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Sample records for brain functional networks

  1. Aging and functional brain networks

    SciTech Connect

    Tomasi D.; Tomasi, D.; Volkow, N.D.

    2011-07-11

    Aging is associated with changes in human brain anatomy and function and cognitive decline. Recent studies suggest the aging decline of major functional connectivity hubs in the 'default-mode' network (DMN). Aging effects on other networks, however, are largely unknown. We hypothesized that aging would be associated with a decline of short- and long-range functional connectivity density (FCD) hubs in the DMN. To test this hypothesis, we evaluated resting-state data sets corresponding to 913 healthy subjects from a public magnetic resonance imaging database using functional connectivity density mapping (FCDM), a voxelwise and data-driven approach, together with parallel computing. Aging was associated with pronounced long-range FCD decreases in DMN and dorsal attention network (DAN) and with increases in somatosensory and subcortical networks. Aging effects in these networks were stronger for long-range than for short-range FCD and were also detected at the level of the main functional hubs. Females had higher short- and long-range FCD in DMN and lower FCD in the somatosensory network than males, but the gender by age interaction effects were not significant for any of the networks or hubs. These findings suggest that long-range connections may be more vulnerable to aging effects than short-range connections and that, in addition to the DMN, the DAN is also sensitive to aging effects, which could underlie the deterioration of attention processes that occurs with aging.

  2. 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

  3. Individual diversity of functional brain network economy

    PubMed Central

    Hahn, Andreas; Kranz, Georg S.; Sladky, Ronald; Ganger, Sebastian; Windischberger, Christian; Kasper, Siegfried; Lanzenberger, Rupert

    2015-01-01

    On average, brain network economy represents a trade-off between communication efficiency, robustness and connection cost, though, an analogous understanding on an individual level is largely missing. Evaluating resting-state networks of 42 healthy participants with 7 Tesla functional MRI 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 3D 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 3 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. PMID:25411715

  4. 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. PMID:25411715

  5. Homological scaffolds of brain functional networks.

    PubMed

    Petri, G; Expert, P; Turkheimer, F; Carhart-Harris, R; Nutt, D; Hellyer, P J; Vaccarino, F

    2014-12-01

    Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci. 10, 186-198. (doi:10.1038/nrn2618)). Traditionally, the structure of complex networks has been studied through their statistical properties and metrics concerned with node and link properties, e.g. degree-distribution, node centrality and modularity. Here, we study the characteristics of functional brain networks at the mesoscopic level from a novel perspective that highlights the role of inhomogeneities in the fabric of functional connections. This can be done by focusing on the features of a set of topological objects-homological cycles-associated with the weighted functional network. We leverage the detected topological information to define the homological scaffolds, a new set of objects designed to represent compactly the homological features of the correlation network and simultaneously make their homological properties amenable to networks theoretical methods. As a proof of principle,we apply these tools to compare resting state functional brain activity in 15 healthy volunteers after intravenous infusion of placebo and psilocybin-the main psychoactive component of magic mushrooms. The results show that the homological structure of the brain's functional patterns undergoes a dramatic change post-psilocybin, characterized by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo. PMID:25401177

  6. Homological scaffolds of brain functional networks

    PubMed Central

    Petri, G.; Expert, P.; Turkheimer, F.; Carhart-Harris, R.; Nutt, D.; Hellyer, P. J.; Vaccarino, F.

    2014-01-01

    Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci. 10, 186–198. (doi:10.1038/nrn2618)). Traditionally, the structure of complex networks has been studied through their statistical properties and metrics concerned with node and link properties, e.g. degree-distribution, node centrality and modularity. Here, we study the characteristics of functional brain networks at the mesoscopic level from a novel perspective that highlights the role of inhomogeneities in the fabric of functional connections. This can be done by focusing on the features of a set of topological objects—homological cycles—associated with the weighted functional network. We leverage the detected topological information to define the homological scaffolds, a new set of objects designed to represent compactly the homological features of the correlation network and simultaneously make their homological properties amenable to networks theoretical methods. As a proof of principle, we apply these tools to compare resting-state functional brain activity in 15 healthy volunteers after intravenous infusion of placebo and psilocybin—the main psychoactive component of magic mushrooms. The results show that the homological structure of the brain's functional patterns undergoes a dramatic change post-psilocybin, characterized by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo. PMID:25401177

  7. 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

  8. Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain*†

    PubMed Central

    Simpson, Sean L.; Bowman, F. DuBois; Laurienti, Paul J.

    2014-01-01

    Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function. PMID:25309643

  9. Resiliency of EEG-Based Brain Functional Networks

    PubMed Central

    Jalili, Mahdi

    2015-01-01

    Applying tools available in network science and graph theory to study brain networks has opened a new era in understanding brain mechanisms. Brain functional networks extracted from EEG time series have been frequently studied in health and diseases. In this manuscript, we studied failure resiliency of EEG-based brain functional networks. The network structures were extracted by analysing EEG time series obtained from 30 healthy subjects in resting state eyes-closed conditions. As the network structure was extracted, we measured a number of metrics related to their resiliency. In general, the brain networks showed worse resilient behaviour as compared to corresponding random networks with the same degree sequences. Brain networks had higher vulnerability than the random ones (P < 0.05), indicating that their global efficiency (i.e., communicability between the regions) is more affected by removing the important nodes. Furthermore, the breakdown happened as a result of cascaded failures in brain networks was severer (i.e., less nodes survived) as compared to randomized versions (P < 0.05). These results suggest that real EEG-based networks have not been evolved to possess optimal resiliency against failures. PMID:26295341

  10. 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

  11. Mapping distributed brain function and networks with diffuse optical tomography

    PubMed Central

    Eggebrecht, Adam T.; Ferradal, Silvina L.; Robichaux-Viehoever, Amy; Hassanpour, Mahlega S.; Dehghani, Hamid; Snyder, Abraham Z.; Hershey, Tamara; Culver, Joseph P.

    2014-01-01

    Mapping of human brain function has revolutionized systems neuroscience. However, traditional functional neuroimaging by positron emission tomography or functional magnetic resonance imaging cannot be used when applications require portability, or are contraindicated because of ionizing radiation (positron emission tomography) or implanted metal (functional magnetic resonance imaging). Optical neuroimaging offers a non-invasive alternative that is radiation free and compatible with implanted metal and electronic devices (for example, pacemakers). However, optical imaging technology has heretofore lacked the combination of spatial resolution and wide field of view sufficient to map distributed brain functions. Here, we present a high-density diffuse optical tomography imaging array that can map higher-order, distributed brain function. The system was tested by imaging four hierarchical language tasks and multiple resting-state networks including the dorsal attention and default mode networks. Finally, we imaged brain function in patients with Parkinson’s disease and implanted deep brain stimulators that preclude functional magnetic resonance imaging. PMID:25083161

  12. Mapping distributed brain function and networks with diffuse optical tomography

    NASA Astrophysics Data System (ADS)

    Eggebrecht, Adam T.; Ferradal, Silvina L.; Robichaux-Viehoever, Amy; Hassanpour, Mahlega S.; Dehghani, Hamid; Snyder, Abraham Z.; Hershey, Tamara; Culver, Joseph P.

    2014-06-01

    Mapping of human brain function has revolutionized systems neuroscience. However, traditional functional neuroimaging by positron emission tomography or functional magnetic resonance imaging cannot be used when applications require portability, or are contraindicated because of ionizing radiation (positron emission tomography) or implanted metal (functional magnetic resonance imaging). Optical neuroimaging offers a non-invasive alternative that is radiation free and compatible with implanted metal and electronic devices (for example, pacemakers). However, optical imaging technology has heretofore lacked the combination of spatial resolution and wide field of view sufficient to map distributed brain functions. Here, we present a high-density diffuse optical tomography imaging array that can map higher-order, distributed brain function. The system was tested by imaging four hierarchical language tasks and multiple resting-state networks including the dorsal attention and default mode networks. Finally, we imaged brain function in patients with Parkinson's disease and implanted deep brain stimulators that preclude functional magnetic resonance imaging.

  13. Hyper-connectivity of functional networks for brain disease diagnosis.

    PubMed

    Jie, Biao; Wee, Chong-Yaw; Shen, Dinggang; Zhang, Daoqiang

    2016-08-01

    Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help

  14. 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. PMID:19621066

  15. 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. PMID:26405867

  16. Complex Networks - A Key to Understanding Brain Function

    ScienceCinema

    Olaf Sporns

    2010-01-08

    The brain is a complex network of neurons, engaging in spontaneous and evoked activity that is thought to be the main substrate of mental life.  How this complex system works together to process information and generate coherent cognitive states, even consciousness, is not yet well understood.  In my talk I will review recent studies that have revealed characteristic structural and functional attributes of brain networks, and discuss efforts to build computational models of the brain that are informed by our growing knowledge of brain anatomy and physiology.

  17. Complex Networks - A Key to Understanding Brain Function

    SciTech Connect

    Sporns, Olaf

    2008-01-23

    The brain is a complex network of neurons, engaging in spontaneous and evoked activity that is thought to be the main substrate of mental life. How this complex system works together to process information and generate coherent cognitive states, even consciousness, is not yet well understood. In my talk I will review recent studies that have revealed characteristic structural and functional attributes of brain networks, and discuss efforts to build computational models of the brain that are informed by our growing knowledge of brain anatomy and physiology.

  18. Complex Networks - A Key to Understanding Brain Function

    SciTech Connect

    Olaf Sporns

    2008-01-23

    The brain is a complex network of neurons, engaging in spontaneous and evoked activity that is thought to be the main substrate of mental life.  How this complex system works together to process information and generate coherent cognitive states, even consciousness, is not yet well understood.  In my talk I will review recent studies that have revealed characteristic structural and functional attributes of brain networks, and discuss efforts to build computational models of the brain that are informed by our growing knowledge of brain anatomy and physiology.

  19. Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

    PubMed Central

    Supekar, Kaustubh; Menon, Vinod; Rubin, Daniel; Musen, Mark; Greicius, Michael D.

    2008-01-01

    Functional brain networks detected in task-free (“resting-state”) functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging. PMID:18584043

  20. Assortative mixing in functional brain networks during epileptic seizures

    NASA Astrophysics Data System (ADS)

    Bialonski, Stephan; Lehnertz, Klaus

    2013-09-01

    We investigate assortativity of functional brain networks before, during, and after one-hundred epileptic seizures with different anatomical onset locations. We construct binary functional networks from multi-channel electroencephalographic data recorded from 60 epilepsy patients; and from time-resolved estimates of the assortativity coefficient, we conclude that positive degree-degree correlations are inherent to seizure dynamics. While seizures evolve, an increasing assortativity indicates a segregation of the underlying functional network into groups of brain regions that are only sparsely interconnected, if at all. Interestingly, assortativity decreases already prior to seizure end. Together with previous observations of characteristic temporal evolutions of global statistical properties and synchronizability of epileptic brain networks, our findings may help to gain deeper insights into the complicated dynamics underlying generation, propagation, and termination of seizures.

  1. Human brain networks function in connectome-specific harmonic waves

    PubMed Central

    Atasoy, Selen; Donnelly, Isaac; Pearson, Joel

    2016-01-01

    A key characteristic of human brain activity is coherent, spatially distributed oscillations forming behaviour-dependent brain networks. However, a fundamental principle underlying these networks remains unknown. Here we report that functional networks of the human brain are predicted by harmonic patterns, ubiquitous throughout nature, steered by the anatomy of the human cerebral cortex, the human connectome. We introduce a new technique extending the Fourier basis to the human connectome. In this new frequency-specific representation of cortical activity, that we call ‘connectome harmonics', oscillatory networks of the human brain at rest match harmonic wave patterns of certain frequencies. We demonstrate a neural mechanism behind the self-organization of connectome harmonics with a continuous neural field model of excitatory–inhibitory interactions on the connectome. Remarkably, the critical relation between the neural field patterns and the delicate excitation–inhibition balance fits the neurophysiological changes observed during the loss and recovery of consciousness. PMID:26792267

  2. Functional brain networks associated with eating behaviors in obesity.

    PubMed

    Park, Bo-Yong; Seo, Jongbum; Park, Hyunjin

    2016-01-01

    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. PMID:27030024

  3. Functional brain networks associated with eating behaviors in obesity

    PubMed Central

    Park, Bo-yong; Seo, Jongbum; Park, Hyunjin

    2016-01-01

    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. PMID:27030024

  4. The brain's default network: anatomy, function, and relevance to disease.

    PubMed

    Buckner, Randy L; Andrews-Hanna, Jessica R; Schacter, Daniel L

    2008-03-01

    Thirty years of brain imaging research has converged to define the brain's default network-a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment. Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system. Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others. Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems. The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation. The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations. These two subsystems converge on important nodes of integration including the posterior cingulate cortex. The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world. We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer's disease. PMID:18400922

  5. 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

  6. Mapping Multiplex Hubs in Human Functional Brain Networks.

    PubMed

    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

  7. Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents.

    PubMed

    Li, Wenjing; Li, Jianhong; Wang, Jieqiong; Zhou, Peng; Wang, Zhenchang; Xian, Junfang; He, Huiguang

    2016-01-01

    Previous neuroimaging studies suggested structural or functional brain reorganizations occurred in prelingually deaf subjects. However, little is known about the reorganizations of brain network architectures in prelingually deaf adolescents. The present study aims to investigate alterations of whole-brain functional network using resting-state fMRI and graph theory analysis. We recruited 16 prelingually deaf adolescents (10~18 years) and 16 normal controls matched in age and gender. Brain networks were constructed from mean time courses of 90 regions. Widely distributed network was observed in deaf subjects, with increased connectivity between the limbic system and regions involved in visual and language processing, suggesting reinforcement of the processing for the visual and verbal information in deaf adolescents. Decreased connectivity was detected between the visual regions and language regions possibly due to inferior reading or speaking skills in deaf subjects. Using graph theory analysis, we demonstrated small-worldness property did not change in prelingually deaf adolescents relative to normal controls. However, compared with healthy adolescents, eight regions involved in visual, language, and auditory processing were identified as hubs only present in prelingually deaf adolescents. These findings revealed reorganization of brain functional networks occurred in prelingually deaf adolescents to adapt to deficient auditory input. PMID:26819781

  8. Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents

    PubMed Central

    Li, Wenjing; Li, Jianhong; Wang, Jieqiong; Zhou, Peng; Wang, Zhenchang; Xian, Junfang; He, Huiguang

    2016-01-01

    Previous neuroimaging studies suggested structural or functional brain reorganizations occurred in prelingually deaf subjects. However, little is known about the reorganizations of brain network architectures in prelingually deaf adolescents. The present study aims to investigate alterations of whole-brain functional network using resting-state fMRI and graph theory analysis. We recruited 16 prelingually deaf adolescents (10~18 years) and 16 normal controls matched in age and gender. Brain networks were constructed from mean time courses of 90 regions. Widely distributed network was observed in deaf subjects, with increased connectivity between the limbic system and regions involved in visual and language processing, suggesting reinforcement of the processing for the visual and verbal information in deaf adolescents. Decreased connectivity was detected between the visual regions and language regions possibly due to inferior reading or speaking skills in deaf subjects. Using graph theory analysis, we demonstrated small-worldness property did not change in prelingually deaf adolescents relative to normal controls. However, compared with healthy adolescents, eight regions involved in visual, language, and auditory processing were identified as hubs only present in prelingually deaf adolescents. These findings revealed reorganization of brain functional networks occurred in prelingually deaf adolescents to adapt to deficient auditory input. PMID:26819781

  9. Functional Connectivity Hubs and Networks in the Awake Marmoset Brain

    PubMed Central

    Belcher, Annabelle M.; Yen, Cecil Chern-Chyi; Notardonato, Lucia; Ross, Thomas J.; Volkow, Nora D.; Yang, Yihong; Stein, Elliot A.; Silva, Afonso C.; Tomasi, Dardo

    2016-01-01

    In combination with advances in analytical methods, resting-state fMRI is allowing unprecedented access to a better understanding of the network organization of the brain. Increasing evidence suggests that this architecture may incorporate highly functionally connected nodes, or “hubs”, and we have recently proposed local functional connectivity density (lFCD) mapping to identify highly-connected nodes in the human brain. Here, we imaged awake nonhuman primates to test whether, like the human brain, the marmoset brain contains FC hubs. Ten adult common marmosets (Callithrix jacchus) were acclimated to mild, comfortable restraint using individualized helmets. Following restraint training, resting BOLD data were acquired during eight consecutive 10 min scans for each subject. lFCD revealed prominent cortical and subcortical hubs of connectivity across the marmoset brain; specifically, in primary and secondary visual cortices (V1/V2), higher-order visual association areas (A19M/V6[DM]), posterior parietal and posterior cingulate areas (PGM and A23b/A31), thalamus, dorsal and ventral striatal areas (caudate, putamen, lateral septal nucleus, and anterior cingulate cortex (A24a). lFCD hubs were highly connected to widespread areas of the brain, and further revealed significant network-network interactions. These data provide a baseline platform for future investigations in a nonhuman primate model of the brain’s network topology. PMID:26973476

  10. 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

    2015-12-01

    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. PMID:26536111

  11. Dynamic reorganization of functional brain networks during picture naming.

    PubMed

    Hassan, Mahmoud; Benquet, Pascal; Biraben, Arnaud; Berrou, Claude; Dufor, Olivier; Wendling, Fabrice

    2015-12-01

    For efficient information processing during cognitive activity, functional brain networks have to rapidly and dynamically reorganize on a sub-second time scale. Tracking the spatiotemporal dynamics of large scale networks over this short time duration is a very challenging issue. Here, we tackle this problem by using dense electroencephalography (EEG) recorded during a picture naming task. We found that (i) the picture naming task can be divided into six brain network states (BNSs) characterized by significantly high synchronization of gamma (30-45 Hz) oscillations, (ii) fast transitions occur between these BNSs that last from 30 msec to 160 msec, (iii) based on the state of the art of the picture naming task, we consider that the spatial location of their nodes and edges, as well as the timing of transitions, indicate that each network can be associated with one or several specific function (from visual processing to articulation) and (iv) the comparison with previously-used approach aimed at localizing the sources showed that the network-based approach reveals networks that are more specific to the performed task. We speculate that the persistence of several brain regions in successive BNSs participates to fast and efficient information processing in the brain. PMID:26478964

  12. Large-scale functional connectivity networks in the rodent brain.

    PubMed

    Gozzi, Alessandro; Schwarz, Adam J

    2016-02-15

    Resting-state functional Magnetic Resonance Imaging (rsfMRI) of the human brain has revealed multiple large-scale neural networks within a hierarchical and complex structure of coordinated functional activity. These distributed neuroanatomical systems provide a sensitive window on brain function and its disruption in a variety of neuropathological conditions. The study of macroscale intrinsic connectivity networks in preclinical species, where genetic and environmental conditions can be controlled and manipulated with high specificity, offers the opportunity to elucidate the biological determinants of these alterations. While rsfMRI methods are now widely used in human connectivity research, these approaches have only relatively recently been back-translated into laboratory animals. Here we review recent progress in the study of functional connectivity in rodent species, emphasising the ability of this approach to resolve large-scale brain networks that recapitulate neuroanatomical features of known functional systems in the human brain. These include, but are not limited to, a distributed set of regions identified in rats and mice that may represent a putative evolutionary precursor of the human default mode network (DMN). The impact and control of potential experimental and methodological confounds are also critically discussed. Finally, we highlight the enormous potential and some initial application of connectivity mapping in transgenic models as a tool to investigate the neuropathological underpinnings of the large-scale connectional alterations associated with human neuropsychiatric and neurological conditions. We conclude by discussing the translational potential of these methods in basic and applied neuroscience. PMID:26706448

  13. Changes in brain functional network connectivity after stroke

    PubMed Central

    Li, Wei; Li, Yapeng; Zhu, Wenzhen; Chen, Xi

    2014-01-01

    Studies have shown that functional network connection models can be used to study brain network changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlated to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea-lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke. PMID:25206743

  14. Functional brain networks underlying detection and integration of disconfirmatory evidence.

    PubMed

    Lavigne, Katie M; Metzak, Paul D; Woodward, Todd S

    2015-05-15

    Processing evidence that disconfirms a prior interpretation is a fundamental aspect of belief revision, and has clear social and clinical relevance. This complex cognitive process requires (at minimum) an alerting stage and an integration stage, and in the current functional magnetic resonance imaging (fMRI) study, we used multivariate analysis methodology on two datasets in an attempt to separate these sequentially-activated cognitive stages and link them to distinct functional brain networks. Thirty-nine healthy participants completed one of two versions of an evidence integration experiment involving rating two consecutive animal images, both of which consisted of two intact images of animal faces morphed together at different ratios (e.g., 70/30 bird/dolphin followed by 10/90 bird/dolphin). The two versions of the experiment differed primarily in terms of stimulus presentation and timing, which facilitated functional interpretation of brain networks based on differences in the hemodynamic response shapes between versions. The data were analyzed using constrained principal component analysis for fMRI (fMRI-CPCA), which allows distinct, simultaneously active task-based networks to be separated, and these were interpreted using both temporal (task-based hemodynamic response shapes) and spatial (dominant brain regions) information. Three networks showed increased activity during integration of disconfirmatory relative to confirmatory evidence: (1) a network involved in alerting to the requirement to revise an interpretation, identified as the salience network (dorsal anterior cingulate cortex and bilateral insula); (2) a sensorimotor response-related network (pre- and post-central gyri, supplementary motor area, and thalamus); and (3) an integration network involving rostral prefrontal, orbitofrontal and posterior parietal cortex. These three networks were staggered in their peak activity (alerting, responding, then integrating), but at certain time points (e

  15. A Mapping Between Structural and Functional Brain Networks.

    PubMed

    Meier, Jil; Tewarie, Prejaas; Hillebrand, Arjan; Douw, Linda; van Dijk, Bob W; Stufflebeam, Steven M; Van Mieghem, Piet

    2016-05-01

    The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent. PMID:26860437

  16. Disrupted Brain Functional Network Architecture in Chronic Tinnitus Patients

    PubMed Central

    Chen, Yu-Chen; Feng, Yuan; Xu, Jin-Jing; Mao, Cun-Nan; Xia, Wenqing; Ren, Jun; Yin, Xindao

    2016-01-01

    Purpose: Resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated the disruptions of multiple brain networks in tinnitus patients. Nonetheless, several studies found no differences in network processing between tinnitus patients and healthy controls (HCs). Its neural bases are poorly understood. To identify aberrant brain network architecture involved in chronic tinnitus, we compared the resting-state fMRI (rs-fMRI) patterns of tinnitus patients and HCs. Materials and Methods: Chronic tinnitus patients (n = 24) with normal hearing thresholds and age-, sex-, education- and hearing threshold-matched HCs (n = 22) participated in the current study and underwent the rs-fMRI scanning. We used degree centrality (DC) to investigate functional connectivity (FC) strength of the whole-brain network and Granger causality to analyze effective connectivity in order to explore directional aspects involved in tinnitus. Results: Compared to HCs, we found significantly increased network centrality in bilateral superior frontal gyrus (SFG). Unidirectionally, the left SFG revealed increased effective connectivity to the left middle orbitofrontal cortex (OFC), left posterior lobe of cerebellum (PLC), left postcentral gyrus, and right middle occipital gyrus (MOG) while the right SFG exhibited enhanced effective connectivity to the right supplementary motor area (SMA). In addition, the effective connectivity from the bilateral SFG to the OFC and SMA showed positive correlations with tinnitus distress. Conclusions: Rs-fMRI provides a new and novel method for identifying aberrant brain network architecture. Chronic tinnitus patients have disrupted FC strength and causal connectivity mostly in non-auditory regions, especially the prefrontal cortex (PFC). The current findings will provide a new perspective for understanding the neuropathophysiological mechanisms in chronic tinnitus. PMID:27458377

  17. Effects of chronic peripheral olfactory loss on functional brain networks.

    PubMed

    Kollndorfer, K; Jakab, A; Mueller, C A; Trattnig, S; Schöpf, V

    2015-12-01

    The effects of sensory loss on central processing in various sensory systems have already been described. The olfactory system holds the special ability to be activated by a sensorimotor act, without the presentation of an odor. In this study, we investigated brain changes related to chronic peripheral smell loss. We included 11 anosmic patients (eight female, three male; mean age, 43.5 years) with smell loss after an infection of the upper respiratory tract (mean disease duration, 4.64 years) and 14 healthy controls (seven female, seven male; mean age, 30.1 years) in a functional magnetic resonance imaging experiment with a sniffing paradigm. Data were analyzed using group-independent component analysis and functional connectivity analysis. Our results revealed a spatially intact olfactory network in patients, whereas major aberrations due to peripheral loss were observed in functional connectivity through a variety of distributed brain areas. This is the first study to show the re-organization caused by the lack of peripheral input. The results of this study indicate that anosmic patients hold the ability to activate an olfaction-related functional network through the sensorimotor component of odor-perception (sniffing). The areas involved were not different from those that emerged in healthy controls. However, functional connectivity appears to be different between the two groups, with a decrease in functional connectivity in the brain in patients with chronic peripheral sensory loss. We can further conclude that the loss of the sense of smell may induce far-reaching effects in the whole brain, which lead to compensatory mechanisms from other sensory systems due to the close interconnectivity of the olfactory system with other functional networks. PMID:26415766

  18. Large-Scale Functional Brain Network Reorganization During Taoist Meditation.

    PubMed

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

    2016-02-01

    Meditation induces a distinct and reversible mental state that provides insights into brain correlates of consciousness. We explored brain network changes related to meditation by graph theoretical analysis of resting-state functional magnetic resonance imaging data. Eighteen Taoist meditators with varying levels of expertise were scanned using a within-subjects counterbalanced design during resting and meditation states. State-related differences in network topology were measured globally and at the level of individual nodes and edges. Although measures of global network topology, such as small-worldness, were unchanged, meditation was characterized by an extensive and expertise-dependent reorganization of the hubs (highly connected nodes) and edges (functional connections). Areas of sensory cortex, especially the bilateral primary visual and auditory cortices, and the bilateral temporopolar areas, which had the highest degree (or connectivity) during the resting state, showed the biggest decrease during meditation. Conversely, bilateral thalamus and components of the default mode network, mainly the bilateral precuneus and posterior cingulate cortex, had low degree in the resting state but increased degree during meditation. Additionally, these changes in nodal degree were accompanied by reorganization of anatomical orientation of the edges. During meditation, long-distance longitudinal (antero-posterior) edges increased proportionally, whereas orthogonal long-distance transverse (right-left) edges connecting bilaterally homologous cortices decreased. Our findings suggest that transient changes in consciousness associated with meditation introduce convergent changes in the topological and spatial properties of brain functional networks, and the anatomical pattern of integration might be as important as the global level of integration when considering the network basis for human consciousness. PMID:26165867

  19. Dynamic functional brain networks involved in simple visual discrimination learning.

    PubMed

    Fidalgo, Camino; Conejo, Nélida María; González-Pardo, Héctor; Arias, Jorge Luis

    2014-10-01

    Visual discrimination tasks have been widely used to evaluate many types of learning and memory processes. However, little is known about the brain regions involved at different stages of visual discrimination learning. We used cytochrome c oxidase histochemistry to evaluate changes in regional brain oxidative metabolism during visual discrimination learning in a water-T maze at different time points during training. As compared with control groups, the results of the present study reveal the gradual activation of cortical (prefrontal and temporal cortices) and subcortical brain regions (including the striatum and the hippocampus) associated to the mastery of a simple visual discrimination task. On the other hand, the brain regions involved and their functional interactions changed progressively over days of training. Regions associated with novelty, emotion, visuo-spatial orientation and motor aspects of the behavioral task seem to be relevant during the earlier phase of training, whereas a brain network comprising the prefrontal cortex was found along the whole learning process. This study highlights the relevance of functional interactions among brain regions to investigate learning and memory processes. PMID:24937013

  20. 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.

  1. Local inhibitory plasticity tunes macroscopic brain dynamics and allows the emergence of functional brain networks.

    PubMed

    Hellyer, Peter J; Jachs, Barbara; Clopath, Claudia; Leech, Robert

    2016-01-01

    Rich, spontaneous brain activity has been observed across a range of different temporal and spatial scales. These dynamics are thought to be important for efficient neural functioning. A range of experimental evidence suggests that these neural dynamics are maintained across a variety of different cognitive states, in response to alterations of the environment and to changes in brain configuration (e.g., across individuals, development and in many neurological disorders). This suggests that the brain has evolved mechanisms to maintain rich dynamics across a broad range of situations. Several mechanisms based around homeostatic plasticity have been proposed to explain how these dynamics emerge from networks of neurons at the microscopic scale. Here we explore how a homeostatic mechanism may operate at the macroscopic scale: in particular, focusing on how it interacts with the underlying structural network topology and how it gives rise to well-described functional connectivity networks. We use a simple mean-field model of the brain, constrained by empirical white matter structural connectivity where each region of the brain is simulated using a pool of excitatory and inhibitory neurons. We show, as with the microscopic work, that homeostatic plasticity regulates network activity and allows for the emergence of rich, spontaneous dynamics across a range of brain configurations, which otherwise show a very limited range of dynamic regimes. In addition, the simulated functional connectivity of the homeostatic model better resembles empirical functional connectivity network. To accomplish this, we show how the inhibitory weights adapt over time to capture important graph theoretic properties of the underlying structural network. Therefore, this work presents suggests how inhibitory homeostatic mechanisms facilitate stable macroscopic dynamics to emerge in the brain, aiding the formation of functional connectivity networks. PMID:26348562

  2. Heritability of the network architecture of intrinsic brain functional connectivity.

    PubMed

    Sinclair, Benjamin; Hansell, Narelle K; Blokland, Gabriëlla A M; Martin, Nicholas G; Thompson, Paul M; Breakspear, Michael; de Zubicaray, Greig I; Wright, Margaret J; McMahon, Katie L

    2015-11-01

    The brain's functional network exhibits many features facilitating functional specialization, integration, and robustness to attack. Using graph theory to characterize brain networks, studies demonstrate their small-world, modular, and "rich-club" properties, with deviations reported in many common neuropathological conditions. Here we estimate the heritability of five widely used graph theoretical metrics (mean clustering coefficient (γ), modularity (Q), rich-club coefficient (ϕnorm), global efficiency (λ), small-worldness (σ)) over a range of connection densities (k=5-25%) in a large cohort of twins (N=592, 84 MZ and 89 DZ twin pairs, 246 single twins, age 23 ± 2.5). We also considered the effects of global signal regression (GSR). We found that the graph metrics were moderately influenced by genetic factors h(2) (γ=47-59%, Q=38-59%, ϕnorm=0-29%, λ=52-64%, σ=51-59%) at lower connection densities (≤ 15%), and when global signal regression was implemented, heritability estimates decreased substantially h(2) (γ=0-26%, Q=0-28%, ϕnorm=0%, λ=23-30%, σ=0-27%). Distinct network features were phenotypically correlated (|r|=0.15-0.81), and γ, Q, and λ were found to be influenced by overlapping genetic factors. Our findings suggest that these metrics may be potential endophenotypes for psychiatric disease and suitable for genetic association studies, but that genetic effects must be interpreted with respect to methodological choices. PMID:26226088

  3. Beyond localized and distributed accounts of brain functions. Comment on “Understanding brain networks and brain organization” by Pessoa

    NASA Astrophysics Data System (ADS)

    Cauda, Franco; Costa, Tommaso; Tamietto, Marco

    2014-09-01

    Recent evidence in cognitive neuroscience lends support to the idea that network models of brain architecture provide a privileged access to the understanding of the relation between brain organization and cognitive processes [1]. The core perspective holds that cognitive processes depend on the interactions among distributed neuronal populations and brain structures, and that the impact of a given region on behavior largely depends on its pattern of anatomical and functional connectivity [2,3].

  4. Quetiapine modulates functional connectivity in brain aggression networks.

    PubMed

    Klasen, Martin; Zvyagintsev, Mikhail; Schwenzer, Michael; Mathiak, Krystyna A; Sarkheil, Pegah; Weber, René; Mathiak, Klaus

    2013-07-15

    Aggressive behavior is associated with dysfunctions in an affective regulation network encompassing amygdala and prefrontal areas such as orbitofrontal (OFC), anterior cingulate (ACC), and dorsolateral prefrontal cortex (DLPFC). In particular, prefrontal regions have been postulated to control amygdala activity by inhibitory projections, and this process may be disrupted in aggressive individuals. The atypical antipsychotic quetiapine successfully attenuates aggressive behavior in various disorders; the underlying neural processes, however, are unknown. A strengthened functional coupling in the prefrontal-amygdala system may account for these anti-aggressive effects. An inhibition of this network has been reported for virtual aggression in violent video games as well. However, there have been so far no in-vivo observations of pharmacological influences on corticolimbic projections during human aggressive behavior. In a double-blind, placebo-controlled study, quetiapine and placebo were administered for three successive days prior to an fMRI experiment. In this experiment, functional brain connectivity was assessed during virtual aggressive behavior in a violent video game and an aggression-free control task in a non-violent modification. Quetiapine increased the functional connectivity of ACC and DLPFC with the amygdala during virtual aggression, whereas OFC-amygdala coupling was attenuated. These effects were observed neither for placebo nor for the non-violent control. These results demonstrate for the first time a pharmacological modification of aggression-related human brain networks in a naturalistic setting. The violence-specific modulation of prefrontal-amygdala networks appears to control aggressive behavior and provides a neurobiological model for the anti-aggressive effects of quetiapine. PMID:23501053

  5. Speech networks at rest and in action: interactions between functional brain networks controlling speech production

    PubMed Central

    Fuertinger, Stefan

    2015-01-01

    Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. PMID:25673742

  6. Speech networks at rest and in action: interactions between functional brain networks controlling speech production.

    PubMed

    Simonyan, Kristina; Fuertinger, Stefan

    2015-04-01

    Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. PMID:25673742

  7. Exploring functional connectivity networks with multichannel brain array coils.

    PubMed

    Anteraper, Sheeba Arnold; Whitfield-Gabrieli, Susan; Keil, Boris; Shannon, Steven; Gabrieli, John D; Triantafyllou, Christina

    2013-01-01

    The use of multichannel array head coils in functional and structural magnetic resonance imaging (MRI) provides increased signal-to-noise ratio (SNR), higher sensitivity, and parallel imaging capabilities. However, their benefits remain to be systematically explored in the context of resting-state functional connectivity MRI (fcMRI). In this study, we compare signal detectability within and between commercially available multichannel brain coils, a 32-Channel (32Ch), and a 12-Channel (12Ch) at 3T, in a high-resolution regime to accurately map resting-state networks. We investigate whether the 32Ch coil can extract and map fcMRI more efficiently and robustly than the 12Ch coil using seed-based and graph-theory-based analyses. Our findings demonstrate that although the 12Ch coil can be used to reveal resting-state connectivity maps, the 32Ch coil provides increased detailed functional connectivity maps (using seed-based analysis) as well as increased global and local efficiency, and cost (using graph-theory-based analysis), in a number of widely reported resting-state networks. The exploration of subcortical networks, which are scarcely reported due to limitations in spatial-resolution and coil sensitivity, also proved beneficial with the 32Ch coil. Further, comparisons regarding the data acquisition time required to successfully map these networks indicated that scan time can be significantly reduced by 50% when a coil with increased number of channels (i.e., 32Ch) is used. Switching to multichannel arrays in resting-state fcMRI could, therefore, provide both detailed functional connectivity maps and acquisition time reductions, which could further benefit imaging special subject populations, such as patients or pediatrics who have less tolerance in lengthy imaging sessions. PMID:23510203

  8. The brain's functional network architecture reveals human motives.

    PubMed

    Hein, Grit; Morishima, Yosuke; Leiberg, Susanne; Sul, Sunhae; Fehr, Ernst

    2016-03-01

    Goal-directed human behaviors are driven by motives. Motives are, however, purely mental constructs that are not directly observable. Here, we show that the brain's functional network architecture captures information that predicts different motives behind the same altruistic act with high accuracy. In contrast, mere activity in these regions contains no information about motives. Empathy-based altruism is primarily characterized by a positive connectivity from the anterior cingulate cortex (ACC) to the anterior insula (AI), whereas reciprocity-based altruism additionally invokes strong positive connectivity from the AI to the ACC and even stronger positive connectivity from the AI to the ventral striatum. Moreover, predominantly selfish individuals show distinct functional architectures compared to altruists, and they only increase altruistic behavior in response to empathy inductions, but not reciprocity inductions. PMID:26941317

  9. The correlation of metrics in complex networks with applications in functional brain networks

    NASA Astrophysics Data System (ADS)

    Li, C.; Wang, H.; de Haan, W.; Stam, C. J.; Van Mieghem, P.

    2011-11-01

    An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we investigate the linear correlation coefficients between widely studied network metrics in three network models (Bárabasi-Albert graphs, Erdös-Rényi random graphs and Watts-Strogatz small-world graphs) as well as in functional brain networks of healthy subjects. The metric correlations, which we have observed and theoretically explained, motivate us to propose a small representative set of metrics by including only one metric from each subset of mutually strongly dependent metrics. The following contributions are considered important. (a) A network with a given degree distribution can indeed be characterized by a small representative set of metrics. (b) Unweighted networks, which are obtained from weighted functional brain networks with a fixed threshold, and Erdös-Rényi random graphs follow a similar degree distribution. Moreover, their metric correlations and the resultant representative metrics are similar as well. This verifies the influence of degree distribution on metric correlations. (c) Most metric correlations can be explained analytically. (d) Interestingly, the most studied metrics so far, the average shortest path length and the clustering coefficient, are strongly correlated and, thus, redundant. Whereas spectral metrics, though only studied recently in the context of complex networks, seem to be essential in network characterizations. This representative set of metrics tends to both sufficiently and effectively characterize networks with a given degree distribution. In the study of a specific network, however, we have to at least consider the representative set so that important network properties will not be neglected.

  10. Understanding entangled cerebral networks: a prerequisite for restoring brain function with brain-computer interfaces

    PubMed Central

    Mandonnet, Emmanuel; Duffau, Hugues

    2014-01-01

    Historically, cerebral processing has been conceptualized as a framework based on statically localized functions. However, a growing amount of evidence supports a hodotopical (delocalized) and flexible organization. A number of studies have reported absence of a permanent neurological deficit after massive surgical resections of eloquent brain tissue. These results highlight the tremendous plastic potential of the brain. Understanding anatomo-functional correlates underlying this cerebral reorganization is a prerequisite to restore brain functions through brain-computer interfaces (BCIs) in patients with cerebral diseases, or even to potentiate brain functions in healthy individuals. Here, we review current knowledge of neural networks that could be utilized in the BCIs that enable movements and language. To this end, intraoperative electrical stimulation in awake patients provides valuable information on the cerebral functional maps, their connectomics and plasticity. Overall, these studies indicate that the complex cerebral circuitry that underpins interactions between action, cognition and behavior should be throughly investigated before progress in BCI approaches can be achieved. PMID:24834030

  11. MIC as an Appropriate Method to Construct the Brain Functional Network

    PubMed Central

    Yi, Ming; Wu, Xia

    2015-01-01

    Using an effective method to measure the brain functional connectivity is an important step to study the brain functional network. The main methods for constructing an undirected brain functional network include correlation coefficient (CF), partial correlation coefficient (PCF), mutual information (MI), wavelet correlation coefficient (WCF), and coherence (CH). In this paper we demonstrate that the maximal information coefficient (MIC) proposed by Reshef et al. is relevant to constructing a brain functional network because it performs best in the comprehensive comparisons in consistency and robustness. Our work can be used to validate the possible new functional connection measures. PMID:25710031

  12. Memory Networks in Tinnitus: A Functional Brain Image Study

    PubMed Central

    Laureano, Maura Regina; Onishi, Ektor Tsuneo; Bressan, Rodrigo Affonseca; Castiglioni, Mario Luiz Vieira; Batista, Ilza Rosa; Reis, Marilia Alves; Garcia, Michele Vargas; de Andrade, Adriana Neves; de Almeida, Roberta Ribeiro; Garrido, Griselda J.; Jackowski, Andrea Parolin

    2014-01-01

    Tinnitus is characterized by the perception of sound in the absence of an external auditory stimulus. The network connectivity of auditory and non-auditory brain structures associated with emotion, memory and attention are functionally altered in debilitating tinnitus. Current studies suggest that tinnitus results from neuroplastic changes in the frontal and limbic temporal regions. The objective of this study was to use Single-Photon Emission Computed Tomography (SPECT) to evaluate changes in the cerebral blood flow in tinnitus patients with normal hearing compared with healthy controls. Methods: Twenty tinnitus patients with normal hearing and 17 healthy controls, matched for sex, age and years of education, were subjected to Single Photon Emission Computed Tomography using the radiotracer ethylenedicysteine diethyl ester, labeled with Technetium 99 m (99 mTc-ECD SPECT). The severity of tinnitus was assessed using the “Tinnitus Handicap Inventory” (THI). The images were processed and analyzed using “Statistical Parametric Mapping” (SPM8). Results: A significant increase in cerebral perfusion in the left parahippocampal gyrus (pFWE <0.05) was observed in patients with tinnitus compared with healthy controls. The average total THI score was 50.8+18.24, classified as moderate tinnitus. Conclusion: It was possible to identify significant changes in the limbic system of the brain perfusion in tinnitus patients with normal hearing, suggesting that central mechanisms, not specific to the auditory pathway, are involved in the pathophysiology of symptoms, even in the absence of clinically diagnosed peripheral changes. PMID:24516567

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

    NASA Astrophysics Data System (ADS)

    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.

  14. 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. PMID:20866854

  15. The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack

    PubMed Central

    Joyce, Karen E.; Hayasaka, Satoru; Laurienti, Paul J.

    2013-01-01

    In recent years, the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner. One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage, even to highly connected regions. However, these highly connected nodes may not be the most critical regions of the brain network, and it is unclear how the network dynamics are impacted by removal of these key nodes. This work seeks to further investigate the resilience of the human functional brain network. Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest (ROI) networks of 5 healthy volunteers. Networks were attacked at key nodes using several criteria for assessing node importance, and the impact on network structure and dynamics was evaluated. The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks, both in terms of network structure and dynamics. PMID:23358557

  16. Functional brain network changes associated with clinical and biochemical measures of the severity of hepatic encephalopathy.

    PubMed

    Jao, Tun; Schröter, Manuel; Chen, Chao-Long; Cheng, Yu-Fan; Lo, Chun-Yi Zac; Chou, Kun-Hsien; Patel, Ameera X; Lin, Wei-Che; Lin, Ching-Po; Bullmore, Edward T

    2015-11-15

    Functional properties of the brain may be associated with changes in complex brain networks. However, little is known about how properties of large-scale functional brain networks may be altered stepwise in patients with disturbance of consciousness, e.g., an encephalopathy. We used resting-state fMRI data on patients suffering from various degrees of hepatic encephalopathy (HE) to explore how topological and spatial network properties of functional brain networks changed at different cognitive and consciousness states. Severity of HE was measured clinically and by neuropsychological tests. Fifty-eight non-alcoholic liver cirrhosis patients and 62 normal controls were studied. Patients were subdivided into liver cirrhosis with no outstanding HE (NoHE, n=23), minimal HE with cognitive impairment only detectable by neuropsychological tests (MHE, n=28), and clinically overt HE (OHE, n=7). From the earliest stage, the NoHE, functional brain networks were progressively more random, less clustered, and less modular. Since the intermediate stage (MHE), increased ammonia level was accompanied by concomitant exponential decay of mean connectivity strength, especially in the primary cortical areas and midline brain structures. Finally, at the OHE stage, there were radical reorganization of the topological centrality-i.e., the relative importance-of the hubs and reorientation of functional connections between nodes. In summary, this study illustrated progressively greater abnormalities in functional brain network organization in patients with clinical and biochemical evidence of more severe hepatic encephalopathy. The early-than-expected brain network dysfunction in cirrhotic patients suggests that brain functional connectivity and network analysis may provide useful and complementary biomarkers for more aggressive and earlier intervention of hepatic encephalopathy. Moreover, the stepwise deterioration of functional brain networks in HE patients may suggest that hierarchical

  17. 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

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

    PubMed Central

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

    2014-01-01

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

  19. A Probabilistic Model of Functional Brain Connectivity Network for Discovering Novel Biomarkers

    PubMed Central

    Bian, Jiang; Xie, Mengjun; Topaloglu, Umit; Cisler, Josh M.

    2013-01-01

    Graph theoretical analyses of functional brain connectivity networks have been limited to a static view of brain activities over the entire timeseries. In this paper, we propose a new probabilistic model of the functional brain connectivity network, the strong-edge model, which incorporates the temporal fluctuation of neurodynamics. We also introduce a systematic approach to identifying biomarkers based on network characteristics that quantitatively describe the organization of the brain network. The evaluation results of the proposed strong-edge network model is quite promising. The biomarkers derived from the strong-edge model have achieved much higher prediction accuracy of 89% (ROCAUC: 0.96) in distinguishing depression subjects from healthy controls in comparison with the conventional network model (accuracy: 76%, ROC-AUC: 0.87). These novel biomarkers have the high potential of being applied clinically in diagnosing neurological and psychiatric brain diseases with noninvasive neuroimaging technologies. PMID:24303289

  20. Modeling dynamic functional information flows on large-scale brain networks.

    PubMed

    Lv, Peili; Guo, Lei; Hu, Xintao; Li, Xiang; Jin, Changfeng; Han, Junwei; Li, Lingjiang; Liu, Tianming

    2013-01-01

    Growing evidence from the functional neuroimaging field suggests that human brain functions are realized via dynamic functional interactions on large-scale structural networks. Even in resting state, functional brain networks exhibit remarkable temporal dynamics. However, it has been rarely explored to computationally model such dynamic functional information flows on large-scale brain networks. In this paper, we present a novel computational framework to explore this problem using multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. Basically, recent literature reports including our own studies have demonstrated that the resting state brain networks dynamically undergo a set of distinct brain states. Within each quasi-stable state, functional information flows from one set of structural brain nodes to other sets of nodes, which is analogous to the message package routing on the Internet from the source node to the destination. Therefore, based on the large-scale structural brain networks constructed from DTI data, we employ a dynamic programming strategy to infer functional information transition routines on structural networks, based on which hub routers that most frequently participate in these routines are identified. It is interesting that a majority of those hub routers are located within the default mode network (DMN), revealing a possible mechanism of the critical functional hub roles played by the DMN in resting state. Also, application of this framework on a post trauma stress disorder (PTSD) dataset demonstrated interesting difference in hub router distributions between PTSD patients and healthy controls. PMID:24579202

  1. Spatial variability of functional brain networks in early-blind and sighted subjects.

    PubMed

    Boldt, Robert; Seppä, Mika; Malinen, Sanna; Tikka, Pia; Hari, Riitta; Carlson, Synnöve

    2014-07-15

    To further the understanding how the human brain adapts to early-onset blindness, we searched in early-blind and normally-sighted subjects for functional brain networks showing the most and least spatial variabilities across subjects. We hypothesized that the functional networks compensating for early-onset blindness undergo cortical reorganization. To determine whether reorganization of functional networks affects spatial variability, we used functional magnetic resonance imaging to compare brain networks, derived by independent component analysis, of 7 early-blind and 7 sighted subjects while they rested or listened to an audio drama. In both conditions, the blind compared with sighted subjects showed more spatial variability in a bilateral parietal network (comprising the inferior parietal and angular gyri and precuneus) and in a bilateral auditory network (comprising the superior temporal gyri). In contrast, a vision-related left-hemisphere-lateralized occipital network (comprising the superior, middle and inferior occipital gyri, fusiform and lingual gyri, and the calcarine sulcus) was less variable in blind than sighted subjects. Another visual network and a tactile network were spatially more variable in the blind than sighted subjects in one condition. We contemplate whether our results on inter-subject spatial variability of brain networks are related to experience-dependent brain plasticity, and we suggest that auditory and parietal networks undergo a stronger experience-dependent reorganization in the early-blind than sighted subjects while the opposite is true for the vision-related occipital network. PMID:24680867

  2. Dynamic brain architectures in local brain activity and functional network efficiency associate with efficient reading in bilinguals.

