Dabbs, Kevin; Hsu, David A; Stafstrom, Carl E; Hermann, Bruce P; Lin, Jack J

2014-08-01

Recent neuroimaging and behavioral studies have revealed that children with new onset epilepsy already exhibit brain structural abnormalities and cognitive impairment. How the organization of large-scale brain structural networks is altered near the time of seizure onset and whether network changes are related to cognitive performances remain unclear. Recent studies also suggest that regional brain volume covariance reflects synchronized brain developmental changes. Here, we test the hypothesis that epilepsy during early-life is associated with abnormalities in brain network organization and cognition. We used graph theory to study structural brain networks based on regional volume covariance in 39 children with new-onset seizures and 28 healthy controls. Children with new-onset epilepsy showed a suboptimal topological structural organization with enhanced network segregation and reduced global integration compared with controls. At the regional level, structural reorganization was evident with redistributed nodes from the posterior to more anterior head regions. The epileptic brain network was more vulnerable to targeted but not random attacks. Finally, a subgroup of children with epilepsy, namely those with lower IQ and poorer executive function, had a reduced balance between network segregation and integration. Taken together, the findings suggest that the neurodevelopmental impact of new onset childhood epilepsies alters large-scale brain networks, resulting in greater vulnerability to network failure and cognitive impairment. Copyright © 2014 Wiley Periodicals, Inc.

Exploration and Modulation of Brain Network Interactions with Noninvasive Brain Stimulation in Combination with Neuroimaging

PubMed Central

Shafi, Mouhsin M.; Westover, M. Brandon; Fox, Michael D.; Pascual-Leone, Alvaro

2012-01-01

Much recent work in systems neuroscience has focused on how dynamic interactions between different cortical regions underlie complex brain functions such as motor coordination, language, and emotional regulation. Various studies using neuroimaging and neurophysiologic techniques have suggested that in many neuropsychiatric disorders, these dynamic brain networks are dysregulated. Here we review the utility of combined noninvasive brain stimulation and neuroimaging approaches towards greater understanding of dynamic brain networks in health and disease. Brain stimulation techniques, such as transcranial magnetic stimulation and transcranial direct current stimulation, use electromagnetic principles to noninvasively alter brain activity, and induce focal but also network effects beyond the stimulation site. When combined with brain imaging techniques such as functional MRI, PET and EEG, these brain stimulation techniques enable a causal assessment of the interaction between different network components, and their respective functional roles. The same techniques can also be applied to explore hypotheses regarding the changes in functional connectivity that occur during task performance and in various disease states such as stroke, depression and schizophrenia. Finally, in diseases characterized by pathologic alterations in either the excitability within a single region or in the activity of distributed networks, such techniques provide a potential mechanism to alter cortical network function and architectures in a beneficial manner. PMID:22429242

Regulation of cerebrospinal fluid (CSF) flow in neurodegenerative, neurovascular and neuroinflammatory disease.

PubMed

Simon, Matthew J; Iliff, Jeffrey J

2016-03-01

Cerebrospinal fluid (CSF) circulation and turnover provides a sink for the elimination of solutes from the brain interstitium, serving an important homeostatic role for the function of the central nervous system. Disruption of normal CSF circulation and turnover is believed to contribute to the development of many diseases, including neurodegenerative conditions such as Alzheimer's disease, ischemic and traumatic brain injury, and neuroinflammatory conditions such as multiple sclerosis. Recent insights into CSF biology suggesting that CSF and interstitial fluid exchange along a brain-wide network of perivascular spaces termed the 'glymphatic' system suggest that CSF circulation may interact intimately with glial and vascular function to regulate basic aspects of brain function. Dysfunction within this glial vascular network, which is a feature of the aging and injured brain, is a potentially critical link between brain injury, neuroinflammation and the development of chronic neurodegeneration. Ongoing research within this field may provide a powerful new framework for understanding the common links between neurodegenerative, neurovascular and neuroinflammatory disease, in addition to providing potentially novel therapeutic targets for these conditions. This article is part of a Special Issue entitled: Neuro Inflammation edited by Helga E. de Vries and Markus Schwaninger. Copyright © 2015 Elsevier B.V. All rights reserved.

Psychotic Experiences, Working Memory, and the Developing Brain: A Multimodal Neuroimaging Study

PubMed Central

Fonville, Leon; Cohen Kadosh, Kathrin; Drakesmith, Mark; Dutt, Anirban; Zammit, Stanley; Mollon, Josephine; Reichenberg, Abraham; Lewis, Glyn; Jones, Derek K.; David, Anthony S.

2015-01-01

Psychotic experiences (PEs) occur in the general population, especially in children and adolescents, and are associated with poor psychosocial outcomes, impaired cognition, and increased risk of transition to psychosis. It is unknown how the presence and persistence of PEs during early adulthood affects cognition and brain function. The current study assessed working memory as well as brain function and structure in 149 individuals, with and without PEs, drawn from a population cohort. Observer-rated PEs were classified as persistent or transient on the basis of longitudinal assessments. Working memory was assessed using the n-back task during fMRI. Dynamic causal modeling (DCM) was used to characterize frontoparietal network configuration and voxel-based morphometry was utilized to examine gray matter. Those with persistent, but not transient, PEs performed worse on the n-back task, compared with controls, yet showed no significant differences in regional brain activation or brain structure. DCM analyses revealed greater emphasis on frontal connectivity within a frontoparietal network in those with PEs compared with controls. We propose that these findings portray an altered configuration of working memory function in the brain, potentially indicative of an adaptive response to atypical development associated with the manifestation of PEs. PMID:26286920

Altered structural connectivity of pain-related brain network in burning mouth syndrome-investigation by graph analysis of probabilistic tractography.

PubMed

Wada, Akihiko; Shizukuishi, Takashi; Kikuta, Junko; Yamada, Haruyasu; Watanabe, Yusuke; Imamura, Yoshiki; Shinozaki, Takahiro; Dezawa, Ko; Haradome, Hiroki; Abe, Osamu

2017-05-01

Burning mouth syndrome (BMS) is a chronic intraoral pain syndrome featuring idiopathic oral pain and burning discomfort despite clinically normal oral mucosa. The etiology of chronic pain syndrome is unclear, but preliminary neuroimaging research has suggested the alteration of volume, metabolism, blood flow, and diffusion at multiple brain regions. According to the neuromatrix theory of Melzack, pain sense is generated in the brain by the network of multiple pain-related brain regions. Therefore, the alteration of pain-related network is also assumed as an etiology of chronic pain. In this study, we investigated the brain network of BMS brain by using probabilistic tractography and graph analysis. Fourteen BMS patients and 14 age-matched healthy controls underwent 1.5T MRI. Structural connectivity was calculated in 83 anatomically defined regions with probabilistic tractography of 60-axis diffusion tensor imaging and 3D T1-weighted imaging. Graph theory network analysis was used to evaluate the brain network at local and global connectivity. In BMS brain, a significant difference of local brain connectivity was recognized at the bilateral rostral anterior cingulate cortex, right medial orbitofrontal cortex, and left pars orbitalis which belong to the medial pain system; however, no significant difference was recognized at the lateral system including the somatic sensory cortex. A strengthened connection of the anterior cingulate cortex and medial prefrontal cortex with the basal ganglia, thalamus, and brain stem was revealed. Structural brain network analysis revealed the alteration of the medial system of the pain-related brain network in chronic pain syndrome.

Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity

PubMed Central

Bassett, Danielle S.; Khambhati, Ankit N.; Grafton, Scott T.

2018-01-01

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain–machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer’s tool kit. PMID:28375650

Association of Structural Global Brain Network Properties with Intelligence in Normal Aging

PubMed Central

Fischer, Florian U.; Wolf, Dominik; Scheurich, Armin; Fellgiebel, Andreas

2014-01-01

Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60–85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R) and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient) were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience. PMID:24465994

Network neuroscience

PubMed Central

Bassett, Danielle S; Sporns, Olaf

2017-01-01

Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system. PMID:28230844

The function of neurocognitive networks. Comment on “Understanding brain networks and brain organization” by Pessoa

NASA Astrophysics Data System (ADS)

Bressler, Steven L.

2014-09-01

Pessoa [5] has performed a valuable service by reviewing the extant literature on brain networks and making a number of interesting proposals about their cognitive function. The term function is at the core of understanding the brain networks of cognition, or neurocognitive networks (NCNs) [1]. The great Russian neuropsychologist, Luria [4], defined brain function as the common task executed by a distributed brain network of complex dynamic structures united by the demands of cognition. Casting Luria in a modern light, we can say that function emerges from the interactions of brain regions in NCNs as they dynamically self-organize according to cognitive demands. Pessoa rightly details the mapping between brain function and structure, emphasizing both its pluripotency (one structure having multiple functions) and degeneracy (many structures having the same function). However, he fails to consider the potential importance of a one-to-one mapping between NCNs and function. If NCNs are uniquely composed of specific collections of brain areas, then each NCN has a unique function determined by that composition.

Development of rostral inferior parietal lobule area functional connectivity from late childhood to early adulthood.

PubMed

Wang, Mengxing; Zhang, Jilei; Dong, Guangheng; Zhang, Hui; Lu, Haifeng; Du, Xiaoxia

2017-06-01

Although the mirror neuron system (MNS) has been extensively studied in monkeys and adult humans, very little is known about its development. Previous studies suggest that the MNS is present by infancy and that the brain and MNS-related cognitive abilities (such as language, empathy, and imitation learning) continue to develop after childhood. In humans, the PFt area of the inferior parietal lobule (IPL) seems to particularly correlate with the functional properties of the PF area in primates, which contains mirror neurons. However, little is known about the functional connectivity (FC) of the PFt area with other brain areas and whether these networks change over time. Here, we investigated the FC development of the PFt area-based network in 59 healthy subjects aged 7-26 years at resting-state to study brain development from late childhood through adolescence to early adulthood. The bilateral PFt showed similar core FC networks, which included the frontal lobe, the cingulate gyri, the insula, the somatosensory cortex, the precuneus, the superior and inferior parietal lobules, the temporal lobe, and the cerebellum posterior lobes. Furthermore, the FC between the left PFt and the left IPL exhibited a significantly positive correlation with age, and the FC between the left PFt and the right postcentral gyrus exhibited a significantly negative correlation with age. In addition, the FC between the right PFt and the right putamen exhibited a significantly negative correlation with age. Our findings suggest that the PFt area-based network develops and is reorganized with age. Copyright © 2017 ISDN. Published by Elsevier Ltd. All rights reserved.

A chronological expression profile of gene activity during embryonic mouse brain development.

PubMed

Goggolidou, P; Soneji, S; Powles-Glover, N; Williams, D; Sethi, S; Baban, D; Simon, M M; Ragoussis, I; Norris, D P

2013-12-01

The brain is a functionally complex organ, the patterning and development of which are key to adult health. To help elucidate the genetic networks underlying mammalian brain patterning, we conducted detailed transcriptional profiling during embryonic development of the mouse brain. A total of 2,400 genes were identified as showing differential expression between three developmental stages. Analysis of the data identified nine gene clusters to demonstrate analogous expression profiles. A significant group of novel genes of as yet undiscovered biological function were detected as being potentially relevant to brain development and function, in addition to genes that have previously identified roles in the brain. Furthermore, analysis for genes that display asymmetric expression between the left and right brain hemispheres during development revealed 35 genes as putatively asymmetric from a combined data set. Our data constitute a valuable new resource for neuroscience and neurodevelopment, exposing possible functional associations between genes, including novel loci, and encouraging their further investigation in human neurological and behavioural disorders.

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

Detecting event-related changes of multivariate phase coupling in dynamic brain networks.

PubMed

Canolty, Ryan T; Cadieu, Charles F; Koepsell, Kilian; Ganguly, Karunesh; Knight, Robert T; Carmena, Jose M

2012-04-01

Oscillatory phase coupling within large-scale brain networks is a topic of increasing interest within systems, cognitive, and theoretical neuroscience. Evidence shows that brain rhythms play a role in controlling neuronal excitability and response modulation (Haider B, McCormick D. Neuron 62: 171-189, 2009) and regulate the efficacy of communication between cortical regions (Fries P. Trends Cogn Sci 9: 474-480, 2005) and distinct spatiotemporal scales (Canolty RT, Knight RT. Trends Cogn Sci 14: 506-515, 2010). In this view, anatomically connected brain areas form the scaffolding upon which neuronal oscillations rapidly create and dissolve transient functional networks (Lakatos P, Karmos G, Mehta A, Ulbert I, Schroeder C. Science 320: 110-113, 2008). Importantly, testing these hypotheses requires methods designed to accurately reflect dynamic changes in multivariate phase coupling within brain networks. Unfortunately, phase coupling between neurophysiological signals is commonly investigated using suboptimal techniques. Here we describe how a recently developed probabilistic model, phase coupling estimation (PCE; Cadieu C, Koepsell K Neural Comput 44: 3107-3126, 2010), can be used to investigate changes in multivariate phase coupling, and we detail the advantages of this model over the commonly employed phase-locking value (PLV; Lachaux JP, Rodriguez E, Martinerie J, Varela F. Human Brain Map 8: 194-208, 1999). We show that the N-dimensional PCE is a natural generalization of the inherently bivariate PLV. Using simulations, we show that PCE accurately captures both direct and indirect (network mediated) coupling between network elements in situations where PLV produces erroneous results. We present empirical results on recordings from humans and nonhuman primates and show that the PCE-estimated coupling values are different from those using the bivariate PLV. Critically on these empirical recordings, PCE output tends to be sparser than the PLVs, indicating fewer significant interactions and perhaps a more parsimonious description of the data. Finally, the physical interpretation of PCE parameters is straightforward: the PCE parameters correspond to interaction terms in a network of coupled oscillators. Forward modeling of a network of coupled oscillators with parameters estimated by PCE generates synthetic data with statistical characteristics identical to empirical signals. Given these advantages over the PLV, PCE is a useful tool for investigating multivariate phase coupling in distributed brain networks.

The brain as a "hyper-network": the key role of neural networks as main producers of the integrated brain actions especially via the "broadcasted" neuroconnectomics.

PubMed

Agnati, Luigi F; Marcoli, Manuela; Maura, Guido; Woods, Amina; Guidolin, Diego

2018-06-01

Investigations of brain complex integrative actions should consider beside neural networks, glial, extracellular molecular, and fluid channels networks. The present paper proposes that all these networks are assembled into the brain hyper-network that has as fundamental components, the tetra-partite synapses, formed by neural, glial, and extracellular molecular networks. Furthermore, peri-synaptic astrocytic processes by modulating the perviousness of extracellular fluid channels control the signals impinging on the tetra-partite synapses. It has also been surmised that global signalling via astrocytes networks and highly pervasive signals, such as electromagnetic fields (EMFs), allow the appropriate integration of the various networks especially at crucial nodes level, the tetra-partite synapses. As a matter of fact, it has been shown that astrocytes can form gap-junction-coupled syncytia allowing intercellular communication characterised by a rapid and possibly long-distance transfer of signals. As far as the EMFs are concerned, the concept of broadcasted neuroconnectomics (BNC) has been introduced to describe highly pervasive signals involved in resetting the information handling of brain networks at various miniaturisation levels. In other words, BNC creates, thanks to the EMFs, generated especially by neurons, different assemblages among the various networks forming the brain hyper-network. Thus, it is surmised that neuronal networks are the "core components" of the brain hyper-network that has as special "nodes" the multi-facet tetra-partite synapses. Furthermore, it is suggested that investigations on the functional plasticity of multi-partite synapses in response to BNC can be the background for a new understanding and perhaps a new modelling of brain morpho-functional organisation and integrative actions.

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.

D-serine signalling as a prominent determinant of neuronal-glial dialogue in the healthy and diseased brain.

PubMed

Billard, J-M

2008-10-01

Rather different from their initial image as passive supportive cells of the CNS, the astrocytes are now considered as active partners at synapses, able to release a set of gliotransmitter-like substances to modulate synaptic communication within neuronal networks. Whereas glutamate and ATP were first regarded as main determinants of gliotransmission, growing evidence indicates now that the amino acid D-serine is another important player in the neuronal-glial dialogue. Through the regulation of glutamatergic neurotransmission through both N-methyl-D-aspartate (NMDA-R) and non-NMDA-R, D-serine is helping in modelling the appropriate connections in the developing brain and influencing the functional plasticity within neuronal networks throughout lifespan. The understanding of D-serine signalling, which has increased linearly in the last few years, gives new insights into the critical role of impaired neuronal-glial communication in the diseased brain, and offers new opportunities for developing relevant strategies to treat cognitive deficits associated to brain disorders.

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.

D-serine signalling as a prominent determinant of neuronal-glial dialogue in the healthy and diseased brain

PubMed Central

Billard, J-M

2008-01-01

Rather different from their initial image as passive supportive cells of the CNS, the astrocytes are now considered as active partners at synapses, able to release a set of gliotransmitter-like substances to modulate synaptic communication within neuronal networks. Whereas glutamate and ATP were first regarded as main determinants of gliotransmission, growing evidence indicates now that the amino acid D-serine is another important player in the neuronal-glial dialogue. Through the regulation of glutamatergic neurotransmission through both N-methyl-D-aspartate (NMDA-R) and non-NMDA-R, D-serine is helping in modelling the appropriate connections in the developing brain and influencing the functional plasticity within neuronal networks throughout lifespan. The understanding of D-serine signalling, which has increased linearly in the last few years, gives new insights into the critical role of impaired neuronal-glial communication in the diseased brain, and offers new opportunities for developing relevant strategies to treat cognitive deficits associated to brain disorders. PMID:18363840

Role of Network Science in the Study of Anesthetic State Transitions.

PubMed

Lee, UnCheol; Mashour, George A

2018-04-23

The heterogeneity of molecular mechanisms, target neural circuits, and neurophysiologic effects of general anesthetics makes it difficult to develop a reliable and drug-invariant index of general anesthesia. No single brain region or mechanism has been identified as the neural correlate of consciousness, suggesting that consciousness might emerge through complex interactions of spatially and temporally distributed brain functions. The goal of this review article is to introduce the basic concepts of networks and explain why the application of network science to general anesthesia could be a pathway to discover a fundamental mechanism of anesthetic-induced unconsciousness. This article reviews data suggesting that reduced network efficiency, constrained network repertoires, and changes in cortical dynamics create inhospitable conditions for information processing and transfer, which lead to unconsciousness. This review proposes that network science is not just a useful tool but a necessary theoretical framework and method to uncover common principles of anesthetic-induced unconsciousness.

A method for validating Rent's rule for technological and biological networks.

PubMed

Alcalde Cuesta, Fernando; González Sequeiros, Pablo; Lozano Rojo, Álvaro

2017-07-14

Rent's rule is empirical power law introduced in an effort to describe and optimize the wiring complexity of computer logic graphs. It is known that brain and neuronal networks also obey Rent's rule, which is consistent with the idea that wiring costs play a fundamental role in brain evolution and development. Here we propose a method to validate this power law for a certain range of network partitions. This method is based on the bifurcation phenomenon that appears when the network is subjected to random alterations preserving its degree distribution. It has been tested on a set of VLSI circuits and real networks, including biological and technological ones. We also analyzed the effect of different types of random alterations on the Rentian scaling in order to test the influence of the degree distribution. There are network architectures quite sensitive to these randomization procedures with significant increases in the values of the Rent exponents.

