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Sample records for brain functional networks

  1. Structure of brain functional networks.

    PubMed

    Kuchaiev, Oleksii; Wang, Po T; Nenadic, Zoran; Przulj, Natasa

    2009-01-01

    Brain is a complex network optimized both for segregated and distributed information processing. To perform cognitive tasks, different areas of the brain must "cooperate," thereby forming complex networks of interactions also known as brain functional networks. Previous studies have shown that these networks exhibit "small-world" characteristics. Small-world topology, however, is a general property of all brain functional networks and does not capture structural changes in these networks in response to different stimuli or cognitive tasks. Here we show how novel graph theoretic techniques can be utilized for precise analysis of brain functional networks. These techniques allow us to detect structural changes in brain functional networks in response to different stimuli or cognitive tasks. For certain types of cognitive tasks we have found that these networks exhibit geometric structure in addition to the small-world topology. The method has been applied to the electrocorticographic signals of six epileptic patients.

  2. 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.

  3. Structure and function of complex brain networks.

    PubMed

    Sporns, Olaf

    2013-09-01

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

  4. Functional brain network efficiency predicts intelligence.

    PubMed

    Langer, Nicolas; Pedroni, Andreas; Gianotti, Lorena R R; Hänggi, Jürgen; Knoch, Daria; Jäncke, Lutz

    2012-06-01

    The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.

  5. Structure and function of complex brain networks

    PubMed Central

    Sporns, Olaf

    2013-01-01

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

  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. Functional brain networks involved in reality monitoring.

    PubMed

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

    2015-08-01

    Source monitoring refers to the recollection of variables that specify the context and conditions in which a memory episode was encoded. This process involves using the qualitative and quantitative features of a memory trace to distinguish its source. One specific class of source monitoring is reality monitoring, which involves distinguishing internally generated from externally generated information, that is, memories of imagined events from real events. The purpose of the present study was to identify functional brain networks that underlie reality monitoring, using an alternative type of source monitoring as a control condition. On the basis of previous studies on self-referential thinking, it was expected that a medial prefrontal cortex (mPFC) based network would be more active during reality monitoring than the control condition, due to the requirement to focus on a comparison of internal (self) and external (other) source information. Two functional brain networks emerged from this analysis, one reflecting increasing task-related activity, and one reflecting decreasing task-related activity. The second network was mPFC based, and was characterized by task-related deactivations in areas resembling the default-mode network; namely, the mPFC, middle temporal gyri, lateral parietal regions, and the precuneus, and these deactivations were diminished during reality monitoring relative to source monitoring, resulting in higher activity during reality monitoring. This result supports previous research suggesting that self-referential thinking involves the mPFC, but extends this to a network-level interpretation of reality monitoring.

  9. The Union of Shortest Path Trees of Functional Brain Networks.

    PubMed

    Meier, Jil; Tewarie, Prejaas; Van Mieghem, Piet

    2015-11-01

    Communication between brain regions is still insufficiently understood. Applying concepts from network science has shown to be successful in gaining insight in the functioning of the brain. Recent work has implicated that especially shortest paths in the structural brain network seem to play a major role in the communication within the brain. So far, for the functional brain network, only the average length of the shortest paths has been analyzed. In this article, we propose to construct the union of shortest path trees (USPT) as a new topology for the functional brain network. The minimum spanning tree, which has been successful in a lot of recent studies to comprise important features of the functional brain network, is always included in the USPT. After interpreting the link weights of the functional brain network as communication probabilities, the USPT of this network can be uniquely defined. Using data from magnetoencephalography, we applied the USPT as a method to find differences in the network topology of multiple sclerosis patients and healthy controls. The new concept of the USPT of the functional brain network also allows interesting interpretations and may represent the highways of the brain.

  10. Manifold learning on brain functional networks in aging.

    PubMed

    Qiu, Anqi; Lee, Annie; Tan, Mingzhen; Chung, Moo K

    2015-02-01

    We propose a new analysis framework to utilize the full information of brain functional networks for computing the mean of a set of brain functional networks and embedding brain functional networks into a low-dimensional space in which traditional regression and classification analyses can be easily employed. For this, we first represent the brain functional network by a symmetric positive matrix computed using sparse inverse covariance estimation. We then impose a Log-Euclidean Riemannian manifold structure on brain functional networks whose norm gives a convenient and practical way to define a mean. Finally, based on the fact that the computation of linear operations can be done in the tangent space of this Riemannian manifold, we adopt Locally Linear Embedding (LLE) to the Log-Euclidean Riemannian manifold space in order to embed the brain functional networks into a low-dimensional space. We show that the integration of the Log-Euclidean manifold with LLE provides more efficient and succinct representation of the functional network and facilitates regression analysis, such as ridge regression, on the brain functional network to more accurately predict age when compared to that of the Euclidean space of functional networks with LLE. Interestingly, using the Log-Euclidean analysis framework, we demonstrate the integration and segregation of cortical-subcortical networks as well as among the salience, executive, and emotional networks across lifespan.

  11. Estimating functional brain networks by incorporating a modularity prior

    PubMed Central

    Qiao, Lishan; Zhang, Han; Kim, Minjeong; Teng, Shenghua; Zhang, Limei; Shen, Dinggang

    2017-01-01

    Functional brain network analysis has become one principled way of revealing informative organization architectures in healthy brains, and providing sensitive biomarkers for diagnosis of neurological disorders. Prior to any post hoc analysis, however, a natural issue is how to construct “ideal” brain networks given, for example, a set of functional magnetic resonance imaging (fMRI) time series associated with different brain regions. Although many methods have been developed, it is currently still an open field to estimate biologically meaningful and statistically robust brain networks due to our limited understanding of the human brain as well as complex noises in the observed data. Motivated by the fact that the brain is organized with modular structures, in this paper, we propose a novel functional brain network modeling scheme by encoding a modularity prior under a matrix-regularized network learning framework, and further formulate it as a sparse low-rank graph learning problem, which can be solved by an efficient optimization algorithm. Then, we apply the learned brain networks to identify patients with mild cognitive impairment (MCI) from normal controls. We achieved 89.01% classification accuracy even with a simple feature selection and classification pipeline, which significantly outperforms the conventional brain network construction methods. Moreover, we further explore brain network features that contributed to MCI identification, and discovered potential biomarkers for personalized diagnosis. PMID:27485752

  12. Structural and functional clusters of complex brain networks

    NASA Astrophysics Data System (ADS)

    Zemanová, Lucia; Zhou, Changsong; Kurths, Jürgen

    2006-12-01

    Recent research using the complex network approach has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. It is of importance to understand the implications of such complex network structures in the functional organization of the brain activities. Here we study this problem from the viewpoint of dynamical complex networks. We investigate synchronization dynamics on the corticocortical network of the cat by modeling each node (cortical area) of the network with a sub-network of interacting excitable neurons. We find that the network displays clustered synchronization behavior, and the dynamical clusters coincide with the topological community structures observed in the anatomical network. Our results provide insights into the relationship between the global organization and the functional specialization of the brain cortex.

  13. Resiliency of EEG-Based Brain Functional Networks.

    PubMed

    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.

  14. 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

  15. Hierarchical organization of brain functional networks during visual tasks.

    PubMed

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

    2011-09-01

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

  16. Hierarchical organization of brain functional networks during visual tasks

    NASA Astrophysics Data System (ADS)

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

    2011-09-01

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

  17. 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.