    PubMed

    Feng, Gangyi; Chen, Hsuan-Chih; Zhu, Zude; He, Yong; Wang, Suiping

    2015-10-01

    The human brain is organized as a dynamic network, in which both regional brain activity and inter-regional connectivity support high-level cognitive processes, such as reading. However, it is still largely unknown how the functional brain network organizes to enable fast and effortless reading processing in the native language (L1) but not in a non-proficient second language (L2), and whether the mechanisms underlying local activity are associated with connectivity dynamics in large-scale brain networks. In the present study, we combined activation-based and multivariate graph-theory analysis with functional magnetic resonance imaging data to address these questions. Chinese-English unbalanced bilinguals read narratives for comprehension in Chinese (L1) and in English (L2). Compared with L2, reading in L1 evoked greater brain activation and recruited a more globally efficient but less clustered network organization. Regions with both increased network efficiency and enhanced brain activation in L1 reading were mostly located in the fronto-temporal reading-related network (RN), whereas regions with decreased global network efficiency, increased clustering, and more deactivation in L2 reading were identified in the default mode network (DMN). Moreover, functional network efficiency was closely associated with local brain activation, and such associations were also modulated by reading efficiency in the two languages. Our results demonstrate that an economical and integrative brain network topology is associated with efficient reading, and further reveal a dynamic association between network efficiency and local activation for both RN and DMN. These findings underscore the importance of considering interregional connectivity when interpreting local BOLD signal changes in bilingual reading. PMID:26095088

  3. 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. PMID:26927726

  4. Hubs of brain functional networks are radically reorganized in comatose patients

    PubMed Central

    Achard, Sophie; Delon-Martin, Chantal; Vértes, Petra E.; Renard, Félix; Schenck, Maleka; Schneider, Francis; Heinrich, Christian; Kremer, Stéphane; Bullmore, Edward T.

    2012-01-01

    Human brain networks have topological properties in common with many other complex systems, prompting the following question: what aspects of brain network organization are critical for distinctive functional properties of the brain, such as consciousness? To address this question, we used graph theoretical methods to explore brain network topology in resting state functional MRI data acquired from 17 patients with severely impaired consciousness and 20 healthy volunteers. We found that many global network properties were conserved in comatose patients. Specifically, there was no significant abnormality of global efficiency, clustering, small-worldness, modularity, or degree distribution in the patient group. However, in every patient, we found evidence for a radical reorganization of high degree or highly efficient “hub” nodes. Cortical regions that were hubs of healthy brain networks had typically become nonhubs of comatose brain networks and vice versa. These results indicate that global topological properties of complex brain networks may be homeostatically conserved under extremely different clinical conditions and that consciousness likely depends on the anatomical location of hub nodes in human brain networks. PMID:23185007

  5. 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

  6. Functional Brain Networks Formed Using Cross-Sample Entropy Are Scale Free

    PubMed Central

    Pritchard, Walter S.; Burdette, Jonathan H.; Hayasaka, Satoru

    2014-01-01

    Abstract Over the previous decade, there has been an explosion of interest in network science, in general, and its application to the human brain, in particular. Most brain network investigations to date have used linear correlations (LinCorr) between brain areas to construct and then interpret brain networks. In this study, we applied an entropy-based method to establish functional connectivity between brain areas. This method is sensitive to both nonlinear and linear associations. The LinCorr-based and entropy-based techniques were applied to resting-state functional magnetic resonance imaging data from 10 subjects, and the resulting networks were compared. The networks derived from the entropy-based method exhibited power-law degree distributions. Moreover, the entropy-based networks had a higher clustering coefficient and a shorter path length compared with that of the LinCorr-based networks. While the LinCorr-based networks were assortative, with nodes with similar degrees preferentially connected, the entropy-based networks were disassortative, with high-degree hubs directly connected to low-degree nodes. It is likely that the differences in clustering and assortativity are due to “mega-hubs” in the entropy-based networks. These mega-hubs connect to a large majority of the nodes in the network. This is the first work clearly demonstrating differences between functional brain networks using linear and nonlinear techniques. The key finding is that the nonlinear technique produced networks with scale-free degree distributions. There remains debate among the neuroscience community as to whether human brains are scale free. These data support the argument that at least some aspects of the human brain are perhaps scale free. PMID:24946057

  7. Functional brain networks formed using cross-sample entropy are scale free.

    PubMed

    Pritchard, Walter S; Laurienti, Paul J; Burdette, Jonathan H; Hayasaka, Satoru

    2014-08-01

    Over the previous decade, there has been an explosion of interest in network science, in general, and its application to the human brain, in particular. Most brain network investigations to date have used linear correlations (LinCorr) between brain areas to construct and then interpret brain networks. In this study, we applied an entropy-based method to establish functional connectivity between brain areas. This method is sensitive to both nonlinear and linear associations. The LinCorr-based and entropy-based techniques were applied to resting-state functional magnetic resonance imaging data from 10 subjects, and the resulting networks were compared. The networks derived from the entropy-based method exhibited power-law degree distributions. Moreover, the entropy-based networks had a higher clustering coefficient and a shorter path length compared with that of the LinCorr-based networks. While the LinCorr-based networks were assortative, with nodes with similar degrees preferentially connected, the entropy-based networks were disassortative, with high-degree hubs directly connected to low-degree nodes. It is likely that the differences in clustering and assortativity are due to "mega-hubs" in the entropy-based networks. These mega-hubs connect to a large majority of the nodes in the network. This is the first work clearly demonstrating differences between functional brain networks using linear and nonlinear techniques. The key finding is that the nonlinear technique produced networks with scale-free degree distributions. There remains debate among the neuroscience community as to whether human brains are scale free. These data support the argument that at least some aspects of the human brain are perhaps scale free. PMID:24946057

  8. A novel pattern mining approach for identifying cognitive activity in EEG based functional brain networks.

    PubMed

    Thilaga, M; Vijayalakshmi, R; Nadarajan, R; Nandagopal, D

    2016-06-01

    The complex nature of neuronal interactions of the human brain has posed many challenges to the research community. To explore the underlying mechanisms of neuronal activity of cohesive brain regions during different cognitive activities, many innovative mathematical and computational models are required. This paper presents a novel Common Functional Pattern Mining approach to demonstrate the similar patterns of interactions due to common behavior of certain brain regions. The electrode sites of EEG-based functional brain network are modeled as a set of transactions and node-based complex network measures as itemsets. These itemsets are transformed into a graph data structure called Functional Pattern Graph. By mining this Functional Pattern Graph, the common functional patterns due to specific brain functioning can be identified. The empirical analyses show the efficiency of the proposed approach in identifying the extent to which the electrode sites (transactions) are similar during various cognitive load states. PMID:27401999

  9. Distinct disruptions of resting-state functional brain networks in familial and sporadic schizophrenia

    PubMed Central

    Zhu, Jiajia; Zhuo, Chuanjun; Liu, Feng; Qin, Wen; Xu, Lixue; Yu, Chunshui

    2016-01-01

    Clinical and brain structural differences have been reported between patients with familial and sporadic schizophrenia; however, little is known about the brain functional differences between the two subtypes of schizophrenia. Twenty-six patients with familial schizophrenia (PFS), 26 patients with sporadic schizophrenia (PSS) and 26 healthy controls (HC) underwent a resting-state functional magnetic resonance imaging. The whole-brain functional network was constructed and analyzed using graph theoretical approaches. Topological properties (including global, nodal and edge measures) were compared among the three groups. We found that PFS, PSS and HC exhibited common small-world architecture of the functional brain networks. However, at a global level, only PFS showed significantly lower normalized clustering coefficient, small-worldness, and local efficiency, indicating a randomization shift of their brain networks. At a regional level, PFS and PSS disrupted different neural circuits, consisting of abnormal nodes (increased or decreased nodal centrality) and edges (decreased functional connectivity strength), which were widely distributed throughout the entire brain. Furthermore, some of these altered network measures were significantly correlated with severity of psychotic symptoms. These results suggest that familial and sporadic schizophrenia had segregated disruptions in the topological organization of the intrinsic functional brain network, which may be due to different etiological contributions. PMID:27032817

  10. Distinct disruptions of resting-state functional brain networks in familial and sporadic schizophrenia.

    PubMed

    Zhu, Jiajia; Zhuo, Chuanjun; Liu, Feng; Qin, Wen; Xu, Lixue; Yu, Chunshui

    2016-01-01

    Clinical and brain structural differences have been reported between patients with familial and sporadic schizophrenia; however, little is known about the brain functional differences between the two subtypes of schizophrenia. Twenty-six patients with familial schizophrenia (PFS), 26 patients with sporadic schizophrenia (PSS) and 26 healthy controls (HC) underwent a resting-state functional magnetic resonance imaging. The whole-brain functional network was constructed and analyzed using graph theoretical approaches. Topological properties (including global, nodal and edge measures) were compared among the three groups. We found that PFS, PSS and HC exhibited common small-world architecture of the functional brain networks. However, at a global level, only PFS showed significantly lower normalized clustering coefficient, small-worldness, and local efficiency, indicating a randomization shift of their brain networks. At a regional level, PFS and PSS disrupted different neural circuits, consisting of abnormal nodes (increased or decreased nodal centrality) and edges (decreased functional connectivity strength), which were widely distributed throughout the entire brain. Furthermore, some of these altered network measures were significantly correlated with severity of psychotic symptoms. These results suggest that familial and sporadic schizophrenia had segregated disruptions in the topological organization of the intrinsic functional brain network, which may be due to different etiological contributions. PMID:27032817

  11. Spontaneous functional network dynamics and associated structural substrates in the human brain

    PubMed Central

    Liao, Xuhong; Yuan, Lin; Zhao, Tengda; Dai, Zhengjia; Shu, Ni; Xia, Mingrui; Yang, Yihong; Evans, Alan; He, Yong

    2015-01-01

    Recent imaging connectomics studies have demonstrated that the spontaneous human brain functional networks derived from resting-state functional MRI (R-fMRI) include many non-trivial topological properties, such as highly efficient small-world architecture and densely connected hub regions. However, very little is known about dynamic functional connectivity (D-FC) patterns of spontaneous human brain networks during rest and about how these spontaneous brain dynamics are constrained by the underlying structural connectivity. Here, we combined sub-second multiband R-fMRI data with graph-theoretical approaches to comprehensively investigate the dynamic characteristics of the topological organization of human whole-brain functional networks, and then employed diffusion imaging data in the same participants to further explore the associated structural substrates. At the connection level, we found that human whole-brain D-FC patterns spontaneously fluctuated over time, while homotopic D-FC exhibited high connectivity strength and low temporal variability. At the network level, dynamic functional networks exhibited time-varying but evident small-world and assortativity architecture, with several regions (e.g., insula, sensorimotor cortex and medial prefrontal cortex) emerging as functionally persistent hubs (i.e., highly connected regions) while possessing large temporal variability in their degree centrality. Finally, the temporal characteristics (i.e., strength and variability) of the connectional and nodal properties of the dynamic brain networks were significantly associated with their structural counterparts. Collectively, we demonstrate the economical, efficient, and flexible characteristics of dynamic functional coordination in large-scale human brain networks during rest, and highlight their relationship with underlying structural connectivity, which deepens our understandings of spontaneous brain network dynamics in humans. PMID:26388757

  12. Modular Brain Networks

    PubMed Central

    Sporns, Olaf; Betzel, Richard F.

    2016-01-01

    The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics. PMID:26393868

  13. Modular Brain Networks.

    PubMed

    Sporns, Olaf; Betzel, Richard F

    2016-01-01

    The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics. PMID:26393868

  14. Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients

    PubMed Central

    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. PMID:27303259

  15. 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. PMID:27303259

  16. White Matter Damage Disorganizes Brain Functional Networks in Amnestic Mild Cognitive Impairment

    PubMed Central

    Garcés, Pilar; López, María Eugenia; Aurtenetxe, Sara; Cuesta, Pablo; Marcos, Alberto; Montejo, Pedro; Yus, Miguel; Hernández-Tamames, Juan Antonio; del Pozo, Francisco; Becker, James T.; Maestú, Fernando

    2014-01-01

    Abstract Although progressive functional brain network disruption has been one of the hallmarks of Alzheimer's Disease, little is known about the origin of this functional impairment that underlies cognitive symptoms. We investigated how the loss of white matter (WM) integrity disrupts the organization of the functional networks at different frequency bands. The analyses were performed in a sample of healthy elders and mild cognitive impairment (MCI) subjects. Spontaneous brain magnetic activity (measured with magnetoencephalography) was characterized with phase synchronization analysis, and graph theory was applied to the functional networks. We identified WM areas (using diffusion weighted magnetic resonance imaging) that showed a statistical dependence between the fractional anisotropy and the graph metrics. These regions are part of an episodic memory network and were also related to cognitive functions. Our data support the hypothesis that disruption of the anatomical networks influences the organization at the functional level resulting in the prodromal dementia syndrome of MCI. PMID:24617580

  17. Graph Analysis of Functional Brain Networks for Cognitive Control of Action in Traumatic Brain Injury

    ERIC Educational Resources Information Center

    Caeyenberghs, Karen; Leemans, Alexander; Heitger, Marcus H.; Leunissen, Inge; Dhollander, Thijs; Sunaert, Stefan; Dupont, Patrick; Swinnen, Stephan P.

    2012-01-01

    Patients with traumatic brain injury show clear impairments in behavioural flexibility and inhibition that often persist beyond the time of injury, affecting independent living and psychosocial functioning. Functional magnetic resonance imaging studies have shown that patients with traumatic brain injury typically show increased and more broadly…

  18. Evidence for Accelerated Decline of Functional Brain Network Efficiency in Schizophrenia.

    PubMed

    Sheffield, Julia M; Repovs, Grega; Harms, Michael P; Carter, Cameron S; Gold, James M; MacDonald, Angus W; Ragland, J Daniel; Silverstein, Steven M; Godwin, Douglass; Barch, Deanna M

    2016-05-01

    Previous work suggests that individuals with schizophrenia display accelerated aging of white matter integrity, however, it is still unknown whether functional brain networks also decline at an elevated rate in schizophrenia. Given the known degradation of functional connectivity and the normal decline in cognitive functioning throughout healthy aging, we aimed to test the hypothesis that efficiency of large-scale functional brain networks supporting overall cognition, as well as integrity of hub nodes within those networks, show evidence of accelerated aging in schizophrenia. Using pseudo-resting state data in 54 healthy controls and 46 schizophrenia patients, in which task-dependent signal from 3 tasks was regressed out to approximate resting-state data, we observed a significant diagnosis by age interaction in the prediction of both global and local efficiency of the cingulo-opercular network, and of the local efficiency of the fronto-parietal network, but no interaction when predicting both default mode network and whole brain efficiency. We also observed a significant diagnosis by age interaction for the node degree of the right anterior insula, left dorsolateral prefrontal cortex, and dorsal anterior cingulate cortex. All interactions were driven by stronger negative associations between age and network metrics in the schizophrenia group than the healthy controls. These data provide evidence that is consistent with accelerated aging of large-scale functional brain networks in schizophrenia that support higher-order cognitive ability. PMID:26472685

  19. Aberrant topologies and reconfiguration pattern of functional brain network in children with second language reading impairment.

    PubMed

    Liu, Lanfang; Li, Hehui; Zhang, Manli; Wang, Zhengke; Wei, Na; Liu, Li; Meng, Xiangzhi; Ding, Guosheng

    2016-07-01

    Prior work has extensively studied neural deficits in children with reading impairment (RI) in their native language but has rarely examined those of RI children in their second language (L2). A recent study revealed that the function of the local brain regions was disrupted in children with RI in L2, but it is not clear whether the disruption also occurs at a large-scale brain network level. Using fMRI and graph theoretical analysis, we explored the topology of the whole-brain functional network during a phonological rhyming task and network reconfigurations across task and short resting phases in Chinese children with English reading impairment versus age-matched typically developing (TD) children. We found that, when completing the phonological task, the RI group exhibited higher local network efficiency and network modularity compared with the TD group. When switching between the phonological task and the short resting phase, the RI group showed difficulty with network reconfiguration, as reflected in fewer changes in the local efficiency and modularity properties and less rearrangement of the modular communities. These findings were reproducible after controlling for the effects of in-scanner accuracy, participant gender, and L1 reading performance. The results from the whole-brain network analyses were largely replicated in the task-activated network. These findings provide preliminary evidence supporting that RI in L2 is associated with not only abnormal functional network organization but also poor flexibility of the neural system in responding to changing cognitive demands. PMID:27321248

  20. Intrinsic intranasal chemosensory brain networks shown by resting-state functional MRI.

    PubMed

    Tobia, Michael J; Yang, Qing X; Karunanayaka, Prasanna

    2016-05-01

    The human brain is organized into functional networks for sensory-motor and cognitive processing. Intrinsic networks are detectable in the absence of stimulation or task demands, whereas extrinsic networks are detectable when stimulated by sensory or cognitive demands. Intranasal chemosensory processing relies on two dissociable networks for processing incoming trigeminal and olfactory stimulation, but it is not known whether these networks are intrinsically organized. The aim of this study was to identify whether brain networks for intranasal chemosensory processing are detectable in functional connectivity resting-state functional MRI (fMRI). Sixteen healthy adults participated in a 5-min resting-state fMRI study. Functional connectivity seeds were defined from coordinates that anchor olfactory (i.e. bilateral piriform and orbitofrontal cortex) and trigeminal (bilateral anterior insula and cingulate cortex) networks in published task activation studies, and the resulting networks were thresholded at P less than 0.001. The olfactory network showed extended functional connectivity to the thalamus, medial prefrontal cortex, caudate, nucleus accumbens, parahippocampal gyrus, and hippocampus. The trigeminal network showed extended functional connectivity to the precuneus, thalamus, caudate, brainstem, and cerebellum. Both networks overlapped in the thalamus, caudate, medial prefrontal cortex, and insula. These results show that brain networks for intranasal chemosensory processing are intrinsically organized, not just extrinsically instantiated in response to task demands, and resemble networks for processing olfactory and trigeminal stimulation. As such, it may be possible to study the functional organization and dynamics of the olfactory network in resting-state fMRI as well as its implications for aging and disease. PMID:27031873

  1. Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks.

    PubMed

    Guye, Maxime; Bettus, Gaelle; Bartolomei, Fabrice; Cozzone, Patrick J

    2010-12-01

    Graph theoretical analysis of structural and functional connectivity MRI data (ie. diffusion tractography or cortical volume correlation and resting-state or task-related (effective) fMRI, respectively) has provided new measures of human brain organization in vivo. The most striking discovery is that the whole-brain network exhibits "small-world" properties shared with many other complex systems (social, technological, information, biological). This topology allows a high efficiency at different spatial and temporal scale with a very low wiring and energy cost. Its modular organization also allows for a high level of adaptation. In addition, degree distribution of brain networks demonstrates highly connected hubs that are crucial for the whole-network functioning. Many of these hubs have been identified in regions previously defined as belonging to the default-mode network (potentially explaining the high basal metabolism of this network) and the attentional networks. This could explain the crucial role of these hub regions in physiology (task-related fMRI data) as well as in pathophysiology. Indeed, such topological definition provides a reliable framework for predicting behavioral consequences of focal or multifocal lesions such as stroke, tumors or multiple sclerosis. It also brings new insights into a better understanding of pathophysiology of many neurological or psychiatric diseases affecting specific local or global brain networks such as epilepsy, Alzheimer's disease or schizophrenia. Graph theoretical analysis of connectivity MRI data provides an outstanding framework to merge anatomical and functional data in order to better understand brain pathologies. PMID:20349109

  2. SSVEP Response Is Related to Functional Brain Network Topology Entrained by the Flickering Stimulus

    PubMed Central

    Zhang, Yangsong; Xu, Peng; Huang, Yingling; Cheng, Kaiwen; Yao, Dezhong

    2013-01-01

    Previous studies have shown that the brain network topology correlates with the cognitive function. However, few studies have investigated the relationship between functional brain networks that process sensory inputs and outputs. In this study, we focus on steady-state paradigms using a periodic visual stimulus, which are increasingly being used in both brain-computer interface (BCI) and cognitive neuroscience researches. Using the graph theoretical analysis, we investigated the relationship between the topology of functional networks entrained by periodic stimuli and steady state visually evoked potentials (SSVEP) using two frequencies and eleven subjects. First, the entire functional network (Network 0) of each frequency for each subject was constructed according to the coherence between electrode pairs. Next, Network 0 was divided into three sub-networks, in which the connection strengths were either significantly (positively for Network 1, negatively for Network 3) or non-significantly (Network 2) correlated with the SSVEP responses. Our results revealed that the SSVEP responses were positively correlated to the mean functional connectivity, clustering coefficient, and global and local efficiencies, while these responses were negatively correlated with the characteristic path length of Networks 0, 1 and 2. Furthermore, the strengths of these connections that significantly correlated with the SSVEP (both positively and negatively) were mainly found to be long-range connections between the parietal-occipital and frontal regions. These results indicate that larger SSVEP responses correspond with better functional network topology structures. This study may provide new insights for understanding brain mechanisms when using SSVEPs as frequency tags. PMID:24039789

  3. Ising-like dynamics in large-scale functional brain networks

    NASA Astrophysics Data System (ADS)

    Fraiman, Daniel; Balenzuela, Pablo; Foss, Jennifer; Chialvo, Dante R.

    2009-06-01

    Brain “rest” is defined—more or less unsuccessfully—as the state in which there is no explicit brain input or output. This work focuses on the question of whether such state can be comparable to any known dynamical state. For that purpose, correlation networks from human brain functional magnetic resonance imaging are contrasted with correlation networks extracted from numerical simulations of the Ising model in two dimensions at different temperatures. For the critical temperature Tc , striking similarities appear in the most relevant statistical properties, making the two networks indistinguishable from each other. These results are interpreted here as lending support to the conjecture that the dynamics of the functioning brain is near a critical point.

  4. High frequency functional brain networks in neonates revealed by rapid acquisition resting state fMRI.

    PubMed

    Smith-Collins, Adam P R; Luyt, Karen; Heep, Axel; Kauppinen, Risto A

    2015-07-01

    Understanding how spatially remote brain regions interact to form functional brain networks, and how these develop during the neonatal period, provides fundamental insights into normal brain development, and how mechanisms of brain disorder and recovery may function in the immature brain. A key imaging tool in characterising functional brain networks is examination of T2*-weighted fMRI signal during rest (resting state fMRI, rs-fMRI). The majority of rs-fMRI studies have concentrated on slow signal fluctuations occurring at <0.1 Hz, even though neuronal rhythms, and haemodynamic responses to these fluctuate more rapidly, and there is emerging evidence for crucial information about functional brain connectivity occurring more rapidly than these limits. The characterisation of higher frequency components has been limited by the sampling frequency achievable with standard T2* echoplanar imaging (EPI) sequences. We describe patterns of neonatal functional brain network connectivity derived using accelerated T2*-weighted EPI MRI. We acquired whole brain rs-fMRI data, at subsecond sampling frequency, from preterm infants at term equivalent age and compared this to rs-fMRI data acquired with standard EPI acquisition protocol. We provide the first evidence that rapid rs-fMRI acquisition in neonates, and adoption of an extended frequency range for analysis, allows identification of a substantial proportion of signal power residing above 0.2 Hz. We thereby describe changes in brain connectivity associated with increasing maturity which are not evident using standard rs-fMRI protocols. Development of optimised neonatal fMRI protocols, including use of high speed acquisition sequences, is crucial for understanding the physiology and pathophysiology of the developing brain. PMID:25787931

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

    PubMed

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

    2014-09-01

    The ability to link variables is critical to many high-order cognitive functions, including reasoning. It has been proposed that limits in relating variables depend critically on relational complexity, defined formally as the number of variables to be related in solving a problem. In humans, the prefrontal cortex is known to be important for reasoning, but recent studies have suggested that such processes are likely to involve widespread functional brain networks. To test this hypothesis, we used functional magnetic resonance imaging and a classic measure of deductive reasoning to examine changes in brain networks as a function of relational complexity. As expected, behavioral performance declined as the number of variables to be related increased. Likewise, increments in relational complexity were associated with proportional enhancements in brain activity and task-based connectivity within and between 2 cognitive control networks: A cingulo-opercular network for maintaining task set, and a fronto-parietal network for implementing trial-by-trial control. Changes in effective connectivity as a function of increased relational complexity suggested a key role for the left dorsolateral prefrontal cortex in integrating and implementing task set in a trial-by-trial manner. Our findings show that limits in relational processing are manifested in the brain as complexity-dependent modulations of large-scale networks. PMID:23563963

  6. Electro-acupuncture at different acupoints modulating the relative specific brain functional network

    NASA Astrophysics Data System (ADS)

    Fang, Jiliang; Wang, Xiaoling; Wang, Yin; Liu, Hesheng; Hong, Yang; Liu, Jun; Zhou, Kehua; Wang, Lei; Xue, Chao; Song, Ming; Liu, Baoyan; Zhu, Bing

    2010-11-01

    Objective: The specific brain effects of acupoint are important scientific concern in acupuncture. However, previous acupuncture fMRI studies focused on acupoints in muscle layer on the limb. Therefore, researches on acupoints within connective tissue at trunk are warranted. Material and Methods: Brain effects of acupuncture on abdomen at acupoints Guanyuan (CV4) and Zhongwan (CV12) were tested using fMRI on 21 healthy volunteers. The data acquisition was performed at resting state, during needle retention, electroacupuncture (EA) and post-EA resting state. Needling sensations were rated after every electroacupuncture (EA) procedure. The needling sensations and the brain functional activity and connectivity were compared between CV4 and CV12 using SPSS, SPM2 and the local and remote connectivity maps. Results and conclusion: EA at CV4 and CV12 induced apparent deactivation effects in the limbic-paralimbic-neocortical network. The default mode of the brain was modified by needle retention and EA, respectively. The functional brain network was significantly changed post EA. However, the minor differences existed between these two acupoints. The results demonstrated similarity between functional brain network mode of acupuncture modulation and functional circuits of emotional and cognitive regulation. Acupuncture may produce analgesia, anti-anxiety and anti-depression via the limbic-paralimbic-neocortical network (LPNN).

  7. Altered brain rhythms and functional network disruptions involved in patients with generalized fixation-off epilepsy.

    PubMed

    Solana, Ana Beatriz; Martínez, Kenia; Hernández-Tamames, Juan Antonio; San Antonio-Arce, Victoria; Toledano, Rafael; García-Morales, Irene; Alvárez-Linera, Juan; Gil-Nágel, Antonio; Del Pozo, Francisco

    2016-06-01

    Generalized Fixation-off Sensitivity (CGE-FoS) patients present abnormal EEG patterns when losing fixation. In the present work, we studied two CGE-FoS epileptic patients with simultaneous EEG-fMRI. We aim to identify brain areas that are specifically related to the pathology by identifying the brain networks that are related to the EEG brain altered rhythms. Three main analyses were performed: EEG standalone, where the voltage fluctuations in delta, alpha, and beta EEG bands were obtained; fMRI standalone, where resting-state fMRI ICA analyses for opened and closed eyes conditions were computed per subject; and, EEG-informed fMRI, where EEG delta, alpha and beta oscillations were used to analyze fMRI. Patient 1 showed EEG abnormalities for lower beta band EEG brain rhythm. Fluctuations of this rhythm were correlated with a brain network mainly composed by temporo-frontal areas only found in the closed eyes condition. Patient 2 presented alterations in all the EEG brain rhythms (delta, alpha, beta) under study when closing eyes. Several biologically relevant brain networks highly correlated (r > 0.7) to each other in the closed eyes condition were found. EEG-informed fMRI results in patient 2 showed hypersynchronized patterns in the fMRI correlation spatial maps. The obtained findings allow a differential diagnosis for each patient and different profiles with respect to healthy volunteers. The results suggest a different disruption in the functional brain networks of these patients that depends on their altered brain rhythms. This knowledge could be used to treat these patients by novel brain stimulation approaches targeting specific altered brain networks in each patient. PMID:26001771

  8. Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?

    NASA Astrophysics Data System (ADS)

    Jalili, Mahdi

    2016-07-01

    The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.

  9. Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?

    PubMed

    Jalili, Mahdi

    2016-01-01

    The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer's Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals. PMID:27417262

  10. Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?

    PubMed Central

    Jalili, Mahdi

    2016-01-01

    The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals. PMID:27417262

  11. Modular structure of brain functional networks: breaking the resolution limit by Surprise

    NASA Astrophysics Data System (ADS)

    Nicolini, Carlo; Bifone, Angelo

    2016-01-01

    The modular organization of brain networks has been widely investigated using graph theoretical approaches. Recently, it has been demonstrated that graph partitioning methods based on the maximization of global fitness functions, like Newman’s Modularity, suffer from a resolution limit, as they fail to detect modules that are smaller than a scale determined by the size of the entire network. Here we explore the effects of this limitation on the study of brain connectivity networks. We demonstrate that the resolution limit prevents detection of important details of the brain modular structure, thus hampering the ability to appreciate differences between networks and to assess the topological roles of nodes. We show that Surprise, a recently proposed fitness function based on probability theory, does not suffer from these limitations. Surprise maximization in brain co-activation and functional connectivity resting state networks reveals the presence of a rich structure of heterogeneously distributed modules, and differences in networks’ partitions that are undetectable by resolution-limited methods. Moreover, Surprise leads to a more accurate identification of the network’s connector hubs, the elements that integrate the brain modules into a cohesive structure.

  12. Modular structure of brain functional networks: breaking the resolution limit by Surprise

    PubMed Central

    Nicolini, Carlo; Bifone, Angelo

    2016-01-01

    The modular organization of brain networks has been widely investigated using graph theoretical approaches. Recently, it has been demonstrated that graph partitioning methods based on the maximization of global fitness functions, like Newman’s Modularity, suffer from a resolution limit, as they fail to detect modules that are smaller than a scale determined by the size of the entire network. Here we explore the effects of this limitation on the study of brain connectivity networks. We demonstrate that the resolution limit prevents detection of important details of the brain modular structure, thus hampering the ability to appreciate differences between networks and to assess the topological roles of nodes. We show that Surprise, a recently proposed fitness function based on probability theory, does not suffer from these limitations. Surprise maximization in brain co-activation and functional connectivity resting state networks reveals the presence of a rich structure of heterogeneously distributed modules, and differences in networks’ partitions that are undetectable by resolution-limited methods. Moreover, Surprise leads to a more accurate identification of the network’s connector hubs, the elements that integrate the brain modules into a cohesive structure. PMID:26763931

  13. A cortical core for dynamic integration of functional networks in the resting human brain

    PubMed Central

    de Pasquale, Francesco; Della Penna, Stefania; Snyder, Abraham Z.; Marzetti, Laura; Pizzella, Vittorio; Romani, Gian Luca; Corbetta, Maurizio

    2012-01-01

    Summary We used magneto-encephalography to study the temporal dynamics of band-limited power correlation at rest within and across six brain networks previously defined by prior fMRI studies. Epochs of transiently high within-network BLP correlation were identified and correlation of BLP time-series across networks was assessed in these epochs. These analyses demonstrate that functional networks are not equivalent with respect to cross-network interactions. The default-mode network and the posterior cingulate cortex, in particular, exhibit the highest degree of transient BLP correlation with other networks especially in the 14–25 Hz (beta band) frequency range. Our results indicate that the previously demonstrated neuroanatomical centrality of the PCC and DMN has a physiological counterpart in the temporal dynamics of network interaction at behaviorally relevant time scales. This interaction involved subsets of nodes from other networks during periods in which their internal correlation was low. PMID:22632732

  14. Aberrant Functional Whole-Brain Network Architecture in Patients With Schizophrenia: A Meta-analysis.

    PubMed

    Kambeitz, Joseph; Kambeitz-Ilankovic, Lana; Cabral, Carlos; Dwyer, Dominic B; Calhoun, Vince D; van den Heuvel, Martijn P; Falkai, Peter; Koutsouleris, Nikolaos; Malchow, Berend

    2016-07-01

    Findings from multiple lines of research provide evidence of aberrant functional brain connectivity in schizophrenia. By using graph-analytical measures, recent studies indicate that patients with schizophrenia exhibit changes in the organizational principles of whole-brain networks and that these changes relate to cognitive symptoms. However, there has not been a systematic investigation of functional brain network changes in schizophrenia to test the consistency of these changes across multiple studies. A comprehensive literature search was conducted to identify all available functional graph-analytical studies in patients with schizophrenia. Effect size measures were derived from each study and entered in a random-effects meta-analytical model. All models were tested for effects of potential moderator variables as well as for the presence of publication bias. The results of a total of n = 13 functional neuroimaging studies indicated that brain networks in patients with schizophrenia exhibit significant decreases in measures of local organization (g = -0.56, P = .02) and significant decreases in small-worldness (g = -0.65, P = .01) whereas global short communication paths seemed to be preserved (g = 0.26, P = .32). There was no evidence for a publication bias or moderator effects. The present meta- analysis demonstrates significant changes in whole brain network architecture associated with schizophrenia across studies. PMID:27460615

  15. The Richness of Task-Evoked Hemodynamic Responses Defines a Pseudohierarchy of Functionally Meaningful Brain Networks.

    PubMed

    Orban, Pierre; Doyon, Julien; Petrides, Michael; Mennes, Maarten; Hoge, Richard; Bellec, Pierre

    2015-09-01

    Functional magnetic resonance imaging can measure distributed and subtle variations in brain responses associated with task performance. However, it is unclear whether the rich variety of responses observed across the brain is functionally meaningful and consistent across individuals. Here, we used a multivariate clustering approach that grouped brain regions into clusters based on the similarity of their task-evoked temporal responses at the individual level, and then established the spatial consistency of these individual clusters at the group level. We observed a stable pseudohierarchy of task-evoked networks in the context of a delayed sequential motor task, where the fractionation of networks was driven by a gradient of involvement in motor sequence preparation versus execution. In line with theories about higher-level cognitive functioning, this gradient evolved in a rostro-caudal manner in the frontal lobe. In addition, parcellations in the cerebellum and basal ganglia matched with known anatomical territories and fiber pathways with the cerebral cortex. These findings demonstrate that subtle variations in brain responses associated with task performance are systematic enough across subjects to define a pseudohierarchy of task-evoked networks. Such networks capture meaningful functional features of brain organization as shaped by a given cognitive context. PMID:24729172

  16. Topology of whole-brain functional MRI networks: Improving the truncated scale-free model

    NASA Astrophysics Data System (ADS)

    Ruiz Vargas, E.; Mitchell, D. G. V.; Greening, S. G.; Wahl, L. M.

    2014-07-01

    Networks of connections within the human brain have been the subject of intense recent research, yet their topology is still only partially understood. We analyze weighted networks calculated from functional magnetic resonance imaging (fMRI) data acquired during task performance. Expanding previous work in the area, our analysis retains all of the connections between all of the voxels in the full brain fMRI data, computing correlations between approximately 200,000 voxels per subject for 10 subjects. We evaluate the extent to which this rich dataset can be described by existing models of scale-free or exponentially truncated scale-free topology, comparing results across a large number of more complex topological models as well. Our results suggest that the novel “log quadratic” model presented in this paper offers a significantly better fit to networks of functional connections at the voxel level in the human brain.

  17. Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence.

    PubMed

    Vakhtin, Andrei A; Ryman, Sephira G; Flores, Ranee A; Jung, Rex E

    2014-12-01

    The refinement of localization of intelligence in the human brain is converging onto a distributed network that broadly conforms to the Parieto-Frontal Integration Theory (P-FIT). While this theory has received support in the neuroimaging literature, no functional magnetic resonance imaging study to date has conducted a whole-brain network-wise examination of the changes during engagement in tasks that are reliable measures of general intelligence (e.g., Raven's Progressive Matrices Test; RPM). Seventy-nine healthy subjects were scanned while solving RPM problems and during rest. Functional networks were extracted from the RPM and resting state data using Independent Component Analysis. Twenty-nine networks were identified, 26 of which were detected in both conditions. Fourteen networks were significantly correlated with the RPM task. The networks' spatial maps and functional connectivity measures at 3 frequency levels (low, medium, & high) were compared between the RPM and rest conditions. The regions involved in the networks that were found to be task related were consistent with the P-FIT, localizing to the bilateral medial frontal and parietal regions, right superior frontal lobule, and the right cingulate gyrus. Functional connectivity in multiple component pairs was differentially affected across all frequency levels during the RPM task. Our findings demonstrate that functional brain networks are more stable than previously thought, and maintain their general features across resting state and engagement in a complex cognitive task. The described spatial and functional connectivity alterations that such components undergo during fluid reasoning provide a network-wise framework of the P-FIT that can be valuable for further, network based, neuroimaging inquiries regarding the neural underpinnings of intelligence. PMID:25284305

  18. Age-related changes in modular organization of human brain functional networks.

    PubMed

    Meunier, David; Achard, Sophie; Morcom, Alexa; Bullmore, Ed

    2009-02-01

    Graph theory allows us to quantify any complex system, e.g., in social sciences, biology or technology, that can be abstractly described as a set of nodes and links. Here we derived human brain functional networks from fMRI measurements of endogenous, low frequency, correlated oscillations in 90 cortical and subcortical regions for two groups of healthy (young and older) participants. We investigated the modular structure of these networks and tested the hypothesis that normal brain aging might be associated with changes in modularity of sparse networks. Newman's modularity metric was maximised and topological roles were assigned to brain regions depending on their specific contributions to intra- and inter-modular connectivity. Both young and older brain networks demonstrated significantly non-random modularity. The young brain network was decomposed into 3 major modules: central and posterior modules, which comprised mainly nodes with few inter-modular connections, and a dorsal fronto-cingulo-parietal module, which comprised mainly nodes with extensive inter-modular connections. The mean network in the older group also included posterior, superior central and dorsal fronto-striato-thalamic modules but the number of intermodular connections to frontal modular regions was significantly reduced, whereas the number of connector nodes in posterior and central modules was increased. PMID:19027073

  19. Lateralized Resting-State Functional Brain Network Organization Changes in Heart Failure

    PubMed Central

    Park, Bumhee; Roy, Bhaswati; Woo, Mary A.; Palomares, Jose A.; Fonarow, Gregg C.; Harper, Ronald M.; Kumar, Rajesh

    2016-01-01

    Heart failure (HF) patients show brain injury in autonomic, affective, and cognitive sites, which can change resting-state functional connectivity (FC), potentially altering overall functional brain network organization. However, the status of such connectivity or functional organization is unknown in HF. Determination of that status was the aim here, and we examined region-to-region FC and brain network topological properties across the whole-brain in 27 HF patients compared to 53 controls with resting-state functional MRI procedures. Decreased FC in HF appeared between the caudate and cerebellar regions, olfactory and cerebellar sites, vermis and medial frontal regions, and precentral gyri and cerebellar areas. However, increased FC emerged between the middle frontal gyrus and sensorimotor areas, superior parietal gyrus and orbito/medial frontal regions, inferior temporal gyrus and lingual gyrus/cerebellar lobe/pallidum, fusiform gyrus and superior orbitofrontal gyrus and cerebellar sites, and within vermis and cerebellar areas; these connections were largely in the right hemisphere (p<0.005; 10,000 permutations). The topology of functional integration and specialized characteristics in HF are significantly changed in regions showing altered FC, an outcome which would interfere with brain network organization (p<0.05; 10,000 permutations). Brain dysfunction in HF extends to resting conditions, and autonomic, cognitive, and affective deficits may stem from altered FC and brain network organization that may contribute to higher morbidity and mortality in the condition. Our findings likely result from the prominent axonal and nuclear structural changes reported earlier in HF; protecting neural tissue may improve FC integrity, and thus, increase quality of life and reduce morbidity and mortality. PMID:27203600

  20. Frontoparietal Connectivity and Hierarchical Structure of the Brain's Functional Network during Sleep.

    PubMed

    Spoormaker, Victor I; Gleiser, Pablo M; Czisch, Michael

    2012-01-01

    Frontal and parietal regions are associated with some of the most complex cognitive functions, and several frontoparietal resting-state networks can be observed in wakefulness. We used functional magnetic resonance imaging data acquired in polysomnographically validated wakefulness, light sleep, and slow-wave sleep to examine the hierarchical structure of a low-frequency functional brain network, and to examine whether frontoparietal connectivity would disintegrate in sleep. Whole-brain analyses with hierarchical cluster analysis on predefined atlases were performed, as well as regression of inferior parietal lobules (IPL) seeds against all voxels in the brain, and an evaluation of the integrity of voxel time-courses in subcortical regions-of-interest. We observed that frontoparietal functional connectivity disintegrated in sleep stage 1 and was absent in deeper sleep stages. Slow-wave sleep was characterized by strong hierarchical clustering of local submodules. Frontoparietal connectivity between IPL and superior medial and right frontal gyrus was lower in sleep stages than in wakefulness. Moreover, thalamus voxels showed maintained integrity in sleep stage 1, making intrathalamic desynchronization an unlikely source of reduced thalamocortical connectivity in this sleep stage. Our data suggest a transition from a globally integrated functional brain network in wakefulness to a disintegrated network consisting of local submodules in slow-wave sleep, in which frontoparietal inter-modular nodes may play a role, possibly in combination with the thalamus. PMID:22629253

  1. Functional brain networks: great expectations, hard times and the big leap forward

    PubMed Central

    Papo, David; Zanin, Massimiliano; Pineda-Pardo, José Angel; Boccaletti, Stefano; Buldú, Javier M.

    2014-01-01

    Many physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks has generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain. We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation of complex network theory, to account for its exquisitely complex functioning mode. PMID:25180303

  2. Cholinergic and serotonergic modulations differentially affect large-scale functional networks in the mouse brain.

    PubMed

    Shah, Disha; Blockx, Ines; Keliris, Georgios A; Kara, Firat; Jonckers, Elisabeth; Verhoye, Marleen; Van der Linden, Annemie

    2016-07-01

    Resting-state functional MRI (rsfMRI) is a widely implemented technique used to investigate large-scale topology in the human brain during health and disease. Studies in mice provide additional advantages, including the possibility to flexibly modulate the brain by pharmacological or genetic manipulations in combination with high-throughput functional connectivity (FC) investigations. Pharmacological modulations that target specific neurotransmitter systems, partly mimicking the effect of pathological events, could allow discriminating the effect of specific systems on functional network disruptions. The current study investigated the effect of cholinergic and serotonergic antagonists on large-scale brain networks in mice. The cholinergic system is involved in cognitive functions and is impaired in, e.g., Alzheimer's disease, while the serotonergic system is involved in emotional and introspective functions and is impaired in, e.g., Alzheimer's disease, depression and autism. Specific interest goes to the default-mode-network (DMN), which is studied extensively in humans and is affected in many neurological disorders. The results show that both cholinergic and serotonergic antagonists impaired the mouse DMN-like network similarly, except that cholinergic modulation additionally affected the retrosplenial cortex. This suggests that both neurotransmitter systems are involved in maintaining integrity of FC within the DMN-like network in mice. Cholinergic and serotonergic modulations also affected other functional networks, however, serotonergic modulation impaired the frontal and thalamus networks more extensively. In conclusion, this study demonstrates the utility of pharmacological rsfMRI in animal models to provide insights into the role of specific neurotransmitter systems on functional networks in neurological disorders. PMID:26195064

  3. Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review.

    PubMed

    Andellini, Martina; Cannatà, Vittorio; Gazzellini, Simone; Bernardi, Bruno; Napolitano, Antonio

    2015-09-30

    The employment of graph theory to analyze spontaneous fluctuations in resting state BOLD fMRI data has become a dominant theme in brain imaging studies and neuroscience. Analysis of resting state functional brain networks based on graph theory has proven to be a powerful tool to quantitatively characterize functional architecture of the brain and it has provided a new platform to explore the overall structure of local and global functional connectivity in the brain. Due to its increased use and possible expansion to clinical use, it is essential that the reliability of such a technique is very strongly assessed. In this review, we explore the outcome of recent studies in network reliability which apply graph theory to analyze connectome resting state networks. Therefore, we investigate which preprocessing steps may affect reproducibility the most. In order to investigate network reliability, we compared the test-retest (TRT) reliability of functional data of published neuroimaging studies with different preprocessing steps. In particular we tested influence of global signal regression, correlation metric choice, binary versus weighted link definition, frequency band selection and length of time-series. Statistical analysis shows that only frequency band selection and length of time-series seem to affect TRT reliability. Our results highlight the importance of the choice of the preprocessing steps to achieve more reproducible measurements. PMID:26072249

  4. A Novel Brain Network Construction Method for Exploring Age-Related Functional Reorganization.

    PubMed

    Li, Wei; Wang, Miao; Li, Yapeng; Huang, Yue; Chen, Xi

    2016-01-01

    The human brain undergoes complex reorganization and changes during aging. Using graph theory, scientists can find differences in topological properties of functional brain networks between young and elderly adults. However, these differences are sometimes significant and sometimes not. Several studies have even identified disparate differences in topological properties during normal aging or in age-related diseases. One possible reason for this issue is that existing brain network construction methods cannot fully extract the "intrinsic edges" to prevent useful signals from being buried into noises. This paper proposes a new subnetwork voting (SNV) method with sliding window to construct functional brain networks for young and elderly adults. Differences in the topological properties of brain networks constructed from the classic and SNV methods were consistent. Statistical analysis showed that the SNV method can identify much more statistically significant differences between groups than the classic method. Moreover, support vector machine was utilized to classify young and elderly adults; its accuracy, based on the SNV method, reached 89.3%, significantly higher than that with classic method. Therefore, the SNV method can improve consistency within a group and highlight differences between groups, which can be valuable for the exploration and auxiliary diagnosis of aging and age-related diseases. PMID:27057155

  5. A Novel Brain Network Construction Method for Exploring Age-Related Functional Reorganization

    PubMed Central

    Li, Wei; Wang, Miao; Li, Yapeng; Huang, Yue; Chen, Xi

    2016-01-01

    The human brain undergoes complex reorganization and changes during aging. Using graph theory, scientists can find differences in topological properties of functional brain networks between young and elderly adults. However, these differences are sometimes significant and sometimes not. Several studies have even identified disparate differences in topological properties during normal aging or in age-related diseases. One possible reason for this issue is that existing brain network construction methods cannot fully extract the “intrinsic edges” to prevent useful signals from being buried into noises. This paper proposes a new subnetwork voting (SNV) method with sliding window to construct functional brain networks for young and elderly adults. Differences in the topological properties of brain networks constructed from the classic and SNV methods were consistent. Statistical analysis showed that the SNV method can identify much more statistically significant differences between groups than the classic method. Moreover, support vector machine was utilized to classify young and elderly adults; its accuracy, based on the SNV method, reached 89.3%, significantly higher than that with classic method. Therefore, the SNV method can improve consistency within a group and highlight differences between groups, which can be valuable for the exploration and auxiliary diagnosis of aging and age-related diseases. PMID:27057155

  6. Structure function relationship in complex brain networks expressed by hierarchical synchronization

    NASA Astrophysics Data System (ADS)

    Zhou, Changsong; Zemanová, Lucia; Zamora-López, Gorka; Hilgetag, Claus C.; Kurths, Jürgen

    2007-06-01

    The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks.

  7. Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

    PubMed Central

    Stevens, Alexander A.; Tappon, Sarah C.; Garg, Arun; Fair, Damien A.

    2012-01-01

    Background Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity. Methodology/Principal Findings Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability. Conclusions/Significance The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules

  8. Resting-brain functional connectivity predicted by analytic measures of network communication.

    PubMed

    Goñi, Joaquín; van den Heuvel, Martijn P; Avena-Koenigsberger, Andrea; Velez de Mendizabal, Nieves; Betzel, Richard F; Griffa, Alessandra; Hagmann, Patric; Corominas-Murtra, Bernat; Thiran, Jean-Philippe; Sporns, Olaf

    2014-01-14

    The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures--search information and path transitivity--which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways. PMID:24379387

  9. Structural architecture supports functional organization in the human aging brain at a regionwise and network level.

    PubMed

    Zimmermann, Joelle; Ritter, Petra; Shen, Kelly; Rothmeier, Simon; Schirner, Michael; McIntosh, Anthony R

    2016-07-01

    Functional interactions in the brain are constrained by the underlying anatomical architecture, and structural and functional networks share network features such as modularity. Accordingly, age-related changes of structural connectivity (SC) may be paralleled by changes in functional connectivity (FC). We provide a detailed qualitative and quantitative characterization of the SC-FC coupling in human aging as inferred from resting-state blood oxygen-level dependent functional magnetic resonance imaging and diffusion-weighted imaging in a sample of 47 adults with an age range of 18-82. We revealed that SC and FC decrease with age across most parts of the brain and there is a distinct age-dependency of regionwise SC-FC coupling and network-level SC-FC relations. A specific pattern of SC-FC coupling predicts age more reliably than does regionwise SC or FC alone (r = 0.73, 95% CI = [0.7093, 0.8522]). Hence, our data propose that regionwise SC-FC coupling can be used to characterize brain changes in aging. Hum Brain Mapp 37:2645-2661, 2016. © 2016 Wiley Periodicals, Inc. PMID:27041212

  10. Spatiotemporal reconfiguration of large-scale brain functional networks during propofol-induced loss of consciousness.