Gender Differences of Brain Glucose Metabolic Networks Revealed by FDG-PET: Evidence from a Large Cohort of 400 Young Adults

PubMed Central

Li, Kai; Zhu, Hong; Qi, Rongfeng; Zhang, Zhiqiang; Lu, Guangming

2013-01-01

Background Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. Materials and Methods FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25∼45 years, mean age±SD: 40.9±3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Results Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. Conclusion This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients. PMID:24358312

Gender differences of brain glucose metabolic networks revealed by FDG-PET: evidence from a large cohort of 400 young adults.

PubMed

Hu, Yuxiao; Xu, Qiang; Li, Kai; Zhu, Hong; Qi, Rongfeng; Zhang, Zhiqiang; Lu, Guangming

2013-01-01

Gender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample. FDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25:45 years, mean age ± SD: 40.9 ± 3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders. Compared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study. This study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients.

Volumetric multimodality neural network for brain tumor segmentation

NASA Astrophysics Data System (ADS)

Silvana Castillo, Laura; Alexandra Daza, Laura; Carlos Rivera, Luis; Arbeláez, Pablo

2017-11-01

Brain lesion segmentation is one of the hardest tasks to be solved in computer vision with an emphasis on the medical field. We present a convolutional neural network that produces a semantic segmentation of brain tumors, capable of processing volumetric data along with information from multiple MRI modalities at the same time. This results in the ability to learn from small training datasets and highly imbalanced data. Our method is based on DeepMedic, the state of the art in brain lesion segmentation. We develop a new architecture with more convolutional layers, organized in three parallel pathways with different input resolution, and additional fully connected layers. We tested our method over the 2015 BraTS Challenge dataset, reaching an average dice coefficient of 84%, while the standard DeepMedic implementation reached 74%.

State of the Art: Novel Applications for Cortical Stimulation.

PubMed

De Ridder, Dirk; Perera, Sanjaya; Vanneste, Sven

2017-04-01

Electrical stimulation via implanted electrodes that overlie the cortex of the brain is an upcoming neurosurgical technique that was hindered for a long time by insufficient knowledge of how the brain functions in a dynamic, physiological, and pathological way, as well as by technological limitations of the implantable stimulation devices. This paper provides an overview of cortex stimulation via implantable devices and introduces future possibilities to improve cortex stimulation. Cortex stimulation was initially used preoperatively as a technique to localize functions in the brain and only later evolved into a treatment technique. It was first used for pain, but more recently a multitude of pathologies are being targeted by cortex stimulation. These disorders are being treated by stimulating different cortical areas of the brain. Risks and complications are essentially similar to those related to deep brain stimulation and predominantly include haemorrhage, seizures, infection, and hardware failures. For cortex stimulation to fully mature, further technological development is required to predict its outcomes and improve stimulation designs. This includes the development of network science-based functional connectivity approaches, genetic analyses, development of navigated high definition transcranial alternating current stimulation, and development of pseudorandom stimulation designs for preventing habituation. In conclusion, cortex stimulation is a nascent but very promising approach to treating a variety of diseases, but requires further technological development for predicting outcomes, such as network science based functional connectivity approaches, genetic analyses, development of navigated transcranial electrical stimulation, and development of pseudorandom stimulation designs for preventing habituation. © 2017 International Neuromodulation Society.

Reduced brain resting-state network specificity in infants compared with adults.

PubMed

Wylie, Korey P; Rojas, Donald C; Ross, Randal G; Hunter, Sharon K; Maharajh, Keeran; Cornier, Marc-Andre; Tregellas, Jason R

2014-01-01

Infant resting-state networks do not exhibit the same connectivity patterns as those of young children and adults. Current theories of brain development emphasize developmental progression in regional and network specialization. We compared infant and adult functional connectivity, predicting that infants would exhibit less regional specificity and greater internetwork communication compared with adults. Functional magnetic resonance imaging at rest was acquired in 12 healthy, term infants and 17 adults. Resting-state networks were extracted, using independent components analysis, and the resulting components were then compared between the adult and infant groups. Adults exhibited stronger connectivity in the posterior cingulate cortex node of the default mode network, but infants had higher connectivity in medial prefrontal cortex/anterior cingulate cortex than adults. Adult connectivity was typically higher than infant connectivity within structures previously associated with the various networks, whereas infant connectivity was frequently higher outside of these structures. Internetwork communication was significantly higher in infants than in adults. We interpret these findings as consistent with evidence suggesting that resting-state network development is associated with increasing spatial specificity, possibly reflecting the corresponding functional specialization of regions and their interconnections through experience.

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

PubMed

Ceschin, Rafael; Zahner, Alexandria; Reynolds, William; Gaesser, Jenna; Zuccoli, Giulio; Lo, Cecilia W; Gopalakrishnan, Vanathi; Panigrahy, Ashok

2018-05-21

Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuroimaging datasets requires adequate sample size for training and well-defined signal intensity based structural differentiation. There is a lack of effective automated diagnostic tools for the reliable detection of brain dysmaturation in the neonatal period, related to small sample size and complex undifferentiated brain structures, despite both translational research and clinical importance. Volumetric information alone is insufficient for diagnosis. In this study, we developed a computational framework for the automated classification of brain dysmaturation from neonatal MRI, by combining a specific deep neural network implementation with neonatal structural brain segmentation as a method for both clinical pattern recognition and data-driven inference into the underlying structural morphology. We implemented three-dimensional convolution neural networks (3D-CNNs) to specifically classify dysplastic cerebelli, a subset of surface-based subcortical brain dysmaturation, in term infants born with congenital heart disease. We obtained a 0.985 ± 0. 0241-classification accuracy of subtle cerebellar dysplasia in CHD using 10-fold cross-validation. Furthermore, the hidden layer activations and class activation maps depicted regional vulnerability of the superior surface of the cerebellum, (composed of mostly the posterior lobe and the midline vermis), in regards to differentiating the dysplastic process from normal tissue. The posterior lobe and the midline vermis provide regional differentiation that is relevant to not only to the clinical diagnosis of cerebellar dysplasia, but also genetic mechanisms and neurodevelopmental outcome correlates. These findings not only contribute to the detection and classification of a subset of neonatal brain dysmaturation, but also provide insight to the pathogenesis of cerebellar dysplasia in CHD. In addition, this is one of the first examples of the application of deep learning to a neuroimaging dataset, in which the hidden layer activation revealed diagnostically and biologically relevant features about the clinical pathogenesis. The code developed for this project is open source, published under the BSD License, and designed to be generalizable to applications both within and beyond neonatal brain imaging. Copyright © 2018 Elsevier Inc. All rights reserved.

Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective.

PubMed

Gilson, Matthieu

2018-04-01

Since the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input-output mapping-determined by EC-for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data-movie viewing versus resting state-illustrates that changes in local variability and changes in brain coordination go hand in hand.

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.

Habit formation.

PubMed

Smith, Kyle S; Graybiel, Ann M

2016-03-01

Habits, both good ones and bad ones, are pervasive in animal behavior. Important frameworks have been developed to understand habits through psychological and neurobiological studies. This work has given us a rich understanding of brain networks that promote habits, and has also helped us to understand what constitutes a habitual behavior as opposed to a behavior that is more flexible and prospective. Mounting evidence from studies using neural recording methods suggests that habit formation is not a simple process. We review this evidence and take the position that habits could be sculpted from multiple dissociable changes in neural activity. These changes occur across multiple brain regions and even within single brain regions. This strategy of classifying components of a habit based on different brain signals provides a potentially useful new way to conceive of disorders that involve overly fixed behaviors as arising from different potential dysfunctions within the brain's habit network.

Habit formation

PubMed Central

Smith, Kyle S.; Graybiel, Ann M.

2016-01-01

Habits, both good ones and bad ones, are pervasive in animal behavior. Important frameworks have been developed to understand habits through psychological and neurobiological studies. This work has given us a rich understanding of brain networks that promote habits, and has also helped us to understand what constitutes a habitual behavior as opposed to a behavior that is more flexible and prospective. Mounting evidence from studies using neural recording methods suggests that habit formation is not a simple process. We review this evidence and take the position that habits could be sculpted from multiple dissociable changes in neural activity. These changes occur across multiple brain regions and even within single brain regions. This strategy of classifying components of a habit based on different brain signals provides a potentially useful new way to conceive of disorders that involve overly fixed behaviors as arising from different potential dysfunctions within the brain's habit network. PMID:27069378

Handedness- and brain size-related efficiency differences in small-world brain networks: a resting-state functional magnetic resonance imaging study.

PubMed

Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong; Yu, Chunshui; Chen, Huafu

2015-05-01

The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size.

A Statistical Method to Distinguish Functional Brain Networks

PubMed Central

Fujita, André; Vidal, Maciel C.; Takahashi, Daniel Y.

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism (p < 0.001). PMID:28261045

A Statistical Method to Distinguish Functional Brain Networks.

PubMed

Fujita, André; Vidal, Maciel C; Takahashi, Daniel Y

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism ( p < 0.001).

The functional neuroanatomy of bipolar disorder: a consensus model

PubMed Central

Strakowski, Stephen M; Adler, Caleb M; Almeida, Jorge; Altshuler, Lori L; Blumberg, Hilary P; Chang, Kiki D; DelBello, Melissa P; Frangou, Sophia; McIntosh, Andrew; Phillips, Mary L; Sussman, Jessika E; Townsend, Jennifer D

2013-01-01

Objectives Functional neuroimaging methods have proliferated in recent years, such that functional magnetic resonance imaging, in particular, is now widely used to study bipolar disorder. However, discrepant findings are common. A workgroup was organized by the Department of Psychiatry, University of Cincinnati (Cincinnati, OH, USA) to develop a consensus functional neuroanatomic model of bipolar I disorder based upon the participants’ work as well as that of others. Methods Representatives from several leading bipolar disorder neuroimaging groups were organized to present an overview of their areas of expertise as well as focused reviews of existing data. The workgroup then developed a consensus model of the functional neuroanatomy of bipolar disorder based upon these data. Results Among the participants, a general consensus emerged that bipolar I disorder arises from abnormalities in the structure and function of key emotional control networks in the human brain. Namely, disruption in early development (e.g., white matter connectivity, prefrontal pruning) within brain networks that modulate emotional behavior leads to decreased connectivity among ventral prefrontal networks and limbic brain regions, especially amygdala. This developmental failure to establish healthy ventral prefrontal–limbic modulation underlies the onset of mania and ultimately, with progressive changes throughout these networks over time and with affective episodes, a bipolar course of illness. Conclusions This model provides a potential substrate to guide future investigations and areas needing additional focus are identified. PMID:22631617

The Schizophrenia Risk Gene MIR137 Acts as a Hippocampal Gene Network Node Orchestrating the Expression of Genes Relevant to Nervous System Development and Function

PubMed Central

Loohuis, Nikkie FM Olde; Kasri, Nael Nadif; Glennon, Jeffrey C; van Bokhoven, Hans; Hébert, Sébastien S; Kaplan, Barry B.; Martens, Gerard JM; Aschrafi, Armaz

2016-01-01

MicroRNAs (miRs) are small regulatory molecules, which orchestrate neuronal development and plasticity through modulation of complex gene networks. microRNA-137 (miR-137) is a brain-enriched RNA with a critical role in regulating brain development and in mediating synaptic plasticity. Importantly, mutations in this miR are associated with the pathoetiology of schizophrenia (SZ), and there is a widespread assumption that disruptions in miR-137 expression lead to aberrant expression of gene regulatory networks associated with SZ. To systematically identify the mRNA targets for this miR, we performed miR-137 gain- and loss-of-function experiments in primary rat hippocampal neurons and profiled differentially expressed mRNAs through next-generation sequencing. We identified 500 genes that were bidirectionally activated or repressed in their expression by the modulation of miR-137 levels. Gene ontology analysis using two independent software resources suggested functions for these miR-137-regulated genes in neurodevelopmental processes, neuronal maturation processes and cell maintenance, all of which known to be critical for proper brain circuitry formation. Since many of the putative miR-137 targets identified here also have been previously shown to be associated with SZ, we propose that this miR acts as a critical gene network hub contributing to the pathophysiology of this neurodevelopmental disorder. PMID:26925706

Cortical brain connectivity evaluated by graph theory in dementia: a correlation study between functional and structural data.

PubMed

Vecchio, Fabrizio; Miraglia, Francesca; Curcio, Giuseppe; Altavilla, Riccardo; Scrascia, Federica; Giambattistelli, Federica; Quattrocchi, Carlo Cosimo; Bramanti, Placido; Vernieri, Fabrizio; Rossini, Paolo Maria

2015-01-01

A relatively new approach to brain function in neuroscience is the "functional connectivity", namely the synchrony in time of activity in anatomically-distinct but functionally-collaborating brain regions. On the other hand, diffusion tensor imaging (DTI) is a recently developed magnetic resonance imaging (MRI)-based technique with the capability to detect brain structural connection with fractional anisotropy (FA) identification. FA decrease has been observed in the corpus callosum of subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI, an AD prodromal stage). Corpus callosum splenium DTI abnormalities are thought to be associated with functional disconnections among cortical areas. This study aimed to investigate possible correlations between structural damage, measured by MRI-DTI, and functional abnormalities of brain integration, measured by characteristic path length detected in resting state EEG source activity (40 participants: 9 healthy controls, 10 MCI, 10 mild AD, 11 moderate AD). For each subject, undirected and weighted brain network was built to evaluate graph core measures. eLORETA lagged linear connectivity values were used as weight of the edges of the network. Results showed that callosal FA reduction is associated to a loss of brain interhemispheric functional connectivity characterized by increased delta and decreased alpha path length. These findings suggest that "global" (average network shortest path length representing an index of how efficient is the information transfer between two parts of the network) functional measure can reflect the reduction of fiber connecting the two hemispheres as revealed by DTI analysis and also anticipate in time this structural loss.

Connectivity Strength-Weighted Sparse Group Representation-Based Brain Network Construction for MCI Classification

PubMed Central

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-01-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. PMID:28150897

Negative functional coupling between the right fronto-parietal and limbic resting state networks predicts increased self-control and later substance use onset in adolescence.

PubMed

Lee, Tae-Ho; Telzer, Eva H

2016-08-01

Recent developmental brain imaging studies have demonstrated that negatively coupled prefrontal-limbic circuitry implicates the maturation of brain development in adolescents. Using resting-state functional magnetic resonance imaging (rs-fMRI) and independent component analysis (ICA), the present study examined functional network coupling between prefrontal and limbic systems and links to self-control and substance use onset in adolescents. Results suggest that negative network coupling (anti-correlated temporal dynamics) between the right fronto-parietal and limbic resting state networks is associated with greater self-control and later substance use onset in adolescents. These findings increase our understanding of the developmental importance of prefrontal-limbic circuitry for adolescent substance use at the resting-state network level. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning.

PubMed

Munsell, B C; Wu, G; Fridriksson, J; Thayer, K; Mofrad, N; Desisto, N; Shen, D; Bonilha, L

2017-09-09

Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorly understood but may indicate a structural disorganization of broadly distributed neuronal networks that support naming ability. Importantly, naming is frequently impaired in other neurological disorders and by contrasting the neuronal structures supporting naming in TLE with other diseases, it will become possible to elucidate the common systems supporting naming. We aimed to evaluate the neuronal networks that support naming in TLE by using a machine learning algorithm intended to predict naming performance in subjects with medication refractory TLE using only the structural brain connectome reconstructed from diffusion tensor imaging. A connectome-based prediction framework was developed using network properties from anatomically defined brain regions across the entire brain, which were used in a multi-task machine learning algorithm followed by support vector regression. Nodal eigenvector centrality, a measure of regional network integration, predicted approximately 60% of the variance in naming. The nodes with the highest regression weight were bilaterally distributed among perilimbic sub-networks involving mainly the medial and lateral temporal lobe regions. In the context of emerging evidence regarding the role of large structural networks that support language processing, our results suggest intact naming relies on the integration of sub-networks, as opposed to being dependent on isolated brain areas. In the case of TLE, these sub-networks may be disproportionately indicative naming processes that are dependent semantic integration from memory and lexical retrieval, as opposed to multi-modal perception or motor speech production. Copyright © 2017. Published by Elsevier Inc.

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification.

PubMed

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-05-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l 1 -norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Resting state brain networks and their implications in neurodegenerative disease

NASA Astrophysics Data System (ADS)

Sohn, William S.; Yoo, Kwangsun; Kim, Jinho; Jeong, Yong

2012-10-01

Neurons are the basic units of the brain, and form network by connecting via synapses. So far, there have been limited ways to measure the brain networks. Recently, various imaging modalities are widely used for this purpose. In this paper, brain network mapping using resting state fMRI will be introduced with several applications including neurodegenerative disease such as Alzheimer's disease, frontotemporal lobar degeneration and Parkinson's disease. The resting functional connectivity using intrinsic functional connectivity in mouse is useful since we can take advantage of perturbation or stimulation of certain nodes of the network. The study of brain connectivity will open a new era in understanding of brain and diseases thus will be an essential foundation for future research.

Graph Frequency Analysis of Brain Signals

PubMed Central

Huang, Weiyu; Goldsberry, Leah; Wymbs, Nicholas F.; Grafton, Scott T.; Bassett, Danielle S.; Ribeiro, Alejandro

2016-01-01

This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations. We relate graph frequency with principal component analysis when the networks of interest denote functional connectivity. The methods are utilized to analyze brain networks and signals as subjects master a simple motor skill. We observe that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning. Further, we notice a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed, and recognize the most contributing and important frequency signatures at different levels of task familiarity. PMID:28439325

Learning about learning: Mining human brain sub-network biomarkers from fMRI data

PubMed Central

Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Singh, Ambuj K.

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in “resting state” employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions. PMID:29016686

Learning about learning: Mining human brain sub-network biomarkers from fMRI data.

PubMed

Bogdanov, Petko; Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S; Wymbs, Nicholas F; Grafton, Scott T; Singh, Ambuj K

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.

The Emerging Role of Epigenetics in Stroke

PubMed Central

Qureshi, Irfan A.; Mehler, Mark F.

2013-01-01

The transplantation of exogenous stem cells and the activation of endogenous neural stem and progenitor cells (NSPCs) are promising treatments for stroke. These cells can modulate intrinsic responses to ischemic injury and may even integrate directly into damaged neural networks. However, the neuroprotective and neural regenerative effects that can be mediated by these cells are limited and may even be deleterious. Epigenetic reprogramming represents a novel strategy for enhancing the intrinsic potential of the brain to protect and repair itself by modulating pathologic neural gene expression and promoting the recapitulation of seminal neural developmental processes. In fact, recent evidence suggests that emerging epigenetic mechanisms are critical for orchestrating nearly every aspect of neural development and homeostasis, including brain patterning, neural stem cell maintenance, neurogenesis and gliogenesis, neural subtype specification, and synaptic and neural network connectivity and plasticity. In this review, we survey the therapeutic potential of exogenous stem cells and endogenous NSPCs and highlight innovative technological approaches for designing, developing, and delivering epigenetic therapies for targeted reprogramming of endogenous pools of NSPCs, neural cells at risk, and dysfunctional neural networks to rescue and restore neurologic function in the ischemic brain. PMID:21403016

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.