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

    PubMed Central

    Jie, Biao; Wee, Chong-Yaw

    2017-01-01

    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

  19. Development of Large-Scale Functional Brain Networks in Children

    PubMed Central

    Supekar, Kaustubh; Musen, Mark; Menon, Vinod

    2009-01-01

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

  20. 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.

  1. Hierarchical organization unveiled by functional connectivity in complex brain networks.

    PubMed

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

    2006-12-08

    How do diverse dynamical patterns arise from the topology of complex networks? We study synchronization dynamics in the cortical brain network of the cat, which displays a hierarchically clustered organization, by modeling each node (cortical area) with a subnetwork of interacting excitable neurons. We find that in the biologically plausible regime the dynamics exhibits a hierarchical modular organization, in particular, revealing functional clusters coinciding with the anatomical communities at different scales. Our results provide insights into the relationship between network topology and functional organization of complex brain networks.

  2. 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.

  3. 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.

  4. Complex Networks - A Key to Understanding Brain Function

    ScienceCinema

    Olaf Sporns

    2016-07-12

    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.

  5. Hemispheric asymmetry of electroencephalography-based functional brain networks.

    PubMed

    Jalili, Mahdi

    2014-11-12

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

  6. 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

  7. Wearable sensor network to study laterality of brain functions.

    PubMed

    Postolache, Gabriela B; Girao, Pedro S; Postolache, Octavian A

    2015-08-01

    In the last decade researches on laterality of brain functions have been reinvigorated. New models of lateralization of brain functions were proposed and new methods for understanding mechanisms of asymmetry between right and left brain functions were described. We design a system to study laterality of motor and autonomic nervous system based on wearable sensors network. A mobile application was developed for analysis of upper and lower limbs movements, cardiac and respiratory function. The functionalities and experience gained with deployment of the system are described.

  8. 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.

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

    PubMed

    Atasoy, Selen; Donnelly, Isaac; Pearson, Joel

    2016-01-21

    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.

  10. 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

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

    PubMed

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

    2016-03-31

    Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from an equal number of people of healthy weight (HW) and non-healthy weight (non-HW). Connectivity matrices were computed with spatial maps derived using a group independent component analysis approach. Brain networks and associated connectivity parameters with significant group-wise differences were identified and correlated with scores on a three-factor eating questionnaire (TFEQ) describing restraint, disinhibition, and hunger eating behaviors. Frontoparietal and cerebellum networks showed group-wise differences between HW and non-HW groups. Frontoparietal network showed a high correlation with TFEQ disinhibition scores. Both frontoparietal and cerebellum networks showed a high correlation with body mass index (BMI) scores. Brain networks with significant group-wise differences between HW and non-HW groups were identified. Parts of the identified networks showed a high correlation with eating behavior scores.

  12. 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

  13. Does sleep restore the topology of functional brain networks?

    PubMed

    Koenis, Maria M G; Romeijn, Nico; Piantoni, Giovanni; Verweij, Ilse; Van der Werf, Ysbrand D; Van Someren, Eus J W; Stam, Cornelis J

    2013-02-01

    Previous studies have shown that healthy anatomical as well as functional brain networks have small-world properties and become less optimal with brain disease. During sleep, the functional brain network becomes more small-world-like. Here we test the hypothesis that the functional brain network during wakefulness becomes less optimal after sleep deprivation (SD). Electroencephalography (EEG) was recorded five times a day after a night of SD and after a night of normal sleep in eight young healthy subjects, both during eyes-closed and eyes-open resting state. Overall synchronization was determined with the synchronization likelihood (SL) and the phase lag index (PLI). From these coupling strength matrices the normalized clustering coefficient C (a measurement of local clustering) and path length L (a measurement of global integration) were computed. Both measures were normalized by dividing them by their corresponding C-s and L-s values of random control networks. SD reduced alpha band C/C-s and L/L-s and theta band C/C-s during eyes-closed resting state. In contrast, SD increased gamma-band C/C-s and L/L-s during eyes-open resting state. Functional relevance of these changes in network properties was suggested by their association with sleep deprivation-induced performance deficits on a sustained attention simple reaction time task. The findings indicate that SD results in a more random network of alpha-coupling and a more ordered network of gamma-coupling. The present study shows that SD induces frequency-specific changes in the functional network topology of the brain, supporting the idea that sleep plays a role in the maintenance of an optimal functional network.

  14. Functional connectivity networks for preoperative brain mapping in neurosurgery.

    PubMed

    Hart, Michael G; Price, Stephen J; Suckling, John

    2016-08-26

    OBJECTIVE Resection of focal brain lesions involves maximizing the resection while preserving brain function. Mapping brain function has entered a new era focusing on distributed connectivity networks at "rest," that is, in the absence of a specific task or stimulus, requiring minimal participant engagement. Central to this frame shift has been the development of methods for the rapid assessment of whole-brain connectivity with functional MRI (fMRI) involving blood oxygenation level-dependent imaging. The authors appraised the feasibility of fMRI-based mapping of a repertoire of functional connectivity networks in neurosurgical patients with focal lesions and the potential benefits of resting-state connectivity mapping for surgical planning. METHODS Resting-state fMRI sequences with a 3-T scanner and multiecho echo-planar imaging coupled to independent component analysis were acquired preoperatively from 5 study participants who had a right temporoparietooccipital glioblastoma. Seed-based functional connectivity analysis was performed with InstaCorr. Network identification focused on 7 major functional connectivity networks described in the literature and a putative language network centered on Broca's area. RESULTS All 8 functional connectivity networks were identified in each participant. Tumor-related topological changes to the default mode network were observed in all participants. In addition, each participant had at least 1 other abnormal network, and each network was abnormal in at least 1 participant. Individual patterns of network irregularities were identified with a qualitative approach and included local displacement due to mass effect, loss of a functional network component, and recruitment of new regions. CONCLUSIONS Resting-state fMRI can reliably and rapidly detect common functional connectivity networks in patients with glioblastoma and also has sufficient sensitivity for identifying patterns of network alterations. Mapping of functional

  15. 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

  16. Correspondence between evoked and intrinsic functional brain network configurations.

    PubMed

    Bolt, Taylor; Nomi, Jason S; Rubinov, Mikail; Uddin, Lucina Q

    2017-04-01

    Much of the literature exploring differences between intrinsic and task-evoked brain architectures has examined changes in functional connectivity patterns between specific brain regions. While informative, this approach overlooks important overall functional changes in hub organization and network topology that may provide insights about differences in integration between intrinsic and task-evoked states. Examination of changes in overall network organization, such as a change in the concentration of hub nodes or a quantitative change in network organization, is important for understanding the underlying processes that differ between intrinsic and task-evoked brain architectures. The present study used graph-theoretical techniques applied to publicly available neuroimaging data collected from a large sample of individuals (N = 202), and a within-subject design where resting-state and several task scans were collected from each participant as part of the Human Connectome Project. We demonstrate that differences between intrinsic and task-evoked brain networks are characterized by a task-general shift in high-connectivity hubs from primarily sensorimotor/auditory processing areas during the intrinsic state to executive control/salience network areas during task performance. In addition, we demonstrate that differences between intrinsic and task-evoked architectures are associated with changes in overall network organization, such as increases in network clustering, global efficiency and integration between modules. These findings offer a new perspective on the principles guiding functional brain organization by identifying unique and divergent properties of overall network organization between the resting-state and task performance. Hum Brain Mapp 38:1992-2007, 2017. © 2017 Wiley Periodicals, Inc.