    PubMed

    Schröter, Manuel S; Spoormaker, Victor I; Schorer, Anna; Wohlschläger, Afra; Czisch, Michael; Kochs, Eberhard F; Zimmer, Claus; Hemmer, Bernhard; Schneider, Gerhard; Jordan, Denis; Ilg, Rüdiger

    2012-09-12

    Applying graph theoretical analysis of spontaneous BOLD fluctuations in functional magnetic resonance imaging (fMRI), we investigated whole-brain functional connectivity of 11 healthy volunteers during wakefulness and propofol-induced loss of consciousness (PI-LOC). After extraction of regional fMRI time series from 110 cortical and subcortical regions, we applied a maximum overlap discrete wavelet transformation and investigated changes in the brain's intrinsic spatiotemporal organization. During PI-LOC, we observed a breakdown of subcortico-cortical and corticocortical connectivity. Decrease of connectivity was pronounced in thalamocortical connections, whereas no changes were found for connectivity within primary sensory cortices. Graph theoretical analyses revealed significant changes in the degree distribution and local organization metrics of brain functional networks during PI-LOC: compared with a random network, normalized clustering was significantly increased, as was small-worldness. Furthermore we observed a profound decline in long-range connections and a reduction in whole-brain spatiotemporal integration, supporting a topological reconfiguration during PI-LOC. Our findings shed light on the functional significance of intrinsic brain activity as measured by spontaneous BOLD signal fluctuations and help to understand propofol-induced loss of consciousness. PMID:22973006

  11. Stress-induced alterations in large-scale functional networks of the rodent brain.

    PubMed

    Henckens, Marloes J A G; van der Marel, Kajo; van der Toorn, Annette; Pillai, Anup G; Fernández, Guillén; Dijkhuizen, Rick M; Joëls, Marian

    2015-01-15

    Stress-related psychopathology is associated with altered functioning of large-scale brain networks. Animal research into chronic stress, one of the most prominent environmental risk factors for development of psychopathology, has revealed molecular and cellular mechanisms potentially contributing to human mental disease. However, so far, these studies have not addressed the system-level changes in extended brain networks, thought to critically contribute to mental disorders. We here tested the effects of chronic stress exposure (10 days immobilization) on the structural integrity and functional connectivity patterns in the brain, using high-resolution structural MRI, diffusion kurtosis imaging, and resting-state functional MRI, while confirming the expected changes in neuronal dendritic morphology using Golgi-staining. Stress effectiveness was confirmed by a significantly lower body weight and increased adrenal weight. In line with previous research, stressed animals displayed neuronal dendritic hypertrophy in the amygdala and hypotrophy in the hippocampal and medial prefrontal cortex. Using independent component analysis of resting-state fMRI data, we identified ten functional connectivity networks in the rodent brain. Chronic stress appeared to increase connectivity within the somatosensory, visual, and default mode networks. Moreover, chronic stress exposure was associated with an increased volume and diffusivity of the lateral ventricles, whereas no other volumetric changes were observed. This study shows that chronic stress exposure in rodents induces alterations in functional network connectivity strength which partly resemble those observed in stress-related psychopathology. Moreover, these functional consequences of stress seem to be more prominent than the effects on gross volumetric change, indicating their significance for future research. PMID:25462693

  12. Identifying important nodes in weighted functional brain networks: A comparison of different centrality approaches

    NASA Astrophysics Data System (ADS)

    Kuhnert, Marie-Therese; Geier, Christian; Elger, Christian E.; Lehnertz, Klaus

    2012-06-01

    We compare different centrality metrics which aim at an identification of important nodes in complex networks. We investigate weighted functional brain networks derived from multichannel electroencephalograms recorded from 23 healthy subject under resting-state eyes-open or eyes-closed conditions. Although we observe the metrics strength, closeness, and betweenness centrality to be related to each other, they capture different spatial and temporal aspects of important nodes in these networks associated with behavioral changes. Identifying and characterizing of these nodes thus benefits from the application of several centrality metrics.

  13. Creating probabilistic maps of the face network in the adolescent brain: a multicentre functional MRI study.

    PubMed

    Tahmasebi, Amir M; Artiges, Eric; Banaschewski, Tobias; Barker, Gareth J; Bruehl, Ruediger; Büchel, Christian; Conrod, Patricia J; Flor, Herta; Garavan, Hugh; Gallinat, Jürgen; Heinz, Andreas; Ittermann, Bernd; Loth, Eva; Mareckova, Klara; Martinot, Jean-Luc; Poline, Jean-Baptiste; Rietschel, Marcella; Smolka, Michael N; Ströhle, Andreas; Schumann, Gunter; Paus, Tomáš

    2012-04-01

    Large-scale magnetic resonance (MR) studies of the human brain offer unique opportunities for identifying genetic and environmental factors shaping the human brain. Here, we describe a dataset collected in the context of a multi-centre study of the adolescent brain, namely the IMAGEN Study. We focus on one of the functional paradigms included in the project to probe the brain network underlying processing of ambiguous and angry faces. Using functional MR (fMRI) data collected in 1,110 adolescents, we constructed probabilistic maps of the neural network engaged consistently while viewing the ambiguous or angry faces; 21 brain regions responding to faces with high probability were identified. We were also able to address several methodological issues, including the minimal sample size yielding a stable location of a test region, namely the fusiform face area (FFA), as well as the effect of acquisition site (eight sites) and scanner (four manufacturers) on the location and magnitude of the fMRI response to faces in the FFA. Finally, we provided a comparison between male and female adolescents in terms of the effect sizes of sex differences in brain response to the ambiguous and angry faces in the 21 regions of interest. Overall, we found a stronger neural response to the ambiguous faces in several cortical regions, including the fusiform face area, in female (vs. male) adolescents, and a slightly stronger response to the angry faces in the amygdala of male (vs. female) adolescents. PMID:21416563

  14. Modalities of Thinking: State and Trait Effects on Cross-Frequency Functional Independent Brain Networks.

    PubMed

    Milz, Patricia; Pascual-Marqui, Roberto D; Lehmann, Dietrich; Faber, Pascal L

    2016-05-01

    Functional states of the brain are constituted by the temporally attuned activity of spatially distributed neural networks. Such networks can be identified by independent component analysis (ICA) applied to frequency-dependent source-localized EEG data. This methodology allows the identification of networks at high temporal resolution in frequency bands of established location-specific physiological functions. EEG measurements are sensitive to neural activity changes in cortical areas of modality-specific processing. We tested effects of modality-specific processing on functional brain networks. Phasic modality-specific processing was induced via tasks (state effects) and tonic processing was assessed via modality-specific person parameters (trait effects). Modality-specific person parameters and 64-channel EEG were obtained from 70 male, right-handed students. Person parameters were obtained using cognitive style questionnaires, cognitive tests, and thinking modality self-reports. EEG was recorded during four conditions: spatial visualization, object visualization, verbalization, and resting. Twelve cross-frequency networks were extracted from source-localized EEG across six frequency bands using ICA. RMANOVAs, Pearson correlations, and path modelling examined effects of tasks and person parameters on networks. Results identified distinct state- and trait-dependent functional networks. State-dependent networks were characterized by decreased, trait-dependent networks by increased alpha activity in sub-regions of modality-specific pathways. Pathways of competing modalities showed opposing alpha changes. State- and trait-dependent alpha were associated with inhibitory and automated processing, respectively. Antagonistic alpha modulations in areas of competing modalities likely prevent intruding effects of modality-irrelevant processing. Considerable research suggested alpha modulations related to modality-specific states and traits. This study identified the

  15. Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia

    PubMed Central

    Lo, Chun-Yi Zac; Su, Tsung-Wei; Huang, Chu-Chung; Hung, Chia-Chun; Chen, Wei-Ling; Lan, Tsuo-Hung; Lin, Ching-Po; Bullmore, Edward T.

    2015-01-01

    Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients’ networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population. PMID:26150519

  16. Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia.

    PubMed

    Lo, Chun-Yi Zac; Su, Tsung-Wei; Huang, Chu-Chung; Hung, Chia-Chun; Chen, Wei-Ling; Lan, Tsuo-Hung; Lin, Ching-Po; Bullmore, Edward T

    2015-07-21

    Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population. PMID:26150519

  17. Graph Theory Analysis of Functional Brain Networks and Mobility Disability in Older Adults

    PubMed Central

    Burdette, Jonathan H.; Morgan, Ashley R.; Williamson, Jeff D.; Kritchevsky, Stephen B.; Laurienti, Paul J.

    2014-01-01

    Background. The brain’s structural integrity is associated with mobility function in older adults. Changes in function may be evident earlier than changes in structure and may be more directly related to mobility. Therefore, we assessed whether functional brain networks varied with mobility function in older adults. Methods. Short Physical Performance Battery (SPPB) and resting state functional magnetic resonance imaging were collected on 24 young (mean age = 26.4±5.1) and 48 older (mean age = 72.04±5.1) participants. Older participants were divided into three groups by SPPB score: Low SPPB (score = 7–9), Mid SPPB (score = 10), High SPPB (score = 11–12).Graph theory–based methods were used to characterize and compare brain network organization. Results. Connectivity in the somatomotor cortex distinguished between groups based on SPPB score. The community structure of the somatomotor cortex was significantly less consistent in the Low SPPB group (mean = 0.097±0.05) compared with Young (mean = 0.163±0.09, p = .03) SPPB group. Striking differences were evident in second-order connections between somatomotor cortex and superior temporal gyrus and insula that reached statistical significance. The Low SPPB group (mean = 140.87±109.30) had a significantly higher number of connections than Young (mean = 45.05±33.79, p = .0003) or High (mean = 49.61±35.31, p = .002) SPPB group. Conclusions. Older adults with poorer mobility function exhibited reduced consistency of somatomotor community structure and a greater number of secondary connections with vestibular and multisensory regions of the brain. Further study is needed to fully interpret these effects, but analysis of functional brain networks adds new insights to the contribution of the brain to mobility. PMID:24717331

  18. Divergent brain functional network alterations in dementia with Lewy bodies and Alzheimer's disease

    PubMed Central

    Peraza, Luis R.; Taylor, John-Paul; Kaiser, Marcus

    2015-01-01

    The clinical phenotype of dementia with Lewy bodies (DLB) is different from Alzheimer's disease (AD), suggesting a divergence between these diseases in terms of brain network organization. To fully understand this, we studied functional networks from resting-state functional magnetic resonance imaging in cognitively matched DLB and AD patients. The DLB group demonstrated a generalized lower synchronization compared with the AD and healthy controls, and this was more severe for edges connecting distant brain regions. Global network measures were significantly different between DLB and AD. For instance, AD showed lower small-worldness than healthy controls, while DLB showed higher small-worldness (AD < controls < DLB), and this was also the case for global efficiency (DLB > controls > AD) and clustering coefficient (DLB < controls < AD). Differences were also found for nodal measures at brain regions associated with each disease. Finally, we found significant associations between network performance measures and global cognitive impairment and severity of cognitive fluctuations in DLB. These results show network divergences between DLB and AD which appear to reflect their neuropathological differences. PMID:26115566

  19. Understanding brain networks and brain organization

    NASA Astrophysics Data System (ADS)

    Pessoa, Luiz

    2014-09-01

    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. However, 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.

  20. 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

  1. Creativity and the default network: A functional connectivity analysis of the creative brain at rest☆

    PubMed Central

    Beaty, Roger E.; Benedek, Mathias; Wilkins, Robin W.; Jauk, Emanuel; Fink, Andreas; Silvia, Paul J.; Hodges, Donald A.; Koschutnig, Karl; Neubauer, Aljoscha C.

    2014-01-01

    The present research used resting-state functional magnetic resonance imaging (fMRI) to examine whether the ability to generate creative ideas corresponds to differences in the intrinsic organization of functional networks in the brain. We examined the functional connectivity between regions commonly implicated in neuroimaging studies of divergent thinking, including the inferior prefrontal cortex and the core hubs of the default network. Participants were prescreened on a battery of divergent thinking tests and assigned to high- and low-creative groups based on task performance. Seed-based functional connectivity analysis revealed greater connectivity between the left inferior frontal gyrus (IFG) and the entire default mode network in the high-creative group. The right IFG also showed greater functional connectivity with bilateral inferior parietal cortex and the left dorsolateral prefrontal cortex in the high-creative group. The results suggest that the ability to generate creative ideas is characterized by increased functional connectivity between the inferior prefrontal cortex and the default network, pointing to a greater cooperation between brain regions associated with cognitive control and low-level imaginative processes. PMID:25245940

  2. 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

  3. 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

  4. Altered functional brain network connectivity and glutamate system function in transgenic mice expressing truncated Disrupted-in-Schizophrenia 1.

    PubMed

    Dawson, N; Kurihara, M; Thomson, D M; Winchester, C L; McVie, A; Hedde, J R; Randall, A D; Shen, S; Seymour, P A; Hughes, Z A; Dunlop, J; Brown, J T; Brandon, N J; Morris, B J; Pratt, J A

    2015-01-01

    Considerable evidence implicates DISC1 as a susceptibility gene for multiple psychiatric diseases. DISC1 has been intensively studied at the molecular, cellular and behavioral level, but its role in regulating brain connectivity and brain network function remains unknown. Here, we utilize a set of complementary approaches to assess the functional brain network abnormalities present in mice expressing a truncated Disc1 gene (Disc1tr Hemi mice). Disc1tr Hemi mice exhibited hypometabolism in the prefrontal cortex (PFC) and reticular thalamus along with a reorganization of functional brain network connectivity that included compromised hippocampal-PFC connectivity. Altered hippocampal-PFC connectivity in Disc1tr Hemi mice was confirmed by electrophysiological analysis, with Disc1tr Hemi mice showing a reduced probability of presynaptic neurotransmitter release in the monosynaptic glutamatergic hippocampal CA1-PFC projection. Glutamate system dysfunction in Disc1tr Hemi mice was further supported by the attenuated cerebral metabolic response to the NMDA receptor (NMDAR) antagonist ketamine and decreased hippocampal expression of NMDAR subunits 2A and 2B in these animals. These data show that the Disc1 truncation in Disc1tr Hemi mice induces a range of translationally relevant endophenotypes underpinned by glutamate system dysfunction and altered brain connectivity. PMID:25989143

  5. 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

  6. 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. PMID:25374531

  7. [Functional connectivity analysis of the brain network using resting-state FMRI].

    PubMed

    Hayashi, Toshihiro

    2011-12-01

    Spatial patterns of spontaneous fluctuations in blood oxygenation level-dependent (BOLD) signals reflect the underlying neural architecture. The study of the brain network based on these self-organized patterns is termed resting-state functional MRI (fMRI). This review article aims at briefly reviewing a basic concept of this technology and discussing its implications for neuropsychological studies. First, the technical aspects of resting-state fMRI, including signal sources, physiological artifacts, image acquisition, and analytical methods such as seed-based correlation analysis and independent component analysis, are explained, followed by a discussion on the major resting-state networks, including the default mode network. In addition, the structure-function correlation studied using diffuse tensor imaging and resting-state fMRI is briefly discussed. Second, I have discussed the reservations and potential pitfalls of 2 major imaging methods: voxel-based lesion-symptom mapping and task fMRI. Problems encountered with voxel-based lesion-symptom mapping can be overcome by using resting-state fMRI and evaluating undamaged brain networks in patients. Regarding task fMRI in patients, I have also emphasized the importance of evaluating the baseline brain activity because the amplitude of activation in BOLD fMRI is hard to interpret as the same baseline cannot be assumed for both patient and normal groups. PMID:22147450

  8. Exploring influence of subliminal interoception on whole-brain functional network connectivity dynamics.

    PubMed

    Jarrahi, Behnaz; Mantini, Dante; Mehnert, Ulrich; Kollias, Spyros

    2015-08-01

    Recent fMRI studies have highlighted a dynamic relation across large-scale intrinsic connectivity networks (ICNs) of human brain. The origin of such temporal variations in functional connectivity especially during the task-free (resting-state) fMRI is still a matter of debate and ongoing investigation. In this exploratory study, we sought to determine whether subliminal differences in interoception (e.g., distention pressure on the viscera) can influence the dynamics of whole-brain functional network connectivity. A group of healthy right-handed female subjects, close in age (n = 15, mean age ± SD = 30.33 ± 8.7 years) underwent a series of eyes-open resting-state fMRI scans under different interoceptive conditions including catheterization and partial bladder filling. Using a high-dimensional independent component analysis, the functional imaging data were parcellated into 75 components, out of which 33 were identified as non-artifactual ICNs. Changes in dynamic functional network connectivity (dFNC) were evaluated using the sliding-time window approach and k-means clustering algorithm. We used subject medians for each cluster state and compared differences in dFNC correlations using a paired t-test. Following a false discovery rate multiple comparison correction threshold of p<;0.05, no significant differences in dFNC were found. However, different dwell times for each (pseudo-)resting-state were observed. More liberal statistical criteria (uncorrected p<;0.005) also indicated differences in dFNC between ICN pairs especially involving the salience, subcortical, sensorimotor, cerebellar and brainstem networks. Further investigations of the effect of internal (bodily) sensations on the time-varying aspects of functional connectivity can improve our understanding of the nature of temporal fluctuations in interrelations between intrinsic brain networks. PMID:26736351

  9. Delayed convergence between brain network structure and function in rolandic epilepsy

    PubMed Central

    Besseling, René M. H.; Jansen, Jacobus F. A.; Overvliet, Geke M.; van der Kruijs, Sylvie J. M.; Ebus, Saskia C. M.; de Louw, Anton J. A.; Hofman, Paul A. M.; Aldenkamp, Albert P.; Backes, Walter H.

    2014-01-01

    Introduction: Rolandic epilepsy (RE) manifests during a critical phase of brain development, and has been associated with language impairments. Concordant abnormalities in structural and functional connectivity (SC and FC) have been described before. As SC and FC are under mutual influence, the current study investigates abnormalities in the SC-FC synergy in RE. Methods: Twenty-two children with RE (age, mean ± SD: 11.3 ± 2.0 y) and 22 healthy controls (age 10.5 ± 1.6 y) underwent structural, diffusion weighted, and resting-state functional magnetic resonance imaging (MRI) at 3T. The probabilistic anatomical landmarks atlas was used to parcellate the (sub)cortical gray matter. Constrained spherical deconvolution tractography and correlation of time series were used to assess SC and FC, respectively. The SC-FC correlation was assessed as a function of age for the non-zero structural connections over a range of sparsity values (0.01–0.75). A modularity analysis was performed on the mean SC network of the controls to localize potential global effects to subnetworks. SC and FC were also assessed separately using graph analysis. Results: The SC-FC correlation was significantly reduced in children with RE compared to healthy controls, especially for the youngest participants. This effect was most pronounced in a left and a right centro-temporal network, as well as in a medial parietal network. Graph analysis revealed no prominent abnormalities in SC or FC network organization. Conclusion: Since SC and FC converge during normal maturation, our finding of reduced SC-FC correlation illustrates impaired synergy between brain structure and function. More specifically, since this effect was most pronounced in the youngest participants, RE may represent a developmental disorder of delayed brain network maturation. The observed effects seem especially attributable to medial parietal connections, which forms an intermediate between bilateral centro-temporal modules of

  10. Changes in functional brain networks following sports-related concussion in adolescents.

    PubMed

    Virji-Babul, Naznin; Hilderman, Courtney G E; Makan, Nadia; Liu, Aiping; Smith-Forrester, Jenna; Franks, Chris; Wang, Z J

    2014-12-01

    Sports-related concussion is a major public health issue; however, little is known about the underlying changes in functional brain networks in adolescents following injury. Our aim was to use the tools from graph theory to evaluate the changes in brain network properties following concussion in adolescent athletes. We recorded resting state electroencephalography (EEG) in 33 healthy adolescent athletes and 9 adolescent athletes with a clinical diagnosis of subacute concussion. Graph theory analysis was applied to these data to evaluate changes in brain networks. Global and local metrics of the structural properties of the graph were calculated for each group and correlated with Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) scores. Brain networks of both groups showed small-world topology with no statistically significant differences in the global metrics; however, significant differences were found in the local metrics. Specifically, in the concussed group, we noted: 1) increased values of betweenness and degree in frontal electrode sites corresponding to the (R) dorsolateral prefrontal cortex and the (R) inferior frontal gyrus and 2) decreased values of degree in the region corresponding to the (R) frontopolar prefrontal cortex. In addition, there was significant negative correlation between degree and hub value, with total symptom score at the electrode site corresponding to the (R) prefrontal cortex. This preliminary report in adolescent athletes shows for the first time that resting-state EEG combined with graph theoretical analysis may provide an objective method of evaluating changes in brain networks following concussion. This approach may be useful in identifying individuals at risk for future injury. PMID:24956041

  11. Complex function in the dynamic brain. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

    NASA Astrophysics Data System (ADS)

    Anderson, Michael L.

    2014-09-01

    There is much to commend in this excellent overview of the progress we've made toward-and the challenges that remain for-developing an empirical framework for neuroscience that is adequate to the dynamic complexity of the brain [17]. Here I will limit myself first to highlighting the concept of dynamic affiliation, which I take to be the central feature of the functional architecture of the brain, and second to clarifying Pessoa's brief discussion of the ontology of cognition, to be sure readers appreciate this crucial issue.

  12. Flexible establishment of functional brain networks supports attentional modulation of unconscious cognition.

    PubMed

    Ulrich, Martin; Adams, Sarah C; Kiefer, Markus

    2014-11-01

    In classical theories of attention, unconscious automatic processes are thought to be independent of higher-level attentional influences. Here, we propose that unconscious processing depends on attentional enhancement of task-congruent processing pathways implemented by a dynamic modulation of the functional communication between brain regions. Using functional magnetic resonance imaging, we tested our model with a subliminally primed lexical decision task preceded by an induction task preparing either a semantic or a perceptual task set. Subliminal semantic priming was significantly greater after semantic compared to perceptual induction in ventral occipito-temporal (vOT) and inferior frontal cortex, brain areas known to be involved in semantic processing. The functional connectivity pattern of vOT varied depending on the induction task and successfully predicted the magnitude of behavioral and neural priming. Together, these findings support the proposal that dynamic establishment of functional networks by task sets is an important mechanism in the attentional control of unconscious processing. PMID:24954512

  13. Resolving Anatomical and Functional Structure in Human Brain Organization: Identifying Mesoscale Organization in Weighted Network Representations

    PubMed Central

    Lohse, Christian; Bassett, Danielle S.; Lim, Kelvin O.; Carlson, Jean M.

    2014-01-01

    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. PMID:25275860

  14. [Macroscopic Functional Networks of the Human Brain when Viewing and Recalling Short Videos].

    PubMed

    Verkhlyutov, V M; Sokolov, P A; Ushakov, V L; Velichkovsky, B M

    2015-01-01

    Macroscopic functional network of the human brain were identified by use of the independent component analysis (ICA) of fMRI while viewing and imaging/recalling stories. The networks were relatively stable in structure, but had a specific dynamics in different experimental conditions. When comparing detected networks with previously detected resting state networks it was found that they coincide on localization. We. discovered also the specificity of activating the peripheral and central parts of retinotopic projections in the visual cortex. The peripheral areas were activated during subject viewing and imaging/recalling. On the contrary, the central departments strengthened their activation when viewing and reduced activity during the imaging/recalling. PMID:26281231

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

    PubMed Central

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

    2014-01-01

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

  16. Autism-Associated Promoter Variant in MET Impacts Functional and Structural Brain Networks

    PubMed Central

    Rudie, J. D.; Hernandez, L. M.; Brown, J. A.; Beck-Pancer, D.; Colich, N. L.; Gorrindo, P.; Thompson, P. M.; Geschwind, D. H.; Bookheimer, S. Y.; Levitt, P.; Dapretto, M.

    2012-01-01

    SUMMARY As genes that confer increased risk for autism spectrum disorder (ASD) are identified, a crucial next step is to determine how these risk factors impact brain structure and function and contribute to disorder heterogeneity. With three converging lines of evidence, we show that a common, functional ASD risk variant in the Met Receptor Tyrosine Kinase (MET) gene is a potent modulator of key social brain circuitry in children and adolescents with and without ASD. MET risk genotype predicted atypical fMRI activation and deactivation patterns to social stimuli (i.e., emotional faces), as well as reduced functional and structural connectivity in temporo-parietal regions known to have high MET expression, particularly within the default mode network. Notably, these effects were more pronounced in individuals with ASD. These findings highlight how genetic stratification may reduce heterogeneity and help elucidate the biological basis of complex neuropsychiatric disorders such as ASD. PMID:22958829

  17. Abnormalities of functional brain networks in pathological gambling: a graph-theoretical approach

    PubMed Central

    Tschernegg, Melanie; Crone, Julia S.; Eigenberger, Tina; Schwartenbeck, Philipp; Fauth-Bühler, Mira; Lemènager, Tagrid; Mann, Karl; Thon, Natasha; Wurst, Friedrich M.; Kronbichler, Martin

    2013-01-01

    Functional neuroimaging studies of pathological gambling (PG) demonstrate alterations in frontal and subcortical regions of the mesolimbic reward system. However, most investigations were performed using tasks involving reward processing or executive functions. Little is known about brain network abnormalities during task-free resting state in PG. In the present study, graph-theoretical methods were used to investigate network properties of resting state functional magnetic resonance imaging data in PG. We compared 19 patients with PG to 19 healthy controls (HCs) using the Graph Analysis Toolbox (GAT). None of the examined global metrics differed between groups. At the nodal level, pathological gambler showed a reduced clustering coefficient in the left paracingulate cortex and the left juxtapositional lobe (supplementary motor area, SMA), reduced local efficiency in the left SMA, as well as an increased node betweenness for the left and right paracingulate cortex and the left SMA. At an uncorrected threshold level, the node betweenness in the left inferior frontal gyrus was decreased and increased in the caudate. Additionally, increased functional connectivity between fronto-striatal regions and within frontal regions has also been found for the gambling patients. These findings suggest that regions associated with the reward system demonstrate reduced segregation but enhanced integration while regions associated with executive functions demonstrate reduced integration. The present study makes evident that PG is also associated with abnormalities in the topological network structure of the brain during rest. Since alterations in PG cannot be explained by direct effects of abused substances on the brain, these findings will be of relevance for understanding functional connectivity in other addictive disorders. PMID:24098282

  18. Network science and the effects of music preference on functional brain connectivity: from Beethoven to Eminem.

    PubMed

    Wilkins, R W; Hodges, D A; Laurienti, P J; Steen, M; Burdette, J H

    2014-01-01

    Most people choose to listen to music that they prefer or 'like' such as classical, country or rock. Previous research has focused on how different characteristics of music (i.e., classical versus country) affect the brain. Yet, when listening to preferred music--regardless of the type--people report they often experience personal thoughts and memories. To date, understanding how this occurs in the brain has remained elusive. Using network science methods, we evaluated differences in functional brain connectivity when individuals listened to complete songs. We show that a circuit important for internally-focused thoughts, known as the default mode network, was most connected when listening to preferred music. We also show that listening to a favorite song alters the connectivity between auditory brain areas and the hippocampus, a region responsible for memory and social emotion consolidation. Given that musical preferences are uniquely individualized phenomena and that music can vary in acoustic complexity and the presence or absence of lyrics, the consistency of our results was unexpected. These findings may explain why comparable emotional and mental states can be experienced by people listening to music that differs as widely as Beethoven and Eminem. The neurobiological and neurorehabilitation implications of these results are discussed. PMID:25167363

  19. Study of amyloid-β peptide functional brain networks in AD, MCI and HC.

    PubMed

    Jiang, Jiehui; Duan, Huoqiang; Huang, Zheming; Yu, Zhihua

    2015-01-01

    One medical challenge in studying the amyloid-β (Aβ) peptide mechanism for Alzheimer's disease (AD) is exploring the law of beta toxic oligomers' diffusion in human brains in vivo. One beneficial means of solving this problem is brain network analysis based on graph theory. In this study, the characteristics of Aβ functional brain networks of Healthy Control (HC), Mild Cognitive Impairment (MCI), and AD groups were compared by applying graph theoretical analyses to Carbon 11-labeled Pittsburgh compound B positron emission tomography (11C PiB-PET) data. 120 groups of PiB-PET images from the ADNI database were analyzed. The results showed that the small-world property of MCI and AD were lost as compared to HC. Furthermore, the local clustering of networks was higher in both MCI and AD as compared to HC, whereas the path length was similar among the three groups. The results also showed that there could be four potential Aβ toxic oligomer seeds: Frontal_Sup_Medial_L, Parietal_Inf_L, Frontal_Med_Orb_R, and Parietal_Inf_R. These four seeds are corresponding to Regions of Interests referred by physicians to clinically diagnose AD. PMID:26405999

  20. Different topological organization of human brain functional networks with eyes open versus eyes closed.

    PubMed

    Xu, Pengfei; Huang, Ruiwang; Wang, Jinhui; Van Dam, Nicholas T; Xie, Teng; Dong, Zhangye; Chen, Chunping; Gu, Ruolei; Zang, Yu-Feng; He, Yong; Fan, Jin; Luo, Yue-jia

    2014-04-15

    Opening and closing the eyes are fundamental behaviors for directing attention to the external versus internal world. However, it remains unclear whether the states of eyes-open (EO) relative to eyes-closed (EC) are associated with different topological organizations of functional neural networks for exteroceptive and interoceptive processing (processing the external world and internal state, respectively). Here, we used resting-state functional magnetic resonance imaging and neural network analysis to investigate the topological properties of functional networks of the human brain when the eyes were open versus closed. The brain networks exhibited higher cliquishness and local efficiency, but lower global efficiency during the EO state compared to the EC state. These properties suggest an increase in specialized information processing along with a decrease in integrated information processing in EO (vs. EC). More importantly, the "exteroceptive" network, including the attentional system (e.g., superior parietal gyrus and inferior parietal lobule), ocular motor system (e.g., precentral gyrus and superior frontal gyrus), and arousal system (e.g., insula and thalamus), showed higher regional nodal properties (nodal degree, efficiency and betweenness centrality) in EO relative to EC. In contrast, the "interoceptive" network, composed of visual system (e.g., lingual gyrus, fusiform gyrus and cuneus), auditory system (e.g., Heschl's gyurs), somatosensory system (e.g., postcentral gyrus), and part of the default mode network (e.g., angular gyrus and anterior cingulate gyrus), showed significantly higher regional properties in EC vs. EO. In addition, the connections across sensory modalities were altered by volitional eye opening. The synchronicity between the visual system and the motor, somatosensory and auditory systems, characteristic of EC, was attenuated in EO. Further, the connections between the visual system and the attention, arousal and subcortical systems were

  1. Linking human brain local activity fluctuations to structural and functional network architectures.

    PubMed

    Baria, A T; Mansour, A; Huang, L; Baliki, M N; Cecchi, G A; Mesulam, M M; Apkarian, A V

    2013-06-01

    Activity of cortical local neuronal populations fluctuates continuously, and a large proportion of these fluctuations are shared across populations of neurons. Here we seek organizational rules that link these two phenomena. Using neuronal activity, as identified by functional MRI (fMRI) and for a given voxel or brain region, we derive a single measure of full bandwidth brain-oxygenation-level-dependent (BOLD) fluctuations by calculating the slope, α, for the log-linear power spectrum. For the same voxel or region, we also measure the temporal coherence of its fluctuations to other voxels or regions, based on exceeding a given threshold, Θ, for zero lag correlation, establishing functional connectivity between pairs of neuronal populations. From resting state fMRI, we calculated whole-brain group-averaged maps for α and for functional connectivity. Both maps showed similar spatial organization, with a correlation coefficient of 0.75 between the two parameters across all brain voxels, as well as variability with hodology. A computational model replicated the main results, suggesting that synaptic low-pass filtering can account for these interrelationships. We also investigated the relationship between α and structural connectivity, as determined by diffusion tensor imaging-based tractography. We observe that the correlation between α and connectivity depends on attentional state; specifically, α correlated more highly to structural connectivity during rest than while attending to a task. Overall, these results provide global rules for the dynamics between frequency characteristics of local brain activity and the architecture of underlying brain networks. PMID:23396160

  2. 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.

  3. Controllability of structural brain networks

    PubMed Central

    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.

    2015-01-01

    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. PMID:26423222

  4. Functional connectivity in BOLD and CBF data: Similarity and reliability of resting brain networks

    PubMed Central

    Jann, Kay; Gee, Dylan G.; Kilroy, Emily; Schwab, Simon; Smith, Robert X.; Cannon, Tyrone D.; Wang, Danny J.J.

    2014-01-01

    Resting-state functional connectivity (FC) fMRI (rs-fcMRI) offers an appealing approach to mapping the brain’s intrinsic functional organization. Blood oxygen level dependent (BOLD) and arterial spin labeling (ASL) are the two main rs-fcMRI approaches to assess alterations in brain networks associated with individual differences, behavior and psychopathology. While the BOLD signal is stronger with a higher temporal resolution, ASL provides quantitative, direct measures of the physiology and metabolism of specific networks. This study systematically investigated the similarity and reliability of resting brain networks (RBNs) in BOLD and ASL. A 2 × 2 × 2 factorial design was employed where each subject underwent repeated BOLD and ASL rs-fcMRI scans on two occasions on two MRI scanners respectively. Both independent and joint FC analyses revealed common RBNs in ASL and BOLD rs-fcMRI with a moderate to high level of spatial overlap, verified by Dice Similarity Coefficients. Test–retest analyses indicated more reliable spatial network patterns in BOLD (average modal Intraclass Correlation Coefficients: 0.905 ± 0.033 between-sessions; 0.885 ± 0.052 between-scanners) than ASL (0.545 ± 0.048; 0.575 ± 0.059). Nevertheless, ASL provided highly reproducible (0.955 ± 0.021; 0.970 ± 0.011) network-specific CBF measurements. Moreover, we observed positive correlations between regional CBF and FC in core areas of all RBNs indicating a relationship between network connectivity and its baseline metabolism. Taken together, the combination of ASL and BOLD rs-fcMRI provides a powerful tool for characterizing the spatiotemporal and quantitative properties of RBNs. These findings pave the way for future BOLD and ASL rs-fcMRI studies in clinical populations that are carried out across time and scanners. PMID:25463468

  5. Brain Functional Effects of Psychopharmacological Treatments in Schizophrenia: A Network-based Functional Perspective Beyond Neurotransmitter Systems

    PubMed Central

    De Rossi, Pietro; Chiapponi, Chiara; Spalletta, Gianfranco

    2015-01-01

    Psychopharmacological treatments for schizophrenia have always been a matter of debate and a very important issue in public health given the chronic, relapsing and disabling nature of the disorder. A thorough understanding of the pros and cons of currently available pharmacological treatments for schizophrenia is critical to better capture the features of treatment-refractory clinical pictures and plan the developing of new treatment strategies. This review focuses on brain functional changes induced by antipsychotic drugs as assessed by modern functional neuroimaging techniques (i.e. fMRI, PET, SPECT, MRI spectroscopy). The most important papers on this topic are reviewed in order to draw an ideal map of the main functional changes occurring in the brain during antipsychotic treatment. This supports the hypothesis that a network-based perspective and a functional connectivity approach are needed to fill the currently existing gap of knowledge in the field of psychotropic drugs and their mechanisms of action beyond neurotransmitter systems. PMID:26412063

  6. Brain Functional Effects of Psychopharmacological Treatments in Schizophrenia: A Network-based Functional Perspective Beyond Neurotransmitter Systems.

    PubMed

    De Rossi, Pietro; Chiapponi, Chiara; Spalletta, Gianfranco

    2015-01-01

    Psychopharmacological treatments for schizophrenia have always been a matter of debate and a very important issue in public health given the chronic, relapsing and disabling nature of the disorder. A thorough understanding of the pros and cons of currently available pharmacological treatments for schizophrenia is critical to better capture the features of treatment-refractory clinical pictures and plan the developing of new treatment strategies. This review focuses on brain functional changes induced by antipsychotic drugs as assessed by modern functional neuroimaging techniques (i.e. fMRI, PET, SPECT, MRI spectroscopy). The most important papers on this topic are reviewed in order to draw an ideal map of the main functional changes occurring in the brain during antipsychotic treatment. This supports the hypothesis that a network-based perspective and a functional connectivity approach are needed to fill the currently existing gap of knowledge in the field of psychotropic drugs and their mechanisms of action beyond neurotransmitter systems. PMID:26412063

  7. Altered brain functional networks in people with Internet gaming disorder: Evidence from resting-state fMRI.

    PubMed

    Wang, Lingxiao; Wu, Lingdan; Lin, Xiao; Zhang, Yifen; Zhou, Hongli; Du, Xiaoxia; Dong, Guangheng

    2016-08-30

    Although numerous neuroimaging studies have detected structural and functional abnormality in specific brain regions and connections in subjects with Internet gaming disorder (IGD), the topological organization of the whole-brain network in IGD remain unclear. In this study, we applied graph theoretical analysis to explore the intrinsic topological properties of brain networks in Internet gaming disorder (IGD). 37 IGD subjects and 35 matched healthy control (HC) subjects underwent a resting-state functional magnetic resonance imaging scan. The functional networks were constructed by thresholding partial correlation matrices of 90 brain regions. Then we applied graph-based approaches to analysis their topological attributes, including small-worldness, nodal metrics, and efficiency. Both IGD and HC subjects show efficient and economic brain network, and small-world topology. Although there was no significant group difference in global topology metrics, the IGD subjects showed reduced regional centralities in the prefrontal cortex, left posterior cingulate cortex, right amygdala, and bilateral lingual gyrus, and increased functional connectivity in sensory-motor-related brain networks compared to the HC subjects. These results imply that people with IGD may be associated with functional network dysfunction, including impaired executive control and emotional management, but enhanced coordination among visual, sensorimotor, auditory and visuospatial systems. PMID:27447451

  8. Hyperthermia-Induced Disruption of Functional Connectivity in the Human Brain Network

    PubMed Central

    Jiang, Qingjun; Liu, Kai; Li, Bo; Li, Min; Zhao, Lun; Zhou, Zhenyu; von Deneen, Karen M.; Liu, Yijun

    2013-01-01

    Background Passive hyperthermia is a potential risk factor to human cognitive performance and work behavior in many extreme work environments. Previous studies have demonstrated significant effects of passive hyperthermia on human cognitive performance and work behavior. However, there is a lack of a clear understanding of the exact affected brain regions and inter-regional connectivities. Methodology and Principal Findings We simulated 1 hour environmental heat exposure to thirty-six participants under two environmental temperature conditions (25°C and 50°C), and collected resting-state functional brain activity. The functional connectivities with a preselected region of interest (ROI) in the posterior cingulate cortex and precuneus (PCC/PCu), furthermore, inter-regional connectivities throughout the entire brain using a prior Anatomical Automatic Labeling (AAL) atlas were calculated. We identified decreased correlations of a set of regions with the PCC/PCu, including the medial orbitofrontal cortex (mOFC) and bilateral medial temporal cortex, as well as increased correlations with the partial orbitofrontal cortex particularly in the bilateral orbital superior frontal gyrus. Compared with the normal control (NC) group, the hyperthermia (HT) group showed 65 disturbed functional connectivities with 50 of them being decreased and 15 of them being increased. While the decreased correlations mainly involved with the mOFC, temporal lobe and occipital lobe, increased correlations were mainly located within the limbic system. In consideration of physiological system changes, we explored the correlations of the number of significantly altered inter-regional connectivities with differential rectal temperatures and weight loss, but failed to obtain significant correlations. More importantly, during the attention network test (ANT) we found that the number of significantly altered functional connectivities was positively correlated with an increase in executive control

  9. Hierarchical organization of functional connectivity in the mouse brain: a complex network approach

    PubMed Central

    Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

    2016-01-01

    This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges. PMID:27534708

  10. Hierarchical organization of functional connectivity in the mouse brain: a complex network approach.

    PubMed

    Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

    2016-01-01

    This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges. PMID:27534708

  11. Waiguan Stimulation May Kindle Anticorrelated Brain Networks: Functional Magnetic Resonance Imaging Data Revisited.

    PubMed

    Wik, Gustav; Huang, Yong; Zeng, Tongjun; Qu, Shanshan; Zheng, Yu; Zhang, Jiping; Lai, Xinsheng; Tang, Chunzhi; Shan, Baoci

    2016-02-01

    Subtraction of functional magnetic resonance imaging activity data results in a loss of information regarding possible general patterns of brain activation under experimental conditions. We, hence, reanalyzed previous Waiguan acupuncture data to qualitatively elucidate patterns of cerebral correlations to acupuncture and placebo conditions. Healthy individuals (n=24) were randomly allocated to true and sham Waiguan acupuncture and to true and sham needling of a nonacupuncture point (nonacupoint), and functional magnetic resonance imaging scans were performed during stimulation. Statistical parametric mapping group comparisons revealed clearly different patterns of activation between Waiguan stimulation and the corresponding stimulation of a nonacupoint. The former condition produced less neocortical activation than the nonacupoint stimulation. Cerebellar activation was typically seen only during true Waiguan acupuncture. The reduced neocortical activity during both true and sham Waiguan acupuncture may indicate that this point activates anticorrelated networks, with possible intrinsic healing properties. Cerebellar activation during true Waiguan acupuncture implies the region's influence on healing networks. PMID:26896073

  12. Functional brain networks related to individual differences in human intelligence at rest.

    PubMed

    Hearne, Luke J; Mattingley, Jason B; Cocchi, Luca

    2016-01-01

    Intelligence is a fundamental ability that sets humans apart from other animal species. Despite its importance in defining human behaviour, the neural networks responsible for intelligence are not well understood. The dominant view from neuroimaging work suggests that intelligent performance on a range of tasks is underpinned by segregated interactions in a fronto-parietal network of brain regions. Here we asked whether fronto-parietal interactions associated with intelligence are ubiquitous, or emerge from more widespread associations in a task-free context. First we undertook an exploratory mapping of the existing literature on functional connectivity associated with intelligence. Next, to empirically test hypotheses derived from the exploratory mapping, we performed network analyses in a cohort of 317 unrelated participants from the Human Connectome Project. Our results revealed a novel contribution of across-network interactions between default-mode and fronto-parietal networks to individual differences in intelligence at rest. Specifically, we found that greater connectivity in the resting state was associated with higher intelligence scores. Our findings highlight the need to broaden the dominant fronto-parietal conceptualisation of intelligence to encompass more complex and context-specific network dynamics. PMID:27561736

  13. Functional brain networks related to individual differences in human intelligence at rest

    PubMed Central

    Hearne, Luke J.; Mattingley, Jason B.; Cocchi, Luca

    2016-01-01

    Intelligence is a fundamental ability that sets humans apart from other animal species. Despite its importance in defining human behaviour, the neural networks responsible for intelligence are not well understood. The dominant view from neuroimaging work suggests that intelligent performance on a range of tasks is underpinned by segregated interactions in a fronto-parietal network of brain regions. Here we asked whether fronto-parietal interactions associated with intelligence are ubiquitous, or emerge from more widespread associations in a task-free context. First we undertook an exploratory mapping of the existing literature on functional connectivity associated with intelligence. Next, to empirically test hypotheses derived from the exploratory mapping, we performed network analyses in a cohort of 317 unrelated participants from the Human Connectome Project. Our results revealed a novel contribution of across-network interactions between default-mode and fronto-parietal networks to individual differences in intelligence at rest. Specifically, we found that greater connectivity in the resting state was associated with higher intelligence scores. Our findings highlight the need to broaden the dominant fronto-parietal conceptualisation of intelligence to encompass more complex and context-specific network dynamics. PMID:27561736

  14. 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. PMID:26834616

  15. 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

  16. Changes of Cerebral Perfusion and Functional Brain Network Organization in Patients with Mild Cognitive Impairment.

    PubMed

    Lou, Wutao; Shi, Lin; Wong, Adrian; Chu, Winnie C W; Mok, Vincent C T; Wang, Defeng

    2016-08-10

    Disruptions of the functional brain network and cerebral blood flow (CBF) have been revealed in patients with mild cognitive impairment (MCI). However, the neurophysiological mechanism of hypoperfusion as well as the reorganization of the intrinsic whole brain network due to the neuropathology of MCI are still unclear. In this study, we aimed to investigate the changes of CBF and the whole brain network organization in MCI by using a multimodal MRI approach. Resting state ASL MRI and BOLD MRI were used to evaluate disruptions of CBF and underlying functional connectivity in 27 patients with MCI and 35 cognitive normal controls (NC). The eigenvector centrality mapping (ECM) was used to assess the whole brain network reorganization in MCI, and a seed-based ECM approach was proposed to reveal the contributions of the whole brain network on the ECM alterations. Significantly decreased perfusion in the posterior parietal cortex as well as its connectivity within the default mode network and occipital cortex were found in the MCI group compared to the NC group. The ECM analysis revealed decreased EC in the middle cingulate cortex, parahippocampal gyrus, medial frontal gyrus, and increased EC in the right calcarine sulcus, superior temporal gyrus, and supplementary motor area in the MCI group. The results of this study indicate that there are deficits in cerebral blood flow and functional connectivity in the default mode network, and that sensory-processing networks might play a compensatory role to make up for the decreased connections in MCI. PMID:27567823

  17. Brain functional network changes following Prelimbic area inactivation in a spatial memory extinction task.

    PubMed

    Méndez-Couz, Marta; Conejo, Nélida M; Vallejo, Guillermo; Arias, Jorge L

    2015-01-01

    Several studies suggest a prefrontal cortex involvement during the acquisition and consolidation of spatial memory, suggesting an active modulating role at late stages of acquisition processes. Recently, we have reported that the prelimbic and infralimbic areas of the prefrontal cortex, among other structures, are also specifically involved in the late phases of spatial memory extinction. This study aimed to evaluate whether the inactivation of the prelimbic area of the prefrontal cortex impaired spatial memory extinction. For this purpose, male Wistar rats were implanted bilaterally with cannulae into the prelimbic region of the prefrontal cortex. Animals were trained during 5 consecutive days in a hidden platform task and tested for reference spatial memory immediately after the last training session. One day after completing the training task, bilateral infusion of the GABAA receptor agonist Muscimol was performed before the extinction protocol was carried out. Additionally, cytochrome c oxidase histochemistry was applied to map the metabolic brain activity related to the spatial memory extinction under prelimbic cortex inactivation. Results show that animals acquired the reference memory task in the water maze, and the extinction task was successfully completed without significant impairment. However, analysis of the functional brain networks involved by cytochrome oxidase activity interregional correlations showed changes in brain networks between the group treated with Muscimol as compared to the saline-treated group, supporting the involvement of the mammillary bodies at a the late stage in the memory extinction process. PMID:25813749

  18. Semi-metric analysis of the functional brain network: Relationship with familial risk for psychotic disorder

    PubMed Central

    Peeters, Sanne; Simas, Tiago; Suckling, John; Gronenschild, Ed; Patel, Ameera; Habets, Petra; van Os, Jim; Marcelis, Machteld

    2015-01-01

    Background Dysconnectivity in schizophrenia can be understood in terms of dysfunctional integration of a distributed network of brain regions. Here we propose a new methodology to analyze complex networks based on semi-metric behavior, whereby higher levels of semi-metricity may represent a higher level of redundancy and dispersed communication. It was hypothesized that individuals with (increased risk for) psychotic disorder would have more semi-metric paths compared to controls and that this would be associated with symptoms. Methods Resting-state functional MRI scans were obtained from 73 patients with psychotic disorder, 83 unaffected siblings and 72 controls. Semi-metric percentages (SMP) at the whole brain, hemispheric and lobar level were the dependent variables in a multilevel random regression analysis to investigate group differences. SMP was further examined in relation to symptomatology (i.e., psychotic/cognitive symptoms). Results At the whole brain and hemispheric level, patients had a significantly higher SMP compared to siblings and controls, with no difference between the latter. In the combined sibling and control group, individuals with high schizotypy had intermediate SMP values in the left hemisphere with respect to patients and individuals with low schizotypy. Exploratory analyses in patients revealed higher SMP in 12 out of 42 lobar divisions compared to controls, of which some were associated with worse PANSS symptomatology (i.e., positive symptoms, excitement and emotional distress) and worse cognitive performance on attention and emotion processing tasks. In the combined group of patients and controls, working memory, attention and social cognition were associated with higher SMP. Discussion The results are suggestive of more dispersed network communication in patients with psychotic disorder, with some evidence for trait-based network alterations in high-schizotypy individuals. Dispersed communication may contribute to the clinical

  19. Extrasynaptic Neurotransmission in the Modulation of Brain Function. Focus on the Striatal Neuronal–Glial Networks

    PubMed Central

    Fuxe, Kjell; Borroto-Escuela, Dasiel O.; Romero-Fernandez, Wilber; Diaz-Cabiale, Zaida; Rivera, Alicia; Ferraro, Luca; Tanganelli, Sergio; Tarakanov, Alexander O.; Garriga, Pere; Narváez, José Angel; Ciruela, Francisco; Guescini, Michele; Agnati, Luigi F.