Brain and Cognitive Reserve: Translation via Network Control Theory

PubMed Central

Medaglia, John Dominic; Pasqualetti, Fabio; Hamilton, Roy H.; Thompson-Schill, Sharon L.; Bassett, Danielle S.

2017-01-01

Traditional approaches to understanding the brain’s resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive “reserve,” associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention. PMID:28104411

Rich club network analysis shows distinct patterns of disruption in frontotemporal dementia and Alzheimer’s disease

PubMed Central

Daianu, Madelaine; Jahanshad, Neda; Villalon-Reina, Julio E.; Mendez, Mario F.; Bartzokis, George; Jimenez, Elvira E.; Joshi, Aditi; Barsuglia, Joseph; Thompson, Paul M.

2015-01-01

Diffusion imaging and brain connectivity analyses can reveal the underlying organizational patterns of the human brain, described as complex networks of densely interlinked regions. Here, we analyzed 1.5-Tesla whole-brain diffusion-weighted images from 64 participants – 15 patients with behavioral variant frontotemporal (bvFTD) dementia, 19 with early-onset Alzheimer’s disease (EOAD), and 30 healthy elderly controls. Based on whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We examined how bvFTD and EOAD disrupt the weighted ‘rich club’ – a network property where high-degree network nodes are more interconnected than expected by chance. bvFTD disrupts both the nodal and global organization of the network in both low- and high-degree regions of the brain. EOAD targets the global connectivity of the brain, mainly affecting the fiber density of high-degree (highly connected) regions that form the rich club network. These rich club analyses suggest distinct patterns of disruptions among different forms of dementia. PMID:26161050

Resting State Network Topology of the Ferret Brain

PubMed Central

Zhou, Zhe Charles; Salzwedel, Andrew P.; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K.; Gilmore, John H.; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-01-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4 tesla MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. PMID:27596024

Hierarchical organization of brain functional networks during visual tasks.

PubMed

Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie

2011-09-01

The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.

fMRI reveals neural activity overlap between adult and infant pain

PubMed Central

Goksan, Sezgi; Hartley, Caroline; Emery, Faith; Cockrill, Naomi; Poorun, Ravi; Moultrie, Fiona; Rogers, Richard; Campbell, Jon; Sanders, Michael; Adams, Eleri; Clare, Stuart; Jenkinson, Mark; Tracey, Irene; Slater, Rebeccah

2015-01-01

Limited understanding of infant pain has led to its lack of recognition in clinical practice. While the network of brain regions that encode the affective and sensory aspects of adult pain are well described, the brain structures involved in infant nociceptive processing are less well known, meaning little can be inferred about the nature of the infant pain experience. Using fMRI we identified the network of brain regions that are active following acute noxious stimulation in newborn infants, and compared the activity to that observed in adults. Significant infant brain activity was observed in 18 of the 20 active adult brain regions but not in the infant amygdala or orbitofrontal cortex. Brain regions that encode sensory and affective components of pain are active in infants, suggesting that the infant pain experience closely resembles that seen in adults. This highlights the importance of developing effective pain management strategies in this vulnerable population. DOI: http://dx.doi.org/10.7554/eLife.06356.001 PMID:25895592

Lateralized theta wave connectivity and language performance in 2- to 5-year-old children.

PubMed

Kikuchi, Mitsuru; Shitamichi, Kiyomi; Yoshimura, Yuko; Ueno, Sanae; Remijn, Gerard B; Hirosawa, Tetsu; Munesue, Toshio; Tsubokawa, Tsunehisa; Haruta, Yasuhiro; Oi, Manabu; Higashida, Haruhiro; Minabe, Yoshio

2011-10-19

Recent neuroimaging studies support the view that a left-lateralized brain network is crucial for language development in children. However, no previous studies have demonstrated a clear link between lateralized brain functional network and language performance in preschool children. Magnetoencephalography (MEG) is a noninvasive brain imaging technique and is a practical neuroimaging method for use in young children. MEG produces a reference-free signal, and is therefore an ideal tool to compute coherence between two distant cortical rhythms. In the present study, using a custom child-sized MEG system, we investigated brain networks while 78 right-handed preschool human children (32-64 months; 96% were 3-4 years old) listened to stories with moving images. The results indicated that left dominance of parietotemporal coherence in theta band activity (6-8 Hz) was specifically correlated with higher performance of language-related tasks, whereas this laterality was not correlated with nonverbal cognitive performance, chronological age, or head circumference. Power analyses did not reveal any specific frequencies that contributed to higher language performance. Our results suggest that it is not the left dominance in theta oscillation per se, but the left-dominant phase-locked connectivity via theta oscillation that contributes to the development of language ability in young children.

Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.

PubMed

Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng

2016-02-01

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

PubMed

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

PubMed Central

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive. PMID:28018162

A Novel Human Body Area Network for Brain Diseases Analysis.

PubMed

Lin, Kai; Xu, Tianlang

2016-10-01

Development of wireless sensor and mobile communication technology provide an unprecedented opportunity for realizing smart and interactive healthcare systems. Designing such systems aims to remotely monitor the health and diagnose the diseases for users. In this paper, we design a novel human body area network for brain diseases analysis, which is named BABDA. Considering the brain is one of the most complex organs in the human body, the BABDA system provides four function modules to ensure the high quality of the analysis result, which includes initial data collection, data correction, data transmission and comprehensive data analysis. The performance evaluation conducted in a realistic environment with several criteria shows the availability and practicability of the BABDA system.

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

Brain Imaging of Human Sexual Response: Recent Developments and Future Directions.

PubMed

Ruesink, Gerben B; Georgiadis, Janniko R

2017-01-01

The purpose of this study is to provide a comprehensive summary of the latest developments in the experimental brain study of human sexuality, focusing on brain connectivity during the sexual response. Stable patterns of brain activation have been established for different phases of the sexual response, especially with regard to the wanting phase, and changes in these patterns can be linked to sexual response variations, including sexual dysfunctions. From this solid basis, connectivity studies of the human sexual response have begun to add a deeper understanding of the brain network function and structure involved. The study of "sexual" brain connectivity is still very young. Yet, by approaching the brain as a connected organ, the essence of brain function is captured much more accurately, increasing the likelihood of finding useful biomarkers and targets for intervention in sexual dysfunction.

Hemispheric lateralization of topological organization in structural brain networks.

PubMed

Caeyenberghs, Karen; Leemans, Alexander

2014-09-01

The study on structural brain asymmetries in healthy individuals plays an important role in our understanding of the factors that modulate cognitive specialization in the brain. Here, we used fiber tractography to reconstruct the left and right hemispheric networks of a large cohort of 346 healthy participants (20-86 years) and performed a graph theoretical analysis to investigate this brain laterality from a network perspective. Findings revealed that the left hemisphere is significantly more "efficient" than the right hemisphere, whereas the right hemisphere showed higher values of "betweenness centrality" and "small-worldness." In particular, left-hemispheric networks displayed increased nodal efficiency in brain regions related to language and motor actions, whereas the right hemisphere showed an increase in nodal efficiency in brain regions involved in memory and visuospatial attention. In addition, we found that hemispheric networks decrease in efficiency with age. Finally, we observed significant gender differences in measures of global connectivity. By analyzing the structural hemispheric brain networks, we have provided new insights into understanding the neuroanatomical basis of lateralized brain functions. Copyright © 2014 Wiley Periodicals, Inc.

The elusive concept of brain network. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

NASA Astrophysics Data System (ADS)

Horwitz, Barry

2014-09-01

As the poet John Donne said of man - "No man is an island entire of itself; every man is a piece of the continent, a part of the main." - so the neuroscience research community now says of brain areas. This is the topic that Luiz Pessoa expands upon in his thorough review of the paradigm shift that has occurred in much of brain research, especially in cognitive neuroscience [1]. His key point is made explicitly in the Abstract: "I argue that a network perspective should supplement the common strategy of understanding the brain in terms of individual regions." In his review, Pessoa covers a large range of topics, including how the network perspective changes the way in which one views the structure-function relationship between brain and behavior, the importance of context in ascertaining how a brain region functions, and the notion of emergent properties as a network feature. Also discussed is graph theory, one of the important mathematical methods used to analyze and describe network structure and function.

Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease.

PubMed

Swann, Nicole C; de Hemptinne, Coralie; Miocinovic, Svjetlana; Qasim, Salman; Ostrem, Jill L; Galifianakis, Nicholas B; Luciano, Marta San; Wang, Sarah S; Ziman, Nathan; Taylor, Robin; Starr, Philip A

2018-02-01

OBJECTIVE Dysfunction of distributed neural networks underlies many brain disorders. The development of neuromodulation therapies depends on a better understanding of these networks. Invasive human brain recordings have a favorable temporal and spatial resolution for the analysis of network phenomena but have generally been limited to acute intraoperative recording or short-term recording through temporarily externalized leads. Here, the authors describe their initial experience with an investigational, first-generation, totally implantable, bidirectional neural interface that allows both continuous therapeutic stimulation and recording of field potentials at multiple sites in a neural network. METHODS Under a physician-sponsored US Food and Drug Administration investigational device exemption, 5 patients with Parkinson's disease were implanted with the Activa PC+S system (Medtronic Inc.). The device was attached to a quadripolar lead placed in the subdural space over motor cortex, for electrocorticography potential recordings, and to a quadripolar lead in the subthalamic nucleus (STN), for both therapeutic stimulation and recording of local field potentials. Recordings from the brain of each patient were performed at multiple time points over a 1-year period. RESULTS There were no serious surgical complications or interruptions in deep brain stimulation therapy. Signals in both the cortex and the STN were relatively stable over time, despite a gradual increase in electrode impedance. Canonical movement-related changes in specific frequency bands in the motor cortex were identified in most but not all recordings. CONCLUSIONS The acquisition of chronic multisite field potentials in humans is feasible. The device performance characteristics described here may inform the design of the next generation of totally implantable neural interfaces. This research tool provides a platform for translating discoveries in brain network dynamics to improved neurostimulation paradigms. Clinical trial registration no.: NCT01934296 (clinicaltrials.gov).

Mapping neurotransmitter networks with PET: an example on serotonin and opioid systems.

PubMed

Tuominen, Lauri; Nummenmaa, Lauri; Keltikangas-Järvinen, Liisa; Raitakari, Olli; Hietala, Jarmo

2014-05-01

All functions of the human brain are consequences of altered activity of specific neural pathways and neurotransmitter systems. Although the knowledge of "system level" connectivity in the brain is increasing rapidly, we lack "molecular level" information on brain networks and connectivity patterns. We introduce novel voxel-based positron emission tomography (PET) methods for studying internal neurotransmitter network structure and intercorrelations of different neurotransmitter systems in the human brain. We chose serotonin transporter and μ-opioid receptor for this analysis because of their functional interaction at the cellular level and similar regional distribution in the brain. Twenty-one healthy subjects underwent two consecutive PET scans using [(11)C]MADAM, a serotonin transporter tracer, and [(11)C]carfentanil, a μ-opioid receptor tracer. First, voxel-by-voxel "intracorrelations" (hub and seed analyses) were used to study the internal structure of opioid and serotonin systems. Second, voxel-level opioid-serotonin intercorrelations (between neurotransmitters) were computed. Regional μ-opioid receptor binding potentials were uniformly correlated throughout the brain. However, our analyses revealed nonuniformity in the serotonin transporter intracorrelations and identified a highly connected local network (midbrain-striatum-thalamus-amygdala). Regionally specific intercorrelations between the opioid and serotonin tracers were found in anteromedial thalamus, amygdala, anterior cingulate cortex, dorsolateral prefrontal cortex, and left parietal cortex, i.e., in areas relevant for several neuropsychiatric disorders, especially affective disorders. This methodology enables in vivo mapping of connectivity patterns within and between neurotransmitter systems. Quantification of functional neurotransmitter balances may be a useful approach in etiological studies of neuropsychiatric disorders and also in drug development as a biomarker-based rationale for targeted modulation of neurotransmitter networks. Copyright © 2013 Wiley Periodicals, Inc.

Development of neural networks for exact and approximate calculation: a FMRI study.

PubMed

Kucian, Karin; von Aster, Michael; Loenneker, Thomas; Dietrich, Thomas; Martin, Ernst

2008-01-01

Neuroimaging findings in adults suggest exact and approximate number processing relying on distinct neural circuits. In the present study we are investigating whether this cortical specialization is already established in 9- and 12-year-old children. Using fMRI, brain activation was measured in 10 third- and 10 sixth-grade school children and 20 adults during trials of symbolic approximate (AP) and exact (EX) calculation, as well as non-symbolic magnitude comparison (MC) of objects. Children activated similar networks like adults, denoting an availability and a similar spatial extent of specified networks as early as third grade. However, brain areas related to number processing become further specialized with schooling. Children showed weaker activation in the intraparietal sulcus during all three tasks, in the left inferior frontal gyrus during EX and in occipital areas during MC. In contrast, activation in the anterior cingulate gyrus, a region associated with attentional effort and working memory load, was enhanced in children. Moreover, children revealed reduced or absent deactivation of regions involved in the so-called default network during symbolic calculation, suggesting a rather general developmental effect. No difference in brain activation patterns between AP and EX was found. Behavioral results indicated major differences between children and adults in AP and EX, but not in MC. Reaction time and accuracy rate were not correlated to brain activation in regions showing developmental changes suggesting rather effects of development than performance differences between children and adults. In conclusion, increasing expertise with age may lead to more automated processing of mental arithmetic, which is reflected by improved performance and by increased brain activation in regions related to number processing and decreased activation in supporting areas.

Neuromodulation: Selected approaches and challenges

PubMed Central

Parpura, Vladimir; Silva, Gabriel A.; Tass, Peter A.; Bennet, Kevin E.; Meyyappan, Meyya; Koehne, Jessica; Lee, Kendall H.; Andrews, Russell J.

2012-01-01

The brain operates through complex interactions in the flow of information and signal processing within neural networks. The “wiring” of such networks, being neuronal or glial, can physically and/or functionally go rogue in various pathological states. Neuromodulation, as a multidisciplinary venture, attempts to correct such faulty nets. In this review, selected approaches and challenges in neuromoduation are discussed. The use of water-dispersible carbon nanotubes have proven effective in modulation of neurite outgrowth in culture as well as in aiding regeneration after spinal cord injury in vivo. Studying neural circuits using computational biology and analytical engineering approaches brings to light geometrical mapping of dynamics within neural networks, much needed information for stimulation interventions in medical practice. Indeed, sophisticated desynchronization approaches used for brain stimulation have been successful in coaxing “misfiring” neuronal circuits to resume productive firing patterns in various human disorders. Devices have been developed for the real time measurement of various neurotransmitters as well as electrical activity in the human brain during electrical deep brain stimulation. Such devices can establish the dynamics of electrochemical changes in the brain during stimulation. With increasing application of nanomaterials in devices for electrical and chemical recording and stimulating in the brain, the era of cellular, and even intracellular, precision neuromodulation will soon be upon us. PMID:23190025

Altered structural brain changes and neurocognitive performance in pediatric HIV.

PubMed

Yadav, Santosh K; Gupta, Rakesh K; Garg, Ravindra K; Venkatesh, Vimala; Gupta, Pradeep K; Singh, Alok K; Hashem, Sheema; Al-Sulaiti, Asma; Kaura, Deepak; Wang, Ena; Marincola, Francesco M; Haris, Mohammad

2017-01-01

Pediatric HIV patients often suffer with neurodevelopmental delay and subsequently cognitive impairment. While tissue injury in cortical and subcortical regions in the brain of adult HIV patients has been well reported there is sparse knowledge about these changes in perinatally HIV infected pediatric patients. We analyzed cortical thickness, subcortical volume, structural connectivity, and neurocognitive functions in pediatric HIV patients and compared with those of pediatric healthy controls. With informed consent, 34 perinatally infected pediatric HIV patients and 32 age and gender matched pediatric healthy controls underwent neurocognitive assessment and brain magnetic resonance imaging (MRI) on a 3 T clinical scanner. Altered cortical thickness, subcortical volumes, and abnormal neuropsychological test scores were observed in pediatric HIV patients. The structural network connectivity analysis depicted lower connection strengths, lower clustering coefficients, and higher path length in pediatric HIV patients than healthy controls. The network betweenness and network hubs in cortico-limbic regions were distorted in pediatric HIV patients. The findings suggest that altered cortical and subcortical structures and regional brain connectivity in pediatric HIV patients may contribute to deficits in their neurocognitive functions. Further, longitudinal studies are required for better understanding of the effect of HIV pathogenesis on brain structural changes throughout the brain development process under standard ART treatment.

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.

Warnings and caveats in brain controllability.

PubMed

Tu, Chengyi; Rocha, Rodrigo P; Corbetta, Maurizio; Zampieri, Sandro; Zorzi, Marco; Suweis, S

2018-08-01

A recent article by Gu et al. (Nat. Commun. 6, 2015) proposed to characterize brain networks, quantified using anatomical diffusion imaging, in terms of their "controllability", drawing on concepts and methods of control theory. They reported that brain activity is controllable from a single node, and that the topology of brain networks provides an explanation for the types of control roles that different regions play in the brain. In this work, we first briefly review the framework of control theory applied to complex networks. We then show contrasting results on brain controllability through the analysis of five different datasets and numerical simulations. We find that brain networks are not controllable (in a statistical significant way) by one single region. Additionally, we show that random null models, with no biological resemblance to brain network architecture, produce the same type of relationship observed by Gu et al. between the average/modal controllability and weighted degree. Finally, we find that resting state networks defined with fMRI cannot be attributed specific control roles. In summary, our study highlights some warning and caveats in the brain controllability framework. Copyright © 2018 Elsevier Inc. All rights reserved.

Clonal development and organization of the adult Drosophila central brain.

PubMed

Yu, Hung-Hsiang; Awasaki, Takeshi; Schroeder, Mark David; Long, Fuhui; Yang, Jacob S; He, Yisheng; Ding, Peng; Kao, Jui-Chun; Wu, Gloria Yueh-Yi; Peng, Hanchuan; Myers, Gene; Lee, Tzumin

2013-04-22

The insect brain can be divided into neuropils that are formed by neurites of both local and remote origin. The complexity of the interconnections obscures how these neuropils are established and interconnected through development. The Drosophila central brain develops from a fixed number of neuroblasts (NBs) that deposit neurons in regional clusters. By determining individual NB clones and pursuing their projections into specific neuropils, we unravel the regional development of the brain neural network. Exhaustive clonal analysis revealed 95 stereotyped neuronal lineages with characteristic cell-body locations and neurite trajectories. Most clones show complex projection patterns, but despite the complexity, neighboring clones often coinnervate the same local neuropil or neuropils and further target a restricted set of distant neuropils. These observations argue for regional clonal development of both neuropils and neuropil connectivity throughout the Drosophila central brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Subjective cognitive impairment and brain structural networks in Chinese gynaecological cancer survivors compared with age-matched controls: a cross-sectional study.