  17. 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

  18. Variability in functional brain networks predicts expertise during action observation.

    PubMed

    Amoruso, Lucía; Ibáñez, Agustín; Fonseca, Bruno; Gadea, Sebastián; Sedeño, Lucas; Sigman, Mariano; García, Adolfo M; Fraiman, Ricardo; Fraiman, Daniel

    2017-02-01

    Observing an action performed by another individual activates, in the observer, similar circuits as those involved in the actual execution of that action. This activation is modulated by prior experience; indeed, sustained training in a particular motor domain leads to structural and functional changes in critical brain areas. Here, we capitalized on a novel graph-theory approach to electroencephalographic data (Fraiman et al., 2016) to test whether variability in functional brain networks implicated in Tango observation can discriminate between groups differing in their level of expertise. We found that experts and beginners significantly differed in the functional organization of task-relevant networks. Specifically, networks in expert Tango dancers exhibited less variability and a more robust functional architecture. Notably, these expertise-dependent effects were captured within networks derived from electrophysiological brain activity recorded in a very short time window (2s). In brief, variability in the organization of task-related networks seems to be a highly sensitive indicator of long-lasting training effects. This finding opens new methodological and theoretical windows to explore the impact of domain-specific expertise on brain plasticity, while highlighting variability as a fruitful measure in neuroimaging research.

  19. Structure-function clustering in multiplex brain networks

    NASA Astrophysics Data System (ADS)

    Crofts, J. J.; Forrester, M.; O'Dea, R. D.

    2016-10-01

    A key question in neuroscience is to understand how a rich functional repertoire of brain activity arises within relatively static networks of structurally connected neural populations: elucidating the subtle interactions between evoked “functional connectivity” and the underlying “structural connectivity” has the potential to address this. These structural-functional networks (and neural networks more generally) are more naturally described using a multilayer or multiplex network approach, in favour of standard single-layer network analyses that are more typically applied to such systems. In this letter, we address such issues by exploring important structure-function relations in the Macaque cortical network by modelling it as a duplex network that comprises an anatomical layer, describing the known (macro-scale) network topology of the Macaque monkey, and a functional layer derived from simulated neural activity. We investigate and characterize correlations between structural and functional layers, as system parameters controlling simulated neural activity are varied, by employing recently described multiplex network measures. Moreover, we propose a novel measure of multiplex structure-function clustering which allows us to investigate the emergence of functional connections that are distinct from the underlying cortical structure, and to highlight the dependence of multiplex structure on the neural dynamical regime.

  20. Breakdown of the brain's functional network modularity with awareness.

    PubMed

    Godwin, Douglass; Barry, Robert L; Marois, René

    2015-03-24

    Neurobiological theories of awareness propose divergent accounts of the spatial extent of brain changes that support conscious perception. Whereas focal theories posit mostly local regional changes, global theories propose that awareness emerges from the propagation of neural signals across a broad extent of sensory and association cortex. Here we tested the scalar extent of brain changes associated with awareness using graph theoretical analysis applied to functional connectivity data acquired at ultra-high field while subjects performed a simple masked target detection task. We found that awareness of a visual target is associated with a degradation of the modularity of the brain's functional networks brought about by an increase in intermodular functional connectivity. These results provide compelling evidence that awareness is associated with truly global changes in the brain's functional connectivity.

  1. Functionally Driven Brain Networks Using Multi-layer Graph Clustering

    PubMed Central

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

    2016-01-01

    Connectivity analysis of resting state brain has provided a novel means of investigating brain networks in the study of neurodevelopmental disorders. The study of functional networks, often represented by high dimensional graphs, predicates on the ability of methods in succinctly extracting meaningful representative connectivity information at the subject and population level. This need motivates the development of techniques that can extract underlying network modules that characterize the connectivity in a population, while capturing variations of these modules at the individual level. In this paper, we propose a multi-layer graph clustering technique that fuses the information from a collection of connectivity networks of a population to extract the underlying common network modules that serve as network hubs for the population. These hubs form a functional network atlas. In addition, our technique provides subject-specific factors designed to characterize and quantify the degree of intra- and inter- connectivity between hubs, thereby providing a representation that is amenable to group level statistical analyses. We demonstrate the utility of the technique by creating a population network atlas of connectivity by examining MEG based functional connectivity in typically developing children, and using this to describe the individualized variation in those diagnosed with autism spectrum disorder. PMID:25320789

  2. A Statistical Method to Distinguish Functional Brain Networks

    PubMed Central

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

    2017-01-01

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

  3. A Statistical Method to Distinguish Functional Brain Networks.

    PubMed

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

    2017-01-01

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

  4. Imaging structural and functional brain networks in temporal lobe epilepsy

    PubMed Central

    Bernhardt, Boris C.; Hong, SeokJun; Bernasconi, Andrea; Bernasconi, Neda

    2013-01-01

    Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy. PMID:24098281

  5. Dynamic reorganization of brain functional networks during cognition.

    PubMed

    Bola, Michał; Sabel, Bernhard A

    2015-07-01

    How does cognition emerge from neural dynamics? The dominant hypothesis states that interactions among distributed brain regions through phase synchronization give basis for cognitive processing. Such phase-synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to perform specific cognitive operations. But unlike resting-state networks, the complex organization of transient cognitive networks is typically not characterized within the graph theory framework. Thus, it is not known whether cognitive processing merely changes the strength of functional connections or, conversely, requires qualitatively new topological arrangements of functional networks. To address this question, we recorded high-density EEG while subjects performed a visual discrimination task. We conducted an event-related network analysis (ERNA) where source-space weighted functional networks were characterized with graph measures. ERNA revealed rapid, transient, and frequency-specific reorganization of the network's topology during cognition. Specifically, cognitive networks were characterized by strong clustering, low modularity, and strong interactions between hub-nodes. Our findings suggest that dense and clustered connectivity between the hub nodes belonging to different modules is the "network fingerprint" of cognition. Such reorganization patterns might facilitate global integration of information and provide a substrate for a "global workspace" necessary for cognition and consciousness to occur. Thus, characterizing topology of the event-related networks opens new vistas to interpret cognitive dynamics in the broader conceptual framework of graph theory.

  6. Graph analysis of functional brain networks for cognitive control of action in traumatic brain injury.

    PubMed

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

    2012-04-01

    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 dispersed frontal and parietal activity during performance of cognitive control tasks. We constructed binary and weighted functional networks and calculated their topological properties using a graph theoretical approach. Twenty-three adults with traumatic brain injury and 26 age-matched controls were instructed to switch between coordination modes while making spatially and temporally coupled circular motions with joysticks during event-related functional magnetic resonance imaging. Results demonstrated that switching performance was significantly lower in patients with traumatic brain injury compared with control subjects. Furthermore, although brain networks of both groups exhibited economical small-world topology, altered functional connectivity was demonstrated in patients with traumatic brain injury. In particular, compared with controls, patients with traumatic brain injury showed increased connectivity degree and strength, and higher values of local efficiency, suggesting adaptive mechanisms in this group. Finally, the degree of increased connectivity was significantly correlated with poorer switching task performance and more severe brain injury. We conclude that analysing the functional brain network connectivity provides new insights into understanding cognitive control changes following brain injury.