    2012-01-01

    Extrasynaptic neurotransmission is an important short distance form of volume transmission (VT) and describes the extracellular diffusion of transmitters and modulators after synaptic spillover or extrasynaptic release in the local circuit regions binding to and activating mainly extrasynaptic neuronal and glial receptors in the neuroglial networks of the brain. Receptor-receptor interactions in G protein-coupled receptor (GPCR) heteromers play a major role, on dendritic spines and nerve terminals including glutamate synapses, in the integrative processes of the extrasynaptic signaling. Heteromeric complexes between GPCR and ion-channel receptors play a special role in the integration of the synaptic and extrasynaptic signals. Changes in extracellular concentrations of the classical synaptic neurotransmitters glutamate and GABA found with microdialysis is likely an expression of the activity of the neuron-astrocyte unit of the brain and can be used as an index of VT-mediated actions of these two neurotransmitters in the brain. Thus, the activity of neurons may be functionally linked to the activity of astrocytes, which may release glutamate and GABA to the extracellular space where extrasynaptic glutamate and GABA receptors do exist. Wiring transmission (WT) and VT are fundamental properties of all neurons of the CNS but the balance between WT and VT varies from one nerve cell population to the other. The focus is on the striatal cellular networks, and the WT and VT and their integration via receptor heteromers are described in the GABA projection neurons, the glutamate, dopamine, 5-hydroxytryptamine (5-HT) and histamine striatal afferents, the cholinergic interneurons, and different types of GABA interneurons. In addition, the role in these networks of VT signaling of the energy-dependent modulator adenosine and of endocannabinoids mainly formed in the striatal projection neurons will be underlined to understand the communication in the striatal cellular networks

  20. The brain network reflecting bodily self-consciousness: a functional connectivity study

    PubMed Central

    Ionta, Silvio; Martuzzi, Roberto; Salomon, Roy

    2014-01-01

    Several brain regions are important for processing self-location and first-person perspective, two important aspects of bodily self-consciousness. However, the interplay between these regions has not been clarified. In addition, while self-location and first-person perspective in healthy subjects are associated with bilateral activity in temporoparietal junction (TPJ), disturbed self-location and first-person perspective result from damage of only the right TPJ. Identifying the involved brain network and understanding the role of hemispheric specializations in encoding self-location and first-person perspective, will provide important information on system-level interactions neurally mediating bodily self-consciousness. Here, we used functional connectivity and showed that right and left TPJ are bilaterally connected to supplementary motor area, ventral premotor cortex, insula, intraparietal sulcus and occipitotemporal cortex. Furthermore, the functional connectivity between right TPJ and right insula had the highest selectivity for changes in self-location and first-person perspective. Finally, functional connectivity revealed hemispheric differences showing that self-location and first-person perspective modulated the connectivity between right TPJ, right posterior insula, and right supplementary motor area, and between left TPJ and right anterior insula. The present data extend previous evidence on healthy populations and clinical observations in neurological deficits, supporting a bilateral, but right-hemispheric dominant, network for bodily self-consciousness. PMID:24396007

  1. Evidence of a Christmas spirit network in the brain: functional MRI study

    PubMed Central

    Hougaard, Anders; Lindberg, Ulrich; Arngrim, Nanna; Larsson, Henrik B W; Olesen, Jes; Amin, Faisal Mohammad; Ashina, Messoud

    2015-01-01

    Objective To detect and localise the Christmas spirit in the human brain. Design Single blinded, cross cultural group study with functional magnetic resonance imaging (fMRI). Setting Functional imaging unit and department of clinical physiology, nuclear medicine and PET in Denmark. Participants 10 healthy people from the Copenhagen area who routinely celebrate Christmas and 10 healthy people living in the same area who have no Christmas traditions. Main outcome measures Brain activation unique to the group with Christmas traditions during visual stimulation with images with a Christmas theme. Methods Functional brain scans optimised for detection of the blood oxygen level dependent (BOLD) response were performed while participants viewed a series of images with Christmas themes interleaved with neutral images having similar characteristics but containing nothing that symbolises Christmas. After scanning, participants answered a questionnaire about their Christmas traditions and the associations they have with Christmas. Brain activation maps from scanning were analysed for Christmas related activation in the “Christmas” and “non-Christmas” groups individually. Subsequently, differences between the two groups were calculated to determine Christmas specific brain activation. Results Significant clusters of increased BOLD activation in the sensory motor cortex, the premotor and primary motor cortex, and the parietal lobule (inferior and superior) were found in scans of people who celebrate Christmas with positive associations compared with scans in a group having no Christmas traditions and neutral associations. These cerebral areas have been associated with spirituality, somatic senses, and recognition of facial emotion among many other functions. Conclusions There is a “Christmas spirit network” in the human brain comprising several cortical areas. This network had a significantly higher activation in a people who celebrate Christmas with

  2. Mapping the mouse brain with rs-fMRI: An optimized pipeline for functional network identification.

    PubMed

    Zerbi, Valerio; Grandjean, Joanes; Rudin, Markus; Wenderoth, Nicole

    2015-12-01

    The use of resting state fMRI (rs-fMRI) in translational research is a powerful tool to assess brain connectivity and investigate neuropathology in mouse models. However, despite encouraging initial results, the characterization of consistent and robust resting state networks in mice remains a methodological challenge. One key reason is that the quality of the measured MR signal is degraded by the presence of structural noise from non-neural sources. Notably, in the current pipeline of the Human Connectome Project, a novel approach has been introduced to clean rs-fMRI data, which involves automatic artifact component classification and data cleaning (FIX). FIX does not require any external recordings of physiology or the segmentation of CSF and white matter. In this study, we evaluated the performance of FIX for analyzing mouse rs-fMRI data. Our results showed that FIX can be easily applied to mouse datasets and detects true signals with 100% accuracy and true noise components with very high accuracy (>98%), thus reducing both within- and between-subject variability of rs-fMRI connectivity measurements. Using this improved pre-processing pipeline, maps of 23 resting state circuits in mice were identified including two networks that displayed default mode network-like topography. Hierarchical clustering grouped these neural networks into meaningful larger functional circuits. These mouse resting state networks, which are publicly available, might serve as a reference for future work using mouse models of neurological disorders. PMID:26296501

  3. The effect of epoch length on estimated EEG functional connectivity and brain network organisation

    NASA Astrophysics Data System (ADS)

    Fraschini, Matteo; Demuru, Matteo; Crobe, Alessandra; Marrosu, Francesco; Stam, Cornelis J.; Hillebrand, Arjan

    2016-06-01

    Objective. Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. Approach. The aim of this study was to provide a network approach insensitive to the effects that epoch length has on functional connectivity and network reconstruction. Two different measures, the phase lag index (PLI) and the amplitude envelope correlation (AEC) were applied to EEG resting-state recordings for a group of 18 healthy volunteers using non-overlapping epochs with variable length (1, 2, 4, 6, 8, 10, 12, 14 and 16 s). Weighted clustering coefficient (CCw), weighted characteristic path length (L w) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. Main results. Results from scalp analysis show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 s for PLI and 6 s for AEC. Moreover, CCw and L w show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1–6 s versus 4–8 s for AEC). At the source-level the results were even more reliable, with stability already at 1 s duration for PLI-based MSTs. Significance. The present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of

  4. Brain functional network connectivity development in very preterm infants: The first six months.

    PubMed

    He, Lili; Parikh, Nehal A

    2016-07-01

    Nearly 10% of premature infants are born very preterm at 32weeks gestational age or less in the United States. Up to 35% of these very preterm survivors are at risk for cognitive and behavioral deficits. Yet accurate diagnosis of such deficits cannot be made until early childhood. Resting-state fMRI provides noninvasive assessment of the brain's functional networks and is a promising tool for early prognostication. In our present study, we enrolled a cohort of very preterm infants soon after birth and performed resting state fMRI at 32, 39 and additionally at 52weeks postmenstrual age. Using group probabilistic independent component analysis, we identified the following resting-state networks: visual, auditory, motor, somatosensory, cerebellum, brainstem, subcortical gray matter, default mode, executive control, and frontoparietal network. We observed increasing functional connectivity strength from 32 to 52weeks postmenstrual age for the auditory, somatosensory, visual, subcortical gray matter, executive control, and frontoparietal networks. Future studies with neurodevelopmental follow-up are needed to potentially identify prognostic biomarkers of long-term cognitive and behavioral deficits. PMID:27351350

  5. Epigenetics, Stress, and Their Potential Impact on Brain Network Function: A Focus on the Schizophrenia Diatheses

    PubMed Central

    Diwadkar, Vaibhav A.; Bustamante, Angela; Rai, Harinder; Uddin, Monica

    2014-01-01

    The recent sociodevelopmental cognitive model of schizophrenia/psychosis is a highly influential and compelling compendium of research findings. Here, we present logical extensions to this model incorporating ideas drawn from epigenetic mediation of psychiatric disease, and the plausible effects of epigenetics on the emergence of brain network function and dysfunction in adolescence. We discuss how gene–environment interactions, effected by epigenetic mechanisms, might in particular mediate the stress response (itself heavily implicated in the emergence of schizophrenia). Next, we discuss the plausible relevance of this framework for adolescent genetic risk populations, a risk group characterized by vexing and difficult-to-explain heterogeneity. We then discuss how exploring relationships between epigenetics and brain network dysfunction (a strongly validated finding in risk populations) can enhance understanding of the relationship between stress, epigenetics, and functional neurobiology, and the relevance of this relationship for the eventual emergence of schizophrenia/psychosis. We suggest that these considerations can expand the impact of models such as the sociodevelopmental cognitive model, increasing their explanatory reach. Ultimately, integration of these lines of research may enhance efforts of early identification, intervention, and treatment in adolescents at-risk for schizophrenia. PMID:25002852

  6. Connectivity disruptions in resting-state functional brain networks in children with temporal lobe epilepsy.

    PubMed

    Mankinen, Katariina; Jalovaara, Paula; Paakki, Jyri-Johan; Harila, Marika; Rytky, Seppo; Tervonen, Osmo; Nikkinen, Juha; Starck, Tuomo; Remes, Jukka; Rantala, Heikki; Kiviniemi, Vesa

    2012-06-01

    Functional resting-state connectivity has been shown to be altered in certain adult epilepsy populations, but few connectivity studies have been performed on pediatric epilepsy patients. Here functional connectivity was measured in pediatric, non-lesional temporal lobe epilepsy patients with normal intelligence and compared with that in age and gender-matched healthy controls using the independent component analysis method. We hypothesized that children with non-lesional temporal lobe epilepsy have disrupted functional connectivity within resting-state networks. Significant differences were demonstrated between the two groups, pointing to a decrease in connectivity. When the results were analyzed according to the interictal electroencephalogram findings, however, the connectivity disruptions were seen in different networks. In addition, increased connectivity and abnormally anti-correlated thalamic activity was detected only in the patients with abnormal electroencephalograms. In summary, connectivity disruptions are already to be seen at an early stage of epilepsy, and epileptiform activity seems to affect connectivity differently. The results indicate that interictal epileptiform activity may lead to reorganization of the resting-state brain networks, but further studies would be needed in order to understand the pathophysiology behind this phenomenon. PMID:22418271

  7. Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state

    PubMed Central

    Yang, Yan-li; Deng, Hong-xia; Xing, Gui-yang; Xia, Xiao-luan; Li, Hai-fang

    2015-01-01

    It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we investigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state. Z-values in the vision-related brain regions were calculated, confirming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental findings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception. PMID:25883631

  8. Functional Brain Networks Are Altered in Type 2 Diabetes and Prediabetes: Signs for Compensation of Cognitive Decrements? The Maastricht Study.

    PubMed

    van Bussel, Frank C G; Backes, Walter H; van Veenendaal, Tamar M; Hofman, Paul A M; van Boxtel, Martin P J; Schram, Miranda T; Sep, Simone J S; Dagnelie, Pieter C; Schaper, Nicolaas; Stehouwer, Coen D A; Wildberger, Joachim E; Jansen, Jacobus F A

    2016-08-01

    Type 2 diabetes is associated with cognitive decrements, accelerated cognitive decline, and increased risk for dementia. Patients with the metabolic syndrome, a major risk factor for diabetes, may display comparable cognitive decrements as seen in type 2 diabetes. Currently, the impact of diabetes and prediabetes on cognition and the underlying organization of functional brain networks still remain to be elucidated. This study investigated whether functional brain networks are affected in type 2 diabetes and prediabetes. Forty-seven participants with diabetes, 47 participants with prediabetes, and 45 control participants underwent detailed cognitive testing and 3-Tesla resting state functional MRI. Graph theoretical network analysis was performed to investigate alterations in functional cerebral networks. Participants with diabetes displayed altered network measures, characterized by a higher normalized cluster coefficient and higher local efficiency, compared with control participants. The network measures of the participants with prediabetes fell between those with diabetes and control participants. Lower processing speed was associated with shorter path length and higher global efficiency. Participants with type 2 diabetes have altered functional brain networks. This alteration is already apparent in the prediabetic stage to a somewhat lower level, hinting at functional reorganization of the cerebral networks as a compensatory mechanism for cognitive decrements. PMID:27217484

  9. Mapping the small-world properties of brain networks in deception with functional near-infrared spectroscopy

    PubMed Central

    Zhang, Jiang; Lin, Xiaohong; Fu, Genyu; Sai, Liyang; Chen, Huafu; Yang, Jianbo; Wang, Mingwen; Liu, Qi; Yang, Gang; Zhang, Junran; Yuan, Zhen

    2016-01-01

    Deception is not a rare occurrence among human behaviors; however, the present brain mapping techniques are insufficient to reveal the neural mechanism of deception under spontaneous or controlled conditions. Interestingly, functional near-infrared spectroscopy (fNIRS) has emerged as a highly promising neuroimaging technique that enables continuous and noninvasive monitoring of changes in blood oxygenation and blood volume in the human brain. In this study, fNIRS was used in combination with complex network theory to extract the attribute features of the functional brain networks underling deception in subjects exhibiting spontaneous or controlled behaviors. Our findings revealed that the small-world networks of the subjects engaged in spontaneous behaviors exhibited greater clustering coefficients, shorter average path lengths, greater average node degrees, and stronger randomness compared with those of subjects engaged in control behaviors. Consequently, we suggest that small-world network topology is capable of distinguishing well between spontaneous and controlled deceptions. PMID:27126145

  10. Mapping the small-world properties of brain networks in deception with functional near-infrared spectroscopy.

    PubMed

    Zhang, Jiang; Lin, Xiaohong; Fu, Genyu; Sai, Liyang; Chen, Huafu; Yang, Jianbo; Wang, Mingwen; Liu, Qi; Yang, Gang; Zhang, Junran; Yuan, Zhen

    2016-01-01

    Deception is not a rare occurrence among human behaviors; however, the present brain mapping techniques are insufficient to reveal the neural mechanism of deception under spontaneous or controlled conditions. Interestingly, functional near-infrared spectroscopy (fNIRS) has emerged as a highly promising neuroimaging technique that enables continuous and noninvasive monitoring of changes in blood oxygenation and blood volume in the human brain. In this study, fNIRS was used in combination with complex network theory to extract the attribute features of the functional brain networks underling deception in subjects exhibiting spontaneous or controlled behaviors. Our findings revealed that the small-world networks of the subjects engaged in spontaneous behaviors exhibited greater clustering coefficients, shorter average path lengths, greater average node degrees, and stronger randomness compared with those of subjects engaged in control behaviors. Consequently, we suggest that small-world network topology is capable of distinguishing well between spontaneous and controlled deceptions. PMID:27126145

  11. Modulation of large-scale brain networks by transcranial direct current stimulation evidenced by resting-state functional MRI

    PubMed Central

    Peña-Gómez, Cleofé; Sala-Lonch, Roser; Junqué, Carme; Clemente, Immaculada C.; Vidal, Dídac; Bargalló, Núria; Falcón, Carles; Valls-Solé, Josep; Pascual-Leone, Álvaro; Bartrés-Faz, David

    2013-01-01

    Background Brain areas interact mutually to perform particular complex brain functions such as memory or language. Furthermore, under resting-state conditions several spatial patterns have been identified that resemble functional systems involved in cognitive functions. Among these, the default-mode network (DMN), which is consistently deactivated during task periods and is related to a variety of cognitive functions, has attracted most attention. In addition, in resting-state conditions some brain areas engaged in focused attention (such as the anticorrelated network, AN) show a strong negative correlation with DMN; as task demand increases, AN activity rises, and DMN activity falls. Objective We combined transcranial direct current stimulation (tDCS) with functional magnetic resonance imaging (fMRI) to investigate these brain network dynamics. Methods Ten healthy young volunteers underwent four blocks of resting-state fMRI (10-minutes), each of them immediately after 20 minutes of sham or active tDCS (2 mA), on two different days. On the first day the anodal electrode was placed over the left dorsolateral prefrontal cortex (DLPFC) (part of the AN) with the cathode over the contralateral supraorbital area, and on the second day, the electrode arrangement was reversed (anode right-DLPFC, cathode left-supraorbital). Results After active stimulation, functional network connectivity revealed increased synchrony within the AN components and reduced synchrony in the DMN components. Conclusions Our study reveals a reconfiguration of intrinsic brain activity networks after active tDCS. These effects may help to explain earlier reports of improvements in cognitive functions after anodal-tDCS, where increasing cortical excitability may have facilitated reconfiguration of functional brain networks to address upcoming cognitive demands. PMID:21962981

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

    PubMed

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

    2016-01-01

    The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures. PMID:27330970

  13. Functional organization and restoration of the brain motor-execution network after stroke and rehabilitation

    PubMed Central

    Bajaj, Sahil; Butler, Andrew J.; Drake, Daniel; Dhamala, Mukesh

    2015-01-01

    Multiple cortical areas of the human brain motor system interact coherently in the low frequency range (<0.1 Hz), even in the absence of explicit tasks. Following stroke, cortical interactions are functionally disturbed. How these interactions are affected and how the functional organization is regained from rehabilitative treatments as people begin to recover motor behaviors has not been systematically studied. We recorded the intrinsic functional magnetic resonance imaging (fMRI) signals from 30 participants: 17 young healthy controls and 13 aged stroke survivors. Stroke participants underwent mental practice (MP) or both mental practice and physical therapy (MP+PT) within 14–51 days following stroke. We investigated the network activity of five core areas in the motor-execution network, consisting of the left primary motor area (LM1), the right primary motor area (RM1), the left pre-motor cortex (LPMC), the right pre-motor cortex (RPMC) and the supplementary motor area (SMA). We discovered that (i) the network activity dominated in the frequency range 0.06–0.08 Hz for all the regions, and for both able-bodied and stroke participants (ii) the causal information flow between the regions: LM1 and SMA, RPMC and SMA, RPMC and LM1, SMA and RM1, SMA and LPMC, was reduced significantly for stroke survivors (iii) the flow did not increase significantly after MP alone and (iv) the flow among the regions during MP+PT increased significantly. We also found that sensation and motor scores were significantly higher and correlated with directed functional connectivity measures when the stroke-survivors underwent MP+PT but not MP alone. The findings provide evidence that a combination of mental practice and physical therapy can be an effective means of treatment for stroke survivors to recover or regain the strength of motor behaviors, and that the spectra of causal information flow can be used as a reliable biomarker for evaluating rehabilitation in stroke survivors. PMID

  14. Properties of functional brain networks correlate with frequency of psychogenic non-epileptic seizures.

    PubMed

    Barzegaran, Elham; Joudaki, Amir; Jalili, Mahdi; Rossetti, Andrea O; Frackowiak, Richard S; Knyazeva, Maria G

    2012-01-01

    Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and small-world structure, estimated with clustering coefficient, modularity, global efficiency, and small-worldness (SW) metrics, respectively. Yet the number of PNES attacks per month correlated with a weakness of local connectedness and a skewed balance between local and global connectedness quantified with SW, all in EEG alpha band. In beta band, patients demonstrated above-normal resiliency, measured with assortativity coefficient, which also correlated with the frequency of PNES attacks. This interictal EEG phenotype may help improve differentiation between PNES and epilepsy. The results also suggest that local connectivity could be a target for therapeutic interventions in PNES. Selective modulation (strengthening) of local connectivity might improve the skewed balance between local and global connectivity and so prevent PNES events. PMID:23267325

  15. Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

    PubMed

    Wang, Yi-Feng; Long, Zhiliang; Cui, Qian; Liu, Feng; Jing, Xiu-Juan; Chen, Heng; Guo, Xiao-Nan; Yan, Jin H; Chen, Hua-Fu

    2016-01-01

    Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities. PMID:26512872

  16. Phase transitions in small-world systems: application to functional brain networks

    NASA Astrophysics Data System (ADS)

    Gadjiev, B. R.; Progulova, T. B.

    2015-04-01

    In the present paper the problem of symmetry breaking in the systems with a small- world property is considered. The obtained results are applied to the description of the functional brain networks. Origin of the entropy of fractal and multifractal small-world systems is discussed. Applying the maximum entropy principle the topology of these networks has been determined. The symmetry of the regular subgroup of a small-world system is described by a discrete subgroup of the Galilean group. The algorithm of determination of this group and transformation properties of the order parameter have been proposed. The integer basis of the irreducible representation is constructed and a free energy functional is introduced. It has been shown that accounting the presence of random connections leads to an integro- differential equation for the order parameter. For q-exponential distributions an equation of motion for the order parameter takes the form of a fractional differential equation. We consider the system that is described by a two-component order parameter and discuss the features of the spatial distribution of solutions.

  17. State-Dependent Changes of Connectivity Patterns and Functional Brain Network Topology in Autism Spectrum Disorder

    ERIC Educational Resources Information Center

    Barttfeld, Pablo; Wicker, Bruno; Cukier, Sebastian; Navarta, Silvana; Lew, Sergio; Leiguarda, Ramon; Sigman, Mariano

    2012-01-01

    Anatomical and functional brain studies have converged to the hypothesis that autism spectrum disorders (ASD) are associated with atypical connectivity. Using a modified resting-state paradigm to drive subjects' attention, we provide evidence of a very marked interaction between ASD brain functional connectivity and cognitive state. We show that…

  18. Functional brain networks underlying latent inhibition of conditioned disgust in rats.

    PubMed

    Gasalla, Patricia; Begega, Azucena; Soto, Alberto; Dwyer, Dominic Michael; López, Matías

    2016-12-15

    The present experiment examined the neuronal networks involved in the latent inhibition of conditioned disgust by measuring brain oxidative metabolism. Rats were given nonreinforced intraoral (IO) exposure to saccharin (exposed groups) or water (non-exposed groups) followed by a conditioning trial in which the animals received an infusion of saccharin paired (or unpaired) with LiCl. On testing, taste reactivity responses displayed by the rats during the infusion of the saccharin were examined. Behavioral data showed that preexposure to saccharin attenuated the development of LiCl-induced conditioned disgust reactions, indicating that the effects of taste aversion on hedonic taste reactivity had been reduced. With respect to cumulative oxidative metabolic activity across the whole study period, the parabrachial nucleus was the only single region examined which showed differential activity between groups which received saccharin-LiCl pairings with and without prior non-reinforced saccharin exposure, suggesting a key role in the effects of latent inhibition of taste aversion learning. In addition, many functional connections between brain regions were revealed through correlational analysis of metabolic activity, in particular an accumbens-amygdala interaction that may be involved in both positive and negative hedonic responses. PMID:27491591

  19. Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI.

    PubMed

    Xu, Tingting; Cullen, Kathryn R; Mueller, Bryon; Schreiner, Mindy W; Lim, Kelvin O; Schulz, S Charles; Parhi, Keshab K

    2016-01-01

    Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03-0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03-0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge

  20. Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

    PubMed Central

    Xu, Tingting; Cullen, Kathryn R.; Mueller, Bryon; Schreiner, Mindy W.; Lim, Kelvin O.; Schulz, S. Charles; Parhi, Keshab K.

    2016-01-01

    Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03–0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03–0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new

  1. Functional Brain Network Abnormalities during Verbal Working Memory Performance in Adolescents and Young Adults with Dyslexia

    ERIC Educational Resources Information Center

    Wolf, Robert Christian; Sambataro, Fabio; Lohr, Christina; Steinbrink, Claudia; Martin, Claudia; Vasic, Nenad

    2010-01-01

    Behavioral and functional neuroimaging studies indicate deficits in verbal working memory (WM) and frontoparietal dysfunction in individuals with dyslexia. Additionally, structural brain abnormalities in dyslexics suggest a dysconnectivity of brain regions associated with phonological processing. However, little is known about the functional…

  2. Prefrontal and limbic resting state brain network functional connectivity differs between nicotine-dependent smokers and non-smoking controls

    PubMed Central

    Janes, Amy C.; Nickerson, Lisa; Frederick, Blaise deB.; Kaufman, Marc J.

    2012-01-01

    Background Brain dysfunction in prefrontal cortex (PFC) and dorsal striatum (DS) contributes to habitual drug use. These regions are constituents of brain networks thought to be involved in drug addiction. To investigate whether networks containing these regions differ between nicotine dependent female smokers and age-matched female non-smokers, we employed functional MRI (fMRI) at rest. Methods Data were processed with independent component analysis (ICA) to identify resting state networks (RSNs). We identified a subcortical limbic network and three discrete PFC networks: a medial prefrontal cortex (mPFC) network and right and left lateralized fronto-parietal networks common to all subjects. We then compared these RSNs between smokers and non-smokers using a dual regression approach. Results Smokers had greater coupling versus non-smokers between left fronto-parietal and mPFC networks. Smokers with the greatest mPFC-left fronto-parietal coupling had the most DS smoking cue reactivity as measured during an fMRI smoking cue reactivity paradigm. This may be important because the DS plays a critical role in maintaining drug-cue associations. Furthermore, subcortical limbic network amplitude was greater in smokers. Conclusions Our results suggest that prefrontal brain networks are more strongly coupled in smokers, which could facilitate drug-cue responding. Our data also are the first to document greater reward-related network fMRI amplitude in smokers. Our findings suggest that resting state PFC network interactions and limbic network amplitude can differentiate nicotine-dependent smokers from controls, and may serve as biomarkers for nicotine dependence severity and treatment efficacy. PMID:22459914

  3. Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

    PubMed Central

    Wang, Jin-Hui; Zuo, Xi-Nian; Gohel, Suril; Milham, Michael P.; Biswal, Bharat B.; He, Yong

    2011-01-01

    Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest. PMID:21818285

  4. 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

  5. Immunity factor contributes to altered brain functional networks in individuals at risk for Alzheimer's disease: Neuroimaging-genetic evidence.

    PubMed

    Bai, Feng; Shi, Yongmei; Yuan, Yonggui; Xie, Chunming; Zhang, Zhijun

    2016-08-01

    Clusterin (CLU) is recognized as a secreted protein that is related to the processes of inflammation and immunity in the pathogenesis of Alzheimer's disease (AD). The effects of the risk variant of the C allele at the rs11136000 locus of the CLU gene are associated with variations in the brain structure and function. However, the relationship of the CLU-C allele to architectural disruptions in resting-state networks in amnestic mild cognitive impairment (aMCI) subjects (i.e., individuals with elevated risk of AD) remains relatively unknown. Using resting-state functional magnetic resonance imaging and an imaging genetic approach, this study investigated whether individual brain functional networks, i.e., the default mode network (DMN) and the task-positive network, were modulated by the CLU-C allele (rs11136000) in 50 elderly participants, including 26 aMCI subjects and 24 healthy controls. CLU-by-aMCI interactions were associated with the information-bridging regions between resting-state networks rather than with the DMN itself, especially in cortical midline regions. Interestingly, the complex communications between resting-state networks were enhanced in aMCI subjects with the CLU rs11136000 CC genotype and were modulated by the degree of memory impairment, suggesting a reconstructed balance of the resting-state networks in these individuals with an elevated risk of AD. The neuroimaging-genetic evidence indicates that immunity factors may contribute to alterations in brain functional networks in aMCI. These findings add to the evidence that the CLU gene may represent a potential therapeutic target for slowing disease progression in AD. PMID:26899953

  6. Functionally Brain Network Connected to the Retrosplenial Cortex of Rats Revealed by 7T fMRI

    PubMed Central

    Wang, Jingjuan; Nie, Binbin; Duan, Shaofeng; Zhu, Haitao; Liu, Hua; Shan, Baoci

    2016-01-01

    Functional networks are regarded as important mechanisms for increasing our understanding of brain function in healthy and diseased states, and increased interest has been focused on extending the study of functional networks to animal models because such models provide a functional understanding of disease progression, therapy and repair. In rodents, the retrosplenial cortex (RSC) is an important cortical region because it has a large size and presents transitional patterns of lamination between the neocortex and archicortex. In addition, a number of invasive studies have highlighted the importance of the RSC for many functions. However, the network based on the RSC in rodents remains unclear. Based on the critical importance of the RSC, we defined the bilateral RSCs as two regions of interest and estimated the network based on the RSC. The results showed that the related regions include the parietal association cortex, hippocampus, thalamus nucleus, midbrain structures, and hypothalamic mammillary bodies. Our findings indicate two possible major networks: a sensory-cognitive network that has a hub in the RSCs and processes sensory information, spatial learning, and episodic memory; and a second network that is involved in the regulation of visceral functions and arousal. In addition, functional asymmetry between the bilateral RSCs was observed. PMID:26745803

  7. Modulating Brain Oscillations to Drive Brain Function

    PubMed Central

    Thut, Gregor

    2014-01-01

    Do neuronal oscillations play a causal role in brain function? In a study in this issue of PLOS Biology, Helfrich and colleagues address this long-standing question by attempting to drive brain oscillations using transcranial electrical current stimulation. Remarkably, they were able to manipulate visual perception by forcing brain oscillations of the left and right visual hemispheres into synchrony using oscillatory currents over both hemispheres. Under this condition, human observers more often perceived an inherently ambiguous visual stimulus in one of its perceptual instantiations. These findings shed light on the mechanisms underlying neuronal computation. They show that it is the neuronal oscillations that drive the visual experience, not the experience driving the oscillations. And they indicate that synchronized oscillatory activity groups brain areas into functional networks. This points to new ways for controlled experimental and possibly also clinical interventions for the study and modulation of brain oscillations and associated functions. PMID:25549340

  8. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research.

    PubMed

    van Diessen, E; Numan, T; van Dellen, E; van der Kooi, A W; Boersma, M; Hofman, D; van Lutterveld, R; van Dijk, B W; van Straaten, E C W; Hillebrand, A; Stam, C J

    2015-08-01

    Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies. PMID:25511636

  9. Writing affects the brain network of reading in Chinese: a functional magnetic resonance imaging study.

    PubMed

    Cao, Fan; Vu, Marianne; Chan, Derek Ho Lung; Lawrence, Jason M; Harris, Lindsay N; Guan, Qun; Xu, Yi; Perfetti, Charles A

    2013-07-01

    We examined the hypothesis that learning to write Chinese characters influences the brain's reading network for characters. Students from a college Chinese class learned 30 characters in a character-writing condition and 30 characters in a pinyin-writing condition. After learning, functional magnetic resonance imaging collected during passive viewing showed different networks for reading Chinese characters and English words, suggesting accommodation to the demands of the new writing system through short-term learning. Beyond these expected differences, we found specific effects of character writing in greater activation (relative to pinyin writing) in bilateral superior parietal lobules and bilateral lingual gyri in both a lexical decision and an implicit writing task. These findings suggest that character writing establishes a higher quality representation of the visual-spatial structure of the character and its orthography. We found a greater involvement of bilateral sensori-motor cortex (SMC) for character-writing trained characters than pinyin-writing trained characters in the lexical decision task, suggesting that learning by doing invokes greater interaction with sensori-motor information during character recognition. Furthermore, we found a correlation of recognition accuracy with activation in right superior parietal lobule, right lingual gyrus, and left SMC, suggesting that these areas support the facilitative effect character writing has on reading. Finally, consistent with previous behavioral studies, we found character-writing training facilitates connections with semantics by producing greater activation in bilateral middle temporal gyri, whereas pinyin-writing training facilitates connections with phonology by producing greater activation in right inferior frontal gyrus. PMID:22378588

  10. A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

    PubMed Central

    2015-01-01

    Background The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. Methods A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. Results The study results show the existence of a statistically significant difference (p < 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. Conclusions The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed. PMID:25953124

  11. Three Large-Scale Functional Brain Networks from Resting-State Functional MRI in Subjects with Different Levels of Cognitive Impairment

    PubMed Central

    Joo, Soo Hyun; Lim, Hyun Kook

    2016-01-01

    Normal aging and to a greater degree degenerative brain diseases such as Alzheimer's disease (AD), cause changes in the brain's structure and function. Degenerative changes in brain structure and decline in its function are associated with declines in cognitive ability. Early detection of AD is a key priority in dementia services and research. However, depending on the disease progression, neurodegenerative manifestations, such as cerebral atrophy, are detected late in course of AD. Functional changes in the brain may be an indirect indicator of trans-synaptic activity and they usually appear prior to structural changes in AD. Resting-state functional magnetic resonance imaging (RS-fMRI) has recently been highlighted as a new technique for interrogating intrinsic functional connectivity networks. Among the majority of RS-fMRI studies, the default mode network (DMN), salience network (SN), and central executive network (CEN) gained particular focus because alterations to their functional connectivity were observed in subjects who had AD, who had mild cognitive impairment (MCI), or who were at high risk for AD. Herein, we present a review of the current research on changes in functional connectivity, as measured by RS-fMRI. We focus on the DMN, SN, and CEN to describe RS-fMRI results from three groups: normal healthy aging, MCI and AD. PMID:26766941

  12. Three Large-Scale Functional Brain Networks from Resting-State Functional MRI in Subjects with Different Levels of Cognitive Impairment.

    PubMed

    Joo, Soo Hyun; Lim, Hyun Kook; Lee, Chang Uk

    2016-01-01

    Normal aging and to a greater degree degenerative brain diseases such as Alzheimer's disease (AD), cause changes in the brain's structure and function. Degenerative changes in brain structure and decline in its function are associated with declines in cognitive ability. Early detection of AD is a key priority in dementia services and research. However, depending on the disease progression, neurodegenerative manifestations, such as cerebral atrophy, are detected late in course of AD. Functional changes in the brain may be an indirect indicator of trans-synaptic activity and they usually appear prior to structural changes in AD. Resting-state functional magnetic resonance imaging (RS-fMRI) has recently been highlighted as a new technique for interrogating intrinsic functional connectivity networks. Among the majority of RS-fMRI studies, the default mode network (DMN), salience network (SN), and central executive network (CEN) gained particular focus because alterations to their functional connectivity were observed in subjects who had AD, who had mild cognitive impairment (MCI), or who were at high risk for AD. Herein, we present a review of the current research on changes in functional connectivity, as measured by RS-fMRI. We focus on the DMN, SN, and CEN to describe RS-fMRI results from three groups: normal healthy aging, MCI and AD. PMID:26766941

  13. Motor Network Plasticity and Low-Frequency Oscillations Abnormalities in Patients with Brain Gliomas: A Functional MRI Study

    PubMed Central

    Niu, Chen; Zhang, Ming; Min, Zhigang; Rana, Netra; Zhang, Qiuli; Liu, Xin; Li, Min; Lin, Pan

    2014-01-01

    Brain plasticity is often associated with the process of slow-growing tumor formation, which remodels neural organization and optimizes brain network function. In this study, we aimed to investigate whether motor function plasticity would display deficits in patients with slow-growing brain tumors located in or near motor areas, but who were without motor neurological deficits. We used resting-state functional magnetic resonance imaging to probe motor networks in 15 patients with histopathologically confirmed brain gliomas and 15 age-matched healthy controls. All subjects performed a motor task to help identify individual motor activity in the bilateral primary motor cortex (PMC) and supplementary motor area (SMA). Frequency-based analysis at three different frequencies was then used to investigate possible alterations in the power spectral density (PSD) of low-frequency oscillations. For each group, the average PSD was determined for each brain region and a nonparametric test was performed to determine the difference in power between the two groups. Significantly reduced inter-hemispheric functional connectivity between the left and right PMC was observed in patients compared with controls (P<0.05). We also found significantly decreased PSD in patients compared to that in controls, in all three frequency bands (low: 0.01–0.02 Hz; middle: 0.02–0.06 Hz; and high: 0.06–0.1 Hz), at three key motor regions. These findings suggest that in asymptomatic patients with brain tumors located in eloquent regions, inter-hemispheric connection may be more vulnerable. A comparison of the two approaches indicated that power spectral analysis is more sensitive than functional connectivity analysis for identifying the neurological abnormalities underlying motor function plasticity induced by slow-growing tumors. PMID:24806463

  14. Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction

    PubMed Central

    Zhang, Zitong; Telesford, Qawi K.; Giusti, Chad; Lim, Kelvin O.; Bassett, Danielle S.

    2016-01-01

    Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24)—each essential parameters in wavelet-based methods—on the estimated values of graph metrics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of graph metrics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of metrics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders. PMID:27355202

  15. Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction.

    PubMed

    Zhang, Zitong; Telesford, Qawi K; Giusti, Chad; Lim, Kelvin O; Bassett, Danielle S

    2016-01-01

    Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24)-each essential parameters in wavelet-based methods-on the estimated values of graph metrics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of graph metrics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of metrics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders. PMID:27355202

  16. Brain imaging and brain function

    SciTech Connect

    Sokoloff, L.

    1985-01-01

    This book is a survey of the applications of imaging studies of regional cerebral blood flow and metabolism to the investigation of neurological and psychiatric disorders. Contributors review imaging techniques and strategies for measuring regional cerebral blood flow and metabolism, for mapping functional neural systems, and for imaging normal brain functions. They then examine the applications of brain imaging techniques to the study of such neurological and psychiatric disorders as: cerebral ischemia; convulsive disorders; cerebral tumors; Huntington's disease; Alzheimer's disease; depression and other mood disorders. A state-of-the-art report on magnetic resonance imaging of the brain and central nervous system rounds out the book's coverage.

  17. Large-scale functional brain networks in human non-rapid eye movement sleep: insights from combined electroencephalographic/functional magnetic resonance imaging studies.

    PubMed

    Spoormaker, Victor I; Czisch, Michael; Maquet, Pierre; Jäncke, Lutz

    2011-10-13

    This paper reviews the existing body of knowledge on the neural correlates of spontaneous oscillations, functional connectivity and brain plasticity in human non-rapid eye movement (NREM) sleep. The first section reviews the evidence that specific sleep events as slow waves and spindles are associated with transient increases in regional brain activity. The second section describes the changes in functional connectivity during NREM sleep, with a particular focus on changes within a low-frequency, large-scale functional brain network. The third section will discuss the possibility that spontaneous oscillations and differential functional connectivity are related to brain plasticity and systems consolidation, with a particular focus on motor skill acquisition. Implications for the mode of information processing per sleep stage and future experimental studies are discussed. PMID:21893524

  18. SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity.

    PubMed

    Lee, Kangjoo; Lina, Jean-Marc; Gotman, Jean; Grova, Christophe

    2016-07-01

    Functional hubs are defined as the specific brain regions with dense connections to other regions in a functional brain network. Among them, connector hubs are of great interests, as they are assumed to promote global and hierarchical communications between functionally specialized networks. Damage to connector hubs may have a more crucial effect on the system than does damage to other hubs. Hubs in graph theory are often identified from a correlation matrix, and classified as connector hubs when the hubs are more connected to regions in other networks than within the networks to which they belong. However, the identification of hubs from functional data is more complex than that from structural data, notably because of the inherent problem of multicollinearity between temporal dynamics within a functional network. In this context, we developed and validated a method to reliably identify connectors and corresponding overlapping network structure from resting-state fMRI. This new method is actually handling the multicollinearity issue, since it does not rely on counting the number of connections from a thresholded correlation matrix. The novelty of the proposed method is that besides counting the number of networks involved in each voxel, it allows us to identify which networks are actually involved in each voxel, using a data-driven sparse general linear model in order to identify brain regions involved in more than one network. Moreover, we added a bootstrap resampling strategy to assess statistically the reproducibility of our results at the single subject level. The unified framework is called SPARK, i.e. SParsity-based Analysis of Reliable k-hubness, where k-hubness denotes the number of networks overlapping in each voxel. The accuracy and robustness of SPARK were evaluated using two dimensional box simulations and realistic simulations that examined detection of artificial hubs generated on real data. Then, test/retest reliability of the method was assessed

  19. Childhood maltreatment is associated with a sex-dependent functional reorganization of a brain inhibitory control network.

    PubMed

    Elton, Amanda; Tripathi, Shanti P; Mletzko, Tanja; Young, Jonathan; Cisler, Josh M; James, G Andrew; Kilts, Clinton D

    2014-04-01

    Childhood adversity represents a major risk factor for drug addiction and other mental disorders. However, the specific mechanisms by which childhood adversity impacts human brain organization to confer greater vulnerability for negative outcomes in adulthood is largely unknown. As an impaired process in drug addiction, inhibitory control of behavior was investigated as a target of childhood maltreatment (abuse and neglect). Forty adults without Axis-I psychiatric disorders (21 females) completed a Childhood Trauma Questionnaire (CTQ) and underwent functional MRI (fMRI) while performing a stop-signal task. A group independent component analysis identified a putative brain inhibitory control network. Graph theoretical analyses and structural equation modeling investigated the impact of childhood maltreatment on the functional organization of this neural processing network. Graph theory outcomes revealed sex differences in the relationship between network functional connectivity and inhibitory control which were dependent on the severity of childhood maltreatment exposure. A network effective connectivity analysis indicated that a maltreatment dose-related negative modulation of dorsal anterior cingulate (dACC) activity by the left inferior frontal cortex (IFC) predicted better response inhibition and lesser attention deficit hyperactivity disorder (ADHD) symptoms in females, but poorer response inhibition and greater ADHD symptoms in males. Less inhibition of the right IFC by dACC in males with higher CTQ scores improved inhibitory control ability. The childhood maltreatment-related reorganization of a brain inhibitory control network provides sex-dependent mechanisms by which childhood adversity may confer greater risk for drug use and related disorders and by which adaptive brain responses protect individuals from this risk factor. PMID:23616424

  20. Childhood maltreatment is associated with a sex-dependent functional reorganization of a brain inhibitory control network

    PubMed Central

    Elton, Amanda; Tripathi, Shanti P.; Mletzko, Tanja; Young, Jonathan; Cisler, Josh M.; James, G. Andrew; Kilts, Clinton D.

    2013-01-01

    Childhood adversity represents a major risk factor for drug addiction and other mental disorders. However the specific mechanisms by which childhood adversity impacts human brain organization to confer greater vulnerability for negative outcomes in adulthood is largely unknown. As an impaired process in drug addiction, inhibitory control of behavior was investigated as a target of childhood maltreatment (abuse and neglect). Forty adults without Axis-I psychiatric disorders (21 female) completed a Childhood Trauma Questionnaire (CTQ) and underwent functional MRI (fMRI) while performing a stop-signal task. A group independent component analysis identified a putative brain inhibitory control network. Graph theoretical analyses and structural equation modeling investigated the impact of childhood maltreatment on the functional organization of this neural processing network. Graph theory outcomes revealed sex differences in the relationship between network functional connectivity and inhibitory control which were dependent on the severity of childhood maltreatment exposure. A network effective connectivity analysis indicated that a maltreatment dose-related negative modulation of dorsal anterior cingulate (dACC) activity by the left inferior frontal cortex (IFC) predicted better response inhibition and lesser attention deficit hyperactivity disorder (ADHD) symptoms in females, but poorer response inhibition and greater ADHD symptoms in males. Less inhibition of the right IFC by dACC in males with higher CTQ scores improved inhibitory control ability. The childhood maltreatment-related reorganization of a brain inhibitory control network provides sex-dependent mechanisms by which childhood adversity may confer greater risk for drug use and related disorders and by which adaptive brain responses protect individuals from this risk factor. PMID:23616424

  1. The re-organization of functional brain networks in pharmaco-resistant epileptic patients who respond to VNS.

    PubMed

    Fraschini, Matteo; Demuru, Matteo; Puligheddu, Monica; Floridia, Simona; Polizzi, Lorenzo; Maleci, Alberto; Bortolato, Marco; Hillebrand, Arjan; Marrosu, Francesco

    2014-09-19

    Vagal nerve stimulation (VNS) is a therapeutic add-on treatment for patients with pharmaco-resistant epilepsy. The mechanism of action is still largely unknown. Previous studies have shown that brain network topology during the inter-ictal period in epileptic patients deviates from normal configuration. In the present paper, we investigate the relationship between clinical improvement induced by VNS and alterations in brain network topology. We hypothesize that, as a consequence of the VNS add-on treatment, functional brain network architecture shifts back toward a more efficient configuration in patients responding to VNS. Electroencephalographic (EEG) recordings from ten patients affected by pharmaco-resistant epilepsy were analyzed in the classical EEG frequency bands. The phase lag index (PLI) was used to estimate functional connectivity between EEG channels and the minimum spanning tree (MST) was computed in order to characterize VNS-induced alterations in network topology in a bias-free way. Our results revealed a clear network re-organization, in terms of MST modification, toward a more integrated architecture in patients responding to the VNS. In particular, the results show a significant interaction effect between benefit from VNS (responders/non-responders) and condition (pre/post VNS implantation) in the theta band. This finding suggests that the positive effect induced by VNS add-on treatment in epileptic patients is related to a clear network re-organization and that this network modification can reveal the long debated mechanism of action of VNS. Therefore, MST analysis could be useful in evaluating and monitoring the efficacy of VNS add-on treatment potentially in both epilepsy and psychiatric diseases. PMID:25123446

  2. Functional cliques in the amygdala and related brain networks driven by fear assessment acquired during movie viewing.

    PubMed

    Kinreich, Sivan; Intrator, Nathan; Hendler, Talma

    2011-01-01

    One of the greatest challenges involved in studying the brain mechanisms of fear is capturing the individual's unique instantaneous experience. Brain imaging studies to date commonly sacrifice valuable information regarding the individual real-time conscious experience, especially when focusing on elucidating the amygdala's activity. Here, we assumed that by using a minimally intrusive cue along with applying a robust clustering approach to probe the amygdala, it would be possible to rate fear in real time and to derive the related network of activation. During functional magnetic resonance imaging scanning, healthy volunteers viewed two excerpts from horror movies and were periodically auditory cued to rate their instantaneous experience of "I'm scared." Using graph theory and community mathematical concepts, data-driven clustering of the fear-related functional cliques in the amygdala was performed guided by the individually marked periods of heightened fear. Individually tailored functions derived from these amygdala activation cliques were subsequently applied as general linear model predictors to a whole-brain analysis to reveal the correlated networks. Our results suggest that by using a localized robust clustering approach, it is possible to probe activation in the right dorsal amygdala that is directly related to individual real-time emotional experience. Moreover, this fear-evoked amygdala revealed two opposing networks of co-activation and co-deactivation, which correspond to vigilance and rest-related circuits, respectively. PMID:22432905

  3. Aberrant Topologies and Reconfiguration Pattern of Functional Brain Network in Children with Second Language Reading Impairment

    ERIC Educational Resources Information Center

    Liu, Lanfang; Li, Hehui; Zhang, Manli; Wang, Zhengke; Wei, Na; Liu, Li; Meng, Xiangzhi; Ding, Guosheng

    2016-01-01

    Prior work has extensively studied neural deficits in children with reading impairment (RI) in their native language but has rarely examined those of RI children in their second language (L2). A recent study revealed that the function of the local brain regions was disrupted in children with RI in L2, but it is not clear whether the disruption…

  4. Is Traumatic Brain Injury Associated with Reduced Inter-Hemispheric Functional Connectivity? A Study of Large-Scale Resting State Networks following Traumatic Brain Injury.