PubMed

Zeng, Yingchun; Cheng, Andy S K; Song, Ting; Sheng, Xiujie; Zhang, Yang; Liu, Xiangyu; Chan, Chetwyn C H

2017-11-28

Subjective cognitive impairment can be a significant and prevalent problem for gynaecological cancer survivors. The aims of this study were to assess subjective cognitive functioning in gynaecological cancer survivors after primary cancer treatment, and to investigate the impact of cancer treatment on brain structural networks and its association with subjective cognitive impairment. This was a cross-sectional survey using a self-reported questionnaire by the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog) to assess subjective cognitive functioning, and applying DTI (diffusion tensor imaging) and graph theoretical analyses to investigate brain structural networks after primary cancer treatment. A total of 158 patients with gynaecological cancer (mean age, 45.86 years) and 130 age-matched non-cancer controls (mean age, 44.55 years) were assessed. Patients reported significantly greater subjective cognitive functioning on the FACT-Cog total score and two subscales of perceived cognitive impairment and perceived cognitive ability (all p values <0.001). Compared with patients who had received surgery only and non-cancer controls, patients treated with chemotherapy indicated the most altered global brain structural networks, especially in one of properties of small-worldness (p = 0.004). Reduced small-worldness was significantly associated with a lower FACT-Cog total score (r = 0.412, p = 0.024). Increased characteristic path length was also significantly associated with more subjective cognitive impairment (r = -0.388, p = 0.034). When compared with non-cancer controls, a considerable proportion of gynaecological cancer survivors may exhibit subjective cognitive impairment. This study provides the first evidence of brain structural network alteration in gynaecological cancer patients at post-treatment, and offers novel insights regarding the possible neurobiological mechanism of cancer-related cognitive impairment (CRCI) in gynaecological cancer patients. As primary cancer treatment can result in a more random organisation of structural brain networks, this may reduce brain functional specificity and segregation, and have implications for cognitive impairment. Future prospective and longitudinal studies are needed to build upon the study findings in order to assess potentially relevant clinical and psychosocial variables and brain network measures, so as to more accurately understand the specific risk factors related to subjective cognitive impairment in the gynaecological cancer population. Such knowledge could inform the development of appropriate treatment and rehabilitation efforts to ameliorate cognitive impairment in gynaecological cancer survivors.

Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques.

PubMed

Sperry, Megan M; Kartha, Sonia; Granquist, Eric J; Winkelstein, Beth A

2018-07-01

Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n = 18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p < 0.00001); however, community structure is similar at network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.

Limitations on the developing preterm brain: impact of periventricular white matter lesions on brain connectivity and cognition.

PubMed

Pavlova, Marina A; Krägeloh-Mann, Ingeborg

2013-04-01

Brain lesions to the white matter in peritrigonal regions, periventricular leukomalacia, in children who were born prematurely represent an important model for studying limitations on brain development. The lesional pattern is of early origin and bilateral, that constrains the compensatory potential of the brain. We suggest that (i) topography and severity of periventricular lesions may have a long-term predictive value for cognitive and social capabilities in preterm birth survivors; and (ii) periventricular lesions may impact cognitive and social functions by affecting brain connectivity, and thereby, the dissociable neural networks underpinning these functions. A further pathway to explore is the relationship between cerebral palsy and cognitive outcome. Restrictions caused by motor disability may affect active exploration of surrounding and social participation that may in turn differentially impinge on cognitive development and social cognition. As an outline for future research, we underscore sex differences, as the sex of a preterm newborn may shape the mechanisms by which the developing brain is affected.

Different alterations in brain functional networks according to direct and indirect topological connections in patients with schizophrenia.

PubMed

Park, Chang-Hyun; Lee, Seungyup; Kim, Taewon; Won, Wang Yeon; Lee, Kyoung-Uk

2017-10-01

Schizophrenia displays connectivity deficits in the brain, but the literature has shown inconsistent findings about alterations in global efficiency of brain functional networks. We supposed that such inconsistency at the whole brain level may be due to a mixture of different portions of global efficiency at sub-brain levels. Accordingly, we considered measuring portions of global efficiency in two aspects: spatial portions by considering sub-brain networks and topological portions by considering contributions to global efficiency according to direct and indirect topological connections. We proposed adjacency and indirect adjacency as new network parameters attributable to direct and indirect topological connections, respectively, and applied them to graph-theoretical analysis of brain functional networks constructed from resting state fMRI data of 22 patients with schizophrenia and 22 healthy controls. Group differences in the network parameters were observed not for whole brain and hemispheric networks, but for regional networks. Alterations in adjacency and indirect adjacency were in opposite directions, such that adjacency increased, but indirect adjacency decreased in patients with schizophrenia. Furthermore, over connections in frontal and parietal regions, increased adjacency was associated with more severe negative symptoms, while decreased adjacency was associated with more severe positive symptoms of schizophrenia. This finding indicates that connectivity deficits associated with positive and negative symptoms of schizophrenia may involve topologically different paths in the brain. In patients with schizophrenia, although changes in global efficiency may not be clearly shown, different alterations in brain functional networks according to direct and indirect topological connections could be revealed at the regional level. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Effects of Different Correlation Metrics and Preprocessing Factors on Small-World Brain Functional Networks: A Resting-State Functional MRI Study

PubMed Central

Liang, Xia; Wang, Jinhui; Yan, Chaogan; Shu, Ni; Xu, Ke; Gong, Gaolang; He, Yong

2012-01-01

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01–0.027 Hz) versus slow-4 (0.027–0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the “best” network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027–0.073 Hz band exhibited greater reliability than those in the 0.01–0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies. PMID:22412922

Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue.

PubMed

Taya, Fumihiko; Dimitriadis, Stavros I; Dragomir, Andrei; Lim, Julian; Sun, Yu; Wong, Kian Foong; Thakor, Nitish V; Bezerianos, Anastasios

2018-04-24

Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue. © 2018 Wiley Periodicals, Inc.

Normative brain size variation and brain shape diversity in humans.

PubMed

Reardon, P K; Seidlitz, Jakob; Vandekar, Simon; Liu, Siyuan; Patel, Raihaan; Park, Min Tae M; Alexander-Bloch, Aaron; Clasen, Liv S; Blumenthal, Jonathan D; Lalonde, Francois M; Giedd, Jay N; Gur, Ruben C; Gur, Raquel E; Lerch, Jason P; Chakravarty, M Mallar; Satterthwaite, Theodore D; Shinohara, Russell T; Raznahan, Armin

2018-06-15

Brain size variation over primate evolution and human development is associated with shifts in the proportions of different brain regions. Individual brain size can vary almost twofold among typically developing humans, but the consequences of this for brain organization remain poorly understood. Using in vivo neuroimaging data from more than 3000 individuals, we find that larger human brains show greater areal expansion in distributed frontoparietal cortical networks and related subcortical regions than in limbic, sensory, and motor systems. This areal redistribution recapitulates cortical remodeling across evolution, manifests by early childhood in humans, and is linked to multiple markers of heightened metabolic cost and neuronal connectivity. Thus, human brain shape is systematically coupled to naturally occurring variations in brain size through a scaling map that integrates spatiotemporally diverse aspects of neurobiology. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Breeding novel solutions in the brain: a model of Darwinian neurodynamics.

PubMed

Szilágyi, András; Zachar, István; Fedor, Anna; de Vladar, Harold P; Szathmáry, Eörs

2016-01-01

Background : The fact that surplus connections and neurons are pruned during development is well established. We complement this selectionist picture by a proof-of-principle model of evolutionary search in the brain, that accounts for new variations in theory space. We present a model for Darwinian evolutionary search for candidate solutions in the brain. Methods : We combine known components of the brain - recurrent neural networks (acting as attractors), the action selection loop and implicit working memory - to provide the appropriate Darwinian architecture. We employ a population of attractor networks with palimpsest memory. The action selection loop is employed with winners-share-all dynamics to select for candidate solutions that are transiently stored in implicit working memory. Results : We document two processes: selection of stored solutions and evolutionary search for novel solutions. During the replication of candidate solutions attractor networks occasionally produce recombinant patterns, increasing variation on which selection can act. Combinatorial search acts on multiplying units (activity patterns) with hereditary variation and novel variants appear due to (i) noisy recall of patterns from the attractor networks, (ii) noise during transmission of candidate solutions as messages between networks, and, (iii) spontaneously generated, untrained patterns in spurious attractors. Conclusions : Attractor dynamics of recurrent neural networks can be used to model Darwinian search. The proposed architecture can be used for fast search among stored solutions (by selection) and for evolutionary search when novel candidate solutions are generated in successive iterations. Since all the suggested components are present in advanced nervous systems, we hypothesize that the brain could implement a truly evolutionary combinatorial search system, capable of generating novel variants.

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.

Small-worldness and gender differences of large scale brain metabolic covariance networks in young adults: a FDG PET study of 400 subjects.

PubMed

Hu, Yuxiao; Xu, Qiang; Shen, Junkang; Li, Kai; Zhu, Hong; Zhang, Zhiqiang; Lu, Guangming

2015-02-01

Many studies have demonstrated the small-worldness of the human brain, and have revealed a sexual dimorphism in brain network properties. However, little is known about the gender effects on the topological organization of the brain metabolic covariance networks. To investigate the small-worldness and the gender differences in the topological architectures of human brain metabolic networks. FDG-PET data of 400 healthy right-handed subjects (200 women and 200 age-matched men) were involved in the present study. Metabolic networks of each gender were constructed by calculating the covariance of regional cerebral glucose metabolism (rCMglc) across subjects on the basis of AAL parcellation. Gender differences of network and nodal properties were investigated by using the graph theoretical approaches. Moreover, the gender-related difference of rCMglc in each brain region was tested for investigating the relationships between the hub regions and the brain regions showing significant gender-related differences in rCMglc. We found prominent small-world properties in the domain of metabolic networks in each gender. No significant gender difference in the global characteristics was found. Gender differences of nodal characteristic were observed in a few brain regions. We also found bilateral and lateralized distributions of network hubs in the females and males. Furthermore, we first reported that some hubs of a gender located in the brain regions showing weaker rCMglc in this gender than the other gender. The present study demonstrated that small-worldness was existed in metabolic networks, and revealed gender differences of organizational patterns in metabolic network. These results maybe provided insights into the understanding of the metabolic substrates underlying individual differences in cognition and behaviors. © The Foundation Acta Radiologica 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Imaging structural and functional brain networks in temporal lobe epilepsy.

PubMed

Bernhardt, Boris C; Hong, Seokjun; Bernasconi, Andrea; Bernasconi, Neda

2013-10-01

Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

Evidence for hubs in human functional brain networks

PubMed Central

Power, Jonathan D; Schlaggar, Bradley L; Lessov-Schlaggar, Christina N; Petersen, Steven E

2013-01-01

Summary Hubs integrate and distribute information in powerful ways due to the number and positioning of their contacts in a network. Several resting state functional connectivity MRI reports have implicated regions of the default mode system as brain hubs; we demonstrate that previous degree-based approaches to hub identification may have identified portions of large brain systems rather than critical nodes of brain networks. We utilize two methods to identify hub-like brain regions: 1) finding network nodes that participate in multiple sub-networks of the brain, and 2) finding spatial locations where several systems are represented within a small volume. These methods converge on a distributed set of regions that differ from previous reports on hubs. This work identifies regions that support multiple systems, leading to spatially constrained predictions about brain function that may be tested in terms of lesions, evoked responses, and dynamic patterns of activity. PMID:23972601

Converging models of schizophrenia - Network alterations of prefrontal cortex underlying cognitive impairments

PubMed Central

Sakurai, Takeshi; Gamo, Nao J; Hikida, Takatoshi; Kim, Sun-Hong; Murai, Toshiya; Tomoda, Toshifumi; Sawa, Akira

2015-01-01

The prefrontal cortex (PFC) and its connections with other brain areas are crucial for cognitive function. Cognitive impairments are one of the core symptoms associated with schizophrenia, and manifest even before the onset of the disorder. Altered neural networks involving PFC contribute to cognitive impairments in schizophrenia. Both genetic and environmental risk factors affect the development of the local circuitry within PFC as well as development of broader brain networks, and make the system vulnerable to further insults during adolescence, leading to the onset of the disorder in young adulthood. Since spared cognitive functions correlate with functional outcome and prognosis, a better understanding of the mechanisms underlying cognitive impairments will have important implications for novel therapeutics for schizophrenia focusing on cognitive functions. Multidisciplinary approaches, from basic neuroscience to clinical studies, are required to link molecules, circuitry, networks, and behavioral phenotypes. Close interactions among such fields by sharing a common language on connectomes, behavioral readouts, and other concepts are crucial for this goal. PMID:26408506

NETWORK ASSISTED ANALYSIS TO REVEAL THE GENETIC BASIS OF AUTISM1

PubMed Central

Liu, Li; Lei, Jing; Roeder, Kathryn

2016-01-01

While studies show that autism is highly heritable, the nature of the genetic basis of this disorder remains illusive. Based on the idea that highly correlated genes are functionally interrelated and more likely to affect risk, we develop a novel statistical tool to find more potentially autism risk genes by combining the genetic association scores with gene co-expression in specific brain regions and periods of development. The gene dependence network is estimated using a novel partial neighborhood selection (PNS) algorithm, where node specific properties are incorporated into network estimation for improved statistical and computational efficiency. Then we adopt a hidden Markov random field (HMRF) model to combine the estimated network and the genetic association scores in a systematic manner. The proposed modeling framework can be naturally extended to incorporate additional structural information concerning the dependence between genes. Using currently available genetic association data from whole exome sequencing studies and brain gene expression levels, the proposed algorithm successfully identified 333 genes that plausibly affect autism risk. PMID:27134692

Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion.

PubMed

Sotiras, Aristeidis; Toledo, Jon B; Gur, Raquel E; Gur, Ruben C; Satterthwaite, Theodore D; Davatzikos, Christos

2017-03-28

During adolescence, the human cortex undergoes substantial remodeling to support a rapid expansion of behavioral repertoire. Accurately quantifying these changes is a prerequisite for understanding normal brain development, as well as the neuropsychiatric disorders that emerge in this vulnerable period. Past accounts have demonstrated substantial regional heterogeneity in patterns of brain development, but frequently have been limited by small samples and analytics that do not evaluate complex multivariate imaging patterns. Capitalizing on recent advances in multivariate analysis methods, we used nonnegative matrix factorization (NMF) to uncover coordinated patterns of cortical development in a sample of 934 youths ages 8-20, who completed structural neuroimaging as part of the Philadelphia Neurodevelopmental Cohort. Patterns of structural covariance (PSCs) derived by NMF were highly reproducible over a range of resolutions, and differed markedly from common gyral-based structural atlases. Moreover, PSCs were largely symmetric and showed correspondence to specific large-scale functional networks. The level of correspondence was ordered according to their functional role and position in the evolutionary hierarchy, being high in lower-order visual and somatomotor networks and diminishing in higher-order association cortex. Furthermore, PSCs showed divergent developmental associations, with PSCs in higher-order association cortex networks showing greater changes with age than primary somatomotor and visual networks. Critically, such developmental changes within PSCs were significantly associated with the degree of evolutionary cortical expansion. Together, our findings delineate a set of structural brain networks that undergo coordinated cortical thinning during adolescence, which is in part governed by evolutionary novelty and functional specialization.

A multisample study of longitudinal changes in brain network architecture in 4-13-year-old children.

PubMed

Wierenga, Lara M; van den Heuvel, Martijn P; Oranje, Bob; Giedd, Jay N; Durston, Sarah; Peper, Jiska S; Brown, Timothy T; Crone, Eveline A

2018-01-01

Recent advances in human neuroimaging research have revealed that white-matter connectivity can be described in terms of an integrated network, which is the basis of the human connectome. However, the developmental changes of this connectome in childhood are not well understood. This study made use of two independent longitudinal diffusion-weighted imaging data sets to characterize developmental changes in the connectome by estimating age-related changes in fractional anisotropy (FA) for reconstructed fibers (edges) between 68 cortical regions. The first sample included 237 diffusion-weighted scans of 146 typically developing children (4-13 years old, 74 females) derived from the Pediatric Longitudinal Imaging, Neurocognition, and Genetics (PLING) study. The second sample included 141 scans of 97 individuals (8-13 years old, 62 females) derived from the BrainTime project. In both data sets, we compared edges that had the most substantial age-related change in FA to edges that showed little change in FA. This allowed us to investigate if developmental changes in white matter reorganize network topology. We observed substantial increases in edges connecting peripheral and a set of highly connected hub regions, referred to as the rich club. Together with the observed topological differences between regions connecting to edges showing the smallest and largest changes in FA, this indicates that changes in white matter affect network organization, such that highly connected regions become even more strongly imbedded in the network. These findings suggest that an important process in brain development involves organizing patterns of inter-regional interactions. Hum Brain Mapp 39:157-170, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A Hybrid CPU-GPU Accelerated Framework for Fast Mapping of High-Resolution Human Brain Connectome

PubMed Central

Ren, Ling; Xu, Mo; Xie, Teng; Gong, Gaolang; Xu, Ningyi; Yang, Huazhong; He, Yong

2013-01-01

Recently, a combination of non-invasive neuroimaging techniques and graph theoretical approaches has provided a unique opportunity for understanding the patterns of the structural and functional connectivity of the human brain (referred to as the human brain connectome). Currently, there is a very large amount of brain imaging data that have been collected, and there are very high requirements for the computational capabilities that are used in high-resolution connectome research. In this paper, we propose a hybrid CPU-GPU framework to accelerate the computation of the human brain connectome. We applied this framework to a publicly available resting-state functional MRI dataset from 197 participants. For each subject, we first computed Pearson’s Correlation coefficient between any pairs of the time series of gray-matter voxels, and then we constructed unweighted undirected brain networks with 58 k nodes and a sparsity range from 0.02% to 0.17%. Next, graphic properties of the functional brain networks were quantified, analyzed and compared with those of 15 corresponding random networks. With our proposed accelerating framework, the above process for each network cost 80∼150 minutes, depending on the network sparsity. Further analyses revealed that high-resolution functional brain networks have efficient small-world properties, significant modular structure, a power law degree distribution and highly connected nodes in the medial frontal and parietal cortical regions. These results are largely compatible with previous human brain network studies. Taken together, our proposed framework can substantially enhance the applicability and efficacy of high-resolution (voxel-based) brain network analysis, and have the potential to accelerate the mapping of the human brain connectome in normal and disease states. PMID:23675425

Association between resting-state brain network topological organization and creative ability: Evidence from a multiple linear regression model.

PubMed

Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming

2017-10-01

Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.

Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study.

PubMed

Kaushal, Mayank; Oni-Orisan, Akinwunmi; Chen, Gang; Li, Wenjun; Leschke, Jack; Ward, Doug; Kalinosky, Benjamin; Budde, Matthew; Schmit, Brian; Li, Shi-Jiang; Muqeet, Vaishnavi; Kurpad, Shekar

2017-09-01

Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.

Sparse brain network using penalized linear regression

NASA Astrophysics Data System (ADS)

Lee, Hyekyoung; Lee, Dong Soo; Kang, Hyejin; Kim, Boong-Nyun; Chung, Moo K.

2011-03-01

Sparse partial correlation is a useful connectivity measure for brain networks when it is difficult to compute the exact partial correlation in the small-n large-p setting. In this paper, we formulate the problem of estimating partial correlation as a sparse linear regression with a l1-norm penalty. The method is applied to brain network consisting of parcellated regions of interest (ROIs), which are obtained from FDG-PET images of the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. To validate the results, we check their reproducibilities of the obtained brain networks by the leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.

Brain and Social Networks: Fundamental Building Blocks of Human Experience.

PubMed

Falk, Emily B; Bassett, Danielle S

2017-09-01

How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Metabolic brain networks in aging and preclinical Alzheimer's disease.