  7. 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

  8. Functional brain network modularity predicts response to cognitive training after brain injury

    PubMed Central

    Chen, Anthony J.-W.; Novakovic-Agopian, Tatjana; Gratton, Caterina; Nomura, Emi M.; D'Esposito, Mark

    2015-01-01

    Objective: We tested the value of measuring modularity, a graph theory metric indexing the relative extent of integration and segregation of distributed functional brain networks, for predicting individual differences in response to cognitive training in patients with brain injury. Methods: Patients with acquired brain injury (n = 11) participated in 5 weeks of cognitive training and a comparison condition (brief education) in a crossover intervention study design. We quantified the measure of functional brain network organization, modularity, from functional connectivity networks during a state of tonic attention regulation measured during fMRI scanning before the intervention conditions. We examined the relationship of baseline modularity with pre- to posttraining changes in neuropsychological measures of attention and executive control. Results: The modularity of brain network organization at baseline predicted improvement in attention and executive function after cognitive training, but not after the comparison intervention. Individuals with higher baseline modularity exhibited greater improvements with cognitive training, suggesting that a more modular baseline network state may contribute to greater adaptation in response to cognitive training. Conclusions: Brain network properties such as modularity provide valuable information for understanding mechanisms that influence rehabilitation of cognitive function after brain injury, and may contribute to the discovery of clinically relevant biomarkers that could guide rehabilitation efforts. PMID:25788557

  9. Dynamic reconfiguration of human brain functional networks through neurofeedback.

    PubMed

    Haller, Sven; Kopel, Rotem; Jhooti, Permi; Haas, Tanja; Scharnowski, Frank; Lovblad, Karl-Olof; Scheffler, Klaus; Van De Ville, Dimitri

    2013-11-01

    Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity.

  10. 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.

  11. 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

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

    PubMed

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

    2009-05-01

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

  13. 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.

  14. Dynamic reorganization of intrinsic functional networks in the mouse brain.

    PubMed

    Grandjean, Joanes; Preti, Maria Giulia; Bolton, Thomas A W; Buerge, Michaela; Seifritz, Erich; Pryce, Christopher R; Van De Ville, Dimitri; Rudin, Markus

    2017-03-14

    Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) allows for the integrative study of neuronal processes at a macroscopic level. The majority of studies to date have assumed stationary interactions between brain regions, without considering the dynamic aspects of network organization. Only recently has the latter received increased attention, predominantly in human studies. Applying dynamic FC (dFC) analysis to mice is attractive given the relative simplicity of the mouse brain and the possibility to explore mechanisms underlying network dynamics using pharmacological, environmental or genetic interventions. Therefore, we have evaluated the feasibility and research potential of mouse dFC using the interventions of social stress or anesthesia duration as two case-study examples. By combining a sliding-window correlation approach with dictionary learning, several dynamic functional states (dFS) with a complex organization were identified, exhibiting highly dynamic inter- and intra-modular interactions. Each dFS displayed a high degree of reproducibility upon changes in analytical parameters and across datasets. They fluctuated at different degrees as a function of anesthetic depth, and were sensitive indicators of pathology as shown for the chronic psychosocial stress mouse model of depression. Dynamic functional states are proposed to make a major contribution to information integration and processing in the healthy and diseased brain.

  15. 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.

  16. Global features of functional brain networks change with contextual disorder

    PubMed Central

    Andric, Michael; Hasson, Uri

    2015-01-01

    It is known that features of stimuli in the environment affect the strength of functional connectivity in the human brain. However, investigations to date have not converged in determining whether these also impact functional networks' global features, such as modularity strength, number of modules, partition structure, or degree distributions. We hypothesized that one environmental attribute that may strongly impact global features is the temporal regularity of the environment, as prior work indicates that differences in regularity impact regions involved in sensory, attentional and memory processes. We examined this with an fMRI study, in which participants passively listened to tonal series that had identical physical features and differed only in their regularity, as defined by the strength of transition structure between tones. We found that series-regularity induced systematic changes to global features of functional networks, including modularity strength, number of modules, partition structure, and degree distributions. In tandem, we used a novel node-level analysis to determine the extent to which brain regions maintained their within-module connectivity across experimental conditions. This analysis showed that primary sensory regions and those associated with default-mode processes are most likely to maintain their within-module connectivity across conditions, whereas prefrontal regions are least likely to do so. Our work documents a significant capacity for global-level brain network reorganization as a function of context. These findings suggest that modularity and other core, global features, while likely constrained by white-matter structural brain connections, are not completely determined by them. PMID:25988223

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

    PubMed

    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.

  18. Reproducibility of graph metrics of human brain functional networks.

    PubMed

    Deuker, Lorena; Bullmore, Edward T; Smith, Marie; Christensen, Soren; Nathan, Pradeep J; Rockstroh, Brigitte; Bassett, Danielle S

    2009-10-01

    Graph theory provides many metrics of complex network organization that can be applied to analysis of brain networks derived from neuroimaging data. Here we investigated the test-retest reliability of graph metrics of functional networks derived from magnetoencephalography (MEG) data recorded in two sessions from 16 healthy volunteers who were studied at rest and during performance of the n-back working memory task in each session. For each subject's data at each session, we used a wavelet filter to estimate the mutual information (MI) between each pair of MEG sensors in each of the classical frequency intervals from gamma to low delta in the overall range 1-60 Hz. Undirected binary graphs were generated by thresholding the MI matrix and 8 global network metrics were estimated: the clustering coefficient, path length, small-worldness, efficiency, cost-efficiency, assortativity, hierarchy, and synchronizability. Reliability of each graph metric was assessed using the intraclass correlation (ICC). Good reliability was demonstrated for most metrics applied to the n-back data (mean ICC=0.62). Reliability was greater for metrics in lower frequency networks. Higher frequency gamma- and beta-band networks were less reliable at a global level but demonstrated high reliability of nodal metrics in frontal and parietal regions. Performance of the n-back task was associated with greater reliability than measurements on resting state data. Task practice was also associated with greater reliability. Collectively these results suggest that graph metrics are sufficiently reliable to be considered for future longitudinal studies of functional brain network changes.

  19. Resting-state functional brain networks in Parkinson's disease.

    PubMed

    Baggio, Hugo C; Segura, Bàrbara; Junque, Carme

    2015-10-01

    The network approach is increasingly being applied to the investigation of normal brain function and its impairment. In the present review, we introduce the main methodological approaches employed for the analysis of resting-state neuroimaging data in Parkinson's disease studies. We then summarize the results of recent studies that used a functional network perspective to evaluate the changes underlying different manifestations of Parkinson's disease, with an emphasis on its cognitive symptoms. Despite the variability reported by many studies, these methods show promise as tools for shedding light on the pathophysiological substrates of different aspects of Parkinson's disease, as well as for differential diagnosis, treatment monitoring and establishment of imaging biomarkers for more severe clinical outcomes.

  20. 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].

  1. 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.

  2. 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.