    PubMed

    Rigon, Arianna; Duff, Melissa C; McAuley, Edward; Kramer, Arthur F; Voss, Michelle W

    2016-06-01

    Traumatic brain injury (TBI) often has long-term debilitating sequelae in cognitive and behavioral domains. Understanding how TBI impacts functional integrity of brain networks that underlie these domains is key to guiding future approaches to TBI rehabilitation. In the current study, we investigated the differences in inter-hemispheric functional connectivity (FC) of resting state networks (RSNs) between chronic mild-to-severe TBI patients and normal comparisons (NC), focusing on two externally oriented networks (i.e., the fronto-parietal network [FPN] and the executive control network [ECN]), one internally oriented network (i.e., the default mode network [DMN]), and one somato-motor network (SMN). Seed voxel correlation analysis revealed that TBI patients displayed significantly less FC between lateralized seeds and both homologous and non-homologous regions in the opposite hemisphere for externally oriented networks but not for DMN or SMN; conversely, TBI patients showed increased FC within regions of the DMN, especially precuneus and parahippocampal gyrus. Region of interest correlation analyses confirmed the presence of significantly higher inter-hemispheric FC in NC for the FPN (p < 0.01), and ECN (p < 0.05), but not for the DMN (p > 0.05) or SMN (p > 0.05). Further analysis revealed that performance on a neuropsychological test measuring organizational skills and visuo-spatial abilities administered to the TBI group, the Rey-Osterrieth Complex Figure Test, positively correlated with FC between the right FPN and homologous regions. Our findings suggest that distinct RSNs display specific patterns of aberrant FC following TBI; this represents a step forward in the search for biomarkers useful for early diagnosis and treatment of TBI-related cognitive impairment. PMID:25719433

  5. Large-scale functional brain network changes in taxi drivers: evidence from resting-state fMRI.

    PubMed

    Wang, Lubin; Liu, Qiang; Shen, Hui; Li, Hong; Hu, Dewen

    2015-03-01

    Driving a car in the environment is a complex behavior that involves cognitive processing of visual information to generate the proper motor outputs and action controls. Previous neuroimaging studies have used virtual simulation to identify the brain areas that are associated with various driving-related tasks. Few studies, however, have focused on the specific patterns of functional organization in the driver's brain. The aim of this study was to assess differences in the resting-state networks (RSNs) of the brains of drivers and nondrivers. Forty healthy subjects (20 licensed taxi drivers, 20 nondrivers) underwent an 8-min resting-state functional MRI acquisition. Using independent component analysis, three sensory (primary and extrastriate visual, sensorimotor) RSNs and four cognitive (anterior and posterior default mode, left and right frontoparietal) RSNs were retrieved from the data. We then examined the group differences in the intrinsic brain activity of each RSN and in the functional network connectivity (FNC) between the RSNs. We found that the drivers had reduced intrinsic brain activity in the visual RSNs and reduced FNC between the sensory RSNs compared with the nondrivers. The major finding of this study, however, was that the FNC between the cognitive and sensory RSNs became more positively or less negatively correlated in the drivers relative to that in the nondrivers. Notably, the strength of the FNC between the left frontoparietal and primary visual RSNs was positively correlated with the number of taxi-driving years. Our findings may provide new insight into how the brain supports driving behavior. PMID:25338709

  6. Disrupted topological organization in the whole-brain functional network of trauma-exposed firefighters: A preliminary study.

    PubMed

    Jung, Wi Hoon; Chang, Ki Jung; Kim, Nam Hee

    2016-04-30

    Given that partial posttraumatic stress disorder (pPTSD) may be a specific risk factor for the development of posttraumatic stress disorder (PTSD), it is important to understand the neurobiology of pPTSD. However, there are few extant studies in this domain. Using resting-state functional magnetic resonance imaging (rs-fMRI) and a graph theoretical approach, we compared the topological organization of the whole-brain functional network in trauma-exposed firefighters with pPTSD (pPTSD group, n=9) with those without pPTSD (PC group, n=8) and non-traumatized healthy controls (HC group, n=11). We also examined changes in the network topology of five individuals with pPTSD before and after eye movement desensitization and reprocessing (EMDR) therapy. Individuals with pPTSD exhibited altered global properties, including a reduction in values of a normalized clustering coefficient, normalized local efficiency, and small-worldness. We also observed altered local properties, particularly in the association cortex, including the temporal and parietal cortices, across groups. These disruptive global and local network properties presented in pPTSD before treatment were ameliorated after treatment. Our preliminary results suggest that subthreshold manifestation of PTSD may be due to a disruption in the optimal balance in the functional brain networks and that this disruption can be ameliorated by psychotherapy. PMID:27107156

  7. Human intelligence and brain networks

    PubMed Central

    Colom, Roberto; Karama, Sherif; Jung, Rex E.; Haier, Richard J.

    2010-01-01

    Intelligence can be defined as a general mental ability for reasoning, problem solving, and learning. Because of its general nature, intelligence integrates cognitive functions such as perception, attention, memory, language, or planning. On the basis of this definition, intelligence can be reliably measured by standardized tests with obtained scores predicting several broad social outcomes such as educational achievement, job performance, health, and longevity. A detailed understanding of the brain mechanisms underlying this general mental ability could provide significant individual and societal benefits. Structural and functional neuroimaging studies have generally supported a frontoparietal network relevant for intelligence. This same network has also been found to underlie cognitive functions related to perception, short-term memory storage, and language. The distributed nature of this network and its involvement in a wide range of cognitive functions fits well with the integrative nature of intelligence. A new key phase of research is beginning to investigate how functional networks relate to structural networks, with emphasis on how distributed brain areas communicate with each other. PMID:21319494

  8. Causal functional contributions and interactions in the attention network of the brain: an objective multi-perturbation analysis.

    PubMed

    Zavaglia, Melissa; Hilgetag, Claus C

    2016-06-01

    Spatial attention is a prime example for the distributed network functions of the brain. Lesion studies in animal models have been used to investigate intact attentional mechanisms as well as perspectives for rehabilitation in the injured brain. Here, we systematically analyzed behavioral data from cooling deactivation and permanent lesion experiments in the cat, where unilateral deactivation of the posterior parietal cortex (in the vicinity of the posterior middle suprasylvian cortex, pMS) or the superior colliculus (SC) cause a severe neglect in the contralateral hemifield. Counterintuitively, additional deactivation of structures in the opposite hemisphere reverses the deficit. Using such lesion data, we employed a game-theoretical approach, multi-perturbation Shapley value analysis (MSA), for inferring functional contributions and network interactions of bilateral pMS and SC from behavioral performance in visual attention studies. The approach provides an objective theoretical strategy for lesion inferences and allows a unique quantitative characterization of regional functional contributions and interactions on the basis of multi-perturbations. The quantitative analysis demonstrated that right posterior parietal cortex and superior colliculus made the strongest positive contributions to left-field orienting, while left brain regions had negative contributions, implying that their perturbation may reverse the effects of contralateral lesions or improve normal function. An analysis of functional modulations and interactions among the regions revealed redundant interactions (implying functional overlap) between regions within each hemisphere, and synergistic interactions between bilateral regions. To assess the reliability of the MSA method in the face of variable and incomplete input data, we performed a sensitivity analysis, investigating how much the contribution values of the four regions depended on the performance of specific configurations and on the

  9. Complex networks in brain electrical activity

    NASA Astrophysics Data System (ADS)

    Ray, C.; Ruffini, G.; Marco-Pallarés, J.; Fuentemilla, L.; Grau, C.

    2007-08-01

    This letter reports a method to extract a functional network of the human brain from electroencephalogram measurements. A network analysis was performed on the resultant network and the statistics of the cluster coefficient, node degree, path length, and physical distance of the links, were studied. Even given the low electrode count of the experimental data the method was able to extract networks with network parameters that clearly depend on the type of stimulus presented to the subject. This type of analysis opens a door to studying the cerebral networks underlying brain electrical activity, and links the fields of complex networks and cognitive neuroscience.

  10. Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity

    PubMed Central

    2013-01-01

    Background Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnection syndrome, supported by converging evidence in both non-syndromic and syndromic ASD. However, the effects of abnormal connectivity on network properties have not been well studied, particularly in syndromic ASD. To close this gap, brain functional networks of electroencephalographic (EEG) connectivity were studied through graph measures in patients with Tuberous Sclerosis Complex (TSC), a disorder with a high prevalence of ASD, as well as in patients with non-syndromic ASD. Methods EEG data were collected from TSC patients with ASD (n = 14) and without ASD (n = 29), from patients with non-syndromic ASD (n = 16), and from controls (n = 46). First, EEG connectivity was characterized by the mean coherence, the ratio of inter- over intra-hemispheric coherence and the ratio of long- over short-range coherence. Next, graph measures of the functional networks were computed and a resilience analysis was conducted. To distinguish effects related to ASD from those related to TSC, a two-way analysis of covariance (ANCOVA) was applied, using age as a covariate. Results Analysis of network properties revealed differences specific to TSC and ASD, and these differences were very consistent across subgroups. In TSC, both with and without a concurrent diagnosis of ASD, mean coherence, global efficiency, and clustering coefficient were decreased and the average path length was increased. These findings indicate an altered network topology. In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence and markedly increased network resilience were found. Conclusions The altered network topology in TSC represents a functional correlate of structural abnormalities and may play a

  11. Development of a large-scale functional brain network during human non-rapid eye movement sleep.

    PubMed

    Spoormaker, Victor I; Schröter, Manuel S; Gleiser, Pablo M; Andrade, Katia C; Dresler, Martin; Wehrle, Renate; Sämann, Philipp G; Czisch, Michael

    2010-08-25

    Graph theoretical analysis of functional magnetic resonance imaging (fMRI) time series has revealed a small-world organization of slow-frequency blood oxygen level-dependent (BOLD) signal fluctuations during wakeful resting. In this study, we used graph theoretical measures to explore how physiological changes during sleep are reflected in functional connectivity and small-world network properties of a large-scale, low-frequency functional brain network. Twenty-five young and healthy participants fell asleep during a 26.7 min fMRI scan with simultaneous polysomnography. A maximum overlap discrete wavelet transformation was applied to fMRI time series extracted from 90 cortical and subcortical regions in normalized space after residualization of the raw signal against unspecific sources of signal fluctuations; functional connectivity analysis focused on the slow-frequency BOLD signal fluctuations between 0.03 and 0.06 Hz. We observed that in the transition from wakefulness to light sleep, thalamocortical connectivity was sharply reduced, whereas corticocortical connectivity increased; corticocortical connectivity subsequently broke down in slow-wave sleep. Local clustering values were closest to random values in light sleep, whereas slow-wave sleep was characterized by the highest clustering ratio (gamma). Our findings support the hypothesis that changes in consciousness in the descent to sleep are subserved by reduced thalamocortical connectivity at sleep onset and a breakdown of general connectivity in slow-wave sleep, with both processes limiting the capacity of the brain to integrate information across functional modules. PMID:20739559

  12. Protecting Neural Structures and Cognitive Function During Prolonged Space Flight by Targeting the Brain Derived Neurotrophic Factor Molecular Network

    NASA Technical Reports Server (NTRS)

    Schmidt, M. A.; Goodwin, T. J.

    2014-01-01

    Brain derived neurotrophic factor (BDNF) is the main activity-dependent neurotrophin in the human nervous system. BDNF is implicated in production of new neurons from dentate gyrus stem cells (hippocampal neurogenesis), synapse formation, sprouting of new axons, growth of new axons, sprouting of new dendrites, and neuron survival. Alterations in the amount or activity of BDNF can produce significant detrimental changes to cortical function and synaptic transmission in the human brain. This can result in glial and neuronal dysfunction, which may contribute to a range of clinical conditions, spanning a number of learning, behavioral, and neurological disorders. There is an extensive body of work surrounding the BDNF molecular network, including BDNF gene polymorphisms, methylated BDNF gene promoters, multiple gene transcripts, varied BDNF functional proteins, and different BDNF receptors (whose activation differentially drive the neuron to neurogenesis or apoptosis). BDNF is also closely linked to mitochondrial biogenesis through PGC-1alpha, which can influence brain and muscle metabolic efficiency. BDNF AS A HUMAN SPACE FLIGHT COUNTERMEASURE TARGET Earth-based studies reveal that BDNF is negatively impacted by many of the conditions encountered in the space environment, including oxidative stress, radiation, psychological stressors, sleep deprivation, and many others. A growing body of work suggests that the BDNF network is responsive to a range of diet, nutrition, exercise, drug, and other types of influences. This section explores the BDNF network in the context of 1) protecting the brain and nervous system in the space environment, 2) optimizing neurobehavioral performance in space, and 3) reducing the residual effects of space flight on the nervous system on return to Earth

  13. Visual Analytics of Brain Networks

    PubMed Central

    Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

    2014-01-01

    Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. PMID:22414991

  14. Visual analytics of brain networks.

    PubMed

    Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

    2012-05-15

    Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. PMID:22414991

  15. Changes in functional connectivity within the fronto-temporal brain network induced by regular and irregular Russian verb production.

    PubMed

    Kireev, Maxim; Slioussar, Natalia; Korotkov, Alexander D; Chernigovskaya, Tatiana V; Medvedev, Svyatoslav V

    2015-01-01

    Functional connectivity between brain areas involved in the processing of complex language forms remains largely unexplored. Contributing to the debate about neural mechanisms underlying regular and irregular inflectional morphology processing in the mental lexicon, we conducted an fMRI experiment in which participants generated forms from different types of Russian verbs and nouns as well as from nonce stimuli. The data were subjected to a whole brain voxel-wise analysis of context dependent changes in functional connectivity [the so-called psychophysiological interaction (PPI) analysis]. Unlike previously reported subtractive results that reveal functional segregation between brain areas, PPI provides complementary information showing how these areas are functionally integrated in a particular task. To date, PPI evidence on inflectional morphology has been scarce and only available for inflectionally impoverished English verbs in a same-different judgment task. Using PPI here in conjunction with a production task in an inflectionally rich language, we found that functional connectivity between the left inferior frontal gyrus (LIFG) and bilateral superior temporal gyri (STG) was significantly greater for regular real verbs than for irregular ones. Furthermore, we observed a significant positive covariance between the number of mistakes in irregular real verb trials and the increase in functional connectivity between the LIFG and the right anterior cingulate cortex in these trails, as compared to regular ones. Our results therefore allow for dissociation between regularity and processing difficulty effects. These results, on the one hand, shed new light on the functional interplay within the LIFG-bilateral STG language-related network and, on the other hand, call for partial reconsideration of some of the previous findings while stressing the role of functional temporo-frontal connectivity in complex morphological processes. PMID:25741262

  16. Functional Brain Imaging

    PubMed Central

    2006-01-01

    Database of Systematic Reviews, CENTRAL, and International Network of Agencies for Health Technology Assessment (INAHTA). The database search was supplemented with a search of relevant Web sites and a review of the bibliographies of selected papers. General inclusion criteria were applied to all conditions. Those criteria included the following: Full reports of systematic reviews, randomized controlled trials (RCTs), cohort-control studies, prospective cohort studies (PCS’), and retrospective studies. Sample sizes of at least 20 patients (≥ 10 with condition being reviewed). English-language studies. Human studies. Any age. Studying at least one of the following: fMRI, PET, MRS, or MEG. Functional brain imaging modality must be compared with a clearly defined reference standard. Must report at least one of the following outcomes: sensitivity, specificity, accuracy, positive predictive value (PPV), receiver operating characteristic curve, outcome measuring impact on diagnostic testing, treatment, patient health, or cost. Summary of Findings There is evidence to indicate that PET can accurately diagnose AD; however, at this time, there is no evidence to suggest that a diagnosis of AD with PET alters the clinical outcomes of patients. The addition of MRS or O-(2-18F-Fluoroethyl)-L-Tyrosine (FET)-PET to gadolinium (Gd)-enhanced MRI for distinguishing malignant from benign tumours during primary diagnosis may provide a higher specificity than Gd-enhanced MRI alone. The clinical utility of additional imaging in patients to distinguish malignant from benign tumours is unclear, because patients with a suspected brain tumour will likely undergo a biopsy despite additional imaging results. The addition of MRS, FET-PET, or MRI T2 to Gd-enhanced MRI for the differentiation of recurrence from radiation necrosis may provide a higher specificity than Gd-enhanced MRI alone. The clinical utility of additional imaging in patients with a suspected recurrence is in the monitoring of

  17. Disrupted Brain Functional Network in Internet Addiction Disorder: A Resting-State Functional Magnetic Resonance Imaging Study

    PubMed Central

    Yap, Pew-Thian; Wu, Guorong; Shi, Feng; Price, True; Du, Yasong; Xu, Jianrong; Zhou, Yan; Shen, Dinggang

    2014-01-01

    Internet addiction disorder (IAD) is increasingly recognized as a mental health disorder, particularly among adolescents. The pathogenesis associated with IAD, however, remains unclear. In this study, we aim to explore the encephalic functional characteristics of IAD adolescents at rest using functional magnetic resonance imaging data. We adopted a graph-theoretic approach to investigate possible disruptions of functional connectivity in terms of network properties including small-worldness, efficiency, and nodal centrality on 17 adolescents with IAD and 16 socio-demographically matched healthy controls. False discovery rate-corrected parametric tests were performed to evaluate the statistical significance of group-level network topological differences. In addition, a correlation analysis was performed to assess the relationships between functional connectivity and clinical measures in the IAD group. Our results demonstrate that there is significant disruption in the functional connectome of IAD patients, particularly between regions located in the frontal, occipital, and parietal lobes. The affected connections are long-range and inter-hemispheric connections. Although significant alterations are observed for regional nodal metrics, there is no difference in global network topology between IAD and healthy groups. In addition, correlation analysis demonstrates that the observed regional abnormalities are correlated with the IAD severity and behavioral clinical assessments. Our findings, which are relatively consistent between anatomically and functionally defined atlases, suggest that IAD causes disruptions of functional connectivity and, importantly, that such disruptions might link to behavioral impairments. PMID:25226035

  18. Avalanche dynamics of idealized neuron function in the brain on an uncorrelated random scale-free network

    NASA Astrophysics Data System (ADS)

    Lee, K. E.; Lee, J. W.

    2006-03-01

    We study a simple model for a neuron function in a collective brain system. The neural network is composed of an uncorrelated configuration model (UCM) for eliminating the degree correlation of dynamical processes. The interaction of neurons is assumed to be isotropic and idealized. These neuron dynamics are similar to biological evolution in extremal dynamics with locally isotropic interaction but has a different time scale. The functioning of neurons takes place as punctuated patterns based on avalanche dynamics. In our model, the avalanche dynamics of neurons exhibit self-organized criticality which shows power-law behavior of the avalanche sizes. For a given network, the avalanche dynamic behavior is not changed with different degree exponents of networks, γ≥2.4 and various refractory periods referred to the memory effect, Tr. Furthermore, the avalanche size distributions exhibit power-law behavior in a single scaling region in contrast to other networks. However, return time distributions displaying spatiotemporal complexity have three characteristic time scaling regimes Thus, we find that UCM may be inefficient for holding a memory.

  19. Violence-related content in video game may lead to functional connectivity changes in brain networks as revealed by fMRI-ICA in young men.

    PubMed

    Zvyagintsev, M; Klasen, M; Weber, R; Sarkheil, P; Esposito, F; Mathiak, K A; Schwenzer, M; Mathiak, K

    2016-04-21

    In violent video games, players engage in virtual aggressive behaviors. Exposure to virtual aggressive behavior induces short-term changes in players' behavior. In a previous study, a violence-related version of the racing game "Carmageddon TDR2000" increased aggressive affects, cognitions, and behaviors compared to its non-violence-related version. This study investigates the differences in neural network activity during the playing of both versions of the video game. Functional magnetic resonance imaging (fMRI) recorded ongoing brain activity of 18 young men playing the violence-related and the non-violence-related version of the video game Carmageddon. Image time series were decomposed into functional connectivity (FC) patterns using independent component analysis (ICA) and template-matching yielded a mapping to established functional brain networks. The FC patterns revealed a decrease in connectivity within 6 brain networks during the violence-related compared to the non-violence-related condition: three sensory-motor networks, the reward network, the default mode network (DMN), and the right-lateralized frontoparietal network. Playing violent racing games may change functional brain connectivity, in particular and even after controlling for event frequency, in the reward network and the DMN. These changes may underlie the short-term increase of aggressive affects, cognitions, and behaviors as observed after playing violent video games. PMID:26855192

  20. Schizophrenia and abnormal brain network hubs

    PubMed Central

    Rubinov, Mikail; Bullmore, Ed.

    2013-01-01

    Schizophrenia is a heterogeneous psychiatric disorder of unknown cause or characteristic pathology. Clinical neuroscientists increasingly postulate that schizophrenia is a disorder of brain network organization. In this article we discuss the conceptual framework of this dysconnection hypothesis, describe the predominant methodological paradigm for testing this hypothesis, and review recent evidence for disruption of central/hub brain regions, as a promising example of this hypothesis. We summarize studies of brain hubs in large-scale structural and functional brain networks and find strong evidence for network abnormalities of prefrontal hubs, and moderate evidence for network abnormalities of limbic, temporal, and parietal hubs. Future studies are needed to differentiate network dysfunction from previously observed gray- and white-matter abnormalities of these hubs, and to link endogenous network dysfunction phenotypes with perceptual, behavioral, and cognitive clinical phenotypes of schizophrenia. PMID:24174905

  1. The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks

    PubMed Central

    Hartman, D.; Hlinka, J.; Paluš, M.; Mantini, D.; Corbetta, M.

    2011-01-01

    In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures—clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable. PMID:21456833

  2. The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks.

    PubMed

    Hartman, D; Hlinka, J; Palus, M; Mantini, D; Corbetta, M

    2011-03-01

    In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures-clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable. PMID:21456833

  3. The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks

    NASA Astrophysics Data System (ADS)

    Hartman, D.; Hlinka, J.; Paluš, M.; Mantini, D.; Corbetta, M.

    2011-03-01

    In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures—clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable.

  4. The Brain Functional Networks Associated to Human and Animal Suffering Differ among Omnivores, Vegetarians and Vegans

    PubMed Central

    Filippi, Massimo; Riccitelli, Gianna; Falini, Andrea; Di Salle, Francesco; Vuilleumier, Patrik; Comi, Giancarlo; Rocca, Maria A.

    2010-01-01

    Empathy and affective appraisals for conspecifics are among the hallmarks of social interaction. Using functional MRI, we hypothesized that vegetarians and vegans, who made their feeding choice for ethical reasons, might show brain responses to conditions of suffering involving humans or animals different from omnivores. We recruited 20 omnivore subjects, 19 vegetarians, and 21 vegans. The groups were matched for sex and age. Brain activation was investigated using fMRI and an event-related design during observation of negative affective pictures of human beings and animals (showing mutilations, murdered people, human/animal threat, tortures, wounds, etc.). Participants saw negative-valence scenes related to humans and animals, alternating with natural landscapes. During human negative valence scenes, compared with omnivores, vegetarians and vegans had an increased recruitment of the anterior cingulate cortex (ACC) and inferior frontal gyrus (IFG). More critically, during animal negative valence scenes, they had decreased amygdala activation and increased activation of the lingual gyri, the left cuneus, the posterior cingulate cortex and several areas mainly located in the frontal lobes, including the ACC, the IFG and the middle frontal gyrus. Nonetheless, also substantial differences between vegetarians and vegans have been found responding to negative scenes. Vegetarians showed a selective recruitment of the right inferior parietal lobule during human negative scenes, and a prevailing activation of the ACC during animal negative scenes. Conversely, during animal negative scenes an increased activation of the inferior prefrontal cortex was observed in vegans. These results suggest that empathy toward non conspecifics has different neural representation among individuals with different feeding habits, perhaps reflecting different motivational factors and beliefs. PMID:20520767

  5. Identification of Focal Epileptogenic Networks in Generalized Epilepsy Using Brain Functional Connectivity Analysis of Bilateral Intracranial EEG Signals.

    PubMed

    Chen, Po-Ching; Castillo, Eduardo M; Baumgartner, James; Seo, Joo Hee; Korostenskaja, Milena; Lee, Ki Hyeong

    2016-09-01

    Simultaneous bilateral onset and bi-synchrony epileptiform discharges in electroencephalogram (EEG) remain hallmarks for generalized seizures. However, the possibility of an epileptogenic focus triggering rapidly generalized epileptiform discharges has been documented in several studies. Previously, a new multi-stage surgical procedure using bilateral intracranial EEG (iEEG) prior to and post complete corpus callosotomy (CC) was developed to uncover seizure focus in non-lateralizing focal epilepsy. Five patients with drug-resistant generalized epilepsy who underwent this procedure were included in the study. Their bilateral iEEG findings prior to complete CC showed generalized epileptiform discharges with no clear lateralization. Nonetheless, the bilateral ictal iEEG findings post complete CC indicated lateralized or localized seizure onset. This study hypothesized that brain functional connectivity analysis, applied to the pre CC bilateral iEEG recordings, could help identify focal epileptogenic networks in generalized epilepsy. The results indicated that despite diffuse epileptiform discharges, focal features can still be observed in apparent generalized seizures through brain connectivity analysis. The seizure onset localization/lateralization from connectivity analysis demonstrated a good agreement with the bilateral iEEG findings post complete CC and final surgical outcomes. Our study supports the role of focal epileptic networks in generalized seizures. PMID:27142358

  6. Alteration of Brain Functional Networks in Early-Stage Parkinson’s Disease: A Resting-State fMRI Study

    PubMed Central

    Wang, Li; Zhang, Jingna; Zhang, Ye; Li, Pengyue; Wang, Jian; Qiu, Mingguo

    2015-01-01

    Although alterations of topological organization have previously been reported in the brain functional network of Parkinson’s disease (PD) patients, the topological properties of the brain network in early-stage PD patients who received antiparkinson treatment are largely unknown. This study sought to determine the topological characteristics of the large-scale functional network in early-stage PD patients. First, 26early-stage PD patients (Hoehn and Yahr stage:1-2) and 30 age-matched normal controls were scanned using resting-state functional MRI. Subsequently, graph theoretical analysis was employed to investigate the abnormal topological configuration of the brain network in early-stage PD patients. We found that both the PD patient and control groups showed small-world properties in their functional brain networks. However, compared with the controls, the early-stage PD patients exhibited abnormal global properties, characterized by lower global efficiency. Moreover, the modular structure and the hub distribution were markedly altered in early-stage PD patients. Furthermore, PD patients exhibited increased nodal centrality, primarily in the bilateral pallidum, the inferior parietal lobule, and the medial superior frontal gyrus, and decreased nodal centrality in the caudate nucleus, the supplementary motor areas, the precentral gyrus, and the middle frontal gyrus. There were significant negative correlations between the Unified Parkinson Disease Rating Scale motor scores and nodal centralities of superior parietal gyrus. These results suggest that the topological organization of the brain functional network was altered in early-stage PD patients who received antiparkinson treatment, and we speculated that the antiparkinson treatment may affect the efficiency of the brain network to effectively relieve clinical symptoms of PD. PMID:26517128

  7. Functional MRI of the vocalization-processing network in the macaque brain

    PubMed Central

    Ortiz-Rios, Michael; Kuśmierek, Paweł; DeWitt, Iain; Archakov, Denis; Azevedo, Frederico A. C.; Sams, Mikko; Jääskeläinen, Iiro P.; Keliris, Georgios A.; Rauschecker, Josef P.

    2015-01-01

    Using functional magnetic resonance imaging in awake behaving monkeys we investigated how species-specific vocalizations are represented in auditory and auditory-related regions of the macaque brain. We found clusters of active voxels along the ascending auditory pathway that responded to various types of complex sounds: inferior colliculus (IC), medial geniculate nucleus (MGN), auditory core, belt, and parabelt cortex, and other parts of the superior temporal gyrus (STG) and sulcus (STS). Regions sensitive to monkey calls were most prevalent in the anterior STG, but some clusters were also found in frontal and parietal cortex on the basis of comparisons between responses to calls and environmental sounds. Surprisingly, we found that spectrotemporal control sounds derived from the monkey calls (“scrambled calls”) also activated the parietal and frontal regions. Taken together, our results demonstrate that species-specific vocalizations in rhesus monkeys activate preferentially the auditory ventral stream, and in particular areas of the antero-lateral belt and parabelt. PMID:25883546

  8. Split Brain Functioning.

    ERIC Educational Resources Information Center

    Cassel, Russell N.

    1978-01-01

    Summarizing recent research, this article defines the functions performed by the left and right sides of the human brain. Attention is given to the right side, or the nondominant side, of the brain and its potential in terms of perception of the environment, music, art, geometry, and the aesthetics. (JC)

  9. COPPER AND BRAIN FUNCTION

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Increasing evidence shows that brain development and function are impaired when the brain is deprived of copper either through dietary copper deficiency or through genetic defects in copper transport. A number of copper-dependent enzymes whose activities are lowered by copper deprivation form the ba...

  10. Testing the hypothesis of neurodegeneracy in respiratory network function with a priori transected arterially perfused brain stem preparation of rat.

    PubMed

    Jones, Sarah E; Dutschmann, Mathias

    2016-05-01

    Degeneracy of respiratory network function would imply that anatomically discrete aspects of the brain stem are capable of producing respiratory rhythm. To test this theory we a priori transected brain stem preparations before reperfusion and reoxygenation at 4 rostrocaudal levels: 1.5 mm caudal to obex (n = 5), at obex (n = 5), and 1.5 (n = 7) and 3 mm (n = 6) rostral to obex. The respiratory activity of these preparations was assessed via recordings of phrenic and vagal nerves and lumbar spinal expiratory motor output. Preparations with a priori transection at level of the caudal brain stem did not produce stable rhythmic respiratory bursting, even when the arterial chemoreceptors were stimulated with sodium cyanide (NaCN). Reperfusion of brain stems that preserved the pre-Bötzinger complex (pre-BötC) showed spontaneous and sustained rhythmic respiratory bursting at low phrenic nerve activity (PNA) amplitude that occurred simultaneously in all respiratory motor outputs. We refer to this rhythm as the pre-BötC burstlet-type rhythm. Conserving circuitry up to the pontomedullary junction consistently produced robust high-amplitude PNA at lower burst rates, whereas sequential motor patterning across the respiratory motor outputs remained absent. Some of the rostrally transected preparations expressed both burstlet-type and regular PNA amplitude rhythms. Further analysis showed that the burstlet-type rhythm and high-amplitude PNA had 1:2 quantal relation, with burstlets appearing to trigger high-amplitude bursts. We conclude that no degenerate rhythmogenic circuits are located in the caudal medulla oblongata and confirm the pre-BötC as the primary rhythmogenic kernel. The absence of sequential motor patterning in a priori transected preparations suggests that pontine circuits govern respiratory pattern formation. PMID:26888109

  11. Approach of Complex Networks for the Determination of Brain Death

    NASA Astrophysics Data System (ADS)

    Sun, Wei-Gang; Cao, Jian-Ting; Wang, Ru-Bin

    2011-06-01

    In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our findings might provide valuable insights on the determination of brain death.

  12. Brain anatomical network and intelligence.

    PubMed

    Li, Yonghui; Liu, Yong; Li, Jun; Qin, Wen; Li, Kuncheng; Yu, Chunshui; Jiang, Tianzi

    2009-05-01

    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

  13. 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

  14. Brain Rhythms Reveal a Hierarchical Network Organization

    PubMed Central

    Steinke, G. Karl; Galán, Roberto F.

    2011-01-01

    Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or “virtual brains”, whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs) and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic), in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states display lower

  15. Developmental process emerges from extended brain-body-behavior networks

    PubMed Central

    Byrge, Lisa; Sporns, Olaf; Smith, Linda B.

    2014-01-01

    Studies of brain connectivity have focused on two modes of networks: structural networks describing neuroanatomy and the intrinsic and evoked dependencies of functional networks at rest and during tasks. Each mode constrains and shapes the other across multiple time scales, and each also shows age-related changes. Here we argue that understanding how brains change across development requires understanding the interplay between behavior and brain networks: changing bodies and activities modify the statistics of inputs to the brain; these changing inputs mold brain networks; these networks, in turn, promote further change in behavior and input. PMID:24862251

  16. Lutein and Brain Function

    PubMed Central

    Erdman, John W.; Smith, Joshua W.; Kuchan, Matthew J.; Mohn, Emily S.; Johnson, Elizabeth J.; Rubakhin, Stanislav S.; Wang, Lin; Sweedler, Jonathan V.; Neuringer, Martha

    2015-01-01

    Lutein is one of the most prevalent carotenoids in nature and in the human diet. Together with zeaxanthin, it is highly concentrated as macular pigment in the foveal retina of primates, attenuating blue light exposure, providing protection from photo-oxidation and enhancing visual performance. Recently, interest in lutein has expanded beyond the retina to its possible contributions to brain development and function. Only primates accumulate lutein within the brain, but little is known about its distribution or physiological role. Our team has begun to utilize the rhesus macaque (Macaca mulatta) model to study the uptake and bio-localization of lutein in the brain. Our overall goal has been to assess the association of lutein localization with brain function. In this review, we will first cover the evolution of the non-human primate model for lutein and brain studies, discuss prior association studies of lutein with retina and brain function, and review approaches that can be used to localize brain lutein. We also describe our approach to the biosynthesis of 13C-lutein, which will allow investigation of lutein flux, localization, metabolism and pharmacokinetics. Lastly, we describe potential future research opportunities. PMID:26566524

  17. 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. PMID:26068849

  18. Change in brain network topology as a function of treatment response in schizophrenia: a longitudinal resting-state fMRI study using graph theory

    PubMed Central

    Hadley, Jennifer Ann; Kraguljac, Nina Vanessa; White, David Matthew; Ver Hoef, Lawrence; Tabora, Janell; Lahti, Adrienne Carol

    2016-01-01

    A number of neuroimaging studies have provided evidence in support of the hypothesis that faulty interactions between spatially disparate brain regions underlie the pathophysiology of schizophrenia, but it remains unclear to what degree antipsychotic medications affect these. We hypothesized that the balance between functional integration and segregation of brain networks is impaired in unmedicated patients with schizophrenia, but that it can be partially restored by antipsychotic medications. We included 32 unmedicated patients with schizophrenia (SZ) and 32 matched healthy controls (HC) in this study. We obtained resting-state scans while unmedicated, and again after 6 weeks of treatment with risperidone to assess functional integration and functional segregation of brain networks using graph theoretical measures. Compared with HC, unmedicated SZ showed reduced global efficiency and increased clustering coefficients. This pattern of aberrant functional network integration and segregation was modulated with antipsychotic medications, but only in those who responded to treatment. Our work lends support to the concept of schizophrenia as a dysconnectivity syndrome, and suggests that faulty brain network topology in schizophrenia is modulated by antipsychotic medication as a function of treatment response. PMID:27336056

  19. Change in brain network topology as a function of treatment response in schizophrenia: a longitudinal resting-state fMRI study using graph theory.

    PubMed

    Hadley, Jennifer Ann; Kraguljac, Nina Vanessa; White, David Matthew; Ver Hoef, Lawrence; Tabora, Janell; Lahti, Adrienne Carol

    2016-01-01

    A number of neuroimaging studies have provided evidence in support of the hypothesis that faulty interactions between spatially disparate brain regions underlie the pathophysiology of schizophrenia, but it remains unclear to what degree antipsychotic medications affect these. We hypothesized that the balance between functional integration and segregation of brain networks is impaired in unmedicated patients with schizophrenia, but that it can be partially restored by antipsychotic medications. We included 32 unmedicated patients with schizophrenia (SZ) and 32 matched healthy controls (HC) in this study. We obtained resting-state scans while unmedicated, and again after 6 weeks of treatment with risperidone to assess functional integration and functional segregation of brain networks using graph theoretical measures. Compared with HC, unmedicated SZ showed reduced global efficiency and increased clustering coefficients. This pattern of aberrant functional network integration and segregation was modulated with antipsychotic medications, but only in those who responded to treatment. Our work lends support to the concept of schizophrenia as a dysconnectivity syndrome, and suggests that faulty brain network topology in schizophrenia is modulated by antipsychotic medication as a function of treatment response. PMID:27336056

  20. Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements

    PubMed Central

    Zhang, Jiang; Liu, Qi; Chen, Huafu; Yuan, Zhen; Huang, Jin; Deng, Lihua; Lu, Fengmei; Zhang, Junpeng; Wang, Yuqing; Wang, Mingwen; Chen, Liangyin

    2015-01-01

    Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks. PMID:26236217

  1. Brain Hemispheric Functioning.

    ERIC Educational Resources Information Center

    Roeper Review, 1981

    1981-01-01

    Four articles consider brain hemisphere functioning of gifted students as it relates to gifted programs; alternation of education methodologies; spatial ability as an element of intellectual gifted functioning; and the interaction between hemisphere specialization, imagery, creative imagination, and sex differentiation. (SB)

  2. Defining nodes in complex brain networks

    PubMed Central

    Stanley, Matthew L.; Moussa, Malaak N.; Paolini, Brielle M.; Lyday, Robert G.; Burdette, Jonathan H.; Laurienti, Paul J.

    2013-01-01

    Network science holds great promise for expanding our understanding of the human brain in health, disease, development, and aging. Network analyses are quickly becoming the method of choice for analyzing functional MRI data. However, many technical issues have yet to be confronted in order to optimize results. One particular issue that remains controversial in functional brain network analyses is the definition of a network node. In functional brain networks a node represents some predefined collection of brain tissue, and an edge measures the functional connectivity between pairs of nodes. The characteristics of a node, chosen by the researcher, vary considerably in the literature. This manuscript reviews the current state of the art based on published manuscripts and highlights the strengths and weaknesses of three main methods for defining nodes. Voxel-wise networks are constructed by assigning a node to each, equally sized brain area (voxel). The fMRI time-series recorded from each voxel is then used to create the functional network. Anatomical methods utilize atlases to define the nodes based on brain structure. The fMRI time-series from all voxels within the anatomical area are averaged and subsequently used to generate the network. Functional activation methods rely on data from traditional fMRI activation studies, often from databases, to identify network nodes. Such methods identify the peaks or centers of mass from activation maps to determine the location of the nodes. Small (~10–20 millimeter diameter) spheres located at the coordinates of the activation foci are then applied to the data being used in the network analysis. The fMRI time-series from all voxels in the sphere are then averaged, and the resultant time series is used to generate the network. We attempt to clarify the discussion and move the study of complex brain networks forward. While the “correct” method to be used remains an open, possibly unsolvable question that deserves

  3. Benefit of interleaved practice of motor skills is associated with changes in functional brain network topology that differ between younger and older adults.

    PubMed

    Lin, Chien-Ho Janice; Knowlton, Barbara J; Wu, Allan D; Iacoboni, Marco; Yang, Ho-Ching; Ye, Yu-Ling; Liu, Kuan-Hong; Chiang, Ming-Chang

    2016-06-01

    Practicing tasks arranged in an interleaved manner generally leads to superior retention compared with practicing tasks repetitively, a phenomenon known as the contextual interference (CI) effect. We investigated the brain network of motor learning under CI, that is, the CI network, and how it was affected by aging. Sixteen younger and 16 older adults practiced motor sequences arranged in a repetitive or an interleaved order over 2 days, followed by a retention test on day 5 to evaluate learning. Network analysis was applied to functional MRI data on retention to define the CI network by identifying brain regions with greater between-region connectivity after interleaved compared with repetitive practice. CI effects were present in both groups but stronger in younger adults. Moreover, CI networks in younger adults exhibited efficient small-world topology, with a significant association between higher network centrality and better learning after interleaved practice. Older adults did not show such favorable network properties. Our findings suggest that aging affects the efficiency of brain networks underlying enhanced motor learning after CI practice. PMID:27143435

  4. Temporal evolution of brain reorganization under cross-modal training: insights into the functional architecture of encoding and retrieval networks

    NASA Astrophysics Data System (ADS)

    Likova, Lora T.

    2015-03-01

    This study is based on the recent discovery of massive and well-structured cross-modal memory activation generated in the primary visual cortex (V1) of totally blind people as a result of novel training in drawing without any vision (Likova, 2012). This unexpected functional reorganization of primary visual cortex was obtained after undergoing only a week of training by the novel Cognitive-Kinesthetic Method, and was consistent across pilot groups of different categories of visual deprivation: congenitally blind, late-onset blind and blindfolded (Likova, 2014). These findings led us to implicate V1 as the implementation of the theoretical visuo-spatial 'sketchpad' for working memory in the human brain. Since neither the source nor the subsequent 'recipient' of this non-visual memory information in V1 is known, these results raise a number of important questions about the underlying functional organization of the respective encoding and retrieval networks in the brain. To address these questions, an individual totally blind from birth was given a week of Cognitive-Kinesthetic training, accompanied by functional magnetic resonance imaging (fMRI) both before and just after training, and again after a two-month consolidation period. The results revealed a remarkable temporal sequence of training-based response reorganization in both the hippocampal complex and the temporal-lobe object processing hierarchy over the prolonged consolidation period. In particular, a pattern of profound learning-based transformations in the hippocampus was strongly reflected in V1, with the retrieval function showing massive growth as result of the Cognitive-Kinesthetic memory training and consolidation, while the initially strong hippocampal response during tactile exploration and encoding became non-existent. Furthermore, after training, an alternating patch structure in the form of a cascade of discrete ventral regions underwent radical transformations to reach complete functional

  5. Task-Based Cohesive Evolution of Dynamic Brain Networks

    NASA Astrophysics Data System (ADS)

    Davison, Elizabeth

    2014-03-01

    Applications of graph theory to neuroscience have resulted in significant progress towards a mechanistic understanding of the brain. Functional network representation of the brain has linked efficient network structure to psychometric intelligence and altered configurations with disease. Dynamic graphs provide us with tools to further study integral properties of the brain; specifically, the mathematical convention of hyperedges has allowed us to study the brain's cross-linked structure. Hyperedges capture the changes in network structure by identifying groups of brain regions with correlation patterns that change cohesively through time. We performed a hyperedge analysis on functional MRI data from 86 subjects and explored the cohesive evolution properties of their functional brain networks as they performed a series of tasks. Our results establish the hypergraph as a useful measure in understanding functional brain dynamics over tasks and reveal characteristic differences in the co-evolution structure of task-specific networks.

  6. Functional Magnetic Resonance Imaging of Chronic Dysarthric Speech after Childhood Brain Injury: Reliance on a Left-Hemisphere Compensatory Network

    ERIC Educational Resources Information Center

    Morgan, Angela T.; Masterton, Richard; Pigdon, Lauren; Connelly, Alan; Liegeois, Frederique J.

    2013-01-01

    Severe and persistent speech disorder, dysarthria, may be present for life after brain injury in childhood, yet the neural correlates of this chronic disorder remain elusive. Although abundant literature is available on language reorganization after lesions in childhood, little is known about the capacity of motor speech networks to reorganize…

  7. Patterns of Cortical Oscillations Organize Neural Activity into Whole-Brain Functional Networks Evident in the fMRI BOLD Signal

    PubMed Central

    Whitman, Jennifer C.; Ward, Lawrence M.; Woodward, Todd S.

    2013-01-01

    Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG/MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks. PMID:23504590

  8. Patterns of Cortical Oscillations Organize Neural Activity into Whole-Brain Functional Networks Evident in the fMRI BOLD Signal.

    PubMed

    Whitman, Jennifer C; Ward, Lawrence M; Woodward, Todd S

    2013-01-01

    Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG/MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks. PMID:23504590

  9. Consciousness, cognition and brain networks: New perspectives.

    PubMed

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

    2016-10-01

    A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks. PMID:26143337

  10. Brain Network Adaptability across Task States

    PubMed Central

    Davison, Elizabeth N.; Schlesinger, Kimberly J.; Bassett, Danielle S.; Lynall, Mary-Ellen; Miller, Michael B.; Grafton, Scott T.; Carlson, Jean M.

    2015-01-01

    Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change. PMID:25569227

  11. Aberrant Functional Connectivity in the Default Mode and Central Executive Networks in Subjects with Schizophrenia – A Whole-Brain Resting-State ICA Study

    PubMed Central

    Littow, Harri; Huossa, Ville; Karjalainen, Sami; Jääskeläinen, Erika; Haapea, Marianne; Miettunen, Jouko; Tervonen, Osmo; Isohanni, Matti; Nikkinen, Juha; Veijola, Juha; Murray, Graham; Kiviniemi, Vesa J.

    2015-01-01

    Neurophysiological changes of schizophrenia are currently linked to disturbances in connectivity between functional brain networks. Functional magnetic resonance imaging studies on schizophrenia have focused on a few selected networks. Also previously, it has not been possible to discern whether the functional alterations in schizophrenia originate from spatial shifting or amplitude alterations of functional connectivity. In this study, we aim to discern the differences in schizophrenia patients with respect to spatial shifting vs. signal amplitude changes in functional connectivity in the whole-brain connectome. We used high model order-independent component analysis to study some 40 resting-state networks (RSN) covering the whole cortex. Group differences were analyzed with dual regression coupled with y-concat correction for multiple comparisons. We investigated the RSNs with and without variance normalization in order to discern spatial shifting from signal amplitude changes in 43 schizophrenia patients and matched controls from the Northern Finland 1966 Birth Cohort. Voxel-level correction for multiple comparisons revealed 18 RSNs with altered functional connectivity, 6 of which had both spatial and signal amplitude changes. After adding the multiple comparison, y-concat correction to the analysis for including the 40 RSNs as well, we found that four RSNs showed still changes. These robust changes actually seem encompass parcellations of the default mode network and central executive networks. These networks both have spatially shifted connectivity and abnormal signal amplitudes. Interestingly the networks seem to mix their functional representations in areas like left caudate nucleus and dorsolateral prefrontal cortex. These changes overlapped with areas that have been related to dopaminergic alterations in patients with schizophrenia compared to controls. PMID:25767449

  12. Functional brain networks and white matter underlying theory-of-mind in autism.

    PubMed

    Kana, Rajesh K; Libero, Lauren E; Hu, Christi P; Deshpande, Hrishikesh D; Colburn, Jeffrey S

    2014-01-01

    Human beings constantly engage in attributing causal explanations to one's own and to others' actions, and theory-of-mind (ToM) is critical in making such inferences. Although children learn causal attribution early in development, children with autism spectrum disorders (ASDs) are known to have impairments in the development of intentional causality. This functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) study investigated the neural correlates of physical and intentional causal attribution in people with ASDs. In the fMRI scanner, 15 adolescents and adults with ASDs and 15 age- and IQ-matched typically developing peers made causal judgments about comic strips presented randomly in an event-related design. All participants showed robust activation in bilateral posterior superior temporal sulcus at the temporo-parietal junction (TPJ) in response to intentional causality. Participants with ASDs showed lower activation in TPJ, right inferior frontal gyrus and left premotor cortex. Significantly weaker functional connectivity was also found in the ASD group between TPJ and motor areas during intentional causality. DTI data revealed significantly reduced fractional anisotropy in ASD participants in white matter underlying the temporal lobe. In addition to underscoring the role of TPJ in ToM, this study found an interaction between motor simulation and mentalizing systems in intentional causal attribution and its possible discord in autism. PMID:22977198

  13. Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient.