PubMed

Arnemann, Katelyn L; Stöber, Franziska; Narayan, Sharada; Rabinovici, Gil D; Jagust, William J

2018-01-01

Metabolic brain networks can provide insight into the network processes underlying progression from healthy aging to Alzheimer's disease. We explore the effect of two Alzheimer's disease risk factors, amyloid-β and ApoE ε4 genotype, on metabolic brain networks in cognitively normal older adults (N = 64, ages 69-89) compared to young adults (N = 17, ages 20-30) and patients with Alzheimer's disease (N = 22, ages 69-89). Subjects underwent MRI and PET imaging of metabolism (FDG) and amyloid-β (PIB). Normal older adults were divided into four subgroups based on amyloid-β and ApoE genotype. Metabolic brain networks were constructed cross-sectionally by computing pairwise correlations of metabolism across subjects within each group for 80 regions of interest. We found widespread elevated metabolic correlations and desegregation of metabolic brain networks in normal aging compared to youth and Alzheimer's disease, suggesting that normal aging leads to widespread loss of independent metabolic function across the brain. Amyloid-β and the combination of ApoE ε4 led to less extensive elevated metabolic correlations compared to other normal older adults, as well as a metabolic brain network more similar to youth and Alzheimer's disease. This could reflect early progression towards Alzheimer's disease in these individuals. Altered metabolic brain networks of older adults and those at the highest risk for progression to Alzheimer's disease open up novel lines of inquiry into the metabolic and network processes that underlie normal aging and Alzheimer's disease.

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

Changing Brain Networks Through Non-invasive Neuromodulation

PubMed Central

To, Wing Ting; De Ridder, Dirk; Hart Jr., John; Vanneste, Sven

2018-01-01

Background/Objective: Non-invasive neuromodulation techniques, such as repetitive Transcranial Magnetic Stimulation (rTMS) and transcranial Direct Current Stimulation (tDCS), have increasingly been investigated for their potential as treatments for neurological and psychiatric disorders. Despite widespread dissemination of these techniques, the underlying therapeutic mechanisms and the ideal stimulation site for a given disorder remain unknown. Increasing evidence support the possibility of non-invasive neuromodulation affecting a brain network rather than just the local stimulation target. In this article, we present evidence in a clinical setting to support the idea that non-invasive neuromodulation changes brain networks. Method: This article addresses the idea that non-invasive neuromodulation modulates brain networks, rather than just the local stimulation target, using neuromodulation studies in tinnitus and major depression as examples. We present studies that support this hypothesis from different perspectives. Main Results/Conclusion: Studies stimulating the same brain region, such as the dorsolateral prefrontal cortex (DLPFC), have shown to be effective for several disorders and studies using different stimulation sites for the same disorder have shown similar results. These findings, as well as results from studies investigating brain network connectivity on both macro and micro levels, suggest that non-invasive neuromodulation affects a brain network rather than just the local stimulation site targeted. We propose that non-invasive neuromodulation should be approached from a network perspective and emphasize the therapeutic potential of this approach through the modulation of targeted brain networks. PMID:29706876

Disruptions of brain structural network in end-stage renal disease patients with long-term hemodialysis and normal-appearing brain tissues.

PubMed

Chou, Ming-Chung; Ko, Chih-Hung; Chang, Jer-Ming; Hsieh, Tsyh-Jyi

2018-05-04

End-stage renal disease (ESRD) patients on hemodialysis were demonstrated to exhibit silent and invisible white-matter alterations which would likely lead to disruptions of brain structural networks. Therefore, the purpose of this study was to investigate the disruptions of brain structural network in ESRD patients. Thiry-three ESRD patients with normal-appearing brain tissues and 29 age- and gender-matched healthy controls were enrolled in this study and underwent both cognitive ability screening instrument (CASI) assessment and diffusion tensor imaging (DTI) acquisition. Brain structural connectivity network was constructed using probabilistic tractography with automatic anatomical labeling template. Graph-theory analysis was performed to detect the alterations of node-strength, node-degree, node-local efficiency, and node-clustering coefficient in ESRD patients. Correlational analysis was performed to understand the relationship between network measures, CASI score, and dialysis duration. Structural connectivity, node-strength, node-degree, and node-local efficiency were significantly decreased, whereas node-clustering coefficient was significantly increased in ESRD patients as compared with healthy controls. The disrupted local structural networks were generally associated with common neurological complications of ESRD patients, but the correlational analysis did not reveal significant correlation between network measures, CASI score, and dialysis duration. Graph-theory analysis was helpful to investigate disruptions of brain structural network in ESRD patients with normal-appearing brain tissues. Copyright © 2018. Published by Elsevier Masson SAS.

Changing Brain Networks Through Non-invasive Neuromodulation.

PubMed

To, Wing Ting; De Ridder, Dirk; Hart, John; Vanneste, Sven

2018-01-01

Background/Objective : Non-invasive neuromodulation techniques, such as repetitive Transcranial Magnetic Stimulation (rTMS) and transcranial Direct Current Stimulation (tDCS), have increasingly been investigated for their potential as treatments for neurological and psychiatric disorders. Despite widespread dissemination of these techniques, the underlying therapeutic mechanisms and the ideal stimulation site for a given disorder remain unknown. Increasing evidence support the possibility of non-invasive neuromodulation affecting a brain network rather than just the local stimulation target. In this article, we present evidence in a clinical setting to support the idea that non-invasive neuromodulation changes brain networks. Method : This article addresses the idea that non-invasive neuromodulation modulates brain networks, rather than just the local stimulation target, using neuromodulation studies in tinnitus and major depression as examples. We present studies that support this hypothesis from different perspectives. Main Results/Conclusion : Studies stimulating the same brain region, such as the dorsolateral prefrontal cortex (DLPFC), have shown to be effective for several disorders and studies using different stimulation sites for the same disorder have shown similar results. These findings, as well as results from studies investigating brain network connectivity on both macro and micro levels, suggest that non-invasive neuromodulation affects a brain network rather than just the local stimulation site targeted. We propose that non-invasive neuromodulation should be approached from a network perspective and emphasize the therapeutic potential of this approach through the modulation of targeted brain networks.

Brain network disturbance related to posttraumatic stress and traumatic brain injury in veterans.

PubMed

Spielberg, Jeffrey M; McGlinchey, Regina E; Milberg, William P; Salat, David H

2015-08-01

Understanding the neural causes and consequences of posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) is a high research priority, given the high rates of associated disability and suicide. Despite remarkable progress in elucidating the brain mechanisms of PTSD and mTBI, a comprehensive understanding of these conditions at the level of brain networks has yet to be achieved. The present study sought to identify functional brain networks and topological properties (measures of network organization and function) related to current PTSD severity and mTBI. Graph theoretic tools were used to analyze resting-state functional magnetic resonance imaging data from 208 veterans of Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn, all of whom had experienced a traumatic event qualifying for PTSD criterion A. Analyses identified brain networks and topological network properties linked to current PTSD symptom severity, mTBI, and the interaction between PTSD and mTBI. Two brain networks were identified in which weaker connectivity was linked to higher PTSD re-experiencing symptoms, one of which was present only in veterans with comorbid mTBI. Re-experiencing was also linked to worse functional segregation (necessary for specialized processing) and diminished influence of key regions on the network, including the hippocampus. Findings of this study demonstrate that PTSD re-experiencing symptoms are linked to weakened connectivity in a network involved in providing contextual information. A similar relationship was found in a separate network typically engaged in the gating of working memory, but only in veterans with mTBI. Published by Elsevier Inc.

Toward Understanding How Early-Life Stress Reprograms Cognitive and Emotional Brain Networks.

PubMed

Chen, Yuncai; Baram, Tallie Z

2016-01-01

Vulnerability to emotional disorders including depression derives from interactions between genes and environment, especially during sensitive developmental periods. Adverse early-life experiences provoke the release and modify the expression of several stress mediators and neurotransmitters within specific brain regions. The interaction of these mediators with developing neurons and neuronal networks may lead to long-lasting structural and functional alterations associated with cognitive and emotional consequences. Although a vast body of work has linked quantitative and qualitative aspects of stress to adolescent and adult outcomes, a number of questions are unclear. What distinguishes 'normal' from pathologic or toxic stress? How are the effects of stress transformed into structural and functional changes in individual neurons and neuronal networks? Which ones are affected? We review these questions in the context of established and emerging studies. We introduce a novel concept regarding the origin of toxic early-life stress, stating that it may derive from specific patterns of environmental signals, especially those derived from the mother or caretaker. Fragmented and unpredictable patterns of maternal care behaviors induce a profound chronic stress. The aberrant patterns and rhythms of early-life sensory input might also directly and adversely influence the maturation of cognitive and emotional brain circuits, in analogy to visual and auditory brain systems. Thus, unpredictable, stress-provoking early-life experiences may influence adolescent cognitive and emotional outcomes by disrupting the maturation of the underlying brain networks. Comprehensive approaches and multiple levels of analysis are required to probe the protean consequences of early-life adversity on the developing brain. These involve integrated human and animal-model studies, and approaches ranging from in vivo imaging to novel neuroanatomical, molecular, epigenomic, and computational methodologies. Because early-life adversity is a powerful determinant of subsequent vulnerabilities to emotional and cognitive pathologies, understanding the underlying processes will have profound implications for the world's current and future children.

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

Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer's disease.

PubMed

Muñoz-Moreno, Emma; Tudela, Raúl; López-Gil, Xavier; Soria, Guadalupe

2018-02-07

Animal models of Alzheimer's disease (AD) are essential to understanding the disease progression and to development of early biomarkers. Because AD has been described as a disconnection syndrome, magnetic resonance imaging (MRI)-based connectomics provides a highly translational approach to characterizing the disruption in connectivity associated with the disease. In this study, a transgenic rat model of AD (TgF344-AD) was analyzed to describe both cognitive performance and brain connectivity at an early stage (5 months of age) before a significant concentration of β-amyloid plaques is present. Cognitive abilities were assessed by a delayed nonmatch-to-sample (DNMS) task preceded by a training phase where the animals learned the task. The number of training sessions required to achieve a learning criterion was recorded and evaluated. After DNMS, MRI acquisition was performed, including diffusion-weighted MRI and resting-state functional MRI, which were processed to obtain the structural and functional connectomes, respectively. Global and regional graph metrics were computed to evaluate network organization in both transgenic and control rats. The results pointed to a delay in learning the working memory-related task in the AD rats, which also completed a lower number of trials in the DNMS task. Regarding connectivity properties, less efficient organization of the structural brain networks of the transgenic rats with respect to controls was observed. Specific regional differences in connectivity were identified in both structural and functional networks. In addition, a strong correlation was observed between cognitive performance and brain networks, including whole-brain structural connectivity as well as functional and structural network metrics of regions related to memory and reward processes. In this study, connectivity and neurocognitive impairments were identified in TgF344-AD rats at a very early stage of the disease when most of the pathological hallmarks have not yet been detected. Structural and functional network metrics of regions related to reward, memory, and sensory performance were strongly correlated with the cognitive outcome. The use of animal models is essential for the early identification of these alterations and can contribute to the development of early biomarkers of the disease based on MRI connectomics.

A network engineering perspective on probing and perturbing cognition with neurofeedback

PubMed Central

Khambhati, Ankit N.

2017-01-01

Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition. PMID:28445589

MicroRNA network changes in the brain stem underlie the development of hypertension.

PubMed

DeCicco, Danielle; Zhu, Haisun; Brureau, Anthony; Schwaber, James S; Vadigepalli, Rajanikanth

2015-09-01

Hypertension is a major chronic disease whose molecular mechanisms remain poorly understood. We compared neuroanatomical patterns of microRNAs in the brain stem of the spontaneous hypertensive rat (SHR) to the Wistar Kyoto rat (WKY, control). We quantified 419 well-annotated microRNAs in the nucleus of the solitary tract (NTS) and rostral ventrolateral medulla (RVLM), from SHR and WKY rats, during three main stages of hypertension development. Changes in microRNA expression were stage- and region-dependent, with a majority of SHR vs. WKY differential expression occurring at the hypertension onset stage in NTS versus at the prehypertension stage in RVLM. Our analysis identified 24 microRNAs showing time-dependent differential expression in SHR compared with WKY in at least one brain region. We predicted potential gene regulatory targets corresponding to catecholaminergic processes, neuroinflammation, and neuromodulation using the miRWALK and RNA22 databases, and we tested those bioinformatics predictions using high-throughput quantitative PCR to evaluate correlations of differential expression between the microRNAs and their predicted gene targets. We found a novel regulatory network motif consisting of microRNAs likely downregulating a negative regulator of prohypertensive processes such as angiotensin II signaling and leukotriene-based inflammation. Our results provide new evidence on the dynamics of microRNA expression in the development of hypertension and predictions of microRNA-mediated regulatory networks playing a region-dependent role in potentially altering brain-stem cardiovascular control circuit function leading to the development of hypertension. Copyright © 2015 the American Physiological Society.

Rapid Postnatal Expansion of Neural Networks Occurs in an Environment of Altered Neurovascular and Neurometabolic Coupling.

PubMed

Kozberg, Mariel G; Ma, Ying; Shaik, Mohammed A; Kim, Sharon H; Hillman, Elizabeth M C

2016-06-22

In the adult brain, increases in neural activity lead to increases in local blood flow. However, many prior measurements of functional hemodynamics in the neonatal brain, including functional magnetic resonance imaging (fMRI) in human infants, have noted altered and even inverted hemodynamic responses to stimuli. Here, we demonstrate that localized neural activity in early postnatal mice does not evoke blood flow increases as in the adult brain, and elucidate the neural and metabolic correlates of these altered functional hemodynamics as a function of developmental age. Using wide-field GCaMP imaging, the development of neural responses to somatosensory stimulus is visualized over the entire bilaterally exposed cortex. Neural responses are observed to progress from tightly localized, unilateral maps to bilateral responses as interhemispheric connectivity becomes established. Simultaneous hemodynamic imaging confirms that spatiotemporally coupled functional hyperemia is not present during these early stages of postnatal brain development, and develops gradually as cortical connectivity is established. Exploring the consequences of this lack of functional hyperemia, measurements of oxidative metabolism via flavoprotein fluorescence suggest that neural activity depletes local oxygen to below baseline levels at early developmental stages. Analysis of hemoglobin oxygenation dynamics at the same age confirms oxygen depletion for both stimulus-evoked and resting-state neural activity. This state of unmet metabolic demand during neural network development poses new questions about the mechanisms of neurovascular development and its role in both normal and abnormal brain development. These results also provide important insights for the interpretation of fMRI studies of the developing brain. This work demonstrates that the postnatal development of neuronal connectivity is accompanied by development of the mechanisms that regulate local blood flow in response to neural activity. Novel in vivo imaging reveals that, in the developing mouse brain, strong and localized GCaMP neural responses to stimulus fail to evoke local blood flow increases, leading to a state in which oxygen levels become locally depleted. These results demonstrate that the development of cortical connectivity occurs in an environment of altered energy availability that itself may play a role in shaping normal brain development. These findings have important implications for understanding the pathophysiology of abnormal developmental trajectories, and for the interpretation of functional magnetic resonance imaging data acquired in the developing brain. Copyright © 2016 the authors 0270-6474/16/366704-14$15.00/0.

A brain-region-based meta-analysis method utilizing the Apriori algorithm.

PubMed

Niu, Zhendong; Nie, Yaoxin; Zhou, Qian; Zhu, Linlin; Wei, Jieyao

2016-05-18

Brain network connectivity modeling is a crucial method for studying the brain's cognitive functions. Meta-analyses can unearth reliable results from individual studies. Meta-analytic connectivity modeling is a connectivity analysis method based on regions of interest (ROIs) which showed that meta-analyses could be used to discover brain network connectivity. In this paper, we propose a new meta-analysis method that can be used to find network connectivity models based on the Apriori algorithm, which has the potential to derive brain network connectivity models from activation information in the literature, without requiring ROIs. This method first extracts activation information from experimental studies that use cognitive tasks of the same category, and then maps the activation information to corresponding brain areas by using the automatic anatomical label atlas, after which the activation rate of these brain areas is calculated. Finally, using these brain areas, a potential brain network connectivity model is calculated based on the Apriori algorithm. The present study used this method to conduct a mining analysis on the citations in a language review article by Price (Neuroimage 62(2):816-847, 2012). The results showed that the obtained network connectivity model was consistent with that reported by Price. The proposed method is helpful to find brain network connectivity by mining the co-activation relationships among brain regions. Furthermore, results of the co-activation relationship analysis can be used as a priori knowledge for the corresponding dynamic causal modeling analysis, possibly achieving a significant dimension-reducing effect, thus increasing the efficiency of the dynamic causal modeling analysis.

Communication of brain network core connections altered in behavioral variant frontotemporal dementia but possibly preserved in early-onset Alzheimer's disease

NASA Astrophysics Data System (ADS)

Daianu, Madelaine; Jahanshad, Neda; Mendez, Mario F.; Bartzokis, George; Jimenez, Elvira E.; Thompson, Paul M.

2015-03-01

Diffusion imaging and brain connectivity analyses can assess white matter deterioration in the brain, revealing the underlying patterns of how brain structure declines. Fiber tractography methods can infer neural pathways and connectivity patterns, yielding sensitive mathematical metrics of network integrity. Here, we analyzed 1.5-Tesla wholebrain diffusion-weighted images from 64 participants - 15 patients with behavioral variant frontotemporal dementia (bvFTD), 19 with early-onset Alzheimer's disease (EOAD), and 30 healthy elderly controls. Using whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We evaluated the brain's networks focusing on the most highly central and connected regions, also known as hubs, in each diagnostic group - specifically the "high-cost" structural backbone used in global and regional communication. The high-cost backbone of the brain, predicted by fiber density and minimally short pathways between brain regions, accounted for 81-92% of the overall brain communication metric in all diagnostic groups. Furthermore, we found that the set of pathways interconnecting high-cost and high-capacity regions of the brain's communication network are globally and regionally altered in bvFTD, compared to healthy participants; however, the overall organization of the high-cost and high-capacity networks were relatively preserved in EOAD participants, relative to controls. Disruption of the major central hubs that transfer information between brain regions may impair neural communication and functional integrity in characteristic ways typical of each subtype of dementia.

EEG classification of emotions using emotion-specific brain functional network.

PubMed

Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

2015-08-01

The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study.

PubMed

Tian, Lixia; Wang, Jinhui; Yan, Chaogan; He, Yong

2011-01-01

We employed resting-state functional MRI (R-fMRI) to investigate hemisphere- and gender-related differences in the topological organization of human brain functional networks. Brain networks were first constructed by measuring inter-regional temporal correlations of R-fMRI data within each hemisphere in 86 young, healthy, right-handed adults (38 males and 48 females) followed by a graph-theory analysis. The hemispheric networks exhibit small-world attributes (high clustering and short paths) that are compatible with previous results in the whole-brain functional networks. Furthermore, we found that compared with females, males have a higher normalized clustering coefficient in the right hemispheric network but a lower clustering coefficient in the left hemispheric network, suggesting a gender-hemisphere interaction. Moreover, we observed significant hemisphere-related differences in the regional nodal characteristics in various brain regions, such as the frontal and occipital regions (leftward asymmetry) and the temporal regions (rightward asymmetry), findings that are consistent with previous studies of brain structural and functional asymmetries. Together, our results suggest that the topological organization of human brain functional networks is associated with gender and hemispheres, and they provide insights into the understanding of functional substrates underlying individual differences in behaviors and cognition. Copyright © 2010 Elsevier Inc. All rights reserved.