  3. Functional brain networks: random, "small world" or deterministic?

    PubMed

    Blinowska, Katarzyna J; Kaminski, Maciej

    2013-01-01

    Lately the problem of connectivity in brain networks is being approached frequently by graph theoretical analysis. In several publications based on bivariate estimators of relations between EEG channels authors reported random or "small world" structure of networks. The results of these works often have no relation to other evidence based on imaging, inverse solutions methods, physiological and anatomical data. Herein we try to find reasons for this discrepancy. We point out that EEG signals are very much interdependent, thus bivariate measures applied to them may produce many spurious connections. In fact, they may outnumber the true connections. Giving all connections equal weights, as it is usual in the framework of graph theoretical analysis, further enhances these spurious links. In effect, close to random and disorganized patterns of connections emerge. On the other hand, multivariate connectivity estimators, which are free of the artificial links, show specific, well determined patterns, which are in a very good agreement with other evidence. The modular structure of brain networks may be identified by multivariate estimators based on Granger causality and formalism of assortative mixing. In this way, the strength of coupling may be evaluated quantitatively. During working memory task, by means of multivariate Directed Transfer Function, it was demonstrated that the modules characterized by strong internal bonds exchange the information by weaker connections.

  4. Bayesian network models in brain functional connectivity analysis

    PubMed Central

    Zhang, Sheng; Li, Chiang-shan R.

    2013-01-01

    Much effort has been made to better understand the complex integration of distinct parts of the human brain using functional magnetic resonance imaging (fMRI). Altered functional connectivity between brain regions is associated with many neurological and mental illnesses, such as Alzheimer and Parkinson diseases, addiction, and depression. In computational science, Bayesian networks (BN) have been used in a broad range of studies to model complex data set in the presence of uncertainty and when expert prior knowledge is needed. However, little is done to explore the use of BN in connectivity analysis of fMRI data. In this paper, we present an up-to-date literature review and methodological details of connectivity analyses using BN, while highlighting caveats in a real-world application. We present a BN model of fMRI dataset obtained from sixty healthy subjects performing the stop-signal task (SST), a paradigm widely used to investigate response inhibition. Connectivity results are validated with the extant literature including our previous studies. By exploring the link strength of the learned BN’s and correlating them to behavioral performance measures, this novel use of BN in connectivity analysis provides new insights to the functional neural pathways underlying response inhibition. PMID:24319317

  5. 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.

  6. 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

  7. 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

  8. Large-scale brain networks in affective and social neuroscience: towards an integrative functional architecture of the brain.

    PubMed

    Barrett, Lisa Feldman; Satpute, Ajay Bhaskar

    2013-06-01

    Understanding how a human brain creates a human mind ultimately depends on mapping psychological categories and concepts to physical measurements of neural response. Although it has long been assumed that emotional, social, and cognitive phenomena are realized in the operations of separate brain regions or brain networks, we demonstrate that it is possible to understand the body of neuroimaging evidence using a framework that relies on domain general, distributed structure-function mappings. We review current research in affective and social neuroscience and argue that the emerging science of large-scale intrinsic brain networks provides a coherent framework for a domain-general functional architecture of the human brain.

  9. 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

  10. Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions.

    PubMed

    Dipasquale, O; Cercignani, Mara

    Non-invasive mapping of brain functional connectivity (FC) has played a fundamental role in neuroscience, and numerous scientists have been fascinated by its ability to reveal the brain's intricate morphology and functional properties. In recent years, two different techniques have been developed that are able to explore FC in pathophysiological conditions and to provide simple and non-invasive biomarkers for the detection of disease onset, severity and progression. These techniques are independent component analysis, which allows a network-based functional exploration of the brain, and graph theory, which provides a quantitative characterization of the whole-brain FC. In this paper we provide an overview of these two techniques and some examples of their clinical applications in the most common neurodegenerative disorders associated with cognitive decline, including mild cognitive impairment, Alzheimer's disease, Parkinson's disease, dementia with Lewy Bodies and behavioral variant frontotemporal dementia.

  11. Dynamic Functional Segregation and Integration in Human Brain Network During Complex Tasks.

    PubMed

    Ren, Shen; Li, Junhua; Taya, Fumihiko; deSouza, Joshua; Thakor, Nitish; Bezerianos, Anastasios

    2016-09-09

    The analysis of the topology and organisation of brain networks is known to greatly benefit from network measures in graph theory. However, to evaluate dynamic changes of brain functional connectivity, more sophisticated quantitative metrics characterising temporal evolution of brain topological features are required. To simplify conversion of time-varying brain connectivity to a static graph representation is straightforward but the procedure loses temporal information that could be critical in understanding the brain functions. To extend the understandings of functional segregation and integration to a dynamic fashion, we recommend dynamic graph metrics to characterise temporal changes of topological features of brain networks. This study investigated functional segregation and integration of brain networks over time by dynamic graph metrics derived from EEG signals during an experimental protocol: performance of complex flight simulation tasks with multiple levels of difficulty. We modelled time-varying brain functional connectivity as multilayer networks, in which each layer models brain connectivity at time window t + t. Dynamic graph metrics were calculated to quantify temporal and topological properties of the network. Results show that brain networks under the performance of complex tasks reveal a dynamic small-world architecture with a number of frequently connected nodes or hubs, which supports the balance of information segregation and integration in brain over time. The results also show that greater cognitive workloads caused by more difficult tasks induced a more globally efficient but less clustered dynamic small-world functional network. Our study illustrates that task-related changes of functional brain network segregation and integration can be characterised by dynamic graph metrics.

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

    PubMed

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

    2014-10-05

    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.

  13. 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

  14. The effects of music on brain functional networks: a network analysis.

    PubMed

    Wu, J; Zhang, J; Ding, X; Li, R; Zhou, C

    2013-10-10

    The human brain can dynamically adapt to the changing surroundings. To explore this issue, we adopted graph theoretical tools to examine changes in electroencephalography (EEG) functional networks while listening to music. Three different excerpts of Chinese Guqin music were played to 16 non-musician subjects. For the main frequency intervals, synchronizations between all pair-wise combinations of EEG electrodes were evaluated with phase lag index (PLI). Then, weighted connectivity networks were created and their organizations were characterized in terms of an average clustering coefficient and characteristic path length. We found an enhanced synchronization level in the alpha2 band during music listening. Music perception showed a decrease of both normalized clustering coefficient and path length in the alpha2 band. Moreover, differences in network measures were not observed between musical excerpts. These experimental results demonstrate an increase of functional connectivity as well as a more random network structure in the alpha2 band during music perception. The present study offers support for the effects of music on human brain functional networks with a trend toward a more efficient but less economical architecture.

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  16. Insights into Brain Architectures from the Homological Scaffolds of Functional Connectivity Networks.