    PubMed

    Boly, M; Tshibanda, L; Vanhaudenhuyse, A; Noirhomme, Q; Schnakers, C; Ledoux, D; Boveroux, P; Garweg, C; Lambermont, B; Phillips, C; Luxen, A; Moonen, G; Bassetti, C; Maquet, P; Laureys, S

    2009-08-01

    Recent studies on spontaneous fluctuations in the functional MRI blood oxygen level-dependent (BOLD) signal in awake healthy subjects showed the presence of coherent fluctuations among functionally defined neuroanatomical networks. However, the functional significance of these spontaneous BOLD fluctuations remains poorly understood. By means of 3 T functional MRI, we demonstrate absent cortico-thalamic BOLD functional connectivity (i.e. between posterior cingulate/precuneal cortex and medial thalamus), but preserved cortico-cortical connectivity within the default network in a case of vegetative state (VS) studied 2.5 years following cardio-respiratory arrest, as documented by extensive behavioral and paraclinical assessments. In the VS patient, as in age-matched controls, anticorrelations could also be observed between posterior cingulate/precuneus and a previously identified task-positive cortical network. Both correlations and anticorrelations were significantly reduced in VS as compared to controls. A similar approach in a brain dead patient did not show any such long-distance functional connectivity. We conclude that some slow coherent BOLD fluctuations previously identified in healthy awake human brain can be found in alive but unaware patients, and are thus unlikely to be uniquely due to ongoing modifications of conscious thoughts. Future studies are needed to give a full characterization of default network connectivity in the VS patients population. PMID:19350563

  14. The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder.

    PubMed

    Wang, Li; Xia, Mingrui; Li, Ke; Zeng, Yawei; Su, Yunai; Dai, Wenji; Zhang, Qinge; Jin, Zhen; Mitchell, Philip B; Yu, Xin; He, Yong; Si, Tianmei

    2015-02-01

    Although most knowledge regarding antidepressant effects is at the receptor level, the neurophysiological correlates of these neurochemical changes remain poorly understood. Such an understanding could benefit from elucidation of antidepressant effects at the level of neural circuits, which would be crucial in identifying biomarkers for monitoring treatment efficacy of antidepressants. In this study, we recruited 20 first-episode drug-naive major depressive disorder (MDD) patients and performed resting-state functional magnetic resonance imaging (MRI) scans before and after 8 weeks of treatment with a selective serotonin reuptake inhibitor-escitalopram. Twenty healthy controls (HCs) were also scanned twice with an 8-week interval. Whole-brain connectivity was analyzed using a graph-theory approach-functional connectivity strength (FCS). The analysis of covariance of FCS was used to determine treatment-related changes. We observed significant group-by-time interaction on FCS in the bilateral dorsomedial prefrontal cortex and bilateral hippocampi. Post hoc analyses revealed that the FCS values in the bilateral dorsomedial prefrontal cortex were significantly higher in the MDD patients compared to HCs at baseline and were significantly reduced after treatment; conversely, the FCS values in the bilateral hippocampi were significantly lower in the patients at baseline and were significantly increased after treatment. Importantly, FCS reduction in the dorsomedial prefrontal cortex was significantly correlated with symptomatic improvement. Together, these findings provided evidence that this commonly used antidepressant can selectively modulate the intrinsic network connectivity associated with the medial prefrontal-limbic system, thus significantly adding to our understanding of antidepressant effects at a circuit level and suggesting potential imaging-based biomarkers for treatment evaluation in MDD. PMID:25332057

  15. 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.

  16. 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. PMID:26723151

  17. Brain networks shaping religious belief.

    PubMed

    Kapogiannis, Dimitrios; Deshpande, Gopikrishna; Krueger, Frank; Thornburg, Matthew P; Grafman, Jordan Henry

    2014-02-01

    We previously demonstrated with functional magnetic resonance imaging (fMRI) that religious belief depends upon three cognitive dimensions, which can be mapped to specific brain regions. In the present study, we considered these co-activated regions as nodes of three networks each one corresponding to a particular dimension, corresponding to each dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory of mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to the evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of supernatural agents (SAs; intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded both on propositional statements for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca's to Wernicke's language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal

  18. Early Brain Response to Low-Dose Radiation Exposure Involves Molecular Networks and Pathways Associated with Cognitive Functions, Advanced Aging and Alzheimer's Disease

    SciTech Connect

    Lowe, Xiu R; Bhattacharya, Sanchita; Marchetti, Francesco; Wyrobek, Andrew J.

    2008-06-06

    Understanding the cognitive and behavioral consequences of brain exposures to low-dose ionizing radiation has broad relevance for health risks from medical radiation diagnostic procedures, radiotherapy, environmental nuclear contamination, as well as earth orbit and space missions. Analyses of transcriptome profiles of murine brain tissue after whole-body radiation showed that low-dose exposures (10 cGy) induced genes not affected by high dose (2 Gy), and low-dose genes were associated with unique pathways and functions. The low-dose response had two major components: pathways that are consistently seen across tissues, and pathways that were brain tissue specific. Low-dose genes clustered into a saturated network (p < 10{sup -53}) containing mostly down-regulated genes involving ion channels, long-term potentiation and depression, vascular damage, etc. We identified 9 neural signaling pathways that showed a high degree of concordance in their transcriptional response in mouse brain tissue after low-dose radiation, in the aging human brain (unirradiated), and in brain tissue from patients with Alzheimer's disease. Mice exposed to high-dose radiation did not show these effects and associations. Our findings indicate that the molecular response of the mouse brain within a few hours after low-dose irradiation involves the down-regulation of neural pathways associated with cognitive dysfunctions that are also down regulated in normal human aging and Alzheimer's disease.

  19. Nonlinear functional connectivity network recovery in the human brain with mutual connectivity analysis (MCA): convergent cross-mapping and non-metric clustering

    NASA Astrophysics Data System (ADS)

    Wismüller, Axel; Abidin, Anas Z.; D'Souza, Adora M.; Wang, Xixi; Hobbs, Susan K.; Leistritz, Lutz; Nagarajan, Mahesh B.

    2015-03-01

    We explore a computational framework for functional connectivity analysis in resting-state functional MRI (fMRI) data acquired from the human brain for recovering the underlying network structure and understanding causality between network components. Termed mutual connectivity analysis (MCA), this framework involves two steps, the first of which is to evaluate the pair-wise cross-prediction performance between fMRI pixel time series within the brain. In a second step, the underlying network structure is subsequently recovered from the affinity matrix using non-metric network clustering approaches, such as the so-called Louvain method. Finally, we use convergent cross-mapping (CCM) to study causality between different network components. We demonstrate our MCA framework in the problem of recovering the motor cortex network associated with hand movement from resting state fMRI data. Results are compared with a ground truth of active motor cortex regions as identified by a task-based fMRI sequence involving a finger-tapping stimulation experiment. Our results regarding causation between regions of the motor cortex revealed a significant directional variability and were not readily interpretable in a consistent manner across subjects. However, our results on whole-slice fMRI analysis demonstrate that MCA-based model-free recovery of regions associated with the primary motor cortex and supplementary motor area are in close agreement with localization of similar regions achieved with a task-based fMRI acquisition. Thus, we conclude that our MCA methodology can extract and visualize valuable information concerning the underlying network structure between different regions of the brain in resting state fMRI.

  20. The brain timewise: how timing shapes and supports brain function

    PubMed Central

    Hari, Riitta; Parkkonen, Lauri

    2015-01-01

    We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function. PMID:25823867

  1. How Should Educational Neuroscience Conceptualise the Relation between Cognition and Brain Function? Mathematical Reasoning as a Network Process

    ERIC Educational Resources Information Center

    Varma, Sashank; Schwartz, Daniel L.

    2008-01-01

    Background: There is increasing interest in applying neuroscience findings to topics in education. Purpose: This application requires a proper conceptualization of the relation between cognition and brain function. This paper considers two such conceptualizations. The area focus understands each cognitive competency as the product of one (and only…

  2. Altered functional connectivity in the brain default-mode network of earthquake survivors persists after 2 years despite recovery from anxiety symptoms.

    PubMed

    Du, Ming-Ying; Liao, Wei; Lui, Su; Huang, Xiao-Qi; Li, Fei; Kuang, Wei-Hong; Li, Jing; Chen, Hua-Fu; Kendrick, Keith Maurice; Gong, Qi-Yong

    2015-11-01

    Although acute impact of traumatic experiences on brain function in disaster survivors is similar to that observed in post-traumatic stress disorders (PTSD), little is known about the long-term impact of this experience. We have used structural and functional magnetic resonance imaging to investigate resting-state functional connectivity and gray and white matter (WM) changes occurring in the brains of healthy Wenchuan earthquake survivors both 3 weeks and 2 years after the disaster. Results show that while functional connectivity changes 3 weeks after the disaster involved both frontal-limbic-striatal and default-mode networks (DMN), at the 2-year follow-up only changes in the latter persisted, despite complete recovery from high initial levels of anxiety. No gray or WM volume changes were found at either time point. Taken together, our findings provide important new evidence that while altered functional connectivity in the frontal-limbic-striatal network may underlie the post-trauma anxiety experienced by survivors, parallel changes in the DMN persist despite the apparent absence of anxiety symptoms. This suggests that long-term changes occur in neural networks involved in core aspects of self-processing, cognitive and emotional functioning in disaster survivors which are independent of anxiety symptoms and which may also confer increased risk of subsequent development of PTSD. PMID:25862672

  3. High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

    PubMed Central

    Plis, Sergey M; Sui, Jing; Lane, Terran; Roy, Sushmita; Clark, Vincent P; Potluru, Vamsi K; Huster, Rene J; Michael, Andrew; Sponheim, Scott R; Weisend, Michael P; Calhoun, Vince D

    2013-01-01

    Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors (“network clusters”). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pair-wise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important. PMID:23876245

  4. Flexible brain network reconfiguration supporting inhibitory control.

    PubMed

    Spielberg, Jeffrey M; Miller, Gregory A; Heller, Wendy; Banich, Marie T

    2015-08-11

    The ability to inhibit distracting stimuli from interfering with goal-directed behavior is crucial for success in most spheres of life. Despite an abundance of studies examining regional brain activation, knowledge of the brain networks involved in inhibitory control remains quite limited. To address this critical gap, we applied graph theory tools to functional magnetic resonance imaging data collected while a large sample of adults (n = 101) performed a color-word Stroop task. Higher demand for inhibitory control was associated with restructuring of the global network into a configuration that was more optimized for specialized processing (functional segregation), more efficient at communicating the output of such processing across the network (functional integration), and more resilient to potential interruption (resilience). In addition, there were regional changes with right inferior frontal sulcus and right anterior insula occupying more central positions as network hubs, and dorsal anterior cingulate cortex becoming more tightly coupled with its regional subnetwork. Given the crucial role of inhibitory control in goal-directed behavior, present findings identifying functional network organization supporting inhibitory control have the potential to provide additional insights into how inhibitory control may break down in a wide variety of individuals with neurological or psychiatric difficulties. PMID:26216985

  5. Can You Hear Me Now? Musical Training Shapes Functional Brain Networks for Selective Auditory Attention and Hearing Speech in Noise

    PubMed Central

    Strait, Dana L.; Kraus, Nina

    2011-01-01

    Even in the quietest of rooms, our senses are perpetually inundated by a barrage of sounds, requiring the auditory system to adapt to a variety of listening conditions in order to extract signals of interest (e.g., one speaker's voice amidst others). Brain networks that promote selective attention are thought to sharpen the neural encoding of a target signal, suppressing competing sounds and enhancing perceptual performance. Here, we ask: does musical training benefit cortical mechanisms that underlie selective attention to speech? To answer this question, we assessed the impact of selective auditory attention on cortical auditory-evoked response variability in musicians and non-musicians. Outcomes indicate strengthened brain networks for selective auditory attention in musicians in that musicians but not non-musicians demonstrate decreased prefrontal response variability with auditory attention. Results are interpreted in the context of previous work documenting perceptual and subcortical advantages in musicians for the hearing and neural encoding of speech in background noise. Musicians’ neural proficiency for selectively engaging and sustaining auditory attention to language indicates a potential benefit of music for auditory training. Given the importance of auditory attention for the development and maintenance of language-related skills, musical training may aid in the prevention, habilitation, and remediation of individuals with a wide range of attention-based language, listening and learning impairments. PMID:21716636

  6. Functional brain networks before the onset of psychosis: A prospective fMRI study with graph theoretical analysis☆☆☆

    PubMed Central

    Lord, Louis-David; Allen, Paul; Expert, Paul; Howes, Oliver; Broome, Matthew; Lambiotte, Renaud; Fusar-Poli, Paolo; Valli, Isabel; McGuire, Philip; Turkheimer, Federico E.

    2012-01-01

    Individuals with an at-risk mental state (ARMS) have a risk of developing a psychotic disorder significantly greater than the general population. However, it is not currently possible to predict which ARMS individuals will develop psychosis from clinical assessment alone. Comparison of ARMS subjects who do, and do not, develop psychosis can reveal which factors are critical for the onset of illness. In the present study, 37 patients with an ARMS were followed clinically at least 24 months subsequent to initial referral. Functional MRI data were collected at the beginning of the follow-up period during performance of an executive task known to recruit frontal lobe networks and to be impaired in psychosis. Graph theoretical analysis was used to compare the organization of a functional brain network in ARMS patients who developed a psychotic disorder following the scan (ARMS-T) to those who did not become ill during the same follow-up period (ARMS-NT) and aged-matched controls. The global properties of each group's representative network were studied (density, efficiency, global average path length) as well as regionally-specific contributions of network nodes to the organization of the system (degree, farness-centrality, betweenness-centrality). We focused our analysis on the dorsal anterior cingulate cortex (ACC), a region known to support executive function that is structurally and functionally impaired in ARMS patients. In the absence of between-group differences in global network organization, we report a significant reduction in the topological centrality of the ACC in the ARMS-T group relative to both ARMS-NT and controls. These results provide evidence that abnormalities in the functional organization of the brain predate the onset of psychosis, and suggest that loss of ACC topological centrality is a potential biomarker for transition to psychosis. PMID:24179741

  7. Genomic connectivity networks based on the BrainSpan atlas of the developing human brain

    NASA Astrophysics Data System (ADS)

    Mahfouz, Ahmed; Ziats, Mark N.; Rennert, Owen M.; Lelieveldt, Boudewijn P. F.; Reinders, Marcel J. T.

    2014-03-01

    The human brain comprises systems of networks that span the molecular, cellular, anatomic and functional levels. Molecular studies of the developing brain have focused on elucidating networks among gene products that may drive cellular brain development by functioning together in biological pathways. On the other hand, studies of the brain connectome attempt to determine how anatomically distinct brain regions are connected to each other, either anatomically (diffusion tensor imaging) or functionally (functional MRI and EEG), and how they change over development. A global examination of the relationship between gene expression and connectivity in the developing human brain is necessary to understand how the genetic signature of different brain regions instructs connections to other regions. Furthermore, analyzing the development of connectivity networks based on the spatio-temporal dynamics of gene expression provides a new insight into the effect of neurodevelopmental disease genes on brain networks. In this work, we construct connectivity networks between brain regions based on the similarity of their gene expression signature, termed "Genomic Connectivity Networks" (GCNs). Genomic connectivity networks were constructed using data from the BrainSpan Transcriptional Atlas of the Developing Human Brain. Our goal was to understand how the genetic signatures of anatomically distinct brain regions relate to each other across development. We assessed the neurodevelopmental changes in connectivity patterns of brain regions when networks were constructed with genes implicated in the neurodevelopmental disorder autism (autism spectrum disorder; ASD). Using graph theory metrics to characterize the GCNs, we show that ASD-GCNs are relatively less connected later in development with the cerebellum showing a very distinct expression of ASD-associated genes compared to other brain regions.

  8. 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. PMID:26802512

  9. Network effects of deep brain stimulation.

    PubMed

    Alhourani, Ahmad; McDowell, Michael M; Randazzo, Michael J; Wozny, Thomas A; Kondylis, Efstathios D; Lipski, Witold J; Beck, Sarah; Karp, Jordan F; Ghuman, Avniel S; Richardson, R Mark

    2015-10-01

    The ability to differentially alter specific brain functions via deep brain stimulation (DBS) represents a monumental advance in clinical neuroscience, as well as within medicine as a whole. Despite the efficacy of DBS in the treatment of movement disorders, for which it is often the gold-standard therapy when medical management becomes inadequate, the mechanisms through which DBS in various brain targets produces therapeutic effects is still not well understood. This limited knowledge is a barrier to improving efficacy and reducing side effects in clinical brain stimulation. A field of study related to assessing the network effects of DBS is gradually emerging that promises to reveal aspects of the underlying pathophysiology of various brain disorders and their response to DBS that will be critical to advancing the field. This review summarizes the nascent literature related to network effects of DBS measured by cerebral blood flow and metabolic imaging, functional imaging, and electrophysiology (scalp and intracranial electroencephalography and magnetoencephalography) in order to establish a framework for future studies. PMID:26269552

  10. 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. PMID:22830966

  11. Altered brain activation and functional connectivity in working memory related networks in patients with type 2 diabetes: An ICA-based analysis.

    PubMed

    Zhang, Yang; Lu, Shan; Liu, Chunlei; Zhang, Huimei; Zhou, Xuanhe; Ni, Changlin; Qin, Wen; Zhang, Quan

    2016-01-01

    Type 2 diabetes mellitus (T2DM) can cause multidimensional cognitive deficits, among which working memory (WM) is usually involved at an early stage. However, the neural substrates underlying impaired WM in T2DM patients are still unclear. To clarify this issue, we utilized functional magnetic resonance imaging (fMRI) and independent component analysis to evaluate T2DM patients for alterations in brain activation and functional connectivity (FC) in WM networks and to determine their associations with cognitive and clinical variables. Twenty complication-free T2DM patients and 19 matched healthy controls (HCs) were enrolled, and fMRI data were acquired during a block-designed 1-back WM task. The WM metrics of the T2DM patients showed no differences compared with those of the HCs, except for a slightly lower accuracy rate in the T2DM patients. Compared with the HCs, the T2DM patients demonstrated increased activation within their WM fronto-parietal networks, and activation strength was significantly correlated with WM performance. The T2DM patients also showed decreased FC within and between their WM networks. Our results indicate that the functional integration of WM sub-networks was disrupted in the complication-free T2DM patients and that strengthened regional activity in fronto-parietal networks may compensate for the WM impairment caused by T2DM. PMID:27021340

  12. Altered brain activation and functional connectivity in working memory related networks in patients with type 2 diabetes: An ICA-based analysis

    PubMed Central

    Zhang, Yang; Lu, Shan; Liu, Chunlei; Zhang, Huimei; Zhou, Xuanhe; Ni, Changlin; Qin, Wen; Zhang, Quan

    2016-01-01

    Type 2 diabetes mellitus (T2DM) can cause multidimensional cognitive deficits, among which working memory (WM) is usually involved at an early stage. However, the neural substrates underlying impaired WM in T2DM patients are still unclear. To clarify this issue, we utilized functional magnetic resonance imaging (fMRI) and independent component analysis to evaluate T2DM patients for alterations in brain activation and functional connectivity (FC) in WM networks and to determine their associations with cognitive and clinical variables. Twenty complication-free T2DM patients and 19 matched healthy controls (HCs) were enrolled, and fMRI data were acquired during a block-designed 1-back WM task. The WM metrics of the T2DM patients showed no differences compared with those of the HCs, except for a slightly lower accuracy rate in the T2DM patients. Compared with the HCs, the T2DM patients demonstrated increased activation within their WM fronto-parietal networks, and activation strength was significantly correlated with WM performance. The T2DM patients also showed decreased FC within and between their WM networks. Our results indicate that the functional integration of WM sub-networks was disrupted in the complication-free T2DM patients and that strengthened regional activity in fronto-parietal networks may compensate for the WM impairment caused by T2DM. PMID:27021340

  13. Acupuncture-Related Modulation of Pain-Associated Brain Networks During Electrical Pain Stimulation: A Functional Magnetic Resonance Imaging Study

    PubMed Central

    Choi, Kyung-Eun; Gizewski, Elke R.; Wen, Ming; Rampp, Thomas; Gasser, Thomas; Dobos, Gustav J.; Forsting, Michael; Musial, Frauke

    2014-01-01

    Abstract Objective: Findings of existing functional MRI (fMRI) studies on the neural mechanisms that mediate effects of acupuncture analgesia are inconsistent. This study analyzes the effects of manual acupuncture on pain ratings and brain activation in response to experimental, electrical pain stimuli. Design: Fourteen healthy volunteers were examined by using a 1.5-T MRI scanner. The intensity of pain stimuli was adjusted to individual pain ratings on a numeric rating scale. Baseline fMRI was performed during electrical pain stimulation in a blocked design. For the second session, manual acupuncture with repeated stimulation was performed on contralateral acupoints—large intestine 4, liver 3, and stomach 36—before imaging. After imaging, subjective pain ratings and ratings of the de qi sensation were assessed. Results: Compared with baseline, volunteers showed modulated brain activity under pain conditions in the cingulate gyrus, insula, primary somatosensory cortex, and prefrontal areas after the acupuncture session. In accordance with the literature, anterior insular and prefrontal activity seemed to be correlated with acupuncture treatment. Conclusion: This study supports the existence of analgesic acupuncture effects that outlast the needling period. Pain-associated brain areas were modulated in direct response to a preceding acupuncture treatment. PMID:25389905

  14. Correlated gene expression supports synchronous activity in brain networks

    PubMed Central

    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.

    2016-01-01

    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. PMID:26068849

  15. 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.

  16. Functional connectivity hubs of the mouse brain.

    PubMed

    Liska, Adam; Galbusera, Alberto; Schwarz, Adam J; Gozzi, Alessandro

    2015-07-15

    Recent advances in functional connectivity methods have made it possible to identify brain hubs - a set of highly connected regions serving as integrators of distributed neuronal activity. The integrative role of hub nodes makes these areas points of high vulnerability to dysfunction in brain disorders, and abnormal hub connectivity profiles have been described for several neuropsychiatric disorders. The identification of analogous functional connectivity hubs in preclinical species like the mouse may provide critical insight into the elusive biological underpinnings of these connectional alterations. To spatially locate functional connectivity hubs in the mouse brain, here we applied a fully-weighted network analysis to map whole-brain intrinsic functional connectivity (i.e., the functional connectome) at a high-resolution voxel-scale. Analysis of a large resting-state functional magnetic resonance imaging (rsfMRI) dataset revealed the presence of six distinct functional modules related to known large-scale functional partitions of the brain, including a default-mode network (DMN). Consistent with human studies, highly-connected functional hubs were identified in several sub-regions of the DMN, including the anterior and posterior cingulate and prefrontal cortices, in the thalamus, and in small foci within well-known integrative cortical structures such as the insular and temporal association cortices. According to their integrative role, the identified hubs exhibited mutual preferential interconnections. These findings highlight the presence of evolutionarily-conserved, mutually-interconnected functional hubs in the mouse brain, and may guide future investigations of the biological foundations of aberrant rsfMRI hub connectivity associated with brain pathological states. PMID:25913701

  17. Functional Lateralization of the Brain.

    ERIC Educational Resources Information Center

    Dean, Raymond S.

    1984-01-01

    Research concerning lateralization of human brain functions is examined in light of the recent publication of the Kaufman Assessment Battery for Children. Following a review of research methodologies and functions ascribed to the hemispheres of the brain, differences are portrayed as complementary and coexisting modes of cognitive processing.…

  18. Strengthening connections: functional connectivity and brain plasticity

    PubMed Central

    Kelly, Clare; Castellanos, F. Xavier

    2014-01-01

    The ascendancy of functional neuroimaging has facilitated the addition of network-based approaches to the neuropsychologist’s toolbox for evaluating the sequelae of brain insult. In particular, intrinsic functional connectivity (iFC) mapping of resting state fMRI (R-fMRI) data constitutes an ideal approach to measuring macro-scale networks in the human brain. Beyond the value of iFC mapping for charting how the functional topography of the brain is altered by insult and injury, iFC analyses can provide insights into effects of experience-dependent plasticity at the macro level of large-scale functional networks. Such insights are foundational to the design of training and remediation interventions that will best facilitate recovery of function. In this review, we consider what is currently known about the origin and function of iFC in the brain, and how this knowledge is informative in neuropsychological settings. We then summarize studies that have examined experience-driven plasticity of iFC in healthy control participants, and frame these findings in terms of a schema that may aid in the interpretation of results and the generation of hypothesis for rehabilitative studies. Finally, we outline some caveats to the R-fMRI approach, as well as some current developments that are likely to bolster the utility of the iFC paradigm for neuropsychology. PMID:24496903

  19. Brain networks: The next steps. Comment on: “Understanding brain networks and brain organization” by Luiz Pessoa

    NASA Astrophysics Data System (ADS)

    Calhoun, Vince D.

    2014-09-01

    The study of brain function from the perspective of whole brain networks has been a focus within the brain imaging community for many years, but has not yet overtaken the traditional approach of focusing on a specific region or set of regions. Pessoa [1] provides a very nice summary of the many reasons why network-based approaches should be used more commonly while also outlining the open questions and challenges, many of which also exist for the predominant region-based approach. One important point to frame the problem well, however, is to define carefully what is meant by the term network, which can be used in many different ways. Pessoa's definition is consistent with that used in the network science field, that is, a graph theoretical perspective based on nodes and edges, though other (useful) definitions are also quite widely used in the brain imaging community and should not be discounted [2]. The concept of networks is a very powerful tool for studying the brain, and also for potentially pointing us to regions that are at high-risk or potentially especially important to protect (or the oft undervalued weak but wide-spread connections as Pessoa points out).

  20. Functional interrelationship of brain aging and delirium.

    PubMed

    Rapazzini, Piero

    2016-02-01

    Theories on the development of delirium are complementary rather than competing and they may relate to each other. Here, we highlight that similar alterations in functional brain connectivity underlie both the observed age-related deficits and episodes of delirium. The default mode network (DMN) is a group of brain regions showing a greater level of activity at rest than during attention-based tasks. These regions include the posteromedial-anteromedial cortices and temporoparietal junctions. Evidence suggests that awareness is subserved through higher order neurons associated with the DMN. By using functional MRI disruption of DMN, connectivity and weaker task-induced deactivations of these regions are observed both in age-related cognitive impairment and during episodes of delirium. We can assume that an acute up-regulation of inhibitory tone within the brain acts to further disrupt network connectivity in vulnerable patients, who are predisposed by a reduced baseline connectivity, and triggers the delirium. PMID:25998952

  1. Alterations in the functional connectivity of a verbal working memory-related brain network in patients with left temporal lobe epilepsy.

    PubMed

    Huang, Wenli; Huang, Donghong; Chen, Zirong; Ye, Wei; Lv, Zongxia; Diao, Limei; Zheng, Jinou

    2015-08-18

    The aim of this study was to investigate the alterations in a verbal working memory (VWM)-related network in left temporal lobe epilepsy (lTLE) at rest. We evaluated 14 patients with lTLE and 14 control subjects by resting-state functional connectivity (RSFC). The region of interest was defined by the voxel with the highest Z-score during a VWM task according to functional magnetic resonance imaging in 16 healthy volunteers. Our study revealed that the network of RSFC was similar to the task-induced network in the healthy volunteers. Moreover, the patients with lTLE exhibited significantly decreased RSFC in the bilateral middle frontal gyrus, the inferior frontal gyrus and the inferior parietal lobule at rest compared to the control subjects. We found no significant correlation between the mean reaction time of the accurate responses in a 2-back task and the mean z-values within the regions that exhibited significant differences in RSFC at the individual level. The alterations in FCs of VWM-related network in lTLE suggested that epileptiform discharges can damage the brain regions, both local focus and remote areas and that the alterations were not associated with VWM performance. PMID:26101832

  2. [Brain mechanisms of male sexual function].

    PubMed

    Wang, Ying; Dou, Xin; Li, Jun-Fa; Luo, Yan-Lin

    2011-08-01

    In this paper, we reviewed the brain imaging studies of male sexual function in recent years from three aspects: the brain mechanism of normal sexual function, the brain mechanism of sexual dysfunction, and the mechanism of drug therapy for sexual dysfunction. Studies show that the development stages of male sexual activities, such as the excitement phase, plateau phase and orgasm phase, are controlled by different neural networks. The mesodiencephalic transition zone may play an important role in the start up of male ejaculation. There are significant differences between sexual dysfunction males and normal males in activation patterns of the brain in sexual arousal. The medial orbitofrontal cortex and inferior frontal gyrus in the abnormal activation pattern are correlated with sexual dysfunction males in sexual arousal. Serum testosterone and morphine are commonly used drugs for male sexual dysfunction, whose mechanisms are to alter the activating levels of the medial orbitofrontal cortex, insula, claustrum and inferior temporal gyrus. PMID:21899000

  3. A splitting brain: imbalanced neural networks in schizophrenia

    PubMed Central

    Li, Mingli; Deng, Wei; He, Zongling; Wang, Qiang; Huang, Chaohua; Jiang, Lijun; Gong, Qiyong; Ziedonis, Doug M.; King, Jean A.; Ma, Xiaohong; Zhang, Nanyin; Li, Tao

    2015-01-01

    Dysconnectivity between key brain systems has been hypothesized to underlie the pathophysiology of schizophrenia. The present study examined the pattern of functional dysconnectivity across whole-brain neural networks in 121 first-episode, treatment-naïve patients with schizophrenia by using resting-state functional magnetic resonance imaging (rsfMRI). Group independent component analysis (ICA) was first applied to rsfMRI data to extract 90 functional components of the brain. The functional connectivity between these ICA components was then evaluated and compared between the patient and control groups. To examine the functional roles of significantly altered between-component connections in patients, each ICA component was ascribed to one of ten previously well-defined brain networks/areas. Relative to healthy controls (n=103), 29 altered functional connections including 19 connections with increased connectivity and 10 connections with decreased connectivity in patients were found. Increased connectivity was mainly within the default mode network (DMN) and between the DMN and cognitive networks, whereas decreased connectivity was predominantly associated with sensory networks. Given the key roles of DMN in internal mental processes and sensory networks in inputs from the external environment, these patterns of altered brain network connectivity could suggest imbalanced neural processing of internal and external information in schizophrenia. PMID:25819347

  4. A splitting brain: Imbalanced neural networks in schizophrenia.

    PubMed

    Li, Mingli; Deng, Wei; He, Zongling; Wang, Qiang; Huang, Chaohua; Jiang, Lijun; Gong, Qiyong; Ziedonis, Doug M; King, Jean A; Ma, Xiaohong; Zhang, Nanyin; Li, Tao

    2015-05-30

    Dysconnectivity between key brain systems has been hypothesized to underlie the pathophysiology of schizophrenia. The present study examined the pattern of functional dysconnectivity across whole-brain neural networks in 121 first-episode, treatment-naïve patients with schizophrenia by using resting-state functional magnetic resonance imaging (rsfMRI). Group independent component analysis (ICA) was first applied to rsfMRI data to extract 90 functional components of the brain. The functional connectivity between these ICA components was then evaluated and compared between the patient and control groups. To examine the functional roles of significantly altered between-component connections in patients, each ICA component was ascribed to one of 10 previously well-defined brain networks/areas. Relative to findings in healthy controls (n=103), 29 altered functional connections including 19 connections with increased connectivity and 10 connections with decreased connectivity in schizophrenia patients were found. Increased connectivity was mainly within the default mode network (DMN) and between the DMN and cognitive networks, whereas decreased connectivity was predominantly associated with sensory networks. Given the key roles of the DMN in internal mental processes and sensory networks in inputs from the external environment, these patterns of altered brain network connectivity could suggest imbalanced neural processing of internal and external information in schizophrenia. PMID:25819347

  5. Nicotinic modulation of intrinsic brain networks in schizophrenia

    PubMed Central

    Smucny, Jason; Tregellas, Jason

    2014-01-01

    The nicotinic receptor is a promising drug target currently being investigated for the treatment of cognitive symptoms in schizophrenia. A key step in this process is the development of noninvasive functional neuroimaging biomarkers that can be used to determine if nicotinic agents are eliciting their targeted biological effect, ideally through modulation of a fundamental aspect of neuronal function. To that end, neuroimaging researchers are beginning to understand how nicotinic modulation affects “intrinsic” brain networks to elicit potentially therapeutic effects. An intrinsic network is a functionally and (often) structurally connected network of brain areas whose activity reflects a fundamental neurobiological organizational principle of the brain. This review summarizes findings of the effects of nicotinic drugs on three topics related to intrinsic brain network activity: (1) the default mode network, a group of brain areas for which activity is maximal at rest and reduced during cognitive tasks, (2) the salience network, which integrates incoming sensory data with prior internal representations to guide future actions and change predictive values, and (3) multi-scale complex network dynamics, which describe these brain’s ability to efficiency integrate information while preserving local functional specialization. These early findings can be used to inform future neuroimaging studies that examine the network effects of nicotinic agents. PMID:23796751

  6. Scaling in topological properties of brain networks

    PubMed Central

    Singh, Soibam Shyamchand; Khundrakpam, Budhachandra; Reid, Andrew T.; Lewis, John D.; Evans, Alan C.; Ishrat, Romana; Sharma, B. Indrajit; Singh, R. K. Brojen

    2016-01-01

    The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws that are signatures of self-organization in complex networks. The modular organization, in terms of modular mass, inter-modular, and intra-modular interaction, also obeys fractal nature. The parameters which characterize topological properties of brain networks follow one parameter scaling theory in all levels of network structure, which reveals the self-similar rules governing the network structure. Further, the calculated fractal dimensions of brain networks of different species are found to decrease when one goes from lower to higher level species which implicates the more ordered and self-organized topography at higher level species. The sparsely distributed hubs in brain networks may be most influencing nodes but their absence may not cause network breakdown, and centrality parameters characterizing them also follow one parameter scaling law indicating self-similar roles of these hubs at different levels of organization in brain networks. The local-community-paradigm decomposition plot and calculated local-community-paradigm-correlation co-efficient of brain networks also shows the evidence for self-organization in these networks. PMID:27112129

  7. Scaling in topological properties of brain networks.

    PubMed

    Singh, Soibam Shyamchand; Khundrakpam, Budhachandra; Reid, Andrew T; Lewis, John D; Evans, Alan C; Ishrat, Romana; Sharma, B Indrajit; Singh, R K Brojen

    2016-01-01

    The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws that are signatures of self-organization in complex networks. The modular organization, in terms of modular mass, inter-modular, and intra-modular interaction, also obeys fractal nature. The parameters which characterize topological properties of brain networks follow one parameter scaling theory in all levels of network structure, which reveals the self-similar rules governing the network structure. Further, the calculated fractal dimensions of brain networks of different species are found to decrease when one goes from lower to higher level species which implicates the more ordered and self-organized topography at higher level species. The sparsely distributed hubs in brain networks may be most influencing nodes but their absence may not cause network breakdown, and centrality parameters characterizing them also follow one parameter scaling law indicating self-similar roles of these hubs at different levels of organization in brain networks. The local-community-paradigm decomposition plot and calculated local-community-paradigm-correlation co-efficient of brain networks also shows the evidence for self-organization in these networks. PMID:27112129

  8. Cocaine addiction related reproducible brain regions of abnormal default-mode network functional connectivity: a group ICA study with different model orders.

    PubMed

    Ding, Xiaoyu; Lee, Seong-Whan

    2013-08-26

    Model order selection in group independent component analysis (ICA) has a significant effect on the obtained components. This study investigated the reproducible brain regions of abnormal default-mode network (DMN) functional connectivity related with cocaine addiction through different model order settings in group ICA. Resting-state fMRI data from 24 cocaine addicts and 24 healthy controls were temporally concatenated and processed by group ICA using model orders of 10, 20, 30, 40, and 50, respectively. For each model order, the group ICA approach was repeated 100 times using the ICASSO toolbox and after clustering the obtained components, centrotype-based anterior and posterior DMN components were selected for further analysis. Individual DMN components were obtained through back-reconstruction and converted to z-score maps. A whole brain mixed effects factorial ANOVA was performed to explore the differences in resting-state DMN functional connectivity between cocaine addicts and healthy controls. The hippocampus, which showed decreased functional connectivity in cocaine addicts for all the tested model orders, might be considered as a reproducible abnormal region in DMN associated with cocaine addiction. This finding suggests that using group ICA to examine the functional connectivity of the hippocampus in the resting-state DMN may provide an additional insight potentially relevant for cocaine-related diagnoses and treatments. PMID:23707901

  9. An algebraic topological method for multimodal brain networks comparisons

    PubMed Central

    Simas, Tiago; Chavez, Mario; Rodriguez, Pablo R.; Diaz-Guilera, Albert

    2015-01-01

    Understanding brain connectivity is one of the most important issues in neuroscience. Nonetheless, connectivity data can reflect either functional relationships of brain activities or anatomical connections between brain areas. Although both representations should be related, this relationship is not straightforward. We have devised a powerful method that allows different operations between networks that share the same set of nodes, by embedding them in a common metric space, enforcing transitivity to the graph topology. Here, we apply this method to construct an aggregated network from a set of functional graphs, each one from a different subject. Once this aggregated functional network is constructed, we use again our method to compare it with the structural connectivity to identify particular brain regions that differ in both modalities (anatomical and functional). Remarkably, these brain regions include functional areas that form part of the classical resting state networks. We conclude that our method -based on the comparison of the aggregated functional network- reveals some emerging features that could not be observed when the comparison is performed with the classical averaged functional network. PMID:26217258

  10. Resolving structural variability in network models and the brain.

    PubMed

    Klimm, Florian; Bassett, Danielle S; Carlson, Jean M; Mucha, Peter J

    2014-03-01

    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

  11. 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

  12. Complex modular structure of large-scale brain networks

    NASA Astrophysics Data System (ADS)

    Valencia, M.; Pastor, M. A.; Fernández-Seara, M. A.; Artieda, J.; Martinerie, J.; Chavez, M.

    2009-06-01

    Modular structure is ubiquitous among real-world networks from related proteins to social groups. Here we analyze the modular organization of brain networks at a large scale (voxel level) extracted from functional magnetic resonance imaging signals. By using a random-walk-based method, we unveil the modularity of brain webs and show modules with a spatial distribution that matches anatomical structures with functional significance. The functional role of each node in the network is studied by analyzing its patterns of inter- and intramodular connections. Results suggest that the modular architecture constitutes the structural basis for the coexistence of functional integration of distant and specialized brain areas during normal brain activities at rest.

  13. The modular and integrative functional architecture of the human brain.

    PubMed

    Bertolero, Maxwell A; Yeo, B T Thomas; D'Esposito, Mark

    2015-12-01

    Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules' processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an author-topic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network's modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules' functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in between-module communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain's modular yet integrated implementation of cognitive functions. PMID:26598686

  14. Brain Network Interactions in Auditory, Visual and Linguistic Processing

    ERIC Educational Resources Information Center

    Horwitz, Barry; Braun, Allen R.

    2004-01-01

    In the paper, we discuss the importance of network interactions between brain regions in mediating performance of sensorimotor and cognitive tasks, including those associated with language processing. Functional neuroimaging, especially PET and fMRI, provide data that are obtained essentially simultaneously from much of the brain, and thus are…

  15. Support Network Responses to Acquired Brain Injury

    ERIC Educational Resources Information Center

    Chleboun, Steffany; Hux, Karen

    2011-01-01

    Acquired brain injury (ABI) affects social relationships; however, the ways social and support networks change and evolve as a result of brain injury is not well understood. This study explored ways in which survivors of ABI and members of their support networks perceive relationship changes as recovery extends into the long-term stage. Two…

  16. Altered Resting State Brain Networks in Parkinson’s Disease

    PubMed Central

    Göttlich, Martin; Münte, Thomas F.; Heldmann, Marcus; Kasten, Meike; Hagenah, Johann; Krämer, Ulrike M.

    2013-01-01

    Parkinson’s disease (PD) is a neurodegenerative disorder affecting dopaminergic neurons in the substantia nigra leading to dysfunctional cortico-striato-thalamic-cortical loops. In addition to the characteristic motor symptoms, PD patients often show cognitive impairments, affective changes and other non-motor symptoms, suggesting system-wide effects on brain function. Here, we used functional magnetic resonance imaging and graph-theory based analysis methods to investigate altered whole-brain intrinsic functional connectivity in PD patients (n = 37) compared to healthy controls (n = 20). Global network properties indicated less efficient processing in PD. Analysis of brain network modules pointed to increased connectivity within the sensorimotor network, but decreased interaction of the visual network with other brain modules. We found lower connectivity mainly between the cuneus and the ventral caudate, medial orbitofrontal cortex and the temporal lobe. To identify regions of altered connectivity, we mapped the degree of intrinsic functional connectivity both on ROI- and on voxel-level across the brain. Compared to healthy controls, PD patients showed lower connectedness in the medial and middle orbitofrontal cortex. The degree of connectivity was also decreased in the occipital lobe (cuneus and calcarine), but increased in the superior parietal cortex, posterior cingulate gyrus, supramarginal gyrus and supplementary motor area. Our results on global network and module properties indicated that PD manifests as a disconnection syndrome. This was most apparent in the visual network module. The higher connectedness within the sensorimotor module in PD patients may be related to compensation mechanism in order to overcome the functional deficit of the striato-cortical motor loops or to loss of mutual inhibition between brain networks. Abnormal connectivity in the visual network may be related to adaptation and compensation processes as a consequence of

  17. 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

  18. Estimating brain's functional graph from the structural graph's Laplacian

    NASA Astrophysics Data System (ADS)

    Abdelnour, F.; Dayan, M.; Devinsky, O.; Thesen, T.; Raj, A.

    2015-09-01

    The interplay between the brain's function and structure has been of immense interest to the neuroscience and connectomics communities. In this work we develop a simple linear model relating the structural network and the functional network. We propose that the two networks are related by the structural network's Laplacian up to a shift. The model is simple to implement and gives accurate prediction of function's eigenvalues at the subject level and its eigenvectors at group level.

  19. Mutated Genes in Schizophrenia Map to Brain Networks

    MedlinePlus

    ... 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the prefrontal cortex area of the brain. ... of spontaneous mutations in genes that form a network in the front region of the brain. The ...

  20. Lead poisoning and brain cell function

    SciTech Connect

    Goldstein, G.W. Kennedy Institute, Baltimore, MD )

    1990-11-01

    Exposure to excessive amounts of inorganic lead during the toddler years may produce lasting adverse effects upon brain function. Maximal ingestion of lead occurs at an age when major changes are occurring in the density of brain synaptic connections. The developmental reorganization of synapses is, in part, mediated by protein kinases, and these enzymes are particularly sensitive to stimulation by lead. By inappropriately activating specific protein kinases, lead poisoning may disrupt the development of neural networks without producing overt pathological alterations. The blood-brain barrier is another potential vulnerable site for the neurotoxic action of lead. protein kinases appear to regulate the development of brain capillaries and the expression of the blood-brain barrier properties. Stimulation of protein kinase by lead may disrupt barrier development and alter the precise regulation of the neuronal environment that is required for normal brain function. Together, these findings suggest that the sensitivity of protein kinases to lead may in part underlie the brain dysfunction observed in children poisoned by this toxicant.

  1. 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. PMID:23781198

  2. 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

  3. The integration of large-scale neural network modeling and functional brain imaging in speech motor control

    PubMed Central

    Golfinopoulos, E.; Tourville, J.A.; Guenther, F.H.

    2009-01-01

    Speech production demands a number of integrated processing stages. The system must encode the speech motor programs that command movement trajectories of the articulators and monitor transient spatiotemporal variations in auditory and somatosensory feedback. Early models of this system proposed that independent neural regions perform specialized speech processes. As technology advanced, neuroimaging data revealed that the dynamic sensorimotor processes of speech require a distributed set of interacting neural regions. The DIVA (Directions into Velocities of Articulators) neurocomputational model elaborates on early theories, integrating existing data and contemporary ideologies, to provide a mechanistic account of acoustic, kinematic, and functional magnetic resonance imaging (fMRI) data on speech acquisition and production. This large-scale neural network model is composed of several interconnected components whose cell activities and synaptic weight strengths are governed by differential equations. Cells in the model are associated with neuroanatomical substrates and have been mapped to locations in Montreal Neurological Institute stereotactic space, providing a means to compare simulated and empirical fMRI data. The DIVA model also provides a computational and neurophysiological framework within which to interpret and organize research on speech acquisition and production in fluent and dysfluent child and adult speakers. The purpose of this review article is to demonstrate how the DIVA model is used to motivate and guide functional imaging studies. We describe how model predictions are evaluated using voxel-based, region-of-interest-based parametric analyses and inter-regional effective connectivity modeling of fMRI data. PMID:19837177

  4. Functional Molecular Ecological Networks

    PubMed Central

    Zhou, Jizhong; Deng, Ye; Luo, Feng; He, Zhili; Tu, Qichao; Zhi, Xiaoyang

    2010-01-01

    Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on “species” richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO2 (eCO2) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO2 enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO2 and ambient CO2 (aCO2) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO2 and aCO2, at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO2 dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change. PMID:20941329

  5. Functional molecular ecological networks.

    PubMed

    Zhou, Jizhong; Deng, Ye; Luo, Feng; He, Zhili; Tu, Qichao; Zhi, Xiaoyang

    2010-01-01

    Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on "species" richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO(2) (eCO(2)) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO(2) enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO(2) and ambient CO(2) (aCO(2)) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO(2) and aCO(2), at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO(2) dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change. PMID:20941329

  6. Small-World Propensity and Weighted Brain Networks

    NASA Astrophysics Data System (ADS)

    Muldoon, Sarah Feldt; Bridgeford, Eric W.; Bassett, Danielle S.

    2016-02-01

    Quantitative descriptions of network structure can provide fundamental insights into the function of interconnected complex systems. Small-world structure, diagnosed by high local clustering yet short average path length between any two nodes, promotes information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-worldness are density dependent and neglect important features such as the strength of network connections, limiting their application in real-world systems. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted brain networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a method for mapping observed brain network data onto the theoretical model. In applying these techniques to compare real-world brain networks, we uncover the surprising fact that the canonical biological small-world network, the C. elegans neuronal network, has strikingly low SWP. These metrics, models, and maps form a coherent toolbox for the assessment and comparison of architectural properties in brain networks.

  7. Small-World Propensity and Weighted Brain Networks

    PubMed Central

    Muldoon, Sarah Feldt; Bridgeford, Eric W.; Bassett, Danielle S.

    2016-01-01

    Quantitative descriptions of network structure can provide fundamental insights into the function of interconnected complex systems. Small-world structure, diagnosed by high local clustering yet short average path length between any two nodes, promotes information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-worldness are density dependent and neglect important features such as the strength of network connections, limiting their application in real-world systems. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted brain networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a method for mapping observed brain network data onto the theoretical model. In applying these techniques to compare real-world brain networks, we uncover the surprising fact that the canonical biological small-world network, the C. elegans neuronal network, has strikingly low SWP. These metrics, models, and maps form a coherent toolbox for the assessment and comparison of architectural properties in brain networks. PMID:26912196

  8. Dynamics of brain networks in the aesthetic appreciation

    PubMed Central

    Cela-Conde, Camilo J.; García-Prieto, Juan; Ramasco, José J.; Mirasso, Claudio R.; Bajo, Ricardo; Munar, Enric; Flexas, Albert; del-Pozo, Francisco; Maestú, Fernando

    2013-01-01

    Neuroimage experiments have been essential for identifying active brain networks. During cognitive tasks as in, e.g., aesthetic appreciation, such networks include regions that belong to the default mode network (DMN). Theoretically, DMN activity should be interrupted during cognitive tasks demanding attention, as is the case for aesthetic appreciation. Analyzing the functional connectivity dynamics along three temporal windows and two conditions, beautiful and not beautiful stimuli, here we report experimental support for the hypothesis that aesthetic appreciation relies on the activation of two different networks, an initial aesthetic network and a delayed aesthetic network, engaged within distinct time frames. Activation of the DMN might correspond mainly to the delayed aesthetic network. We discuss adaptive and evolutionary explanations for the relationships existing between the DMN and aesthetic networks and offer unique inputs to debates on the mind/brain interaction. PMID:23754437

  9. Structural and functional connectivity in traumatic brain injury

    PubMed Central

    Xiao, Hui; Yang, Yang; Xi, Ji-hui; Chen, Zi-qian

    2015-01-01

    Traumatic brain injury survivors often experience cognitive deficits and neuropsychiatric symptoms. However, the neurobiological mechanisms underlying specific impairments are not fully understood. Advances in neuroimaging techniques (such as diffusion tensor imaging and functional MRI) have given us new insights on structural and functional connectivity patterns of the human brain in both health and disease. The connectome derived from connectivity maps reflects the entire constellation of distributed brain networks. Using these powerful neuroimaging approaches, changes at the microstructural level can be detected through regional and global properties of neuronal networks. Here we will review recent developments in the study of brain network abnormalities in traumatic brain injury, mainly focusing on structural and functional connectivity. Some connectomic studies have provided interesting insights into the neurological dysfunction that occurs following traumatic brain injury. These techniques could eventually be helpful in developing imaging biomarkers of cognitive and neurobehavioral sequelae, as well as predicting outcome and prognosis. PMID:26889200

  10. Communication Networks in the Brain

    PubMed Central

    Lovinger, David M.