Understanding emotion with brain networks.

PubMed

Pessoa, Luiz

2018-02-01

Emotional processing appears to be interlocked with perception, cognition, motivation, and action. These interactions are supported by the brain's large-scale non-modular anatomical and functional architectures. An important component of this organization involves characterizing the brain in terms of networks. Two aspects of brain networks are discussed: brain networks should be considered as inherently overlapping (not disjoint) and dynamic (not static). Recent work on multivariate pattern analysis shows that affective dimensions can be detected in the activity of distributed neural systems that span cortical and subcortical regions. More broadly, the paper considers how we should think of causation in complex systems like the brain, so as to inform the relationship between emotion and other mental aspects, such as cognition.

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.

Brain activation patterns elicited by the 'Faces Symbol Test' -- a pilot fMRI study.

PubMed

Grabner, Rh; Popotnig, F; Ropele, S; Neuper, C; Gorani, F; Petrovic, K; Ebner, F; Strasser-Fuchs, S; Fazekas, F; Enzinger, C

2008-04-01

The Faces Symbol Test (FST) has recently been proposed as a brief and patient-friendly screening instrument for the assessment of cognitive dysfunction in patients with multiple sclerosis (MS). However, in contrast to well-established MS screening tests such as the Paced Auditory Serial Addition Test, the neural correlates of the FST have not been investigated so far. In the present study, we developed a functional MRI (fMRI) version of the FST to provide first data on brain regions and networks involved in this test. A sample of 19 healthy participants completed a version of the FST adapted for fMRI, requiring matching of faces and symbols in a multiple choice test and two further experimental conditions drawing on cognitive subcomponents (face matching and symbol matching). Imaging data showed a differential involvement of a fronto-parieto-occipital network in the three conditions. The most demanding FST condition elicited brain activation patterns related with sustained attention and executive control. These results suggest that the FST recruits brain networks critical for higher-order cognitive functions often impaired in MS patients.

Multimodal Imaging of Alzheimer Pathophysiology in the Brain's Default Mode Network

DOE PAGES

Shin, Jonghan; Kepe, Vladimir; Small, Gary W.; ...

2011-01-01

The spatial correlations between the brain's default mode network (DMN) and the brain regions known to develop pathophysiology in Alzheimer's disease (AD) have recently attracted much attention. In this paper, we compare results of different functional and structural imaging modalities, including MRI and PET, and highlight different patterns of anomalies observed within the DMN. Multitracer PET imaging in subjects with and without dementia has demonstrated that [C-11]PIB- and [F-18]FDDNP-binding patterns in patients with AD overlap within nodes of the brain's default network including the prefrontal, lateral parietal, lateral temporal, and posterior cingulate cortices, with the exception of the medial temporalmore » cortex (especially, the hippocampus) where significant discrepancy between increased [F-18]FDDNP binding and negligible [C-11]PIB-binding was observed. [F-18]FDDNP binding in the medial temporal cortex—a key constituent of the DMN—coincides with both the presence of amyloid and tau pathology, and also with cortical areas with maximal atrophy as demonstrated by T1-weighted MR imaging of AD patients.« less

Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients.

PubMed

Qiu, Xiangzhe; Zhang, Yanjun; Feng, Hongbo; Jiang, Donglang

2016-01-01

Recent studies have demonstrated alterations in the topological organization of structural brain networks in diabetes mellitus (DM). However, the DM-related changes in the topological properties in functional brain networks are unexplored so far. We therefore used fluoro-D-glucose positron emission tomography (FDG-PET) data to construct functional brain networks of 73 DM patients and 91 sex- and age-matched normal controls (NCs), followed by a graph theoretical analysis. We found that both DM patients and NCs had a small-world topology in functional brain network. In comparison to the NC group, the DM group was found to have significantly lower small-world index, lower normalized clustering coefficients and higher normalized characteristic path length. Moreover, for diabetic patients, the nodal centrality was significantly reduced in the right rectus, the right cuneus, the left middle occipital gyrus, and the left postcentral gyrus, and it was significantly increased in the orbitofrontal region of the left middle frontal gyrus, the left olfactory region, and the right paracentral lobule. Our results demonstrated that the diabetic brain was associated with disrupted topological organization in the functional PET network, thus providing functional evidence for the abnormalities of brain networks in DM.

Resting state network topology of the ferret brain.

PubMed

Zhou, Zhe Charles; Salzwedel, Andrew P; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K; Gilmore, John H; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-12-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4T MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. Copyright © 2016 Elsevier Inc. All rights reserved.

Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions

PubMed Central

Wang, Xiang; Öngür, Dost; Auerbach, Randy P.; Yao, Shuqiao

2016-01-01

Abstract Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network–mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. PMID:27148911

Growth and development of the brain and impact on cognitive outcomes.

PubMed

Hüppi, Petra S

2010-01-01

Understanding human brain development from the fetal life to adulthood is of great clinical importance as many neurological and neurobehavioral disorders have their origin in early structural and functional cerebral maturation. The developing brain is particularly prone to being affected by endogenous and exogenous events through the fetal and early postnatal life. The concept of 'developmental plasticity or disruption of the developmental program' summarizes these events. Increases in white matter, which speed up communication between brain cells, growing complexity of neuronal networks suggested by gray and white matter changes, and environmentally sensitive plasticity are all essential aspects in a child's ability to mentalize and maintain the adaptive flexibility necessary for achieving high sociocognitive functioning. Advancement in neuroimaging has opened up new ways for examining the developing human brain in vivo, the study of the effects of early antenatal, perinatal and neonatal events on later structural and functional brain development resulting in developmental disabilities or developmental resilience. In this review, methods of quantitative assessment of human brain development, such as 3D-MRI with image segmentation, diffusion tensor imaging to assess connectivity and functional MRI to visualize brain function will be presented. Copyright (c) 2010 S. Karger AG, Basel.

Mapping Multiplex Hubs in Human Functional Brain Networks

PubMed Central

De Domenico, Manlio; Sasai, Shuntaro; Arenas, Alex

2016-01-01

Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches. PMID:27471443

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

PubMed

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

Growth-related neural reorganization and the autism phenotype: a test of the hypothesis that altered brain growth leads to altered connectivity

PubMed Central

Lewis, John D.; Elman, Jeffrey L.

2009-01-01

Theoretical considerations, and findings from computational modeling, comparative neuroanatomy and developmental neuroscience, motivate the hypothesis that a deviant brain growth trajectory will lead to deviant patterns of change in cortico-cortical connectivity. Differences in brain size during development will alter the relative cost and effectiveness of short- and long-distance connections, and should thus impact the growth and retention of connections. Reduced brain size should favor long-distance connectivity; brain overgrowth should favor short-distance connectivity; and inconsistent deviations from the normal growth trajectory – as occurs in autism – should result in potentially disruptive changes to established patterns of functional and physical connectivity during development. To explore this hypothesis, neural networks which modeled inter-hemispheric interaction were grown at the rate of either typically developing children or children with autism. The influence of the length of the inter-hemispheric connections was analyzed at multiple developmental time-points. The networks that modeled autistic growth were less affected by removal of the inter-hemispheric connections than those that modeled normal growth – indicating a reduced reliance on long-distance connections – for short response times, and this difference increased substantially at approximately 24 simulated months of age. The performance of the networks showed a corresponding decline during development. And direct analysis of the connection weights showed a parallel reduction in connectivity. These modeling results support the hypothesis that the deviant growth trajectory in autism spectrum disorders may lead to a disruption of established patterns of functional connectivity during development, with potentially negative behavioral consequences, and a subsequent reduction in physical connectivity. The results are discussed in relation to the growing body of evidence of reduced functional and structural connectivity in autism, and in relation to the behavioral phenotype, particularly the developmental aspects. PMID:18171375

Hubs of Anticorrelation in High-Resolution Resting-State Functional Connectivity Network Architecture.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Cabanban, Romeo; Crosson, Bruce A

2015-06-01

A major focus of brain research recently has been to map the resting-state functional connectivity (rsFC) network architecture of the normal brain and pathology through functional magnetic resonance imaging. However, the phenomenon of anticorrelations in resting-state signals between different brain regions has not been adequately examined. The preponderance of studies on resting-state fMRI (rsFMRI) have either ignored anticorrelations in rsFC networks or adopted methods in data analysis, which have rendered anticorrelations in rsFC networks uninterpretable. The few studies that have examined anticorrelations in rsFC networks using conventional methods have found anticorrelations to be weak in strength and not very reproducible across subjects. Anticorrelations in rsFC network architecture could reflect mechanisms that subserve a number of important brain processes. In this preliminary study, we examined the properties of anticorrelated rsFC networks by systematically focusing on negative cross-correlation coefficients (CCs) among rsFMRI voxel time series across the brain with graph theory-based network analysis. A number of methods were implemented to enhance the neuronal specificity of resting-state functional connections that yield negative CCs, although at the cost of decreased sensitivity. Hubs of anticorrelation were seen in a number of cortical and subcortical brain regions. Examination of the anticorrelation maps of these hubs indicated that negative CCs in rsFC network architecture highlight a number of regulatory interactions between brain networks and regions, including reciprocal modulations, suppression, inhibition, and neurofeedback.

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.

Concerted Perturbation Observed in a Hub Network in Alzheimer’s Disease

PubMed Central

Liang, Dapeng; Han, Guangchun; Feng, Xuemei; Sun, Jiya; Duan, Yong; Lei, Hongxing

2012-01-01

Alzheimer’s disease (AD) is a progressive neurodegenerative disease involving the alteration of gene expression at the whole genome level. Genome-wide transcriptional profiling of AD has been conducted by many groups on several relevant brain regions. However, identifying the most critical dys-regulated genes has been challenging. In this work, we addressed this issue by deriving critical genes from perturbed subnetworks. Using a recent microarray dataset on six brain regions, we applied a heaviest induced subgraph algorithm with a modular scoring function to reveal the significantly perturbed subnetwork in each brain region. These perturbed subnetworks were found to be significantly overlapped with each other. Furthermore, the hub genes from these perturbed subnetworks formed a connected hub network consisting of 136 genes. Comparison between AD and several related diseases demonstrated that the hub network was robustly and specifically perturbed in AD. In addition, strong correlation between the expression level of these hub genes and indicators of AD severity suggested that this hub network can partially reflect AD progression. More importantly, this hub network reflected the adaptation of neurons to the AD-specific microenvironment through a variety of adjustments, including reduction of neuronal and synaptic activities and alteration of survival signaling. Therefore, it is potentially useful for the development of biomarkers and network medicine for AD. PMID:22815752

Describing functional diversity of brain regions and brain networks

PubMed Central

Anderson, Michael L.; Kinnison, Josh; Pessoa, Luiz

2013-01-01

Despite the general acceptance that functional specialization plays an important role in brain function, there is little consensus about its extent in the brain. We sought to advance the understanding of this question by employing a data-driven approach that capitalizes on the existence of large databases of neuroimaging data. We quantified the diversity of activation in brain regions as a way to characterize the degree of functional specialization. To do so, brain activations were classified in terms of task domains, such as vision, attention, and language, which determined a region’s functional fingerprint. We found that the degree of diversity varied considerably across the brain. We also quantified novel properties of regions and of networks that inform our understanding of several task-positive and task-negative networks described in the literature, including defining functional fingerprints for entire networks and measuring their functional assortativity, namely the degree to which they are composed of regions with similar functional fingerprints. Our results demonstrate that some brain networks exhibit strong assortativity, whereas other networks consist of relatively heterogeneous parts. In sum, rather than characterizing the contributions of individual brain regions using task-based functional attributions, we instead quantified their dispositional tendencies, and related those to each region’s affiliative properties in both task-positive and task-negative contexts. PMID:23396162

Diminished neural network dynamics after moderate and severe traumatic brain injury.

PubMed

Gilbert, Nicholas; Bernier, Rachel A; Calhoun, Vincent D; Brenner, Einat; Grossner, Emily; Rajtmajer, Sarah M; Hillary, Frank G

2018-01-01

Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain's subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity. In this study, we examine the dynamic connectivity with focus on state-level connectivity (state) and evaluate the reliability of dynamic network states over the course of two runs of intermittent task and resting data. The goal was to examine the dynamic properties of neural networks engaged periodically with task stimulation in order to determine: 1) the reliability of inter-nodal and network-level characteristics over time and 2) the transitions between distinct network states after traumatic brain injury. To do so, we enrolled 23 individuals with moderate and severe TBI at least 1-year post injury and 19 age- and education-matched healthy adults using functional MRI methods, dynamic connectivity modeling, and graph theory. The results reveal several distinct network "states" that were reliably evident when comparing runs; the overall frequency of dynamic network states are highly reproducible (r-values>0.8) for both samples. Analysis of movement between states resulted in fewer state transitions in the TBI sample and, in a few cases, brain injury resulted in the appearance of states not exhibited by the healthy control (HC) sample. Overall, the findings presented here demonstrate the reliability of observable dynamic mental states during periods of on-task performance and support emerging evidence that brain injury may result in diminished network dynamics.

Is the Internet gaming-addicted brain close to be in a pathological state?

PubMed

Park, Chang-Hyun; Chun, Ji-Won; Cho, Huyn; Jung, Young-Chul; Choi, Jihye; Kim, Dai Jin

2017-01-01

Internet gaming addiction (IGA) is becoming a common and widespread mental health concern. Although IGA induces a variety of negative psychosocial consequences, it is yet ambiguous whether the brain addicted to Internet gaming is considered to be in a pathological state. We investigated IGA-induced abnormalities of the brain specifically from the network perspective and qualitatively assessed whether the Internet gaming-addicted brain is in a state similar to the pathological brain. Topological properties of brain functional networks were examined by applying a graph-theoretical approach to analyzing functional magnetic resonance imaging data acquired during a resting state in 19 IGA adolescents and 20 age-matched healthy controls. We compared functional distance-based measures, global and local efficiency of resting state brain functional networks between the two groups to assess how the IGA subjects' brain was topologically altered from the controls' brain. The IGA subjects had severer impulsiveness and their brain functional networks showed higher global efficiency and lower local efficiency relative to the controls. These topological differences suggest that IGA induced brain functional networks to shift toward the random topological architecture, as exhibited in other pathological states. Furthermore, for the IGA subjects, the topological alterations were specifically attributable to interregional connections incident on the frontal region, and the degree of impulsiveness was associated with the topological alterations over the frontolimbic connections. The current findings lend support to the proposition that the Internet gaming-addicted brain could be in the state similar to pathological states in terms of topological characteristics of brain functional networks. © 2015 Society for the Study of Addiction.

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

An Adaptive Complex Network Model for Brain Functional Networks

PubMed Central

Gomez Portillo, Ignacio J.; Gleiser, Pablo M.

2009-01-01

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution. PMID:19738902

Stronger default mode network connectivity is associated with poorer clinical insight in youth at ultra high-risk for psychotic disorders.

PubMed

Clark, Sarah V; Mittal, Vijay A; Bernard, Jessica A; Ahmadi, Aral; King, Tricia Z; Turner, Jessica A

2018-03-01

Impaired clinical insight (CI) is a common symptom of psychotic disorders and a promising treatment target. However, to date, our understanding of how variability in CI is tied to underlying brain dysfunction in the clinical high-risk period is limited. Developing a stronger conception of this link will be a vital first step for efforts to determine if CI can serve as a useful prognostic indicator. The current study investigated whether variability in CI is related to major brain networks in adolescents and young adults at ultra high-risk (UHR) of developing psychosis. Thirty-five UHR youth were administered structured clinical interviews as well as an assessment for CI and underwent resting-state magnetic resonance imaging scans. Functional connectivity was calculated in the default mode network (DMN) and fronto-parietal network (FPN), two major networks that are dysfunctional in psychosis and are hypothesized to affect insight. Greater DMN connectivity between the posterior cingulate/precuneus and ventromedial prefrontal cortex (DMN) was related to poorer CI (R 2 =0.399). There were no significant relationships between insight and the FPN. This is the first study to relate a major brain network to clinical insight before the onset of psychosis. Findings are consistent with evidence if a hyperconnected DMN in schizophrenia and UHR, and similar to a previous study of insight and connectivity in schizophrenia. Results suggest that a strongly connected DMN may be related to poor self-awareness of subthreshold psychotic symptoms in UHR adolescents and young adults. Copyright © 2017 Elsevier B.V. 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.

Connectivity supporting attention in children with attention deficit hyperactivity disorder.

PubMed

Barber, Anita D; Jacobson, Lisa A; Wexler, Joanna L; Nebel, Mary Beth; Caffo, Brian S; Pekar, James J; Mostofsky, Stewart H

2015-01-01

Intra-subject variability (ISV) is the most consistent behavioral deficit in Attention Deficit Hyperactivity Disorder (ADHD). ISV may be associated with networks involved in sustaining task control (cingulo-opercular network: CON) and self-reflective lapses of attention (default mode network: DMN). The current study examined whether connectivity supporting attentional control is atypical in children with ADHD. Group differences in full-brain connection strength and brain-behavior associations with attentional control measures were examined for the late-developing CON and DMN in 50 children with ADHD and 50 typically-developing (TD) controls (ages 8-12 years). Children with ADHD had hyper-connectivity both within the CON and within the DMN. Full-brain behavioral associations were found for a number of between-network connections. Across both groups, more anti-correlation between DMN and occipital cortex supported better attentional control. However, in the TD group, this brain-behavior association was stronger and occurred for a more extensive set of DMN-occipital connections. Differential support for attentional control between the two groups occurred with a number of CON-DMN connections. For all CON-DMN connections identified, increased between-network anti-correlation was associated with better attentional control for the ADHD group, but worse attentional control in the TD group. A number of between-network connections with the medial frontal cortex, in particular, showed this relationship. Follow-up analyses revealed that these associations were specific to attentional control and were not due to individual differences in working memory, IQ, motor control, age, or scan motion. While CON-DMN anti-correlation is associated with improved attention in ADHD, other circuitry supports improved attention in TD children. Greater CON-DMN anti-correlation supported better attentional control in children with ADHD, but worse attentional control in TD children. On the other hand, greater DMN-occipital anti-correlation supported better attentional control in TD children.

Familiarity and prevalence of Facebook use for social networking among individuals with traumatic brain injury.

PubMed

Tsaousides, Theodore; Matsuzawa, Yuka; Lebowitz, Matthew

2011-01-01

To examine use of Facebook among individuals with traumatic brain injury (TBI) and to identify barriers preventing Facebook use. An online survey was developed assessing frequency and barriers to use of Facebook. The survey was distributed electronically to individuals with TBI through four state brain injury associations. Ninety-six individuals with TBI completed the survey (60% female, age range: 23-70). The relative majority of respondents (60%) reported using Facebook on a regular basis. Among those who reported not using Facebook, the most commonly reported barriers were security concerns and cognitive deficits. Approximately half of non-users indicated interest in learning to use the site, with 70% reporting that they would use it more if they were more knowledgeable about it. Both users and non-users indicated that they would be interested in receiving training to learn how to use Facebook better. Social networking sites are increasingly important in creating and maintaining social networks. A significant number of survey respondents expressed interest in further training on Facebook use. Increased use of social networking may have important implications for social integration among individuals with TBI.