    PubMed

    Lord, Louis-David; Expert, Paul; Fernandes, Henrique M; Petri, Giovanni; Van Hartevelt, Tim J; Vaccarino, Francesco; Deco, Gustavo; Turkheimer, Federico; Kringelbach, Morten L

    2016-01-01

    In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain's functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain's complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e., interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: (i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and (ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the

  17. Graph Analysis of Functional Brain Networks in Patients with Mild Traumatic Brain Injury

    PubMed Central

    van der Horn, Harm J.; Liemburg, Edith J.; Scheenen, Myrthe E.; de Koning, Myrthe E.; Spikman, Jacoba M.; van der Naalt, Joukje

    2017-01-01

    Mild traumatic brain injury (mTBI) is one of the most common neurological disorders worldwide. Posttraumatic complaints are frequently reported, interfering with outcome. However, a consistent neural substrate has not yet been found. We used graph analysis to further unravel the complex interactions between functional brain networks, complaints, anxiety and depression in the sub-acute stage after mTBI. This study included 54 patients with uncomplicated mTBI and 20 matched healthy controls. Posttraumatic complaints, anxiety and depression were measured at two weeks post-injury. Patients were selected based on presence (n = 34) or absence (n = 20) of complaints. Resting-state fMRI scans were made approximately four weeks post-injury. High order independent component analysis resulted in 89 neural components that were included in subsequent graph analyses. No differences in graph measures were found between patients with mTBI and healthy controls. Regarding the two patient subgroups, degree, strength, local efficiency and eigenvector centrality of the bilateral posterior cingulate/precuneus and bilateral parahippocampal gyrus were higher, and eigenvector centrality of the frontal pole/ bilateral middle & superior frontal gyrus was lower in patients with complaints compared to patients without complaints. In patients with mTBI, higher degree, strength and eigenvector centrality of default mode network components were related to higher depression scores, and higher degree and eigenvector centrality of executive network components were related to lower depression scores. In patients without complaints, one extra module was found compared to patients with complaints and healthy controls, consisting of the cingulate areas. In conclusion, this research extends the knowledge of functional network connectivity after mTBI. Specifically, our results suggest that an imbalance in the function of the default mode- and executive network plays a central role in the interaction

  18. 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.

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

    PubMed Central

    Shi, Ran

    2016-01-01

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

  20. 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.

  1. Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity

    PubMed Central

    García-Prieto, Juan; Bajo, Ricardo; Pereda, Ernesto

    2017-01-01

    Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations. Furthermore, network theory related measures like Strength, Shortest Path Length, Clustering Coefficient, and Betweenness Centrality are also implemented showing computational times up to thousands of times faster than most well-known implementations. Altogether, this work significantly expands what can be computed in feasible times, even enabling whole-head real-time network analysis of brain function. PMID:28220071

  2. Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity.

    PubMed

    García-Prieto, Juan; Bajo, Ricardo; Pereda, Ernesto

    2017-01-01

    Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations. Furthermore, network theory related measures like Strength, Shortest Path Length, Clustering Coefficient, and Betweenness Centrality are also implemented showing computational times up to thousands of times faster than most well-known implementations. Altogether, this work significantly expands what can be computed in feasible times, even enabling whole-head real-time network analysis of brain function.

  3. Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions

    PubMed Central

    Dipasquale, Ottavia; Cercignani, Mara

    2016-01-01

    Summary Non-invasive mapping of brain functional connectivity (FC) has played a fundamental role in neuroscience, and numerous scientists have been fascinated by its ability to reveal the brain’s intricate morphology and functional properties. In recent years, two different techniques have been developed that are able to explore FC in pathophysiological conditions and to provide simple and non-invasive biomarkers for the detection of disease onset, severity and progression. These techniques are independent component analysis, which allows a network-based functional exploration of the brain, and graph theory, which provides a quantitative characterization of the whole-brain FC. In this paper we provide an overview of these two techniques and some examples of their clinical applications in the most common neurodegenerative disorders associated with cognitive decline, including mild cognitive impairment, Alzheimer’s disease, Parkinson’s disease, dementia with Lewy Bodies and behavioral variant frontotemporal dementia. PMID:28072380

  4. 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.

  5. 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.

  6. 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

  7. Default mode network functional and structural connectivity after traumatic brain injury.

    PubMed

    Sharp, David J; Beckmann, Christian F; Greenwood, Richard; Kinnunen, Kirsi M; Bonnelle, Valerie; De Boissezon, Xavier; Powell, Jane H; Counsell, Serena J; Patel, Maneesh C; Leech, Robert

    2011-08-01

    Traumatic brain injury often results in cognitive impairments that limit recovery. The underlying pathophysiology of these impairments is uncertain, which restricts clinical assessment and management. Here, we use magnetic resonance imaging to test the hypotheses that: (i) traumatic brain injury results in abnormalities of functional connectivity within key cognitive networks; (ii) these changes are correlated with cognitive performance; and (iii) functional connectivity within these networks is influenced by underlying changes in structural connectivity produced by diffuse axonal injury. We studied 20 patients in the chronic phase after traumatic brain injury compared with age-matched controls. Network function was investigated in detail using functional magnetic resonance imaging to analyse both regional brain activation, and the interaction of brain regions within a network (functional connectivity). We studied patients during performance of a simple choice-reaction task and at 'rest'. Since functional connectivity reflects underlying structural connectivity, diffusion tensor imaging was used to quantify axonal injury, and test whether structural damage correlated with functional change. The patient group showed typical impairments in information processing and attention, when compared with age-matched controls. Patients were able to perform the task accurately, but showed slow and variable responses. Brain regions activated by the task were similar between the groups, but patients showed greater deactivation within the default mode network, in keeping with an increased cognitive load. A multivariate analysis of 'resting' state functional magnetic resonance imaging was then used to investigate whether changes in network function were present in the absence of explicit task performance. Overall, default mode network functional connectivity was increased in the patient group. Patients with the highest functional connectivity had the least cognitive impairment. In

  8. Random matrix theory for analyzing the brain functional network in attention deficit hyperactivity disorder

    NASA Astrophysics Data System (ADS)

    Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan

    2016-11-01

    Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6 -7 % of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.

  9. Random matrix theory for analyzing the brain functional network in attention deficit hyperactivity disorder.

    PubMed

    Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan

    2016-11-01

    Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6-7% of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.

  10. Alteration and Reorganization of Functional Networks: A New Perspective in Brain Injury Study

    PubMed Central

    Castellanos, Nazareth P.; Bajo, Ricardo; Cuesta, Pablo; Villacorta-Atienza, José Antonio; Paúl, Nuria; Garcia-Prieto, Juan; del-Pozo, Francisco; Maestú, Fernando

    2011-01-01

    Plasticity is the mechanism underlying the brain’s potential capability to compensate injury. Recently several studies have shown how functional connections among the brain areas are severely altered by brain injury and plasticity leading to a reorganization of the networks. This new approach studies the impact of brain injury by means of alteration of functional interactions. The concept of functional connectivity refers to the statistical interdependencies between physiological time series simultaneously recorded in various areas of the brain and it could be an essential tool for brain functional studies, being its deviation from healthy reference an indicator for damage. In this article, we review studies investigating functional connectivity changes after brain injury and subsequent recovery, providing an accessible introduction to common mathematical methods to infer functional connectivity, exploring their capabilities, future perspectives, and clinical uses in brain injury studies. PMID:21960965

  11. 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.