    2008-01-01

    Nerve cells (i.e., neurons) communicate via a combination of electrical and chemical signals. Within the neuron, electrical signals driven by charged particles allow rapid conduction from one end of the cell to the other. Communication between neurons occurs at tiny gaps called synapses, where specialized parts of the two cells (i.e., the presynaptic and postsynaptic neurons) come within nanometers of one another to allow for chemical transmission. The presynaptic neuron releases a chemical (i.e., a neurotransmitter) that is received by the postsynaptic neuron’s specialized proteins called neurotransmitter receptors. The neurotransmitter molecules bind to the receptor proteins and alter postsynaptic neuronal function. Two types of neurotransmitter receptors exist—ligand-gated ion channels, which permit rapid ion flow directly across the outer cell membrane, and G-protein–coupled receptors, which set into motion chemical signaling events within the cell. Hundreds of molecules are known to act as neurotransmitters in the brain. Neuronal development and function also are affected by peptides known as neurotrophins and by steroid hormones. This article reviews the chemical nature, neuronal actions, receptor subtypes, and therapeutic roles of several transmitters, neurotrophins, and hormones. It focuses on neurotransmitters with important roles in acute and chronic alcohol effects on the brain, such as those that contribute to intoxication, tolerance, dependence, and neurotoxicity, as well as maintained alcohol drinking and addiction. PMID:23584863

  11. Bioprinting: Functional droplet networks

    NASA Astrophysics Data System (ADS)

    Durmus, Naside Gozde; Tasoglu, Savas; Demirci, Utkan

    2013-06-01

    Tissue-mimicking printed networks of droplets separated by lipid bilayers that can be functionalized with membrane proteins are able to spontaneously fold and transmit electrical currents along predefined paths.

  12. 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 brainbrain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be

  13. 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 brainbrain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be

  14. Differential functional brain network connectivity during visceral interoception as revealed by independent component analysis of fMRI TIME-series.

    PubMed

    Jarrahi, Behnaz; Mantini, Dante; Balsters, Joshua Henk; Michels, Lars; Kessler, Thomas M; Mehnert, Ulrich; Kollias, Spyros S

    2015-11-01

    Influential theories of brain-viscera interactions propose a central role for interoception in basic motivational and affective feeling states. Recent neuroimaging studies have underlined the insula, anterior cingulate, and ventral prefrontal cortices as the neural correlates of interoception. However, the relationships between these distributed brain regions remain unclear. In this study, we used spatial independent component analysis (ICA) and functional network connectivity (FNC) approaches to investigate time course correlations across the brain regions during visceral interoception. Functional magnetic resonance imaging (fMRI) was performed in thirteen healthy females who underwent viscerosensory stimulation of bladder as a representative internal organ at different prefill levels, i.e., no prefill, low prefill (100 ml saline), and high prefill (individually adapted to the sensations of persistent strong desire to void), and with different infusion temperatures, i.e., body warm (∼37°C) or ice cold (4-8°C) saline solution. During Increased distention pressure on the viscera, the insula, striatum, anterior cingulate, ventromedial prefrontal cortex, amygdalo-hippocampus, thalamus, brainstem, and cerebellar components showed increased activation. A second group of components encompassing the insula and anterior cingulate, dorsolateral prefrontal and posterior parietal cortices and temporal-parietal junction showed increased activity with innocuous temperature stimulation of bladder mucosa. Significant differences in the FNC were found between the insula and amygdalo-hippocampus, the insula and ventromedial prefrontal cortex, and the ventromedial prefrontal cortex and temporal-parietal junction as the distention pressure on the viscera increased. These results provide new insight into the supraspinal processing of visceral interoception originating from an internal organ. PMID:26249369

  15. Dynamic connectivity and dynamic affiliation. Comment on “Understanding brain networks and brain organization” by L. Pessoa

    NASA Astrophysics Data System (ADS)

    Uddin, Lucina Q.

    2014-09-01

    Structure-function mapping in the human brain is a non-trivial endeavor that has occupied neuroscientists for many years. While a network approach, elegantly summarized by Pessoa [1], has for the most part supplanted simple one-to-one mapping schemes, several outstanding issues remain. The concept of "dynamic affiliation" of a particular brain region with a network in a certain context goes a long way towards providing a more sophisticated description of how any given region may participate in multiple cognitive processes. Despite the considerable insights into brain function provided by the network perspectives summarized by Pessoa, it is apparent that "rigorous characterization of brain networks is in its infancy".

  16. Brain Network Activity in Monolingual and Bilingual Older Adults

    PubMed Central

    Grady, Cheryl L.; Luk, Gigi; Craik, Fergus I.M.; Bialystok, Ellen

    2016-01-01

    Bilingual older adults typically have better performance on tasks of executive control (EC) than do their monolingual peers, but differences in brain activity due to language experience are not well understood. Based on studies showing a relation between the dynamic range of brain network activity and performance on EC tasks, we hypothesized that life-long bilingual older adults would show increased functional connectivity relative to monolinguals in networks related to EC. We assessed intrinsic functional connectivity and modulation of activity in task vs. fixation periods in two brain networks that are active when EC is engaged, the frontoparietal control network (FPC) and the salience network (SLN). We also examined the default mode network (DMN), which influences behavior through reduced activity during tasks. We found stronger intrinsic functional connectivity in the FPC and DMN in bilinguals than in monolinguals. Although there were no group differences in the modulation of activity across tasks and fixation, bilinguals showed stronger correlations than monolinguals between intrinsic connectivity in the FPC and task-related increases of activity in prefrontal and parietal regions. This bilingual difference in network connectivity suggests that language experience begun in childhood and continued throughout adulthood influences brain networks in ways that may provide benefits in later life. PMID:25445783

  17. Pain: A Distributed Brain Information Network?

    PubMed Central

    Mano, Hiroaki; Seymour, Ben

    2015-01-01

    Understanding how pain is processed in the brain has been an enduring puzzle, because there doesn't appear to be a single “pain cortex” that directly codes the subjective perception of pain. An emerging concept is that, instead, pain might emerge from the coordinated activity of an integrated brain network. In support of this view, Woo and colleagues present evidence that distinct brain networks support the subjective changes in pain that result from nociceptive input and self-directed cognitive modulation. This evidence for the sensitivity of distinct neural subsystems to different aspects of pain opens up the way to more formal computational network theories of pain. PMID:25562782

  18. Brain networks that track musical structure.

    PubMed

    Janata, Petr

    2005-12-01

    As the functional neuroimaging literature grows, it becomes increasingly apparent that music and musical activities engage diverse regions of the brain. In this paper I discuss two studies to illustrate that exactly which brain areas are observed to be responsive to musical stimuli and tasks depends on the tasks and the methods used to describe the tasks and the stimuli. In one study, subjects listened to polyphonic music and were asked to either orient their attention selectively to individual instruments or in a divided or holistic manner across multiple instruments. The network of brain areas that was recruited changed subtly with changes in the task instructions. The focus of the second study was to identify brain regions that follow the pattern of movement of a continuous melody through the tonal space defined by the major and minor keys of Western tonal music. Such an area was identified in the rostral medial prefrontal cortex. This observation is discussed in the context of other neuroimaging studies that implicate this region in inwardly directed mental states involving decisions about the self, autobiographical memory, the cognitive regulation of emotion, affective responses to musical stimuli, and familiarity judgments about musical stimuli. Together with observations that these regions are among the last to atrophy in Alzheimer disease, and that these patients appear to remain responsive to autobiographically salient musical stimuli, very early evidence is emerging from the literature for the hypothesis that the rostral medial prefrontal cortex is a node that is important for binding music with memories within a broader music-responsive network. PMID:16597758

  19. Noninvasive brain stimulation: from physiology to network dynamics and back

    PubMed Central

    Dayan, Eran; Censor, Nitzan; Buch, Ethan R; Sandrini, Marco; Cohen, Leonardo G

    2016-01-01

    Noninvasive brain stimulation techniques have been widely used for studying the physiology of the CNS, identifying the functional role of specific brain structures and, more recently, exploring large-scale network dynamics. Here we review key findings that contribute to our understanding of the mechanisms underlying the physiological and behavioral effects of these techniques. We highlight recent innovations using noninvasive stimulation to investigate global brain network dynamics and organization. New combinations of these techniques, in conjunction with neuroimaging, will further advance the utility of their application. PMID:23799477

  20. Natriuretic Hormones in Brain Function

    PubMed Central

    Hodes, Anastasia; Lichtstein, David

    2014-01-01

    Natriuretic hormones (NH) include three groups of compounds: the natriuretic peptides (ANP, BNP and CNP), the gastrointestinal peptides (guanylin and uroguanylin), and endogenous cardiac steroids. These substances induce the kidney to excrete sodium and therefore participate in the regulation of sodium and water homeostasis, blood volume, and blood pressure (BP). In addition to their peripheral functions, these hormones act as neurotransmitters or neuromodulators in the brain. In this review, the established information on the biosynthesis, release and function of NH is discussed, with particular focus on their role in brain function. The available literature on the expression patterns of each of the NH and their receptors in the brain is summarized, followed by the evidence for their roles in modulating brain function. Although numerous open questions exist regarding this issue, the available data support the notion that NH participate in the central regulation of BP, neuroprotection, satiety, and various psychiatric conditions, including anxiety, addiction, and depressive disorders. In addition, the interactions between the different NH in the periphery and the brain are discussed. PMID:25506340

  1. The modular and integrative functional architecture of the human brain

    PubMed Central

    Bertolero, Maxwell A.; Yeo, B. T. Thomas; D’Esposito, Mark

    2015-01-01

    Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules’ processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an author–topic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network’s modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules’ functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in between-module communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain’s modular yet integrated implementation of cognitive functions. PMID:26598686

  2. Atomoxetine Treatment Strengthens an Anti-Correlated Relationship between Functional Brain Networks in Medication-Naïve Adults with Attention-Deficit Hyperactivity Disorder: A Randomized Double-Blind Placebo-Controlled Clinical Trial

    PubMed Central

    Lin, Hsiang-Yuan

    2016-01-01

    Background: Although atomoxetine demonstrates efficacy in individuals with attention-deficit hyperactivity disorder, its treatment effects on brain resting-state functional connectivity remain unknown. Therefore, we aimed to investigate major brain functional networks in medication-naïve adults with attention-deficit hyperactivity disorder and the efficacy of atomoxetine treatment on resting-state functional connectivity. Methods: After collecting baseline resting-state functional MRI scans from 24 adults with attention-deficit hyperactivity disorder (aged 18–52 years) and 24 healthy controls (matched in demographic characteristics), the participants with attention-deficit hyperactivity disorder were randomly assigned to atomoxetine (n=12) and placebo (n=12) arms in an 8-week, double-blind, placebo-controlled trial. The primary outcome was functional connectivity assessed by a resting-state functional MRI. Seed-based functional connectivity was calculated and compared for the affective, attention, default, and cognitive control networks. Results: At baseline, we found atypical cross talk between the default, cognitive control, and dorsal attention networks and hypoconnectivity within the dorsal attention and default networks in adults with attention-deficit hyperactivity disorder. Our first-ever placebo-controlled clinical trial incorporating resting-state functional MRI showed that treatment with atomoxetine strengthened an anticorrelated relationship between the default and task-positive networks and modulated all major brain networks. The strengthened anticorrelations were associated with improving clinical symptoms in the atomoxetine-treated adults. Conclusions: Our results support the idea that atypical default mode network task-positive network interaction plays an important role in the pathophysiology of adult attention-deficit hyperactivity disorder. Strengthening this atypical relationship following atomoxetine treatment suggests an important pathway to

  3. 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.

  4. Consensus between Pipelines in Structural Brain Networks

    PubMed Central

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

    2014-01-01

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

  5. Development of Human Brain Structural Networks Through Infancy and Childhood

    PubMed Central

    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

    2015-01-01

    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. PMID:24335033

  6. Medial Prefrontal and Anterior Insular Connectivity in Early Schizophrenia and Major Depressive Disorder: A Resting Functional MRI Evaluation of Large-Scale Brain Network Models.

    PubMed

    Penner, Jacob; Ford, Kristen A; Taylor, Reggie; Schaefer, Betsy; Théberge, Jean; Neufeld, Richard W J; Osuch, Elizabeth A; Menon, Ravi S; Rajakumar, Nagalingam; Allman, John M; Williamson, Peter C

    2016-01-01

    Anomalies in the medial prefrontal cortex, anterior insulae, and large-scale brain networks associated with them have been proposed to underlie the pathophysiology of schizophrenia and major depressive disorder (MDD). In this study, we examined the connectivity of the medial prefrontal cortices and anterior insulae in 24 healthy controls, 24 patients with schizophrenia, and 24 patients with MDD early in illness with seed-based resting state functional magnetic resonance imaging analysis using Statistical Probability Mapping. As hypothesized, reduced connectivity was found between the medial prefrontal cortex and the dorsal anterior cingulate cortex and other nodes associated with directed effort in patients with schizophrenia compared to controls while patients with MDD had reduced connectivity between the medial prefrontal cortex and ventral prefrontal emotional encoding regions compared to controls. Reduced connectivity was found between the anterior insulae and the medial prefrontal cortex in schizophrenia compared to controls, but contrary to some models emotion processing regions failed to demonstrate increased connectivity with the medial prefrontal cortex in MDD compared to controls. Although, not statistically significant after correction for multiple comparisons, patients with schizophrenia tended to demonstrate decreased connectivity between basal ganglia-thalamocortical regions and the medial prefrontal cortex compared to patients with MDD, which might be expected as these regions effect action. Results were interpreted to support anomalies in nodes associated with directed effort in schizophrenia and nodes associated with emotional encoding network in MDD compared to healthy controls. PMID:27064387

  7. Medial Prefrontal and Anterior Insular Connectivity in Early Schizophrenia and Major Depressive Disorder: A Resting Functional MRI Evaluation of Large-Scale Brain Network Models

    PubMed Central

    Penner, Jacob; Ford, Kristen A.; Taylor, Reggie; Schaefer, Betsy; Théberge, Jean; Neufeld, Richard W. J.; Osuch, Elizabeth A.; Menon, Ravi S.; Rajakumar, Nagalingam; Allman, John M.; Williamson, Peter C.

    2016-01-01

    Anomalies in the medial prefrontal cortex, anterior insulae, and large-scale brain networks associated with them have been proposed to underlie the pathophysiology of schizophrenia and major depressive disorder (MDD). In this study, we examined the connectivity of the medial prefrontal cortices and anterior insulae in 24 healthy controls, 24 patients with schizophrenia, and 24 patients with MDD early in illness with seed-based resting state functional magnetic resonance imaging analysis using Statistical Probability Mapping. As hypothesized, reduced connectivity was found between the medial prefrontal cortex and the dorsal anterior cingulate cortex and other nodes associated with directed effort in patients with schizophrenia compared to controls while patients with MDD had reduced connectivity between the medial prefrontal cortex and ventral prefrontal emotional encoding regions compared to controls. Reduced connectivity was found between the anterior insulae and the medial prefrontal cortex in schizophrenia compared to controls, but contrary to some models emotion processing regions failed to demonstrate increased connectivity with the medial prefrontal cortex in MDD compared to controls. Although, not statistically significant after correction for multiple comparisons, patients with schizophrenia tended to demonstrate decreased connectivity between basal ganglia-thalamocortical regions and the medial prefrontal cortex compared to patients with MDD, which might be expected as these regions effect action. Results were interpreted to support anomalies in nodes associated with directed effort in schizophrenia and nodes associated with emotional encoding network in MDD compared to healthy controls. PMID:27064387

  8. Evolving networks in the human epileptic brain

    NASA Astrophysics Data System (ADS)

    Lehnertz, Klaus; Ansmann, Gerrit; Bialonski, Stephan; Dickten, Henning; Geier, Christian; Porz, Stephan

    2014-01-01

    Network theory provides novel concepts that promise an improved characterization of interacting dynamical systems. Within this framework, evolving networks can be considered as being composed of nodes, representing systems, and of time-varying edges, representing interactions between these systems. This approach is highly attractive to further our understanding of the physiological and pathophysiological dynamics in human brain networks. Indeed, there is growing evidence that the epileptic process can be regarded as a large-scale network phenomenon. We here review methodologies for inferring networks from empirical time series and for a characterization of these evolving networks. We summarize recent findings derived from studies that investigate human epileptic brain networks evolving on timescales ranging from few seconds to weeks. We point to possible pitfalls and open issues, and discuss future perspectives.

  9. Modulatory Interactions of Resting-State Brain Functional Connectivity

    PubMed Central

    Di, Xin; Biswal, Bharat B.

    2013-01-01

    The functional brain connectivity studies are generally based on the synchronization of the resting-state functional magnetic resonance imaging (fMRI) signals. Functional connectivity measures usually assume a stable relationship over time; however, accumulating studies have reported time-varying properties of strength and spatial distribution of functional connectivity. The present study explored the modulation of functional connectivity between two regions by a third region using the physiophysiological interaction (PPI) technique. We first identified eight brain networks and two regions of interest (ROIs) representing each of the networks using a spatial independent component analysis. A voxel-wise analysis was conducted to identify regions that showed modulatory interactions (PPI) with the two ROIs of each network. Mostly, positive modulatory interactions were observed within regions involved in the same system. For example, the two regions of the dorsal attention network revealed modulatory interactions with the regions related to attention, while the two regions of the extrastriate network revealed modulatory interactions with the regions in the visual cortex. In contrast, the two regions of the default mode network (DMN) revealed negative modulatory interactions with the regions in the executive network, and vice versa, suggesting that the activities of one network may be associated with smaller within network connectivity of the competing network. These results validate the use of PPI analysis to study modulation of resting-state functional connectivity by a third region. The modulatory effects may provide a better understanding of complex brain functions. PMID:24023609

  10. Adaptive Transfer Function Networks

    SciTech Connect

    Goulding, J.R. |

    1993-06-01

    Real-time pattern classification and time-series forecasting applications continue to drive artificial neural network (ANN) technology. As ANNs increase in complexity, the throughput of digital computer simulations decreases. A novel ANN, the Adaptive Transfer Function Network (ATF-Net), directly addresses the issue of throughput. ATF-Nets are global mapping equations generated by the superposition of ensembles of neurodes having arbitrary continuous functions receiving encoded input data. ATF-Nets may be implemented on parallel digital computers. An example is presented which illustrates a four-fold increase in computational throughput.

  11. Adaptive Transfer Function Networks

    SciTech Connect

    Goulding, J.R. Portland State Univ., OR . Dept. of Electrical Engineering)

    1993-01-01

    Real-time pattern classification and time-series forecasting applications continue to drive artificial neural network (ANN) technology. As ANNs increase in complexity, the throughput of digital computer simulations decreases. A novel ANN, the Adaptive Transfer Function Network (ATF-Net), directly addresses the issue of throughput. ATF-Nets are global mapping equations generated by the superposition of ensembles of neurodes having arbitrary continuous functions receiving encoded input data. ATF-Nets may be implemented on parallel digital computers. An example is presented which illustrates a four-fold increase in computational throughput.

  12. Efficiency of weak brain connections support general cognitive functioning.

    PubMed

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

    2014-09-01

    Brain network topology provides valuable information on healthy and pathological brain functioning. Novel approaches for brain network analysis have shown an association between topological properties and cognitive functioning. Under the assumption that "stronger is better", the exploration of brain properties has generally focused on the connectivity patterns of the most strongly correlated regions, whereas the role of weaker brain connections has remained obscure for years. Here, we assessed whether the different strength of connections between brain regions may explain individual differences in intelligence. We analyzed-functional connectivity at rest in ninety-eight healthy individuals of different age, and correlated several connectivity measures with full scale, verbal, and performance Intelligent Quotients (IQs). Our results showed that the variance in IQ levels was mostly explained by the distributed communication efficiency of brain networks built using moderately weak, long-distance connections, with only a smaller contribution of stronger connections. The variability in individual IQs was associated with the global efficiency of a pool of regions in the prefrontal lobes, hippocampus, temporal pole, and postcentral gyrus. These findings challenge the traditional view of a prominent role of strong functional brain connections in brain topology, and highlight the importance of both strong and weak connections in determining the functional architecture responsible for human intelligence variability. PMID:24585433

  13. Sub-hubs of baseline functional brain networks are related to early improvement following two-week pharmacological therapy for major depressive disorder.

    PubMed

    Shen, Yuedi; Yao, Jiashu; Jiang, Xueyan; Zhang, Lei; Xu, Luoyi; Feng, Rui; Cai, Liqiang; Liu, Jing; Wang, Jinhui; Chen, Wei

    2015-08-01

    Accumulating evidence suggests that early improvement after two-week antidepressant treatment is predictive of later outcomes of patients with major depressive disorder (MDD); however, whether this early improvement is associated with baseline neural architecture remains largely unknown. Utilizing resting-state functional MRI data and graph-based network approaches, this study calculated voxel-wise degree centrality maps for 24 MDD patients at baseline and linked them with changes in the Hamilton Rating Scale for Depression (HAMD) scores after two weeks of medication. Six clusters exhibited significant correlations of their baseline degree centrality with treatment-induced HAMD changes for the patients, which were mainly categorized into the posterior default-mode network (i.e., the left precuneus, supramarginal gyrus, middle temporal gyrus, and right angular gyrus) and frontal regions. Receiver operating characteristic curve and logistic regression analyses convergently revealed excellent performance of these regions in discriminating the early improvement status for the patients, especially the angular gyrus (sensitivity and specificity of 100%). Moreover, the angular gyrus was identified as the optimal regressor as determined by stepwise regression. Interestingly, these regions possessed higher centrality than others in the brain (P < 10(-3)) although they were not the most highly connected hubs. Finally, we demonstrate a high reproducibility of our findings across several factors (e.g., threshold choice, anatomical distance, and temporal cutting) in our analyses. Together, these preliminary exploratory analyses demonstrate the potential of neuroimaging-based network analysis in predicting the early therapeutic improvement of MDD patients and have important implications in guiding earlier personalized therapeutic regimens for possible treatment-refractory depression. PMID:25930660

  14. Fast transient networks in spontaneous human brain activity

    PubMed Central

    Baker, Adam P; Brookes, Matthew J; Rezek, Iead A; Smith, Stephen M; Behrens, Timothy; Probert Smith, Penny J; Woolrich, Mark

    2014-01-01

    To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states. DOI: http://dx.doi.org/10.7554/eLife.01867.001 PMID:24668169

  15. Frequency-specific network topologies in the resting human brain.

    PubMed

    Sasai, Shuntaro; Homae, Fumitaka; Watanabe, Hama; Sasaki, Akihiro T; Tanabe, Hiroki C; Sadato, Norihiro; Taga, Gentaro

    2014-01-01

    A community is a set of nodes with dense inter-connections, while there are sparse connections between different communities. A hub is a highly connected node with high centrality. It has been shown that both "communities" and "hubs" exist simultaneously in the brain's functional connectivity network (FCN), as estimated by correlations among low-frequency spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signal changes (0.01-0.10 Hz). This indicates that the brain has a spatial organization that promotes both segregation and integration of information. Here, we demonstrate that frequency-specific network topologies that characterize segregation and integration also exist within this frequency range. In investigating the coherence spectrum among 87 brain regions, we found that two frequency bands, 0.01-0.03 Hz (very low frequency [VLF] band) and 0.07-0.09 Hz (low frequency [LF] band), mainly contributed to functional connectivity. Comparing graph theoretical indices for the VLF and LF bands revealed that the network in the former had a higher capacity for information segregation between identified communities than the latter. Hubs in the VLF band were mainly located within the anterior cingulate cortices, whereas those in the LF band were located in the posterior cingulate cortices and thalamus. Thus, depending on the timescale of brain activity, at least two distinct network topologies contributed to information segregation and integration. This suggests that the brain intrinsically has timescale-dependent functional organizations. PMID:25566037

  16. Frequency-specific network topologies in the resting human brain

    PubMed Central

    Sasai, Shuntaro; Homae, Fumitaka; Watanabe, Hama; Sasaki, Akihiro T.; Tanabe, Hiroki C.; Sadato, Norihiro; Taga, Gentaro

    2014-01-01

    A community is a set of nodes with dense inter-connections, while there are sparse connections between different communities. A hub is a highly connected node with high centrality. It has been shown that both “communities” and “hubs” exist simultaneously in the brain's functional connectivity network (FCN), as estimated by correlations among low-frequency spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signal changes (0.01–0.10 Hz). This indicates that the brain has a spatial organization that promotes both segregation and integration of information. Here, we demonstrate that frequency-specific network topologies that characterize segregation and integration also exist within this frequency range. In investigating the coherence spectrum among 87 brain regions, we found that two frequency bands, 0.01–0.03 Hz (very low frequency [VLF] band) and 0.07–0.09 Hz (low frequency [LF] band), mainly contributed to functional connectivity. Comparing graph theoretical indices for the VLF and LF bands revealed that the network in the former had a higher capacity for information segregation between identified communities than the latter. Hubs in the VLF band were mainly located within the anterior cingulate cortices, whereas those in the LF band were located in the posterior cingulate cortices and thalamus. Thus, depending on the timescale of brain activity, at least two distinct network topologies contributed to information segregation and integration. This suggests that the brain intrinsically has timescale-dependent functional organizations. PMID:25566037

  17. Why network neuroscience? Compelling evidence and current frontiers. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

    NASA Astrophysics Data System (ADS)

    Muldoon, Sarah Feldt; Bassett, Danielle S.

    2014-09-01

    The recent application of network theory to neuroscience has brought new insights into understanding the relationship between brain structure and function [1]. As Pessoa describes in his extensive review [2], the organization of the brain can be viewed as a complex system of connected components that interact at many scales [3], both in the underlying structural architecture and through temporal functional relationships. Importantly, he emphasizes that we must shed the view that a specific brain region can be tied to a specific function and instead view the brain as a dynamic and evolving network in which overlapping sub-networks of brain regions work together to produce different functions. In fact, the complexity of these evolving interactions is now driving the future of network science [4], as efforts focus on developing novel metrics to capture the dynamic essence of these interconnected networks.

  18. Does IQ affect the functional brain network involved in pseudoword reading in students with reading disability? A magnetoencephalography study.

    PubMed

    Simos, Panagiotis G; Rezaie, Roozbeh; Papanicolaou, Andrew C; Fletcher, Jack M

    2014-01-01

    The study examined whether individual differences in performance and verbal IQ affect the profiles of reading-related regional brain activation in 127 students experiencing reading difficulties and typical readers. Using magnetoencephalography in a pseudoword read-aloud task, we compared brain activation profiles of students experiencing word-level reading difficulties who did (n = 29) or did not (n = 36) meet the IQ-reading achievement discrepancy criterion. Typical readers assigned to a lower-IQ (n = 18) or a higher IQ (n = 44) subgroup served as controls. Minimum norm estimates of regional cortical activity revealed that the degree of hypoactivation in the left superior temporal and supramarginal gyri in both RD subgroups was not affected by IQ. Moreover, IQ did not moderate the positive association between degree of activation in the left fusiform gyrus and phonological decoding ability. We did find, however, that the hypoactivation of the left pars opercularis in RD was restricted to lower-IQ participants. In accordance with previous morphometric and fMRI studies, degree of activity in inferior frontal, and inferior parietal regions correlated with IQ across reading ability subgroups. Results are consistent with current views questioning the relevance of IQ-discrepancy criteria in the diagnosis of dyslexia. PMID:24409136

  19. Does IQ affect the functional brain network involved in pseudoword reading in students with reading disability? A magnetoencephalography study

    PubMed Central

    Simos, Panagiotis G.; Rezaie, Roozbeh; Papanicolaou, Andrew C.; Fletcher, Jack M.

    2014-01-01

    The study examined whether individual differences in performance and verbal IQ affect the profiles of reading-related regional brain activation in 127 students experiencing reading difficulties and typical readers. Using magnetoencephalography in a pseudoword read-aloud task, we compared brain activation profiles of students experiencing word-level reading difficulties who did (n = 29) or did not (n = 36) meet the IQ-reading achievement discrepancy criterion. Typical readers assigned to a lower-IQ (n = 18) or a higher IQ (n = 44) subgroup served as controls. Minimum norm estimates of regional cortical activity revealed that the degree of hypoactivation in the left superior temporal and supramarginal gyri in both RD subgroups was not affected by IQ. Moreover, IQ did not moderate the positive association between degree of activation in the left fusiform gyrus and phonological decoding ability. We did find, however, that the hypoactivation of the left pars opercularis in RD was restricted to lower-IQ participants. In accordance with previous morphometric and fMRI studies, degree of activity in inferior frontal, and inferior parietal regions correlated with IQ across reading ability subgroups. Results are consistent with current views questioning the relevance of IQ-discrepancy criteria in the diagnosis of dyslexia. PMID:24409136

  20. 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…

  1. Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI

    PubMed Central

    Kong, Xiang-zhen; Liu, Zhaoguo; Huang, Lijie; Wang, Xu; Yang, Zetian; Zhou, Guangfu; Zhen, Zonglei; Liu, Jia

    2015-01-01

    Representing brain morphology as a network has the advantage that the regional morphology of ‘isolated’ structures can be described statistically based on graph theory. However, very few studies have investigated brain morphology from the holistic perspective of complex networks, particularly in individual brains. We proposed a new network framework for individual brain morphology. Technically, in the new network, nodes are defined as regions based on a brain atlas, and edges are estimated using our newly-developed inter-regional relation measure based on regional morphological distributions. This implementation allows nodes in the brain network to be functionally/anatomically homogeneous but different with respect to shape and size. We first demonstrated the new network framework in a healthy sample. Thereafter, we studied the graph-theoretical properties of the networks obtained and compared the results with previous morphological, anatomical, and functional networks. The robustness of the method was assessed via measurement of the reliability of the network metrics using a test-retest dataset. Finally, to illustrate potential applications, the networks were used to measure age-related changes in commonly used network metrics. Results suggest that the proposed method could provide a concise description of brain organization at a network level and be used to investigate interindividual variability in brain morphology from the perspective of complex networks. Furthermore, the method could open a new window into modeling the complexly distributed brain and facilitate the emerging field of human connectomics. PMID:26536598

  2. Electroencephalographic imaging of higher brain function

    NASA Technical Reports Server (NTRS)

    Gevins, A.; Smith, M. E.; McEvoy, L. K.; Leong, H.; Le, J.

    1999-01-01

    High temporal resolution is necessary to resolve the rapidly changing patterns of brain activity that underlie mental function. Electroencephalography (EEG) provides temporal resolution in the millisecond range. However, traditional EEG technology and practice provide insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. Recent advances help to overcome this problem by recording EEGs from more electrodes, by registering EEG data with anatomical images, and by correcting the distortion caused by volume conduction of EEG signals through the skull and scalp. In addition, statistical measurements of sub-second interdependences between EEG time-series recorded from different locations can help to generate hypotheses about the instantaneous functional networks that form between different cortical regions during perception, thought and action. Example applications are presented from studies of language, attention and working memory. Along with its unique ability to monitor brain function as people perform everyday activities in the real world, these advances make modern EEG an invaluable complement to other functional neuroimaging modalities.

  3. Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path

    PubMed Central

    Cheng, Bastian; Messé, Arnaud; Thomalla, Götz; Gerloff, Christian; König, Peter

    2016-01-01

    In this study, we investigate if phase-locking of fast oscillatory activity relies on the anatomical skeleton and if simple computational models informed by structural connectivity can help further to explain missing links in the structure-function relationship. We use diffusion tensor imaging data and alpha band-limited EEG signal recorded in a group of healthy individuals. Our results show that about 23.4% of the variance in empirical networks of resting-state functional connectivity is explained by the underlying white matter architecture. Simulating functional connectivity using a simple computational model based on the structural connectivity can increase the match to 45.4%. In a second step, we use our modeling framework to explore several technical alternatives along the modeling path. First, we find that an augmentation of homotopic connections in the structural connectivity matrix improves the link to functional connectivity while a correction for fiber distance slightly decreases the performance of the model. Second, a more complex computational model based on Kuramoto oscillators leads to a slight improvement of the model fit. Third, we show that the comparison of modeled and empirical functional connectivity at source level is much more specific for the underlying structural connectivity. However, different source reconstruction algorithms gave comparable results. Of note, as the fourth finding, the model fit was much better if zero-phase lag components were preserved in the empirical functional connectome, indicating a considerable amount of functionally relevant synchrony taking place with near zero or zero-phase lag. The combination of the best performing alternatives at each stage in the pipeline results in a model that explains 54.4% of the variance in the empirical EEG functional connectivity. Our study shows that large-scale brain circuits of fast neural network synchrony strongly rely upon the structural connectome and simple computational

  4. Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path.

    PubMed

    Finger, Holger; Bönstrup, Marlene; Cheng, Bastian; Messé, Arnaud; Hilgetag, Claus; Thomalla, Götz; Gerloff, Christian; König, Peter

    2016-08-01

    In this study, we investigate if phase-locking of fast oscillatory activity relies on the anatomical skeleton and if simple computational models informed by structural connectivity can help further to explain missing links in the structure-function relationship. We use diffusion tensor imaging data and alpha band-limited EEG signal recorded in a group of healthy individuals. Our results show that about 23.4% of the variance in empirical networks of resting-state functional connectivity is explained by the underlying white matter architecture. Simulating functional connectivity using a simple computational model based on the structural connectivity can increase the match to 45.4%. In a second step, we use our modeling framework to explore several technical alternatives along the modeling path. First, we find that an augmentation of homotopic connections in the structural connectivity matrix improves the link to functional connectivity while a correction for fiber distance slightly decreases the performance of the model. Second, a more complex computational model based on Kuramoto oscillators leads to a slight improvement of the model fit. Third, we show that the comparison of modeled and empirical functional connectivity at source level is much more specific for the underlying structural connectivity. However, different source reconstruction algorithms gave comparable results. Of note, as the fourth finding, the model fit was much better if zero-phase lag components were preserved in the empirical functional connectome, indicating a considerable amount of functionally relevant synchrony taking place with near zero or zero-phase lag. The combination of the best performing alternatives at each stage in the pipeline results in a model that explains 54.4% of the variance in the empirical EEG functional connectivity. Our study shows that large-scale brain circuits of fast neural network synchrony strongly rely upon the structural connectome and simple computational

  5. 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/). PMID:23861951

  6. Directed progression brain networks in Alzheimer's disease: properties and classification.

    PubMed

    Friedman, Eric J; Young, Karl; Asif, Danial; Jutla, Inderjit; Liang, Michael; Wilson, Scott; Landsberg, Adam S; Schuff, Norbert

    2014-06-01

    This article introduces a new approach in brain connectomics aimed at characterizing the temporal spread in the brain of pathologies like Alzheimer's disease (AD). The main instrument is the development of "directed progression networks" (DPNets), wherein one constructs directed edges between nodes based on (weakly) inferred directions of the temporal spreading of the pathology. This stands in contrast to many previously studied brain networks where edges represent correlations, physical connections, or functional progressions. In addition, this is one of a few studies showing the value of using directed networks in the study of AD. This article focuses on the construction of DPNets for AD using longitudinal cortical thickness measurements from magnetic resonance imaging data. The network properties are then characterized, providing new insights into AD progression, as well as novel markers for differentiating normal cognition (NC) and AD at the group level. It also demonstrates the important role of nodal variations for network classification (i.e., the significance of standard deviations, not just mean values of nodal properties). Finally, the DPNets are utilized to classify subjects based on their global network measures using a variety of data-mining methodologies. In contrast to most brain networks, these DPNets do not show high clustering and small-world properties. PMID:24901258

  7. Big Words, Halved Brains and Small Worlds: Complex Brain Networks of Figurative Language Comprehension

    PubMed Central

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

    2011-01-01

    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. PMID:21556324

  8. Dynamic geometry, brain function modeling, and consciousness.

    PubMed

    Roy, Sisir; Llinás, Rodolfo

    2008-01-01

    Pellionisz and Llinás proposed, years ago, a geometric interpretation towards understanding brain function. This interpretation assumes that the relation between the brain and the external world is determined by the ability of the central nervous system (CNS) to construct an internal model of the external world using an interactive geometrical relationship between sensory and motor expression. This approach opened new vistas not only in brain research but also in understanding the foundations of geometry itself. The approach named tensor network theory is sufficiently rich to allow specific computational modeling and addressed the issue of prediction, based on Taylor series expansion properties of the system, at the neuronal level, as a basic property of brain function. It was actually proposed that the evolutionary realm is the backbone for the development of an internal functional space that, while being purely representational, can interact successfully with the totally different world of the so-called "external reality". Now if the internal space or functional space is endowed with stochastic metric tensor properties, then there will be a dynamic correspondence between events in the external world and their specification in the internal space. We shall call this dynamic geometry since the minimal time resolution of the brain (10-15 ms), associated with 40 Hz oscillations of neurons and their network dynamics, is considered to be responsible for recognizing external events and generating the concept of simultaneity. The stochastic metric tensor in dynamic geometry can be written as five-dimensional space-time where the fifth dimension is a probability space as well as a metric space. This extra dimension is considered an imbedded degree of freedom. It is worth noticing that the above-mentioned 40 Hz oscillation is present both in awake and dream states where the central difference is the inability of phase resetting in the latter. This framework of dynamic

  9. Task induced modulation of neural oscillations in electrophysiological brain networks.

    PubMed

    Brookes, M J; Liddle, E B; Hale, J R; Woolrich, M W; Luckhoo, H; Liddle, P F; Morris, P G

    2012-12-01

    In recent years, one of the most important findings in systems neuroscience has been the identification of large scale distributed brain networks. These networks support healthy brain function and are perturbed in a number of neurological disorders (e.g. schizophrenia). Their study is therefore an important and evolving focus for neuroscience research. The majority of network studies are conducted using functional magnetic resonance imaging (fMRI) which relies on changes in blood oxygenation induced by neural activity. However recently, a small number of studies have begun to elucidate the electrical origin of fMRI networks by searching for correlations between neural oscillatory signals from spatially separate brain areas in magnetoencephalography (MEG) data. Here we advance this research area. We introduce two methodological extensions to previous independent component analysis (ICA) approaches to MEG network characterisation: 1) we show how to derive pan-spectral networks that combine independent components computed within individual frequency bands. 2) We show how to measure the temporal evolution of each network with millisecond temporal resolution. We apply our approach to ~10h of MEG data recorded in 28 experimental sessions during 3 separate cognitive tasks showing that a number of networks could be identified and were robust across time, task, subject and recording session. Further, we show that neural oscillations in those networks are modulated by memory load, and task relevance. This study furthers recent findings on electrodynamic brain networks and paves the way for future clinical studies in patients in which abnormal connectivity is thought to underlie core symptoms. PMID:22906787

  10. Hyper-Brain Networks Support Romantic Kissing in Humans

    PubMed Central

    Müller, Viktor; Lindenberger, Ulman

    2014-01-01

    Coordinated social interaction is associated with, and presumably dependent on, oscillatory couplings within and between brains, which, in turn, consist of an interplay across different frequencies. Here, we introduce a method of network construction based on the cross-frequency coupling (CFC) and examine whether coordinated social interaction is associated with CFC within and between brains. Specifically, we compare the electroencephalograms (EEG) of 15 heterosexual couples during romantic kissing to kissing one’s own hand, and to kissing one another while performing silent arithmetic. Using graph-theory methods, we identify theta–alpha hyper-brain networks, with alpha serving a cleaving or pacemaker function. Network strengths were higher and characteristic path lengths shorter when individuals were kissing each other than when they were kissing their own hand. In both partner-oriented kissing conditions, greater strength and shorter path length for 5-Hz oscillation nodes correlated reliably with greater partner-oriented kissing satisfaction. This correlation was especially strong for inter-brain connections in both partner-oriented kissing conditions but not during kissing one’s own hand. Kissing quality assessed after the kissing with silent arithmetic correlated reliably with intra-brain strength of 10-Hz oscillation nodes during both romantic kissing and kissing with silent arithmetic. We conclude that hyper-brain networks based on CFC may capture neural mechanisms that support interpersonally coordinated voluntary action and bonding behavior. PMID:25375132

  11. Creative Cognition and Brain Network Dynamics.

    PubMed

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

    2016-02-01

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

  12. Decreased Functional Brain Connectivity in Adolescents with Internet Addiction

    PubMed Central

    Hong, Soon-Beom; Zalesky, Andrew; Cocchi, Luca; Fornito, Alex; Choi, Eun-Jung; Kim, Ho-Hyun; Suh, Jeong-Eun; Kim, Chang-Dai; Kim, Jae-Won; Yi, Soon-Hyung

    2013-01-01

    Background Internet addiction has become increasingly recognized as a mental disorder, though its neurobiological basis is unknown. This study used functional neuroimaging to investigate whole-brain functional connectivity in adolescents diagnosed with internet addiction. Based on neurobiological changes seen in other addiction related disorders, it was predicted that connectivity disruptions in adolescents with internet addiction would be most prominent in cortico-striatal circuitry. Methods Participants were 12 adolescents diagnosed with internet addiction and 11 healthy comparison subjects. Resting-state functional magnetic resonance images were acquired, and group differences in brain functional connectivity were analyzed using the network-based statistic. We also analyzed network topology, testing for between-group differences in key graph-based network measures. Results Adolescents with internet addiction showed reduced functional connectivity spanning a distributed network. The majority of impaired connections involved cortico-subcortical circuits (∼24% with prefrontal and ∼27% with parietal cortex). Bilateral putamen was the most extensively involved subcortical brain region. No between-group difference was observed in network topological measures, including the clustering coefficient, characteristic path length, or the small-worldness ratio. Conclusions Internet addiction is associated with a widespread and significant decrease of functional connectivity in cortico-striatal circuits, in the absence of global changes in brain functional network topology. PMID:23451272

  13. Analysing Local Sparseness in the Macaque Brain Network.

    PubMed

    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

  14. Realistic modeling of neurons and networks: towards brain simulation

    PubMed Central

    D’Angelo, Egidio; Solinas, Sergio; Garrido, Jesus; Casellato, Claudia; Pedrocchi, Alessandra; Mapelli, Jonathan; Gandolfi, Daniela; Prestori, Francesca

    Summary Realistic modeling is a new advanced methodology for investigating brain functions. Realistic modeling is based on a detailed biophysical description of neurons and synapses, which can be integrated into microcircuits. The latter can, in turn, be further integrated to form large-scale brain networks and eventually to reconstruct complex brain systems. Here we provide a review of the realistic simulation strategy and use the cerebellar network as an example. This network has been carefully investigated at molecular and cellular level and has been the object of intense theoretical investigation. The cerebellum is thought to lie at the core of the forward controller operations of the brain and to implement timing and sensory prediction functions. The cerebellum is well described and provides a challenging field in which one of the most advanced realistic microcircuit models has been generated. We illustrate how these models can be elaborated and embedded into robotic control systems to gain insight into how the cellular properties of cerebellar neurons emerge in integrated behaviors. Realistic network modeling opens up new perspectives for the investigation of brain pathologies and for the neurorobotic field. PMID:24139652

  15. The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain

    PubMed Central

    Simpson, Sean L.; Burdette, Jonathan H.; Hayasaka, Satoru; Laurienti, Paul J.

    2011-01-01

    Abstract Although graph theory has been around since the 18th century, the field of network science is more recent and continues to gain popularity, particularly in the field of neuroimaging. The field was propelled forward when Watts and Strogatz introduced their small-world network model, which described a network that provided regional specialization with efficient global information transfer. This model is appealing to the study of brain connectivity, as the brain can be viewed as a system with various interacting regions that produce complex behaviors. In practice, graph metrics such as clustering coefficient, path length, and efficiency measures are often used to characterize system properties. Centrality metrics such as degree, betweenness, closeness, and eigenvector centrality determine critical areas within the network. Community structure is also essential for understanding network organization and topology. Network science has led to a paradigm shift in the neuroscientific community, but it should be viewed as more than a simple “tool du jour.” To fully appreciate the utility of network science, a greater understanding of how network models apply to the brain is needed. An integrated appraisal of multiple network analyses should be performed to better understand network structure rather than focusing on univariate comparisons to find significant group differences; indeed, such comparisons, popular with traditional functional magnetic resonance imaging analyses, are arguably no longer relevant with graph-theory based approaches. These methods necessitate a philosophical shift toward complexity science. In this context, when correctly applied and interpreted, network scientific methods have a chance to revolutionize the understanding of brain function. PMID:22432419

  16. Dynamic Functional Brain Connectivity for Face Perception

    PubMed Central

    Yang, Yuan; Qiu, Yihong; Schouten, Alfred C.

    2015-01-01

    Face perception is mediated by a distributed brain network comprised of the core system at occipito-temporal areas and the extended system at other relevant brain areas involving bilateral hemispheres. In this study we explored how the brain connectivity changes over the time for face-sensitive processing. We investigated the dynamic functional connectivity in face perception by analyzing time-dependent EEG phase synchronization in four different frequency bands: theta (4–7 Hz), alpha (8–14 Hz), beta (15–24 Hz), and gamma (25–45 Hz) bands in the early stages of face processing from 30 to 300 ms. High-density EEG were recorded from subjects who were passively viewing faces, buildings, and chairs. The dynamic connectivity within the core system and between the extended system were investigated. Significant differences between faces and non-faces mainly appear in theta band connectivity: (1) at the time segment of 90–120 ms between parietal area and occipito-temporal area in the right hemisphere, and (2) at the time segment of 150–180 ms between bilateral occipito-temporal areas. These results indicate (1) the importance of theta-band connectivity in the face-sensitive processing, and (2) that different parts of network are involved for the initial stage of face categorization and the stage of face structural encoding. PMID:26696870

  17. Bayesian approach for network modeling of brain structural features

    NASA Astrophysics Data System (ADS)

    Joshi, Anand A.; Joshi, Shantanu H.; Leahy, Richard M.; Shattuck, David W.; Dinov, Ivo; Toga, Arthur W.

    2010-03-01

    Brain connectivity patterns are useful in understanding brain function and organization. Anatomical brain connectivity is largely determined using the physical synaptic connections between neurons. In contrast statistical brain connectivity in a given brain population refers to the interaction and interdependencies of statistics of multitudes of brain features including cortical area, volume, thickness etc. Traditionally, this dependence has been studied by statistical correlations of cortical features. In this paper, we propose the use of Bayesian network modeling for inferring statistical brain connectivity patterns that relate to causal (directed) as well as non-causal (undirected) relationships between cortical surface areas. We argue that for multivariate cortical data, the Bayesian model provides for a more accurate representation by removing the effect of confounding correlations that get introduced due to canonical dependence between the data. Results are presented for a population of 466 brains, where a SEM (structural equation modeling) approach is used to generate a Bayesian network model, as well as a dependency graph for the joint distribution of cortical areas.

  18. Neural Substrate Expansion for the Restoration of Brain Function

    PubMed Central

    Chen, H. Isaac; Jgamadze, Dennis; Serruya, Mijail D.; Cullen, D. Kacy; Wolf, John A.; Smith, Douglas H.