Selective impairment of hippocampus and posterior hub areas in Alzheimer's disease: an MEG-based multiplex network study.

PubMed

Yu, Meichen; Engels, Marjolein M A; Hillebrand, Arjan; van Straaten, Elisabeth C W; Gouw, Alida A; Teunissen, Charlotte; van der Flier, Wiesje M; Scheltens, Philip; Stam, Cornelis J

2017-05-01

Although frequency-specific network analyses have shown that functional brain networks are altered in patients with Alzheimer's disease, the relationships between these frequency-specific network alterations remain largely unknown. Multiplex network analysis is a novel network approach to study complex systems consisting of subsystems with different types of connectivity patterns. In this study, we used magnetoencephalography to integrate five frequency-band specific brain networks in a multiplex framework. Previous structural and functional brain network studies have consistently shown that hub brain areas are selectively disrupted in Alzheimer's disease. Accordingly, we hypothesized that hub regions in the multiplex brain networks are selectively targeted in patients with Alzheimer's disease in comparison to healthy control subjects. Eyes-closed resting-state magnetoencephalography recordings from 27 patients with Alzheimer's disease (60.6 ± 5.4 years, 12 females) and 26 controls (61.8 ± 5.5 years, 14 females) were projected onto atlas-based regions of interest using beamforming. Subsequently, source-space time series for both 78 cortical and 12 subcortical regions were reconstructed in five frequency bands (delta, theta, alpha 1, alpha 2 and beta band). Multiplex brain networks were constructed by integrating frequency-specific magnetoencephalography networks. Functional connections between all pairs of regions of interests were quantified using a phase-based coupling metric, the phase lag index. Several multiplex hub and heterogeneity metrics were computed to capture both overall importance of each brain area and heterogeneity of the connectivity patterns across frequency-specific layers. Different nodal centrality metrics showed consistently that several hub regions, particularly left hippocampus, posterior parts of the default mode network and occipital regions, were vulnerable in patients with Alzheimer's disease compared to control subjects. Of note, these detected vulnerable hubs in Alzheimer's disease were absent in each individual frequency-specific network, thus showing the value of integrating the networks. The connectivity patterns of these vulnerable hub regions in the patients were heterogeneously distributed across layers. Perturbed cognitive function and abnormal cerebrospinal fluid amyloid-β42 levels correlated positively with the vulnerability of the hub regions in patients with Alzheimer's disease. Our analysis therefore demonstrates that the magnetoencephalography-based multiplex brain networks contain important information that cannot be revealed by frequency-specific brain networks. Furthermore, this indicates that functional networks obtained in different frequency bands do not act as independent entities. Overall, our multiplex network study provides an effective framework to integrate the frequency-specific networks with different frequency patterns and reveal neuropathological mechanism of hub disruption in Alzheimer's disease. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Brain structural covariance network centrality in maltreated youth with PTSD and in maltreated youth resilient to PTSD.

PubMed

Sun, Delin; Haswell, Courtney C; Morey, Rajendra A; De Bellis, Michael D

2018-04-10

Child maltreatment is a major cause of pediatric posttraumatic stress disorder (PTSD). Previous studies have not investigated potential differences in network architecture in maltreated youth with PTSD and those resilient to PTSD. High-resolution magnetic resonance imaging brain scans at 3 T were completed in maltreated youth with PTSD (n = 31), without PTSD (n = 32), and nonmaltreated controls (n = 57). Structural covariance network architecture was derived from between-subject intraregional correlations in measures of cortical thickness in 148 cortical regions (nodes). Interregional positive partial correlations controlling for demographic variables were assessed, and those correlations that exceeded specified thresholds constituted connections in cortical brain networks. Four measures of network centrality characterized topology, and the importance of cortical regions (nodes) within the network architecture were calculated for each group. Permutation testing and principle component analysis method were employed to calculate between-group differences. Principle component analysis is a methodological improvement to methods used in previous brain structural covariance network studies. Differences in centrality were observed between groups. Larger centrality was found in maltreated youth with PTSD in the right posterior cingulate cortex; smaller centrality was detected in the right inferior frontal cortex compared to youth resilient to PTSD and controls, demonstrating network characteristics unique to pediatric maltreatment-related PTSD. Larger centrality was detected in right frontal pole in maltreated youth resilient to PTSD compared to youth with PTSD and controls, demonstrating structural covariance network differences in youth resilience to PTSD following maltreatment. Smaller centrality was found in the left posterior cingulate cortex and in the right inferior frontal cortex in maltreated youth compared to controls, demonstrating attributes of structural covariance network topology that is unique to experiencing maltreatment. This work is the first to identify cortical thickness-based structural covariance network differences between maltreated youth with and without PTSD. We demonstrated network differences in both networks unique to maltreated youth with PTSD and those resilient to PTSD. The networks identified are important for the successful attainment of age-appropriate social cognition, attention, emotional processing, and inhibitory control. Our findings in maltreated youth with PTSD versus those without PTSD suggest vulnerability mechanisms for developing PTSD.

Stochastic Geometric Network Models for Groups of Functional and Structural Connectomes

PubMed Central

Friedman, Eric J.; Landsberg, Adam S.; Owen, Julia P.; Li, Yi-Ou; Mukherjee, Pratik

2014-01-01

Structural and functional connectomes are emerging as important instruments in the study of normal brain function and in the development of new biomarkers for a variety of brain disorders. In contrast to single-network studies that presently dominate the (non-connectome) network literature, connectome analyses typically examine groups of empirical networks and then compare these against standard (stochastic) network models. Current practice in connectome studies is to employ stochastic network models derived from social science and engineering contexts as the basis for the comparison. However, these are not necessarily best suited for the analysis of connectomes, which often contain groups of very closely related networks, such as occurs with a set of controls or a set of patients with a specific disorder. This paper studies important extensions of standard stochastic models that make them better adapted for analysis of connectomes, and develops new statistical fitting methodologies that account for inter-subject variations. The extensions explicitly incorporate geometric information about a network based on distances and inter/intra hemispherical asymmetries (to supplement ordinary degree-distribution information), and utilize a stochastic choice of networks' density levels (for fixed threshold networks) to better capture the variance in average connectivity among subjects. The new statistical tools introduced here allow one to compare groups of networks by matching both their average characteristics and the variations among them. A notable finding is that connectomes have high “smallworldness” beyond that arising from geometric and degree considerations alone. PMID:25067815

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

Brain network alterations and vulnerability to simulated neurodegeneration in breast cancer.

PubMed

Kesler, Shelli R; Watson, Christa L; Blayney, Douglas W

2015-08-01

Breast cancer and its treatments are associated with mild cognitive impairment and brain changes that could indicate an altered or accelerated brain aging process. We applied diffusion tensor imaging and graph theory to measure white matter organization and connectivity in 34 breast cancer survivors compared with 36 matched healthy female controls. We also investigated how brain networks (connectomes) in each group responded to simulated neurodegeneration based on network attack analysis. Compared with controls, the breast cancer group demonstrated significantly lower fractional anisotropy, altered small-world connectome properties, lower brain network tolerance to systematic region (node), and connection (edge) attacks and significant cognitive impairment. Lower tolerance to network attack was associated with cognitive impairment in the breast cancer group. These findings provide further evidence of diffuse white matter pathology after breast cancer and extend the literature in this area with unique data demonstrating increased vulnerability of the post-breast cancer brain network to future neurodegenerative processes. Copyright © 2015 Elsevier Inc. All rights reserved.

Information and the Origin of Qualia

PubMed Central

Orpwood, Roger

2017-01-01

This article argues that qualia are a likely outcome of the processing of information in local cortical networks. It uses an information-based approach and makes a distinction between information structures (the physical embodiment of information in the brain, primarily patterns of action potentials), and information messages (the meaning of those structures to the brain, and the basis of qualia). It develops formal relationships between these two kinds of information, showing how information structures can represent messages, and how information messages can be identified from structures. The article applies this perspective to basic processing in cortical networks or ensembles, showing how networks can transform between the two kinds of information. The article argues that an input pattern of firing is identified by a network as an information message, and that the output pattern of firing generated is a representation of that message. If a network is encouraged to develop an attractor state through attention or other re-entrant processes, then the message identified each time physical information is cycled through the network becomes “representation of the previous message”. Using an example of olfactory perception, it is shown how this piggy-backing of messages on top of previous messages could lead to olfactory qualia. The message identified on each pass of information could evolve from inner identity, to inner form, to inner likeness or image. The outcome is an olfactory quale. It is shown that the same outcome could result from information cycled through a hierarchy of networks in a resonant state. The argument for qualia generation is applied to other sensory modalities, showing how, through a process of brain-wide constraint satisfaction, a particular state of consciousness could develop at any given moment. Evidence for some of the key predictions of the theory is presented, using ECoG data and studies of gamma oscillations and attractors, together with an outline of what further evidence is needed to provide support for the theory. PMID:28484376

Multichannel activity propagation across an engineered axon network

NASA Astrophysics Data System (ADS)

Chen, H. Isaac; Wolf, John A.; Smith, Douglas H.

2017-04-01

Objective. Although substantial progress has been made in mapping the connections of the brain, less is known about how this organization translates into brain function. In particular, the massive interconnectivity of the brain has made it difficult to specifically examine data transmission between two nodes of the connectome, a central component of the ‘neural code.’ Here, we investigated the propagation of multiple streams of asynchronous neuronal activity across an isolated in vitro ‘connectome unit.’ Approach. We used the novel technique of axon stretch growth to create a model of a long-range cortico-cortical network, a modular system consisting of paired nodes of cortical neurons connected by axon tracts. Using optical stimulation and multi-electrode array recording techniques, we explored how input patterns are represented by cortical networks, how these representations shift as they are transmitted between cortical nodes and perturbed by external conditions, and how well the downstream node distinguishes different patterns. Main results. Stimulus representations included direct, synaptic, and multiplexed responses that grew in complexity as the distance between the stimulation source and recorded neuron increased. These representations collapsed into patterns with lower information content at higher stimulation frequencies. With internodal activity propagation, a hierarchy of network pathways, including latent circuits, was revealed using glutamatergic blockade. As stimulus channels were added, divergent, non-linear effects were observed in local versus distant network layers. Pairwise difference analysis of neuronal responses suggested that neuronal ensembles generally outperformed individual cells in discriminating input patterns. Significance. Our data illuminate the complexity of spiking activity propagation in cortical networks in vitro, which is characterized by the transformation of an input into myriad outputs over several network layers. These results provide insight into how the brain potentially processes information and generates the neural code and could guide the development of clinical therapies based on multichannel brain stimulation.

Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity.

PubMed

Napadow, Vitaly; LaCount, Lauren; Park, Kyungmo; As-Sanie, Sawsan; Clauw, Daniel J; Harris, Richard E

2010-08-01

Fibromyalgia (FM) is considered to be the prototypical central chronic pain syndrome and is associated with widespread pain that fluctuates spontaneously. Multiple studies have demonstrated altered brain activity in these patients. The objective of this study was to investigate the degree of connectivity between multiple brain networks in patients with FM, as well as how activity in these networks correlates with the level of spontaneous pain. Resting-state functional magnetic resonance imaging (FMRI) data from 18 patients with FM and 18 age-matched healthy control subjects were analyzed using dual-regression independent components analysis, which is a data-driven approach for the identification of independent brain networks. Intrinsic, or resting-state, connectivity was evaluated in multiple brain networks: the default mode network (DMN), the executive attention network (EAN), and the medial visual network (MVN), with the MVN serving as a negative control. Spontaneous pain levels were also analyzed for covariance with intrinsic connectivity. Patients with FM had greater connectivity within the DMN and right EAN (corrected P [P(corr)] < 0.05 versus controls), and greater connectivity between the DMN and the insular cortex, which is a brain region known to process evoked pain. Furthermore, greater intensity of spontaneous pain at the time of the FMRI scan correlated with greater intrinsic connectivity between the insula and both the DMN and right EAN (P(corr) < 0.05). These findings indicate that resting brain activity within multiple networks is associated with spontaneous clinical pain in patients with FM. These findings may also have broader implications for how subjective experiences such as pain arise from a complex interplay among multiple brain networks.

Intrinsic Brain Connectivity in Fibromyalgia is Associated with Chronic Pain Intensity

PubMed Central

Napadow, Vitaly; LaCount, Lauren; Park, Kyungmo; As-Sanie, Suzie; Clauw, Daniel J; Harris, Richard E

2010-01-01

OBJECTIVE Fibromyalgia (FM) is considered to be the prototypical central chronic pain syndrome and is associated with widespread pain that fluctuates spontaneously. Multiple studies have demonstrated altered brain activity in these patients. Our objective was to investigate the degree of connectivity between multiple brain networks in FM, as well as how activity in these networks correlates with spontaneous pain. METHODS Resting functional magnetic resonance imaging (fMRI) data in FM patients (n=18) and age-matched healthy controls (HC, n=18) were analyzed using dual regression independent component analysis (ICA) - a data driven approach used to identify independent brain networks. We evaluated intrinsic, or resting, connectivity in multiple brain networks: the default mode network (DMN), the executive attention network (EAN), and the medial visual network (MVN), with the MVN serving as a negative control. Spontaneous pain levels were also covaried with intrinsic connectivity. RESULTS We found that FM patients had greater connectivity within the DMN and right EAN (rEAN; p<0.05, corrected), and greater connectivity between the DMN and the insular cortex – a brain region known to process evoked pain. Furthermore, greater spontaneous pain at the time of the scan correlated with greater intrinsic connectivity between the insula and both the DMN and rEAN (p<0.05, corrected). CONCLUSION Our findings indicate that resting brain activity within multiple networks is associated with spontaneous clinical pain in FM. These findings may also have broader implications for how subjective experiences such as pain arise from a complex interplay amongst multiple brain networks. PMID:20506181

Minimum spanning tree analysis of the human connectome.

PubMed

van Dellen, Edwin; Sommer, Iris E; Bohlken, Marc M; Tewarie, Prejaas; Draaisma, Laurijn; Zalesky, Andrew; Di Biase, Maria; Brown, Jesse A; Douw, Linda; Otte, Willem M; Mandl, René C W; Stam, Cornelis J

2018-06-01

One of the challenges of brain network analysis is to directly compare network organization between subjects, irrespective of the number or strength of connections. In this study, we used minimum spanning tree (MST; a unique, acyclic subnetwork with a fixed number of connections) analysis to characterize the human brain network to create an empirical reference network. Such a reference network could be used as a null model of connections that form the backbone structure of the human brain. We analyzed the MST in three diffusion-weighted imaging datasets of healthy adults. The MST of the group mean connectivity matrix was used as the empirical null-model. The MST of individual subjects matched this reference MST for a mean 58%-88% of connections, depending on the analysis pipeline. Hub nodes in the MST matched with previously reported locations of hub regions, including the so-called rich club nodes (a subset of high-degree, highly interconnected nodes). Although most brain network studies have focused primarily on cortical connections, cortical-subcortical connections were consistently present in the MST across subjects. Brain network efficiency was higher when these connections were included in the analysis, suggesting that these tracts may be utilized as the major neural communication routes. Finally, we confirmed that MST characteristics index the effects of brain aging. We conclude that the MST provides an elegant and straightforward approach to analyze structural brain networks, and to test network topological features of individual subjects in comparison to empirical null models. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Changes in Brain Structural Networks and Cognitive Functions in Testicular Cancer Patients Receiving Cisplatin-Based Chemotherapy.

PubMed

Amidi, Ali; Hosseini, S M Hadi; Leemans, Alexander; Kesler, Shelli R; Agerbæk, Mads; Wu, Lisa M; Zachariae, Robert

2017-12-01

Cisplatin-based chemotherapy may have neurotoxic effects within the central nervous system. The aims of this study were 1) to longitudinally investigate the impact of cisplatin-based chemotherapy on whole-brain networks in testicular cancer patients undergoing treatment and 2) to explore whether possible changes are related to decline in cognitive functioning. Sixty-four newly orchiectomized TC patients underwent structural magnetic resonance imaging (T1-weighted and diffusion-weighted imaging) and cognitive testing at baseline prior to further treatment and again at a six-month follow-up. At follow-up, 22 participants had received cisplatin-based chemotherapy (CT) while 42 were in active surveillance (S). Brain structural networks were constructed for each participant, and network properties were investigated using graph theory and longitudinally compared across groups. Cognitive functioning was evaluated using standardized neuropsychological tests. All statistical tests were two-sided. Compared with the S group, the CT group demonstrated altered global and local brain network properties from baseline to follow-up as evidenced by decreases in important brain network properties such as small-worldness (P = .04), network clustering (P = .04), and local efficiency (P = .02). In the CT group, poorer overall cognitive performance was associated with decreased small-worldness (r = -0.46, P = .04) and local efficiency (r = -0.51, P = .02), and verbal fluency was associated with decreased local efficiency (r = -0.55, P = .008). Brain structural networks may be disrupted following treatment with cisplatin-based chemotherapy. Impaired brain networks may underlie poorer performance over time on both specific and nonspecific cognitive functions in patients undergoing chemotherapy. To the best of our knowledge, this is the first study to longitudinally investigate changes in structural brain networks in a cancer population, providing novel insights regarding the neurobiological mechanisms of cancer-related cognitive impairment.

Anomalous single-subject based morphological cortical networks in drug-naive, first-episode major depressive disorder.

PubMed

Chen, Taolin; Kendrick, Keith M; Wang, Jinhui; Wu, Min; Li, Kaiming; Huang, Xiaoqi; Luo, Yuejia; Lui, Su; Sweeney, John A; Gong, Qiyong

2017-05-01

Major depressive disorder (MDD) has been associated with disruptions in the topological organization of brain morphological networks in group-level data. Such disruptions have not yet been identified in single-patients, which is needed to show relations with symptom severity and to evaluate their potential as biomarkers for illness. To address this issue, we conducted a cross-sectional structural brain network study of 33 treatment-naive, first-episode MDD patients and 33 age-, gender-, and education-matched healthy controls (HCs). Weighted graph-theory based network models were used to characterize the topological organization of brain networks between the two groups. Compared with HCs, MDD patients exhibited lower normalized global efficiency and higher modularity in their whole-brain morphological networks, suggesting impaired integration and increased segregation of morphological brain networks in the patients. Locally, MDD patients exhibited lower efficiency in anatomic organization for transferring information predominantly in default-mode regions including the hippocampus, parahippocampal gyrus, precuneus and superior parietal lobule, and higher efficiency in the insula, calcarine and posterior cingulate cortex, and in the cerebellum. Morphological connectivity comparisons revealed two subnetworks that exhibited higher connectivity strength in MDD mainly involving neocortex-striatum-thalamus-cerebellum and thalamo-hippocampal circuitry. MDD-related alterations correlated with symptom severity and differentiated individuals with MDD from HCs with a sensitivity of 87.9% and specificity of 81.8%. Our findings indicate that single subject grey matter morphological networks are often disrupted in clinically relevant ways in treatment-naive, first episode MDD patients. Circuit-specific changes in brain anatomic network organization suggest alterations in the efficiency of information transfer within particular brain networks in MDD. Hum Brain Mapp 38:2482-2494, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks

PubMed Central

Hramov, Alexander E.; Maksimenko, Vladimir A.; Pchelintseva, Svetlana V.; Runnova, Anastasiya E.; Grubov, Vadim V.; Musatov, Vyacheslav Yu.; Zhuravlev, Maksim O.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2017-01-01

In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces. PMID:29255403

Analysis of the characteristics of the synchronous clusters in the adaptive Kuramoto network and neural network of the epileptic brain

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Kharchenko, Alexander A.; Makarov, Vladimir V.; Khramova, Marina V.; Koronovskii, Alexey A.; Pavlov, Alexey N.; Dana, Syamal K.