  12. Empirical Network Model of Human Higher Cognitive Brain Functions

    DTIC Science & Technology

    1990-03-31

    forms as val. and to 8 lags ( -,-/- 62 msec) for the 4-7 potentials, in keeping with common usage. Hz-filtered intervals. The ERC was defined as the...Functional topography of the graded. Further refinement of the techniques used human brain. In: G. Pfurtscheller and F.H. Lopes da Silva (Eds...sufficiently dense spatial sampling and techniques of spatial enhancement such as Laplacian Transform. Classical neurological and neurolinguistic thinking

  13. Changes in topological organization of functional PET brain network with normal aging.

    PubMed

    Liu, Zhiliang; Ke, Lining; Liu, Huafeng; Huang, Wenhua; Hu, Zhenghui

    2014-01-01

    Recent studies about brain network have suggested that normal aging is associated with alterations in coordinated patterns of the large-scale brain functional and structural systems. However, age-related changes in functional networks constructed via positron emission tomography (PET) data are still barely understood. Here, we constructed functional brain networks composed of 90 regions in younger (mean age 36.5 years) and older (mean age 56.3 years) age groups with PET data. 113 younger and 110 older healthy individuals were separately selected for two age groups, from a physical examination database. Corresponding brain functional networks of the two groups were constructed by thresholding average cerebral glucose metabolism correlation matrices of 90 regions and analysed using graph theoretical approaches. Although both groups showed normal small-world architecture in the PET networks, increased clustering and decreased efficiency were found in older subjects, implying a degeneration process that brain system shifts from a small-world network to regular one along with normal aging. Moreover, normal senescence was related to changed nodal centralities predominantly in association and paralimbic cortex regions, e.g. increasing in orbitofrontal cortex (middle) and decreasing in left hippocampus. Additionally, the older networks were about equally as robust to random failures as younger counterpart, but more vulnerable against targeted attacks. Finally, methods in the construction of the PET networks revealed reasonable robustness. Our findings enhanced the understanding about the topological principles of PET networks and changes related to normal aging.

  14. Changes in Topological Organization of Functional PET Brain Network with Normal Aging

    PubMed Central

    Liu, Huafeng; Huang, Wenhua; Hu, Zhenghui

    2014-01-01

    Recent studies about brain network have suggested that normal aging is associated with alterations in coordinated patterns of the large-scale brain functional and structural systems. However, age-related changes in functional networks constructed via positron emission tomography (PET) data are still barely understood. Here, we constructed functional brain networks composed of regions in younger (mean age years) and older (mean age years) age groups with PET data. younger and older healthy individuals were separately selected for two age groups, from a physical examination database. Corresponding brain functional networks of the two groups were constructed by thresholding average cerebral glucose metabolism correlation matrices of regions and analysed using graph theoretical approaches. Although both groups showed normal small-world architecture in the PET networks, increased clustering and decreased efficiency were found in older subjects, implying a degeneration process that brain system shifts from a small-world network to regular one along with normal aging. Moreover, normal senescence was related to changed nodal centralities predominantly in association and paralimbic cortex regions, e.g. increasing in orbitofrontal cortex (middle) and decreasing in left hippocampus. Additionally, the older networks were about equally as robust to random failures as younger counterpart, but more vulnerable against targeted attacks. Finally, methods in the construction of the PET networks revealed reasonable robustness. Our findings enhanced the understanding about the topological principles of PET networks and changes related to normal aging. PMID:24586370

  15. Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging.

    PubMed

    Voss, Michelle W; Weng, Timothy B; Burzynska, Agnieszka Z; Wong, Chelsea N; Cooke, Gillian E; Clark, Rachel; Fanning, Jason; Awick, Elizabeth; Gothe, Neha P; Olson, Erin A; McAuley, Edward; Kramer, Arthur F

    2016-05-01

    Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the default mode network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks.

  16. 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

  17. 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

  18. Phase synchronization in brain networks derived from correlation between probabilities of recurrences in functional MRI data.

    PubMed

    Rangaprakash, D; Hu, Xiaoping; Deshpande, Gopikrishna

    2013-04-01

    It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

  19. 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

  20. 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

  1. Brain network analysis of EEG functional connectivity during imagery hand movements.

    PubMed

    Demuru, Matteo; Fara, Francesca; Fraschini, Matteo

    2013-12-01

    The characterization of human neural activity during imaginary movement tasks represent an important challenge in order to develop effective applications that allow the control of a machine. Yet methods based on brain network analysis of functional connectivity have been scarcely investigated. As a result we use graph theoretic methods to investigate the functional connectivity and brain network measures in order to characterize imagery hand movements in a set of healthy subjects. The results of the present study show that functional connectivity analysis and minimum spanning tree (MST) parameters allow to successfully discriminate between imagery hand movements (both right and left) and resting state conditions. In conclusion, this paper shows that brain network analysis of EEG functional connectivity could represent an efficient alternative to more classical local activation based approaches. Furthermore, it also suggests the shift toward methods based on the characterization of a limited set of fundamental functional connections that disclose salient network topological features.

  2. Functional Modularity of Background Activities in Normal and Epileptic Brain Networks

    NASA Astrophysics Data System (ADS)

    Chavez, M.; Valencia, M.; Navarro, V.; Latora, V.; Martinerie, J.

    2010-03-01

    We analyze the connectivity structure of weighted brain networks extracted from spontaneous magnetoencephalographic signals of healthy subjects and epileptic patients (suffering from absence seizures) recorded at rest. We find that, for the activities in the 5-14 Hz range, healthy brains exhibit a sparse connectivity, whereas the brain networks of patients display a rich connectivity with a clear modular structure. Our results suggest that modularity plays a key role in the functional organization of brain areas during normal and pathological neural activities at rest.

  3. Functional brain networks and abnormal connectivity in the movement disorders

    PubMed Central

    Poston, Kathleen L.; Eidelberg, David

    2012-01-01

    Clinical manifestations of movement disorders, such as Parkinson’s disease (PD) and dystonia, arise from neurophysiological changes within the cortico-striato-pallidothalamocortical (CSPTC) and cerebello-thalamo-cortical (CbTC) circuits. Neuroimaging techniques that probe connectivity within these circuits can be used to understand how these disorders develop as well as identify potential targets for medical and surgical therapies. Indeed, network analysis of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) has identified abnormal metabolic networks associated with the cardinal motor symptoms of PD, such as akinesia and tremor, as well as PD-related cognitive dysfunction. More recent task-based and resting state functional magnetic resonance imaging studies have reproduced several of the altered connectivity patterns identified in these abnormal PD-related networks. A similar network analysis approach in dystonia revealed abnormal disease related metabolic patterns in both manifesting and non-manifesting carriers of dystonia mutations. Other multimodal imaging approaches using magnetic resonance diffusion tensor imaging in patients with primary genetic dystonia suggest abnormal connectivity within the CbTC circuits mediate the clinical manifestations of this inherited neurodevelopmental disorder. Ongoing developments in functional imaging and future studies in early patients are likely to enhance our understanding of these movement disorders and guide novel targets for future therapies. PMID:22206967

  4. 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…

  5. Small-World Brain Network and Dynamic Functional Distribution in Patients with Subcortical Vascular Cognitive Impairment

    PubMed Central

    Yu, Yongqiang; Zhou, Xia; Wang, Haibao; Hu, Xiaopeng; Zhu, Xiaoqun; Xu, Liyan; Zhang, Chao; Sun, Zhongwu

    2015-01-01

    To investigate the topological properties of the functional connectivity and their relationships with cognition impairment in subcortical vascular cognitive impairment (SVCI) patients, resting-state fMRI and graph theory approaches were employed in 23 SVCI patients and 20 healthy controls. Functional connectivity between 90 brain regions was estimated using bivariate correlation analysis and thresholded to construct a set of undirected graphs. Moreover, all of them were subjected to a battery of cognitive assessment, and the correlations between graph metrics and cognitive performance were further analyzed. Our results are as follows: functional brain networks of both SVCI patients and controls showed small-world attributes over a range of thresholds(0.15≤sparsity≤0.40). However, global topological organization of the functional brain networks in SVCI was significantly disrupted, as indicated by reduced global and local efficiency, clustering coefficients and increased characteristic path lengths relative to normal subjects. The decreased activity areas in SVCI predominantly targeted in the frontal-temporal lobes, while subcortical regions showed increased topological properties, which are suspected to compensate for the inefficiency of the functional network. We also demonstrated that altered brain network properties in SVCI are closely correlated with general cognitive and praxis dysfunction. The disruption of whole-brain topological organization of the functional connectome provides insight into the functional changes in the human brain in SVCI. PMID:26132397