    2016-01-01

    Restoring neurological and cognitive function in individuals who have suffered brain damage is one of the principal objectives of modern translational neuroscience. Electrical stimulation approaches, such as deep-brain stimulation, have achieved the most clinical success, but they ultimately may be limited by the computational capacity of the residual cerebral circuitry. An alternative strategy is brain substrate expansion, in which the computational capacity of the brain is augmented through the addition of new processing units and the reconstitution of network connectivity. This latter approach has been explored to some degree using both biological and electronic means but thus far has not demonstrated the ability to reestablish the function of large-scale neuronal networks. In this review, we contend that fulfilling the potential of brain substrate expansion will require a significant shift from current methods that emphasize direct manipulations of the brain (e.g., injections of cellular suspensions and the implantation of multi-electrode arrays) to the generation of more sophisticated neural tissues and neural-electric hybrids in vitro that are subsequently transplanted into the brain. Drawing from neural tissue engineering, stem cell biology, and neural interface technologies, this strategy makes greater use of the manifold techniques available in the laboratory to create biocompatible constructs that recapitulate brain architecture and thus are more easily recognized and utilized by brain networks. PMID:26834579

  19. Neural Substrate Expansion for the Restoration of Brain Function.

    PubMed

    Chen, H Isaac; Jgamadze, Dennis; Serruya, Mijail D; Cullen, D Kacy; Wolf, John A; Smith, Douglas H

    2016-01-01

    Restoring neurological and cognitive function in individuals who have suffered brain damage is one of the principal objectives of modern translational neuroscience. Electrical stimulation approaches, such as deep-brain stimulation, have achieved the most clinical success, but they ultimately may be limited by the computational capacity of the residual cerebral circuitry. An alternative strategy is brain substrate expansion, in which the computational capacity of the brain is augmented through the addition of new processing units and the reconstitution of network connectivity. This latter approach has been explored to some degree using both biological and electronic means but thus far has not demonstrated the ability to reestablish the function of large-scale neuronal networks. In this review, we contend that fulfilling the potential of brain substrate expansion will require a significant shift from current methods that emphasize direct manipulations of the brain (e.g., injections of cellular suspensions and the implantation of multi-electrode arrays) to the generation of more sophisticated neural tissues and neural-electric hybrids in vitro that are subsequently transplanted into the brain. Drawing from neural tissue engineering, stem cell biology, and neural interface technologies, this strategy makes greater use of the manifold techniques available in the laboratory to create biocompatible constructs that recapitulate brain architecture and thus are more easily recognized and utilized by brain networks. PMID:26834579

  20. Parcellating Cortical Functional Networks in Individuals

    PubMed Central

    Wang, Danhong; Buckner, Randy L.; Fox, Michael D.; Holt, Daphne J.; Holmes, Avram J.; Stoecklein, Sophia; Langs, Georg; Pan, Ruiqi; Qian, Tianyi; Li, Kuncheng; Baker, Justin T.; Stufflebeam, Steven M.; Wang, Kai; Wang, Xiaomin; Hong, Bo; Liu, Hesheng

    2015-01-01

    The capacity to identify the unique functional architecture of an individual’s brain is a critical step towards personalized medicine and understanding the neural basis of variations in human cognition and behavior. Here, we developed a novel cortical parcellation approach to accurately map functional organization at the individual level using resting-state fMRI. A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting great potential for use in clinical applications. PMID:26551545

  1. Dynamic imaging of brain function

    PubMed Central

    Hyder, Fahmeed

    2013-01-01

    In recent years, there have been unprecedented methodological advances in the dynamic imaging of brain activities. Electrophysiological, optical, and magnetic resonance methods now allow mapping of functional activation (or deactivation) by measurement of neuronal activity (e.g., membrane potential, ion flux, neurotransmitter flux), energy metabolism (e.g., glucose consumption, oxygen consumption, creatine kinase flux), and functional hyperemia (e.g., blood oxygenation, blood flow, blood volume). Properties of the glutamatergic synapse are used as a model to reveal activities at the nerve terminal and their associated changes in energy demand and blood flow. This approach reveals that each method measures different tissue- and/or cell-specific components with specified spatiotemporal resolution. While advantages and disadvantages of different methods are apparent and often used to supersede one another in terms of specificity and/or sensitivity, no particular technique is the optimal dynamic brain imaging method because each method is unique in some respect. Because the demand for energy substrates is a fundamental requirement for function, energy-based methods may allow quantitative dynamic imaging in vivo. However there are exclusive neurobiological insights gained by combining some of these different dynamic imaging techniques. PMID:18839085

  2. Default network connectivity decodes brain states with simulated microgravity.

    PubMed

    Zeng, Ling-Li; Liao, Yang; Zhou, Zongtan; Shen, Hui; Liu, Yadong; Liu, Xufeng; Hu, Dewen

    2016-04-01

    With great progress of space navigation technology, it becomes possible to travel beyond Earth's gravity. So far, it remains unclear whether the human brain can function normally within an environment of microgravity and confinement. Particularly, it is a challenge to figure out some neuroimaging-based markers for rapid screening diagnosis of disrupted brain function in microgravity environment. In this study, a 7-day -6° head down tilt bed rest experiment was used to simulate the microgravity, and twenty healthy male participants underwent resting-state functional magnetic resonance imaging scans at baseline and after the simulated microgravity experiment. We used a multivariate pattern analysis approach to distinguish the brain states with simulated microgravity from normal gravity based on the functional connectivity within the default network, resulting in an accuracy of no less than 85 % via cross-validation. Moreover, most discriminative functional connections were mainly located between the limbic system and cortical areas and were enhanced after simulated microgravity, implying a self-adaption or compensatory enhancement to fulfill the need of complex demand in spatial navigation and motor control functions in microgravity environment. Overall, the findings suggest that the brain states in microgravity are likely different from those in normal gravity and that brain connectome could act as a biomarker to indicate the brain state in microgravity. PMID:27066149

  3. Robust Reproducible Resting State Networks in the Awake Rodent Brain

    PubMed Central

    Becerra, Lino; Pendse, Gautam; Chang, Pei-Ching; Bishop, James; Borsook, David

    2011-01-01

    Resting state networks (RSNs) have been studied extensively with functional MRI in humans in health and disease to reflect brain function in the un-stimulated state as well as reveal how the brain is altered with disease. Rodent models of disease have been used comprehensively to understand the biology of the disease as well as in the development of new therapies. RSN reported studies in rodents, however, are few, and most studies are performed with anesthetized rodents that might alter networks and differ from their non-anesthetized state. Acquiring RSN data in the awake rodent avoids the issues of anesthesia effects on brain function. Using high field fMRI we determined RSNs in awake rats using an independent component analysis (ICA) approach, however, ICA analysis can produce a large number of components, some with biological relevance (networks). We further have applied a novel method to determine networks that are robust and reproducible among all the components found with ICA. This analysis indicates that 7 networks are robust and reproducible in the rat and their putative role is discussed. PMID:22028788

  4. Robust reproducible resting state networks in the awake rodent brain.

    PubMed

    Becerra, Lino; Pendse, Gautam; Chang, Pei-Ching; Bishop, James; Borsook, David

    2011-01-01

    Resting state networks (RSNs) have been studied extensively with functional MRI in humans in health and disease to reflect brain function in the un-stimulated state as well as reveal how the brain is altered with disease. Rodent models of disease have been used comprehensively to understand the biology of the disease as well as in the development of new therapies. RSN reported studies in rodents, however, are few, and most studies are performed with anesthetized rodents that might alter networks and differ from their non-anesthetized state. Acquiring RSN data in the awake rodent avoids the issues of anesthesia effects on brain function. Using high field fMRI we determined RSNs in awake rats using an independent component analysis (ICA) approach, however, ICA analysis can produce a large number of components, some with biological relevance (networks). We further have applied a novel method to determine networks that are robust and reproducible among all the components found with ICA. This analysis indicates that 7 networks are robust and reproducible in the rat and their putative role is discussed. PMID:22028788

  5. Large-scale brain networks in cognition: emerging methods and principles.

    PubMed

    Bressler, Steven L; Menon, Vinod

    2010-06-01

    An understanding of how the human brain produces cognition ultimately depends on knowledge of large-scale brain organization. Although it has long been assumed that cognitive functions are attributable to the isolated operations of single brain areas, we demonstrate that the weight of evidence has now shifted in support of the view that cognition results from the dynamic interactions of distributed brain areas operating in large-scale networks. We review current research on structural and functional brain organization, and argue that the emerging science of large-scale brain networks provides a coherent framework for understanding of cognition. Critically, this framework allows a principled exploration of how cognitive functions emerge from, and are constrained by, core structural and functional networks of the brain. PMID:20493761

  6. 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

  7. Topological Isomorphisms of Human Brain and Financial Market Networks

    PubMed Central

    Vértes, Petra E.; Nicol, Ruth M.; Chapman, Sandra C.; Watkins, Nicholas W.; Robertson, Duncan A.; Bullmore, Edward T.

    2011-01-01

    Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets – the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular – more highly optimized for information processing – than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets. PMID:22007161

  8. The functional brain connectome of the child and autism spectrum disorders.

    PubMed

    Mevel, Katell; Fransson, Peter

    2016-09-01

    Brain connectomics is a relatively new field of research that maps the brain's large-scale structural and functional networks at rest. The connectome of the human brain develops progressively from early infancy to late adolescence, and this review describes the theory behind the concept and its applicability to studying the development and dynamics of brain networks through graph theoretical metrics. We also describe how the brain connectome concept could further our understanding of autism spectrum disorders (ASD) CONCLUSION: Further research into the functional child brain connectome concept could enhance our understanding of atypical brain connectivity patterns presumed to be linked to ASD. PMID:27228241

  9. Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.

    PubMed

    Stephen, Emily P; Lepage, Kyle Q; Eden, Uri T; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S; Guenther, Frank H; Kramer, Mark A

    2014-01-01

    The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty-both in the functional network edges and the corresponding aggregate measures of network topology-are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here-appropriate for static and dynamic network inference and different statistical measures of coupling-permits the evaluation of confidence in network measures in a variety of settings common to neuroscience. PMID:24678295

  10. Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses

    PubMed Central

    Stephen, Emily P.; Lepage, Kyle Q.; Eden, Uri T.; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S.; Guenther, Frank H.; Kramer, Mark A.

    2014-01-01

    The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience. PMID:24678295

  11. Functional brain correlates of heterosexual paedophilia.

    PubMed

    Schiffer, Boris; Paul, Thomas; Gizewski, Elke; Forsting, Michael; Leygraf, Norbert; Schedlowski, Manfred; Kruger, Tillmann H C

    2008-05-15

    Although the neuronal mechanisms underlying normal sexual motivation and function have recently been examined, the alterations in brain function in deviant sexual behaviours such as paedophilia are largely unknown. The objective of this study was to identify paedophilia-specific functional networks implicated in sexual arousal. Therefore a consecutive sample of eight paedophile forensic inpatients, exclusively attracted to females, and 12 healthy age-matched heterosexual control participants from a comparable socioeconomic stratum participated in a visual sexual stimulation procedure during functional magnetic resonance imaging. The visual stimuli were sexually stimulating photographs and emotionally neutral photographs. Immediately after the imaging session subjective responses pertaining to sexual desire were recorded. Principally, the brain response of heterosexual paedophiles to heteropaedophilic stimuli was comparable to that of heterosexual males to heterosexual stimuli, including different limbic structures (amygdala, cingulate gyrus, and hippocampus), the substantia nigra, caudate nucleus, as well as the anterior cingulate cortex, different thalamic nuclei, and associative cortices. However, responses to visual sexual stimulation were found in the orbitofrontal cortex in healthy heterosexual males, but not in paedophiles, in whom abnormal activity in the dorsolateral prefrontal cortex was observed. Thus, in line with clinical observations and neuropsychological studies, it seems that central processing of sexual stimuli in heterosexual paedophiles may be altered by a disturbance in the prefrontal networks, which, as has already been hypothesized, may be associated with stimulus-controlled behaviours, such as sexual compulsive behaviours. Moreover, these findings may suggest a dysfunction (in the functional and effective connectivity) at the cognitive stage of sexual arousal processing. PMID:18358744

  12. Computational Neuropsychiatry – Schizophrenia as a Cognitive Brain Network Disorder

    PubMed Central

    Dauvermann, Maria R.; Whalley, Heather C.; Schmidt, André; Lee, Graham L.; Romaniuk, Liana; Roberts, Neil; Johnstone, Eve C.; Lawrie, Stephen M.; Moorhead, Thomas W. J.

    2014-01-01

    Computational modeling of functional brain networks in fMRI data has advanced the understanding of higher cognitive function. It is hypothesized that functional networks mediating higher cognitive processes are disrupted in people with schizophrenia. In this article, we review studies that applied measures of functional and effective connectivity to fMRI data during cognitive tasks, in particular working memory fMRI studies. We provide a conceptual summary of the main findings in fMRI data and their relationship with neurotransmitter systems, which are known to be altered in individuals with schizophrenia. We consider possible developments in computational neuropsychiatry, which are likely to further our understanding of how key functional networks are altered in schizophrenia. PMID:24723894

  13. 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

  14. Plasticity of brain wave network interactions and evolution across physiologic states.

    PubMed

    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

  15. Reorganization of Functional Connectivity as a Correlate of Cognitive Recovery in Acquired Brain Injury

    ERIC Educational Resources Information Center

    Castellanos, Nazareth P.; Paul, Nuria; Ordonez, Victoria E.; Demuynck, Olivier; Bajo, Ricardo; Campo, Pablo; Bilbao, Alvaro; Ortiz, Tomas; del-Pozo, Francisco; Maestu, Fernando

    2010-01-01

    Cognitive processes require a functional interaction between specialized multiple, local and remote brain regions. Although these interactions can be strongly altered by an acquired brain injury, brain plasticity allows network reorganization to be principally responsible for recovery. The present work evaluates the impact of brain injury on…

  16. Dolichol alters brain membrane functions

    SciTech Connect

    Sun, G.Y.; Sun, A.Y.; Schroeder, F.; Wood, G.; Strong, R.

    1986-03-05

    It has been well demonstrated that there is a direct correlation between increase in dolichol level in brain and aging. An abnormally high level of dolichol was found in brain tissue of patients with pathological aging disorders. The aim of this study is to examine the physiological significance of dolichol affecting membrane transport activity and phospholipid acyl group turnover. Dolichol added to synaptic plasma membranes resulted in a biphasic effect on (Na/sup +/, K/sup +/)-ATPase, i.e., an enhancement of activity at low concentrations (5 ..mu..g/125 mg protein) and an inhibition of activity at high concentrations (40-100 ..mu..g). To probe the membrane acyl group turnover, the incorporation of (/sup 14/C)-arachidonate into plasma membrane phospholipids was examined in the presence and absence of dolichol. Dolichol elicited an increase in the incorporation of label into phospholipids. However, the effects varied depending on whether BSA is present. In the absence of BSA, the increase in labeling of phosphatidylinositols is higher than that of phosphatidylcholines. These results suggest that dolichols, when inserted into membranes, may alter membrane functions.

  17. The application of graph theoretical analysis to complex networks in the brain.

    PubMed

    Reijneveld, Jaap C; Ponten, Sophie C; Berendse, Henk W; Stam, Cornelis J

    2007-11-01

    Considering the brain as a complex network of interacting dynamical systems offers new insights into higher level brain processes such as memory, planning, and abstract reasoning as well as various types of brain pathophysiology. This viewpoint provides the opportunity to apply new insights in network sciences, such as the discovery of small world and scale free networks, to data on anatomical and functional connectivity in the brain. In this review we start with some background knowledge on the history and recent advances in network theories in general. We emphasize the correlation between the structural properties of networks and the dynamics of these networks. We subsequently demonstrate through evidence from computational studies, in vivo experiments, and functional MRI, EEG and MEG studies in humans, that both the functional and anatomical connectivity of the healthy brain have many features of a small world network, but only to a limited extent of a scale free network. The small world structure of neural networks is hypothesized to reflect an optimal configuration associated with rapid synchronization and information transfer, minimal wiring costs, resilience to certain types of damage, as well as a balance between local processing and global integration. Eventually, we review the current knowledge on the effects of focal and diffuse brain disease on neural network characteristics, and demonstrate increasing evidence that both cognitive and psychiatric disturbances, as well as risk of epileptic seizures, are correlated with (changes in) functional network architectural features. PMID:17900977

  18. Measuring Asymmetric Interactions in Resting State Brain Networks*

    PubMed Central

    Joshi, Anand A.; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M.

    2015-01-01

    Directed graph representations of brain networks are increasingly being used in brain image analysis to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

  19. 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. PMID:25876038

  20. Construction of Multi-Scale Consistent Brain Networks: Methods and Applications

    PubMed Central

    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. PMID:25876038

  1. Preserved Modular Network Organization in the Sedated Rat Brain

    PubMed Central

    Bruns, Andreas; Künnecke, Basil; von Kienlin, Markus; Van der Linden, Annemie; Mueggler, Thomas; Verhoye, Marleen

    2014-01-01

    Translation of resting-state functional connectivity (FC) magnetic resonance imaging (rs-fMRI) applications from human to rodents has experienced growing interest, and bears a great potential in pre-clinical imaging as it enables assessing non-invasively the topological organization of complex FC networks (FCNs) in rodent models under normal and various pathophysiological conditions. However, to date, little is known about the organizational architecture of FCNs in rodents in a mentally healthy state, although an understanding of the same is of paramount importance before investigating networks under compromised states. In this study, we characterized the properties of resting-state FCN in an extensive number of Sprague-Dawley rats (n = 40) under medetomidine sedation by evaluating its modular organization and centrality of brain regions and tested for reproducibility. Fully-connected large-scale complex networks of positively and negatively weighted connections were constructed based on Pearson partial correlation analysis between the time courses of 36 brain regions encompassing almost the entire brain. Applying recently proposed complex network analysis measures, we show that the rat FCN exhibits a modular architecture, comprising six modules with a high between subject reproducibility. In addition, we identified network hubs with strong connections to diverse brain regions. Overall our results obtained under a straight medetomidine protocol show for the first time that the community structure of the rat brain is preserved under pharmacologically induced sedation with a network modularity contrasting from the one reported for deep anesthesia but closely resembles the organization described for the rat in conscious state. PMID:25181007

  2. Aberrant functional brain connectome in people with antisocial personality disorder.

    PubMed

    Tang, Yan; Long, Jun; Wang, Wei; Liao, Jian; Xie, Hua; Zhao, Guihu; Zhang, Hao

    2016-01-01

    Antisocial personality disorder (ASPD) is characterised by a disregard for social obligations and callous unconcern for the feelings of others. Studies have demonstrated that ASPD is associated with abnormalities in brain regions and aberrant functional connectivity. In this paper, topological organisation was examined in resting-state fMRI data obtained from 32 ASPD patients and 32 non-ASPD controls. The frequency-dependent functional networks were constructed using wavelet-based correlations over 90 brain regions. The topology of the functional networks of ASPD subjects was analysed via graph theoretical analysis. Furthermore, the abnormal functional connectivity was determined with a network-based statistic (NBS) approach. Our results revealed that, compared with the controls, the ASPD patients exhibited altered topological configuration of the functional connectome in the frequency interval of 0.016-0.031 Hz, as indicated by the increased clustering coefficient and decreased betweenness centrality in the medial superior frontal gyrus, precentral gyrus, Rolandic operculum, superior parietal gyrus, angular gyrus, and middle temporal pole. In addition, the ASPD patients showed increased functional connectivity mainly located in the default-mode network. The present study reveals an aberrant topological organisation of the functional brain network in individuals with ASPD. Our findings provide novel insight into the neuropathological mechanisms of ASPD. PMID:27257047

  3. Aberrant functional brain connectome in people with antisocial personality disorder

    PubMed Central

    Tang, Yan; Long, Jun; Wang, Wei; Liao, Jian; Xie, Hua; Zhao, Guihu; Zhang, Hao

    2016-01-01

    Antisocial personality disorder (ASPD) is characterised by a disregard for social obligations and callous unconcern for the feelings of others. Studies have demonstrated that ASPD is associated with abnormalities in brain regions and aberrant functional connectivity. In this paper, topological organisation was examined in resting-state fMRI data obtained from 32 ASPD patients and 32 non-ASPD controls. The frequency-dependent functional networks were constructed using wavelet-based correlations over 90 brain regions. The topology of the functional networks of ASPD subjects was analysed via graph theoretical analysis. Furthermore, the abnormal functional connectivity was determined with a network-based statistic (NBS) approach. Our results revealed that, compared with the controls, the ASPD patients exhibited altered topological configuration of the functional connectome in the frequency interval of 0.016–0.031 Hz, as indicated by the increased clustering coefficient and decreased betweenness centrality in the medial superior frontal gyrus, precentral gyrus, Rolandic operculum, superior parietal gyrus, angular gyrus, and middle temporal pole. In addition, the ASPD patients showed increased functional connectivity mainly located in the default-mode network. The present study reveals an aberrant topological organisation of the functional brain network in individuals with ASPD. Our findings provide novel insight into the neuropathological mechanisms of ASPD. PMID:27257047

  4. Rounding of abrupt phase transitions in brain networks

    NASA Astrophysics Data System (ADS)

    Villa Martín, Paula; Moretti, Paolo; Muñoz, Miguel A.

    2015-01-01

    The observation of critical-like behavior in cortical networks represents a major step forward in elucidating how the brain manages information. Understanding the origin and functionality of critical-like dynamics, as well as its robustness, is a major challenge in contemporary neuroscience. Here, we present an extensive numerical study of a family of simple dynamical models, which describe activity propagation in brain networks through the integration of different neighboring spiking potentials, mimicking basic neural interactions. The requirement of signal integration may lead to discontinuous phase transitions in networks that are well described by the mean-field approximation, thus preventing the emergence of critical points in such systems. Brain networks, however, are finite dimensional and exhibit a heterogeneous hierarchical structure that cannot be encoded in mean-field models. Here we propose that, as a consequence of the presence of such a heterogeneous substrate with its concomitant structural disorder, critical-like features may emerge even in the presence of integration. These conclusions may prove significant in explaining the observation of traits of critical behavior in large-scale measurements of brain activity.

  5. An exploration of graph metric reproducibility in complex brain networks

    PubMed Central

    Telesford, Qawi K.; Burdette, Jonathan H.; Laurienti, Paul J.

    2013-01-01

    The application of graph theory to brain networks has become increasingly popular in the neuroimaging community. These investigations and analyses have led to a greater understanding of the brain's complex organization. More importantly, it has become a useful tool for studying the brain under various states and conditions. With the ever expanding popularity of network science in the neuroimaging community, there is increasing interest to validate the measurements and calculations derived from brain networks. Underpinning these studies is the desire to use brain networks in longitudinal studies or as clinical biomarkers to understand changes in the brain. A highly reproducible tool for brain imaging could potentially prove useful as a clinical tool. In this review, we examine recent studies in network reproducibility and their implications for analysis of brain networks. PMID:23717257

  6. Plasticity of Brain Networks in a Randomized Intervention Trial of Exercise Training in Older Adults

    PubMed Central

    Voss, Michelle W.; Prakash, Ruchika S.; Erickson, Kirk I.; Basak, Chandramallika; Chaddock, Laura; Kim, Jennifer S.; Alves, Heloisa; Heo, Susie; Szabo, Amanda N.; White, Siobhan M.; Wójcicki, Thomas R.; Mailey, Emily L.; Gothe, Neha; Olson, Erin A.; McAuley, Edward; Kramer, Arthur F.

    2010-01-01

    Research has shown the human brain is organized into separable functional networks during rest and varied states of cognition, and that aging is associated with specific network dysfunctions. The present study used functional magnetic resonance imaging (fMRI) to examine low-frequency (0.008 < f < 0.08 Hz) coherence of cognitively relevant and sensory brain networks in older adults who participated in a 1-year intervention trial, comparing the effects of aerobic and non-aerobic fitness training on brain function and cognition. Results showed that aerobic training improved the aging brain's resting functional efficiency in higher-level cognitive networks. One year of walking increased functional connectivity between aspects of the frontal, posterior, and temporal cortices within the Default Mode Network and a Frontal Executive Network, two brain networks central to brain dysfunction in aging. Length of training was also an important factor. Effects in favor of the walking group were observed only after 12 months of training, compared to non-significant trends after 6 months. A non-aerobic stretching and toning group also showed increased functional connectivity in the DMN after 6 months and in a Frontal Parietal Network after 12 months, possibly reflecting experience-dependent plasticity. Finally, we found that changes in functional connectivity were behaviorally relevant. Increased functional connectivity was associated with greater improvement in executive function. Therefore the study provides the first evidence for exercise-induced functional plasticity in large-scale brain systems in the aging brain, using functional connectivity techniques, and offers new insight into the role of aerobic fitness in attenuating age-related brain dysfunction. PMID:20890449

  7. 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. PMID:26595445

  8. scMRI Reveals Large-Scale Brain Network Abnormalities in Autism

    PubMed Central

    Zielinski, Brandon A.; Anderson, Jeffrey S.; Froehlich, Alyson L.; Prigge, Molly B. D.; Nielsen, Jared A.; Cooperrider, Jason R.; Cariello, Annahir N.; Fletcher, P. Thomas; Alexander, Andrew L.; Lange, Nicholas; Bigler, Erin D.; Lainhart, Janet E.

    2012-01-01

    Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a ‘posteriorization’ of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI. PMID:23185305

  9. The Functional Connectivity Landscape of the Human Brain

    PubMed Central

    Fatima, Zainab; Jonides, John; McIntosh, Anthony R.

    2014-01-01

    Functional brain networks emerge and dissipate over a primarily static anatomical foundation. The dynamic basis of these networks is inter-regional communication involving local and distal regions. It is assumed that inter-regional distances play a pivotal role in modulating network dynamics. Using three different neuroimaging modalities, 6 datasets were evaluated to determine whether experimental manipulations asymmetrically affect functional relationships based on the distance between brain regions in human participants. Contrary to previous assumptions, here we show that short- and long-range connections are equally likely to strengthen or weaken in response to task demands. Additionally, connections between homotopic areas are the most stable and less likely to change compared to any other type of connection. Our results point to a functional connectivity landscape characterized by fluid transitions between local specialization and global integration. This ability to mediate functional properties irrespective of spatial distance may engender a diverse repertoire of cognitive processes when faced with a dynamic environment. PMID:25350370

  10. Promoting motor function by exercising the brain.

    PubMed

    Perrey, Stephane

    2013-01-01

    Exercise represents a behavioral intervention that enhances brain health and motor function. The increase in cerebral blood volume in response to physical activity may be responsible for improving brain function. Among the various neuroimaging techniques used to monitor brain hemodynamic response during exercise, functional near-infrared spectroscopy could facilitate the measurement of task-related cortical responses noninvasively and is relatively robust with regard to the subjects' motion. Although the components of optimal exercise interventions have not been determined, evidence from animal and human studies suggests that aerobic exercise with sufficiently high intensity has neuroprotective properties and promotes motor function. This review provides an insight into the effect of physical activity (based on endurance and resistance exercises) on brain function for producing movement. Since most progress in the study of brain function has come from patients with neurological disorders (e.g., stroke and Parkinson's patients), this review presents some findings emphasizing training paradigms for restoring motor function. PMID:24961309

  11. Promoting Motor Function by Exercising the Brain

    PubMed Central

    Perrey, Stephane

    2013-01-01

    Exercise represents a behavioral intervention that enhances brain health and motor function. The increase in cerebral blood volume in response to physical activity may be responsible for improving brain function. Among the various neuroimaging techniques used to monitor brain hemodynamic response during exercise, functional near-infrared spectroscopy could facilitate the measurement of task-related cortical responses noninvasively and is relatively robust with regard to the subjects’ motion. Although the components of optimal exercise interventions have not been determined, evidence from animal and human studies suggests that aerobic exercise with sufficiently high intensity has neuroprotective properties and promotes motor function. This review provides an insight into the effect of physical activity (based on endurance and resistance exercises) on brain function for producing movement. Since most progress in the study of brain function has come from patients with neurological disorders (e.g., stroke and Parkinson’s patients), this review presents some findings emphasizing training paradigms for restoring motor function. PMID:24961309

  12. Persistent brain network homology from the perspective of dendrogram.

    PubMed

    Lee, Hyekyoung; Kang, Hyejin; Chung, Moo K; Kim, Bung-Nyun; Lee, Dong Soo

    2012-12-01

    The brain network is usually constructed by estimating the connectivity matrix and thresholding it at an arbitrary level. The problem with this standard method is that we do not have any generally accepted criteria for determining a proper threshold. Thus, we propose a novel multiscale framework that models all brain networks generated over every possible threshold. Our approach is based on persistent homology and its various representations such as the Rips filtration, barcodes, and dendrograms. This new persistent homological framework enables us to quantify various persistent topological features at different scales in a coherent manner. The barcode is used to quantify and visualize the evolutionary changes of topological features such as the Betti numbers over different scales. By incorporating additional geometric information to the barcode, we obtain a single linkage dendrogram that shows the overall evolution of the network. The difference between the two networks is then measured by the Gromov-Hausdorff distance over the dendrograms. As an illustration, we modeled and differentiated the FDG-PET based functional brain networks of 24 attention-deficit hyperactivity disorder children, 26 autism spectrum disorder children, and 11 pediatric control subjects. PMID:23008247

  13. Network Perspectives on the Mechanisms of Deep Brain Stimulation

    PubMed Central

    McIntyre, Cameron C.; Hahn, Philip J.

    2009-01-01

    Deep brain stimulation (DBS) is an established medical therapy for the treatment of movement disorders and shows great promise for several other neurological disorders. However, after decades of clinical utility the underlying therapeutic mechanisms remain undefined. Early attempts to explain the mechanisms of DBS focused on hypotheses that mimicked an ablative lesion to the stimulated brain region. More recent scientific efforts have explored the wide-spread changes in neural activity generated throughout the stimulated brain network. In turn, new theories on the mechanisms of DBS have taken a systems-level approach to begin to decipher the network activity. This review provides an introduction to some of the network based theories on the function and pathophysiology of the cortico-basal-ganglia-thalamo-cortical loops commonly targeted by DBS. We then analyze some recent results on the effects of DBS on these networks, with a focus on subthalamic DBS for the treatment of Parkinson's disease. Finally we attempt to summarize how DBS could be achieving its therapeutic effects by overriding pathological network activity. PMID:19804831

  14. Investigating a Novel Measure of Brain Networking Following Sports Concussion.

    PubMed

    Broglio, S P; Rettmann, A; Greer, J; Brimacombe, S; Moore, B; Narisetty, N; He, X; Eckner, J

    2016-08-01

    Clinicians managing sports-related concussions are left to their clinical judgment in making diagnoses and return-to-play decisions. This study was designed to evaluate the utility of a novel measure of functional brain networking for concussion management. 24 athletes with acutely diagnosed concussion and 21 control participants were evaluated in a research laboratory. At each of the 4 post-injury time points, participants completed the Axon assessment of neurocognitive function, a self-report symptom inventory, and the auditory oddball and go/no-go tasks while electroencephalogram (EEG) readings were recorded. Brain Network Activation (BNA) scores were calculated from EEG data related to the auditory oddball and go/no-go tasks. BNA scores were unable to differentiate between the concussed and control groups or by self-report symptom severity. These findings conflict with previous work implementing electrophysiological assessments in concussed athletes, suggesting that BNA requires additional investigation and refinement before clinical implementation. PMID:27286176

  15. Functional Localization of Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Eto, Shinji; Hirasawa, Kotaro; Hu, Jinglu

    According to the knowledge of brain science, it is suggested that there exists cerebral functional localization, which means that a specific part of the cerebrum is activated depending on various kinds of information human receives. The aim of this paper is to build an artificial model to realize functional localization based on Genetic Network Programming (GNP), a new evolutionary computation method recently developed. GNP has a directed graph structure suitable for realizing functional localization. We studied the basic characteristics of the proposed system by making GNP work in a functionally localized way.

  16. Connectivity and functional profiling of abnormal brain structures in pedophilia

    PubMed Central

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

    2015-01-01

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

  17. Connectivity and functional profiling of abnormal brain structures in pedophilia.

    PubMed

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

    2015-06-01

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

  18. Brain networks underlying bistable perception.

    PubMed

    Baker, Daniel H; Karapanagiotidis, Theodoros; Coggan, David D; Wailes-Newson, Kirstie; Smallwood, Jonathan

    2015-10-01

    Bistable stimuli, such as the Necker Cube, demonstrate that experience can change in the absence of changes in the environment. Such phenomena can be used to assess stimulus-independent aspects of conscious experience. The current study used resting state functional magnetic resonance imaging (rs-fMRI) to index stimulus-independent changes in neural activity to understand the neural architecture that determines dominance durations during bistable perception (using binocular rivalry and Necker cube stimuli). Anterior regions of the Superior Parietal Lobule (SPL) exhibited robust connectivity with regions of primary sensorimotor cortex. The strength of this region's connectivity with the striatum predicted shorter dominance durations during binocular rivalry, whereas its connectivity to pre-motor cortex predicted longer dominance durations for the Necker Cube. Posterior regions of the SPL, on the other hand, were coupled to associative cortex in the temporal and frontal lobes. The posterior SPL's connectivity to the temporal lobe predicted longer dominance during binocular rivalry. In conjunction with prior work, these data suggest that the anterior SPL contributes to perceptual rivalry through the inhibition of incongruent bottom up information, whereas the posterior SPL influences rivalry by supporting the current interpretation of a bistable stimulus. Our data suggests that the functional connectivity of the SPL with regions of sensory, motor, and associative cortex allows it to regulate the interpretation of the environment that forms the focus of conscious attention at a specific moment in time. PMID:26123379

  19. Measuring Asymmetric Interactions in Resting State Brain Networks.

    PubMed

    Joshi, Anand A; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M

    2015-01-01

    Directed graph representations of brain networks are increasingly being used to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

  20. Network Coding for Function Computation

    ERIC Educational Resources Information Center

    Appuswamy, Rathinakumar

    2011-01-01

    In this dissertation, the following "network computing problem" is considered. Source nodes in a directed acyclic network generate independent messages and a single receiver node computes a target function f of the messages. The objective is to maximize the average number of times f can be computed per network usage, i.e., the "computing…

  1. Network measures predict neuropsychological outcome after brain injury

    PubMed Central

    Warren, David E.; Power, Jonathan D.; Bruss, Joel; Denburg, Natalie L.; Waldron, Eric J.; Sun, Haoxin; Petersen, Steven E.; Tranel, Daniel

    2014-01-01

    Hubs are network components that hold positions of high importance for network function. Previous research has identified hubs in human brain networks derived from neuroimaging data; however, there is little consensus on the localization of such hubs. Moreover, direct evidence regarding the role of various proposed hubs in network function (e.g., cognition) is scarce. Regions of the default mode network (DMN) have been frequently identified as “cortical hubs” of brain networks. On theoretical grounds, we have argued against some of the methods used to identify these hubs and have advocated alternative approaches that identify different regions of cortex as hubs. Our framework predicts that our proposed hub locations may play influential roles in multiple aspects of cognition, and, in contrast, that hubs identified via other methods (including salient regions in the DMN) might not exert such broad influence. Here we used a neuropsychological approach to directly test these predictions by studying long-term cognitive and behavioral outcomes in 30 patients, 19 with focal lesions to six “target” hubs identified by our approaches (high system density and participation coefficient) and 11 with focal lesions to two “control” hubs (high degree centrality). In support of our predictions, we found that damage to target locations produced severe and widespread cognitive deficits, whereas damage to control locations produced more circumscribed deficits. These findings support our interpretation of how neuroimaging-derived network measures relate to cognition and augment classic neuroanatomically based predictions about cognitive and behavioral outcomes after focal brain injury. PMID:25225403

  2. Brain networks modulated by subthalamic nucleus deep brain stimulation.

    PubMed

    Accolla, Ettore A; Herrojo Ruiz, Maria; Horn, Andreas; Schneider, Gerd-Helge; Schmitz-Hübsch, Tanja; Draganski, Bogdan; Kühn, Andrea A

    2016-09-01

    Deep brain stimulation of the subthalamic nucleus is an established treatment for the motor symptoms of Parkinson's disease. Given the frequent occurrence of stimulation-induced affective and cognitive adverse effects, a better understanding about the role of the subthalamic nucleus in non-motor functions is needed. The main goal of this study is to characterize anatomical circuits modulated by subthalamic deep brain stimulation, and infer about the inner organization of the nucleus in terms of motor and non-motor areas. Given its small size and anatomical intersubject variability, functional organization of the subthalamic nucleus is difficult to investigate in vivo with current methods. Here, we used local field potential recordings obtained from 10 patients with Parkinson's disease to identify a subthalamic area with an analogous electrophysiological signature, namely a predominant beta oscillatory activity. The spatial accuracy was improved by identifying a single contact per macroelectrode for its vicinity to the electrophysiological source of the beta oscillation. We then conducted whole brain probabilistic tractography seeding from the previously identified contacts, and further described connectivity modifications along the macroelectrode's main axis. The designated subthalamic 'beta' area projected predominantly to motor and premotor cortical regions additional to connections to limbic and associative areas. More ventral subthalamic areas showed predominant connectivity to medial temporal regions including amygdala and hippocampus. We interpret our findings as evidence for the convergence of different functional circuits within subthalamic nucleus' portions deemed to be appropriate as deep brain stimulation target to treat motor symptoms in Parkinson's disease. Potential clinical implications of our study are illustrated by an index case where deep brain stimulation of estimated predominant non-motor subthalamic nucleus induced hypomanic behaviour. PMID

  3. Disrupted functional brain connectome in unilateral sudden sensorineural hearing loss.

    PubMed

    Xu, Haibo; Fan, Wenliang; Zhao, Xueyan; Li, Jing; Zhang, Wenjuan; Lei, Ping; Liu, Yuan; Wang, Haha; Cheng, Huamao; Shi, Hong

    2016-05-01

    Sudden sensorineural hearing loss (SSNHL) is generally defined as sensorineural hearing loss of 30 dB or greater over at least three contiguous audiometric frequencies and within a three-day period. This hearing loss is usually unilateral and can be associated with tinnitus and vertigo. The pathogenesis of unilateral sudden sensorineural hearing loss is still unknown, and the alterations in the functional connectivity are suspected to involve one possible pathogenesis. Despite scarce findings with respect to alterations in brain functional networks in unilateral sudden sensorineural hearing loss, the alterations of the whole brain functional connectome and whether these alterations were already in existence in the acute period remains unknown. The aim of this study was to investigate the alterations of brain functional connectome in two large samples of unilateral sudden sensorineural hearing loss patients and to investigate the correlation between unilateral sudden sensorineural hearing loss characteristics and changes in the functional network properties. Pure tone audiometry was performed to assess hearing ability. Abnormal changes in the peripheral auditory system were examined using conventional magnetic resonance imaging. The graph theoretical network analysis method was used to detect brain connectome alterations in unilateral sudden sensorineural hearing loss. Compared with the control groups, both groups of unilateral SSNHL patients exhibited a significantly increased clustering coefficient, global efficiency, and local efficiency but a significantly decreased characteristic path length. In addition, the primary increased nodal strength (e.g., nodal betweenness, hubs) was observed in several regions primarily, including the limbic and paralimbic systems, and in the auditory network brain areas. These findings suggest that the alteration of network organization already exists in unilateral sudden sensorineural hearing loss patients within the acute period

  4. Brain foods: the effects of nutrients on brain function

    PubMed Central

    Gómez-Pinilla, Fernando

    2009-01-01

    It has long been suspected that the relative abundance of specific nutrients can affect cognitive processes and emotions. Newly described influences of dietary factors on neuronal function and synaptic plasticity have revealed some of the vital mechanisms that are responsible for the action of diet on brain health and mental function. Several gut hormones that can enter the brain, or that are produced in the brain itself, influence cognitive ability. In addition, well-established regulators of synaptic plasticity, such as brain-derived neurotrophic factor, can function as metabolic modulators, responding to peripheral signals such as food intake. Understanding the molecular basis of the effects of food on cognition will help us to determine how best to manipulate diet in order to increase the resistance of neurons to insults and promote mental fitness. PMID:18568016

  5. Dynamic functional network connectivity using distance correlation

    NASA Astrophysics Data System (ADS)

    Rudas, Jorge; Guaje, Javier; Demertzi, Athena; Heine, Lizette; Tshibanda, Luaba; Soddu, Andrea; Laureys, Steven; Gómez, Francisco

    2015-01-01

    Investigations about the intrinsic brain organization in resting-state are critical for the understanding of healthy, pathological and pharmacological cerebral states. Recent studies on fMRI suggest that resting state activity is organized on large scale networks of coordinated activity, in the so called, Resting State Networks (RSNs). The assessment of the interactions among these functional networks plays an important role for the understanding of different brain pathologies. Current methods to quantify these interactions commonly assume that the underlying coordination mechanisms are stationary and linear through the whole recording of the resting state phenomena. Nevertheless, recent evidence suggests that rather than stationary, these mechanisms may exhibit a rich set of time-varying repertoires. In addition, these approaches do not consider possible non-linear relationships maybe linked to feed-back communication mechanisms between RSNs. In this work, we introduce a novel approach for dynamical functional network connectivity for functional magnetic resonance imaging (fMRI) resting activity, which accounts for non-linear dynamic relationships between RSNs. The proposed method is based on a windowed distance correlations computed on resting state time-courses extracted at single subject level. We showed that this strategy is complementary to the current approaches for dynamic functional connectivity and will help to enhance the discrimination capacity of patients with disorder of consciousness.

  6. Predicting individual brain maturity using dynamic functional connectivity

    PubMed Central

    Qin, Jian; Chen, Shan-Guang; Hu, Dewen; Zeng, Ling-Li; Fan, Yi-Ming; Chen, Xiao-Ping; Shen, Hui

    2015-01-01

    Neuroimaging-based functional connectivity (FC) analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited. Here, we examined age-related differences in the temporal variability of FC dynamics with data publicly released by the Nathan Kline Institute (NKI; n = 183, ages 7–30) and showed that dynamic inter-region interactions can be used to accurately predict individual brain maturity across development. Furthermore, we identified a significant age-dependent trend underlying dynamic inter-network FC, including increasing variability of the connections between the visual network, default mode network (DMN) and cerebellum as well as within the cerebellum and DMN and decreasing variability within the cerebellum and between the cerebellum and DMN as well as the cingulo-opercular network. Overall, the results suggested significant developmental changes in dynamic inter-network interaction, which may shed new light on the functional organization of typical developmental brains. PMID:26236224

  7. 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

  8. The restless brain: how intrinsic activity organizes brain function.

    PubMed

    Raichle, Marcus E

    2015-05-19

    Traditionally studies of brain function have focused on task-evoked responses. By their very nature such experiments tacitly encourage a reflexive view of brain function. While such an approach has been remarkably productive at all levels of neuroscience, it ignores the alternative possibility that brain functions are mainly intrinsic and ongoing, involving information processing for interpreting, responding to and predicting environmental demands. I suggest that the latter view best captures the essence of brain function, a position that accords well with the allocation of the brain's energy resources, its limited access to sensory information and a dynamic, intrinsic functional organization. The nature of this intrinsic activity, which exhibits a surprising level of organization with dimensions of both space and time, is revealed in the ongoing activity of the brain and its metabolism. As we look to the future, understanding the nature of this intrinsic activity will require integrating knowledge from cognitive and systems neuroscience with cellular and molecular neuroscience where ion channels, receptors, components of signal transduction and metabolic pathways are all in a constant state of flux. The reward for doing so will be a much better understanding of human behaviour in health and disease. PMID:25823869

  9. The restless brain: how intrinsic activity organizes brain function

    PubMed Central

    Raichle, Marcus E.

    2015-01-01

    Traditionally studies of brain function have focused on task-evoked responses. By their very nature such experiments tacitly encourage a reflexive view of brain function. While such an approach has been remarkably productive at all levels of neuroscience, it ignores the alternative possibility that brain functions are mainly intrinsic and ongoing, involving information processing for interpreting, responding to and predicting environmental demands. I suggest that the latter view best captures the essence of brain function, a position that accords well with the allocation of the brain's energy resources, its limited access to sensory information and a dynamic, intrinsic functional organization. The nature of this intrinsic activity, which exhibits a surprising level of organization with dimensions of both space and time, is revealed in the ongoing activity of the brain and its metabolism. As we look to the future, understanding the nature of this intrinsic activity will require integrating knowledge from cognitive and systems neuroscience with cellular and molecular neuroscience where ion channels, receptors, components of signal transduction and metabolic pathways are all in a constant state of flux. The reward for doing so will be a much better understanding of human behaviour in health and disease. PMID:25823869

  10. Mapping human brain networks with cortico-cortical evoked potentials

    PubMed Central

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

    2014-01-01

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

  11. Mapping human brain networks with cortico-cortical evoked potentials.

    PubMed

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

    2014-10-01

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

  12. Functional and structural syntax networks in aging.

    PubMed

    Antonenko, Daria; Brauer, Jens; Meinzer, Marcus; Fengler, Anja; Kerti, Lucia; Friederici, Angela D; Flöel, Agnes

    2013-12-01

    Language abilities are known to deteriorate in aging, possibly related to decreased functional and structural connectivity within specialized brain networks. Here, we investigated syntactic ability in healthy young and older adults using a comprehensive assessment of behavioral performance, task-independent functional (FC) and structural brain connectivity (SC). Seed-based FC originating from left pars opercularis (part of Broca's area) known to support syntactic processes was assessed using resting-state functional magnetic resonance imaging, and SC using fractional anisotropy from diffusion weighted imaging, in the dorsally located superior longitudinal and the ventrally located uncinate fasciculi (SLF, UF) and forceps minor. Young compared to older adults exhibited superior syntactic performance and stronger FC within the mainly left-lateralized syntax network, which was beneficial for performance. In contrast, in older adults, FC within the mainly left-lateralized syntax network was reduced and did not correlate with performance; inter-hemispheric FC to right inferior frontal and angular gyri was detrimental for performance. In both groups, performance was positively correlated with inter-hemispheric SC. For intra-hemispheric SC, performance correlated with structural integrity of SLF in young adults and with integrity of UF in older adults. Our data show that reduced syntactic ability in older adults is associated with decreased FC within dedicated syntax networks. Moreover, young adults showed an association of syntactic ability with structural integrity of the dorsal tract, while older adults rely more on ventral fibers. In sum, our study provided novel insight into the relationship between connectivity and syntactic performance in young and older adults. In addition to elucidating age-related changes in syntax networks and their behavioral relevance, our results contribute to a better understanding of age-related changes in functional and structural brain

  13. Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome

    NASA Astrophysics Data System (ADS)

    Haimovici, Ariel; Tagliazucchi, Enzo; Balenzuela, Pablo; Chialvo, Dante R.

    2013-04-01

    The relation between large-scale brain structure and function is an outstanding open problem in neuroscience. We approach this problem by studying the dynamical regime under which realistic spatiotemporal patterns of brain activity emerge from the empirically derived network of human brain neuroanatomical connections. The results show that critical dynamics unfolding on the structural connectivity of the human brain allow the recovery of many key experimental findings obtained from functional magnetic resonance imaging, such as divergence of the correlation length, the anomalous scaling of correlation fluctuations, and the emergence of large-scale resting state networks.

  14. ABCD: a functional database for the avian brain.

    PubMed

    Schrott, Aniko; Kabai, Peter

    2008-01-30

    Here we present the first database developed for storing, retrieving and cross-referencing neuroscience information about the connectivity of the avian brain. The Avian Brain Circuitry Database (ABCD) contains entries about the new and old terminology of the areas and their hierarchy, data on connections between brain regions, as well as a functional keyword system linked to brain regions and connections. Data were collected from the primary literature and textbooks, and an online submission system was developed to facilitate further data collection directly from researchers. The database aims to help spread the results of avian connectivity studies, the recently revised nomenclature and also to provide data for brain network research. ABCD is freely available at http://www.behav.org/abcd. PMID:17889371

  15. Functional constraints in the evolution of brain circuits

    PubMed Central

    Bosman, Conrado A.; Aboitiz, Francisco

    2015-01-01

    Regardless of major anatomical and neurodevelopmental differences, the vertebrate isocortex shows a remarkably well-conserved organization. In the isocortex, reciprocal connections between excitatory and inhibitory neurons are distributed across multiple layers, encompassing modular, dynamical and recurrent functional networks during information processing. These dynamical brain networks are often organized in neuronal assemblies interacting through rhythmic phase relationships. Accordingly, these oscillatory interactions are observed across multiple brain scale levels, and they are associated with several sensory, motor, and cognitive processes. Most notably, oscillatory interactions are also found in the complete spectrum of vertebrates. Yet, it is unknown why this functional organization is so well conserved in evolution. In this perspective, we propose some ideas about how functional requirements of the isocortex can account for the evolutionary stability observed in microcircuits across vertebrates. We argue that isocortex architectures represent canonical microcircuits resulting from: (i) the early selection of neuronal architectures based on the oscillatory excitatory-inhibitory balance, which lead to the implementation of compartmentalized oscillations and (ii) the subsequent emergence of inferential coding strategies (predictive coding), which are able to expand computational capacities. We also argue that these functional constraints may be the result of several advantages that oscillatory activity contributes to brain network processes, such as information transmission and code reliability. In this manner, similarities in mesoscale brain circuitry and input-output organization between different vertebrate groups may reflect evolutionary constraints imposed by these functional requirements, which may or may not be traceable to a common ancestor. PMID:26388716