2016-04-01

In the paper we study the mechanisms of phase synchronization in the adaptive model network of Kuramoto oscillators and the neural network of brain by consideration of the integral characteristics of the observed networks signals. As the integral characteristics of the model network we consider the summary signal produced by the oscillators. Similar to the model situation we study the ECoG signal as the integral characteristic of neural network of the brain. We show that the establishment of the phase synchronization results in the increase of the peak, corresponding to synchronized oscillators, on the wavelet energy spectrum of the integral signals. The observed correlation between the phase relations of the elements and the integral characteristics of the whole network open the way to detect the size of synchronous clusters in the neural networks of the epileptic brain before and during seizure.

Using Inspiration from Synaptic Plasticity Rules to Optimize Traffic Flow in Distributed Engineered Networks.

PubMed

Suen, Jonathan Y; Navlakha, Saket

2017-05-01

Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that depends only on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules, long-term potentiation and long-term depression, can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both by simulation and analytically, how different forms of edge-weight-update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.

Single-subject structural networks with closed-form rotation invariant matching mprove power in developmental studies of the cortex.

PubMed

Kandel, Benjamin M; Wang, Danny J J; Gee, James C; Avants, Brian B

2014-01-01

Although much attention has recently been focused on single-subject functional networks, using methods such as resting-state functional MRI, methods for constructing single-subject structural networks are in their infancy. Single-subject cortical networks aim to describe the self-similarity across the cortical structure, possibly signifying convergent developmental pathways. Previous methods for constructing single-subject cortical networks have used patch-based correlations and distance metrics based on curvature and thickness. We present here a method for constructing similarity-based cortical structural networks that utilizes a rotation-invariant representation of structure. The resulting graph metrics are closely linked to age and indicate an increasing degree of closeness throughout development in nearly all brain regions, perhaps corresponding to a more regular structure as the brain matures. The derived graph metrics demonstrate a four-fold increase in power for detecting age as compared to cortical thickness. This proof of concept study indicates that the proposed metric may be useful in identifying biologically relevant cortical patterns.

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

Test-Retest Reliability of fMRI Brain Activity during Memory Encoding

PubMed Central

Brandt, David J.; Sommer, Jens; Krach, Sören; Bedenbender, Johannes; Kircher, Tilo; Paulus, Frieder M.; Jansen, Andreas

2013-01-01

The mechanisms underlying hemispheric specialization of memory are not completely understood. Functional magnetic resonance imaging (fMRI) can be used to develop and test models of hemispheric specialization. In particular for memory tasks however, the interpretation of fMRI results is often hampered by the low reliability of the data. In the present study we therefore analyzed the test-retest reliability of fMRI brain activation related to an implicit memory encoding task, with a particular focus on brain activity of the medial temporal lobe (MTL). Fifteen healthy subjects were scanned with fMRI on two sessions (average retest interval 35 days) using a commonly applied novelty encoding paradigm contrasting known and unknown stimuli. To assess brain lateralization, we used three different stimuli classes that differed in their verbalizability (words, scenes, fractals). Test-retest reliability of fMRI brain activation was assessed by an intraclass-correlation coefficient (ICC), describing the stability of inter-individual differences in the brain activation magnitude over time. We found as expected a left-lateralized brain activation network for the words paradigm, a bilateral network for the scenes paradigm, and predominantly right-hemispheric brain activation for the fractals paradigm. Although these networks were consistently activated in both sessions on the group level, across-subject reliabilities were only poor to fair (ICCs ≤ 0.45). Overall, the highest ICC values were obtained for the scenes paradigm, but only in strongly activated brain regions. In particular the reliability of brain activity of the MTL was poor for all paradigms. In conclusion, for novelty encoding paradigms the interpretation of fMRI results on a single subject level is hampered by its low reliability. More studies are needed to optimize the retest reliability of fMRI activation for memory tasks. PMID:24367338

Structural network alterations and neurological dysfunction in cerebral amyloid angiopathy

PubMed Central

Reijmer, Yael D.; Fotiadis, Panagiotis; Martinez-Ramirez, Sergi; Salat, David H.; Schultz, Aaron; Shoamanesh, Ashkan; Ayres, Alison M.; Vashkevich, Anastasia; Rosas, Diana; Schwab, Kristin; Leemans, Alexander; Biessels, Geert-Jan; Rosand, Jonathan; Johnson, Keith A.; Viswanathan, Anand; Gurol, M. Edip

2015-01-01

Cerebral amyloid angiopathy is a common form of small-vessel disease and an important risk factor for cognitive impairment. The mechanisms linking small-vessel disease to cognitive impairment are not well understood. We hypothesized that in patients with cerebral amyloid angiopathy, multiple small spatially distributed lesions affect cognition through disruption of brain connectivity. We therefore compared the structural brain network in patients with cerebral amyloid angiopathy to healthy control subjects and examined the relationship between markers of cerebral amyloid angiopathy-related brain injury, network efficiency, and potential clinical consequences. Structural brain networks were reconstructed from diffusion-weighted magnetic resonance imaging in 38 non-demented patients with probable cerebral amyloid angiopathy (69 ± 10 years) and 29 similar aged control participants. The efficiency of the brain network was characterized using graph theory and brain amyloid deposition was quantified by Pittsburgh compound B retention on positron emission tomography imaging. Global efficiency of the brain network was reduced in patients compared to controls (0.187 ± 0.018 and 0.201 ± 0.015, respectively, P < 0.001). Network disturbances were most pronounced in the occipital, parietal, and posterior temporal lobes. Among patients, lower global network efficiency was related to higher cortical amyloid load (r = −0.52; P = 0.004), and to magnetic resonance imaging markers of small-vessel disease including increased white matter hyperintensity volume (P < 0.001), lower total brain volume (P = 0.02), and number of microbleeds (trend P = 0.06). Lower global network efficiency was also related to worse performance on tests of processing speed (r = 0.58, P < 0.001), executive functioning (r = 0.54, P = 0.001), gait velocity (r = 0.41, P = 0.02), but not memory. Correlations with cognition were independent of age, sex, education level, and other magnetic resonance imaging markers of small-vessel disease. These findings suggest that reduced structural brain network efficiency might mediate the relationship between advanced cerebral amyloid angiopathy and neurologic dysfunction and that such large-scale brain network measures may represent useful outcome markers for tracking disease progression. PMID:25367025

Brain connectivity dynamics during social interaction reflect social network structure

PubMed Central

Schmälzle, Ralf; Brook O’Donnell, Matthew; Garcia, Javier O.; Cascio, Christopher N.; Bayer, Joseph; Vettel, Jean M.

2017-01-01

Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants’ friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics. PMID:28465434

A Social-Interactive Neuroscience Approach to Understanding the Developing Brain.

PubMed

Redcay, Elizabeth; Warnell, Katherine Rice

2018-01-01

From birth onward, social interaction is central to our everyday lives. Our ability to seek out social partners, flexibly navigate and learn from social interactions, and develop social relationships is critically important for our social and cognitive development and for our mental and physical health. Despite the importance of our social interactions, the neurodevelopmental bases of such interactions are underexplored, as most research examines social processing in noninteractive contexts. We begin this chapter with evidence from behavioral work and adult neuroimaging studies demonstrating how social-interactive context fundamentally alters cognitive and neural processing. We then highlight four brain networks that play key roles in social interaction and, drawing on existing developmental neuroscience literature, posit the functional roles these networks may play in social-interactive development. We conclude by discussing how a social-interactive neuroscience approach holds great promise for advancing our understanding of both typical and atypical social development. © 2018 Elsevier Inc. All rights reserved.

Nanotomography of brain networks

NASA Astrophysics Data System (ADS)

Saiga, Rino; Mizutani, Ryuta; Takekoshi, Susumu; Osawa, Motoki; Arai, Makoto; Takeuchi, Akihisa; Uesugi, Kentaro; Terada, Yasuko; Suzuki, Yoshio; de Andrade, Vincent; de Carlo, Francesco

The first step to understanding how the brain functions is to analyze its 3D network. The brain network consists of neurons having micrometer to nanometer sized structures. Therefore, 3D analysis of brain tissue at the relevant resolution is essential for elucidating brain's functional mechanisms. Here, we report 3D structures of human and fly brain networks revealed with synchrotron radiation nanotomography, or nano-CT. Neurons were stained with high-Z elements to visualize their structures with X-rays. Nano-CT experiments were then performed at the 32-ID beamline of the Advanced Photon Source, Argonne National Laboratory and at the BL37XU and BL47XU beamlines of SPring-8. Reconstructed 3D images illustrated precise structures of human neurons, including dendritic spines responsible for synaptic connections. The network of the fly brain hemisphere was traced to build a skeletonized wire model. An article reviewing our study appeared in MIT Technology Review. Movies of the obtained structures can be found in our YouTube channel.

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.

The Virtual Brain: a simulator of primate brain network dynamics.

PubMed

Sanz Leon, Paula; Knock, Stuart A; Woodman, M Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R; Jirsa, Viktor

2013-01-01

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

The Virtual Brain: a simulator of primate brain network dynamics

PubMed Central

Sanz Leon, Paula; Knock, Stuart A.; Woodman, M. Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R.; Jirsa, Viktor

2013-01-01

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications. PMID:23781198

Sigmund Freud-early network theories of the brain.

PubMed

Surbeck, Werner; Killeen, Tim; Vetter, Johannes; Hildebrandt, Gerhard

2018-06-01

Since the early days of modern neuroscience, psychological models of brain function have been a key component in the development of new knowledge. These models aim to provide a framework that allows the integration of discoveries derived from the fundamental disciplines of neuroscience, including anatomy and physiology, as well as clinical neurology and psychiatry. During the initial stages of his career, Sigmund Freud (1856-1939), became actively involved in these nascent fields with a burgeoning interest in functional neuroanatomy. In contrast to his contemporaries, Freud was convinced that cognition could not be localised to separate modules and that the brain processes cognition not in a merely serial manner but in a parallel and dynamic fashion-anticipating fundamental aspects of current network theories of brain function. This article aims to shed light on Freud's seminal, yet oft-overlooked, early work on functional neuroanatomy and his reasons for finally abandoning the conventional neuroscientific "brain-based" reference frame in order to conceptualise the mind from a purely psychological perspective.

Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network.

PubMed

McConnell, Michael J; Moran, John V; Abyzov, Alexej; Akbarian, Schahram; Bae, Taejeong; Cortes-Ciriano, Isidro; Erwin, Jennifer A; Fasching, Liana; Flasch, Diane A; Freed, Donald; Ganz, Javier; Jaffe, Andrew E; Kwan, Kenneth Y; Kwon, Minseok; Lodato, Michael A; Mills, Ryan E; Paquola, Apua C M; Rodin, Rachel E; Rosenbluh, Chaggai; Sestan, Nenad; Sherman, Maxwell A; Shin, Joo Heon; Song, Saera; Straub, Richard E; Thorpe, Jeremy; Weinberger, Daniel R; Urban, Alexander E; Zhou, Bo; Gage, Fred H; Lehner, Thomas; Senthil, Geetha; Walsh, Christopher A; Chess, Andrew; Courchesne, Eric; Gleeson, Joseph G; Kidd, Jeffrey M; Park, Peter J; Pevsner, Jonathan; Vaccarino, Flora M

2017-04-28

Neuropsychiatric disorders have a complex genetic architecture. Human genetic population-based studies have identified numerous heritable sequence and structural genomic variants associated with susceptibility to neuropsychiatric disease. However, these germline variants do not fully account for disease risk. During brain development, progenitor cells undergo billions of cell divisions to generate the ~80 billion neurons in the brain. The failure to accurately repair DNA damage arising during replication, transcription, and cellular metabolism amid this dramatic cellular expansion can lead to somatic mutations. Somatic mutations that alter subsets of neuronal transcriptomes and proteomes can, in turn, affect cell proliferation and survival and lead to neurodevelopmental disorders. The long life span of individual neurons and the direct relationship between neural circuits and behavior suggest that somatic mutations in small populations of neurons can significantly affect individual neurodevelopment. The Brain Somatic Mosaicism Network has been founded to study somatic mosaicism both in neurotypical human brains and in the context of complex neuropsychiatric disorders. Copyright © 2017, American Association for the Advancement of Science.

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

PubMed Central

Dura-Bernal, Salvador; Li, Kan; Neymotin, Samuel A.; Francis, Joseph T.; Principe, Jose C.; Lytton, William W.

2016-01-01

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors. PMID:26903796

Early-life exposure to caffeine affects the construction and activity of cortical networks in mice.

PubMed

Fazeli, Walid; Zappettini, Stefania; Marguet, Stephan Lawrence; Grendel, Jasper; Esclapez, Monique; Bernard, Christophe; Isbrandt, Dirk

2017-09-01

The consumption of psychoactive drugs during pregnancy can have deleterious effects on newborns. It remains unclear whether early-life exposure to caffeine, the most widely consumed psychoactive substance, alters brain development. We hypothesized that maternal caffeine ingestion during pregnancy and the early postnatal period in mice affects the construction and activity of cortical networks in offspring. To test this hypothesis, we focused on primary visual cortex (V1) as a model neocortical region. In a study design mimicking the daily consumption of approximately three cups of coffee during pregnancy in humans, caffeine was added to the drinking water of female mice and their offspring were compared to control offspring. Caffeine altered the construction of GABAergic neuronal networks in V1, as reflected by a reduced number of somatostatin-containing GABA neurons at postnatal days 6-7, with the remaining ones showing poorly developed dendritic arbors. These findings were accompanied by increased synaptic activity in vitro and elevated network activity in vivo in V1. Similarly, in vivo hippocampal network activity was altered from the neonatal period until adulthood. Finally, caffeine-exposed offspring showed increased seizure susceptibility in a hyperthermia-induced seizure model. In summary, our results indicate detrimental effects of developmental caffeine exposure on mouse brain development. Copyright © 2017 Elsevier Inc. All rights reserved.

Chips of Hope: Neuro-Electronic Hybrids for Brain Repair

NASA Astrophysics Data System (ADS)

Ben-Jacob, Eshel

2010-03-01

The field of Neuro-Electronic Hybrids kicked off 30 years ago when researchers in the US first tweaked the technology of recording and stimulation of networks of live neurons grown in a Petri dish and interfaced with a computer via an array of electrodes. Since then, many researchers have searched for ways to imprint in neural networks new ``memories" without erasing old ones. I will describe our new generation of Neuro-Electronic Hybrids and how we succeeded to turn them into the first learning Neurochips - memory and information processing chips made of live neurons. To imprint multiple memories in our new chip we used chemical stimulation at specific locations that were selected by analyzing the networks activity in real time according to our new information encoding principle. Currently we develop new-generation of neuro chips using special carbon nano tubes (CNT). These electrodes enable to engineer the networks topology and efficient electrical interfacing with the neurons. This advance bears the promise to pave the way for building a new experimental platform for testing new drugs and developing new methods for neural networks repair and regeneration. Looking into the future, the development brings us a step closer towards the dream of Brain Repair by implementable Neuro-Electronic hybrid chips.

Synthetic event-related potentials: a computational bridge between neurolinguistic models and experiments.

PubMed

Barrès, Victor; Simons, Arthur; Arbib, Michael

2013-01-01

Our previous work developed Synthetic Brain Imaging to link neural and schema network models of cognition and behavior to PET and fMRI studies of brain function. We here extend this approach to Synthetic Event-Related Potentials (Synthetic ERP). Although the method is of general applicability, we focus on ERP correlates of language processing in the human brain. The method has two components: Phase 1: To generate cortical electro-magnetic source activity from neural or schema network models; and Phase 2: To generate known neurolinguistic ERP data (ERP scalp voltage topographies and waveforms) from putative cortical source distributions and activities within a realistic anatomical model of the human brain and head. To illustrate the challenges of Phase 2 of the methodology, spatiotemporal information from Friederici's 2002 model of auditory language comprehension was used to define cortical regions and time courses of activation for implementation within a forward model of ERP data. The cortical regions from the 2002 model were modeled using atlas-based masks overlaid on the MNI high definition single subject cortical mesh. The electromagnetic contribution of each region was modeled using current dipoles whose position and orientation were constrained by the cortical geometry. In linking neural network computation via EEG forward modeling to empirical results in neurolinguistics, we emphasize the need for neural network models to link their architecture to geometrically sound models of the cortical surface, and the need for conceptual models to refine and adopt brain-atlas based approaches to allow precise brain anchoring of their modules. The detailed analysis of Phase 2 sets the stage for a brief introduction to Phase 1 of the program, including the case for a schema-theoretic approach to language production and perception presented in detail elsewhere. Unlike Dynamic Causal Modeling (DCM) and Bojak's mean field model, Synthetic ERP builds on models of networks that mediate the relation between the brain's inputs, outputs, and internal states in executing a specific task. The neural networks used for Synthetic ERP must include neuroanatomically realistic placement and orientation of the cortical pyramidal neurons. These constraints pose exciting challenges for future work in neural network modeling that is applicable to systems and cognitive neuroscience. Copyright © 2012 Elsevier Ltd. All rights reserved.

Functional brain networks associated with eating behaviors in obesity.

PubMed

Park, Bo-Yong; Seo, Jongbum; Park, Hyunjin

2016-03-31

Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from an equal number of people of healthy weight (HW) and non-healthy weight (non-HW). Connectivity matrices were computed with spatial maps derived using a group independent component analysis approach. Brain networks and associated connectivity parameters with significant group-wise differences were identified and correlated with scores on a three-factor eating questionnaire (TFEQ) describing restraint, disinhibition, and hunger eating behaviors. Frontoparietal and cerebellum networks showed group-wise differences between HW and non-HW groups. Frontoparietal network showed a high correlation with TFEQ disinhibition scores. Both frontoparietal and cerebellum networks showed a high correlation with body mass index (BMI) scores. Brain networks with significant group-wise differences between HW and non-HW groups were identified. Parts of the identified networks showed a high correlation with eating behavior scores.

Small-worldness characteristics and its gender relation in specific hemispheric networks.

PubMed

Miraglia, F; Vecchio, F; Bramanti, P; Rossini, P M

2015-12-03

Aim of this study was to verify whether the topological organization of human brain functional networks is different for males and females in resting state EEGs. Undirected and weighted brain networks were computed by eLORETA lagged linear connectivity in 130 subjects (59 males and 71 females) within each hemisphere and in four resting state networks (Attentional Network (AN), Frontal Network (FN), Sensorimotor Network (SN), Default Mode Network (DMN)). We found that small-world (SW) architecture in the left hemisphere Frontal network presented differences in both delta and alpha band, in particular lower values in delta and higher in alpha 2 in males respect to females while in the right hemisphere differences were found in lower values of SW in males respect to females in gamma Attentional, delta Sensorimotor and delta and gamma DMNs. Gender small-worldness differences in some of resting state networks indicated that there are specific brain differences in the EEG rhythms when the brain is in the resting-state condition. These specific regions could be considered related to the functions of behavior and cognition and should be taken into account both for research on healthy and brain diseased subjects. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

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.