  6. Functional and structural brain networks in epilepsy: what have we learned?

    PubMed

    van Diessen, Eric; Diederen, Sander J H; Braun, Kees P J; Jansen, Floor E; Stam, Cornelis J

    2013-11-01

    Brain functioning is increasingly seen as a complex interplay of dynamic neural systems that rely on the integrity of structural and functional networks. Recent studies that have investigated functional and structural networks in epilepsy have revealed specific disruptions in connectivity and network topology and, consequently, have led to a shift from "focus" to "networks" in modern epilepsy research. Disruptions in these networks may be associated with cognitive and behavioral impairments often seen in patients with chronic epilepsy. In this review, we aim to provide an overview that would introduce the clinical neurologist and epileptologist to this new theoretical paradigm. We focus on the application of a theory, called "network analysis," to characterize resting-state functional and structural networks and discuss current and future clinical applications of network analysis in patients with epilepsy.

  7. 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.

  8. Insights into Brain Architectures from the Homological Scaffolds of Functional Connectivity Networks

    PubMed Central

    Lord, Louis-David; Expert, Paul; Fernandes, Henrique M.; Petri, Giovanni; Van Hartevelt, Tim J.; Vaccarino, Francesco; Deco, Gustavo; Turkheimer, Federico; Kringelbach, Morten L.

    2016-01-01

    In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain's functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain's complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e., interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: (i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and (ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the

  9. Investigation of the large-scale functional brain networks modulated by acupuncture.

    PubMed

    Feng, Yuanyuan; Bai, Lijun; Ren, Yanshuang; Wang, Hu; Liu, Zhenyu; Zhang, Wensheng; Tian, Jie

    2011-09-01

    Previous neuroimaging studies have primarily focused on the neural activities involving the acute effects of acupuncture. Considering that acupuncture can induce long-lasting effects, several researchers have begun to pay attention to the sustained effects of acupuncture on the resting brain. Most of these researchers adopted functional connectivity analysis based on one or a few preselected brain regions and demonstrated various function-guided brain networks underlying the specific effect of acupuncture. Few have investigated how these brain networks interacted at the whole-brain level. In this study, we sought to investigate the functional correlations throughout the entire brain following acupuncture at acupoint ST36 (ACUP) in comparison with acupuncture at nearby nonacupoint (SHAM). We divided the whole brain into 90 regions and constructed functional brain network for each condition. Then we examined the network hubs and identified statistically significant differences in functional correlations between the two conditions. Following ACUP, but not SHAM, the limbic/paralimbic regions such as the amygdala, hippocampus and anterior cingulate gyrus emerged as network hubs. For direct comparisons, increased correlations for ACUP compared to SHAM were primarily related with the limbic/paralimbic and subcortical regions such as the insula, amygdala, anterior cingulate gyrus, and thalamus, whereas decreased correlations were mainly related with the sensory and frontal cortex. The heterogeneous modulation patterns between the two conditions may relate to the functional specific modulatory effects of acupuncture. The preliminary findings may help us to better understand the long-lasting effects of acupuncture on the entire resting brain, as well as the neurophysiological mechanisms underlying acupuncture.

  10. Associations among executive function, cardiorespiratory fitness, and brain network properties in older adults

    PubMed Central

    Kawagoe, Toshikazu; Onoda, Keiichi; Yamaguchi, Shuhei

    2017-01-01

    Aging is associated with deterioration in a number of cognitive functions. Previous reports have demonstrated the beneficial effect of physical fitness on cognitive function, especially executive function (EF). The graph theoretical approach models the brain as a complex network represented graphically as nodes and edges. We analyzed several measures of EF, an index of physical fitness, and resting-state functional magnetic resonance imaging data from healthy older volunteers to elucidate the associations among EF, cardiorespiratory fitness, and brain network properties. The topological neural properties were significantly related to the level of EF and/or physical fitness. Global efficiency, which represents how well the whole brain is integrated, was positively related, whereas local efficiency, which represents how well the brain is functionally segregated, was negatively related, to the level of EF and fitness. The associations among EF, physical fitness and topological resting-state functional network property appear related to compensation and dedifferentiation in older age. A mediation analysis showed that high-fit older adults gain higher global efficiency of the brain at the expense of lower local efficiency. The results suggest that physical fitness may be beneficial in maintaining EF in healthy aging by enhancing the efficiency of the global brain network. PMID:28054664

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

    PubMed Central

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

    2013-01-01

    Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain's ‘functional connectivity' is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks. PMID:23756687

  12. Task-related changes in functional properties of the human brain network underlying attentional control.

    PubMed

    Kida, Tetsuo; Kakigi, Ryusuke

    2013-01-01

    Previous studies have demonstrated task-related changes in brain activation and inter-regional connectivity but the temporal dynamics of functional properties of the brain during task execution is still unclear. In the present study, we investigated task-related changes in functional properties of the human brain network by applying graph-theoretical analysis to magnetoencephalography (MEG). Subjects performed a cue-target attention task in which a visual cue informed them of the direction of focus for incoming auditory or tactile target stimuli, but not the sensory modality. We analyzed the MEG signal in the cue-target interval to examine network properties during attentional control. Cluster-based non-parametric permutation tests with the Monte-Carlo method showed that in the cue-target interval, beta activity was desynchronized in the sensori-motor region including premotor and posterior parietal regions in the hemisphere contralateral to the attended side. Graph-theoretical analysis revealed that, in beta frequency, global hubs were found around the sensori-motor and prefrontal regions, and functional segregation over the entire network was decreased during attentional control compared to the baseline. Thus, network measures revealed task-related temporal changes in functional properties of the human brain network, leading to the understanding of how the brain dynamically responds to task execution as a network.

  13. Large scale brain functional networks support sentence comprehension: evidence from both explicit and implicit language tasks.

    PubMed

    Zhu, Zude; Fan, Yuanyuan; Feng, Gangyi; Huang, Ruiwang; Wang, Suiping

    2013-01-01

    Previous studies have indicated that sentences are comprehended via widespread brain regions in the fronto-temporo-parietal network in explicit language tasks (e.g., semantic congruency judgment tasks), and through restricted temporal or frontal regions in implicit language tasks (e.g., font size judgment tasks). This discrepancy has raised questions regarding a common network for sentence comprehension that acts regardless of task effect and whether different tasks modulate network properties. To this end, we constructed brain functional networks based on 27 subjects' fMRI data that was collected while performing explicit and implicit language tasks. We found that network properties and network hubs corresponding to the implicit language task were similar to those associated with the explicit language task. We also found common hubs in occipital, temporal and frontal regions in both tasks. Compared with the implicit language task, the explicit language task resulted in greater global efficiency and increased integrated betweenness centrality of the left inferior frontal gyrus, which is a key region related to sentence comprehension. These results suggest that brain functional networks support both explicit and implicit sentence comprehension; in addition, these two types of language tasks may modulate the properties of brain functional networks.

  14. Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury.

    PubMed

    Stevens, Michael C; Lovejoy, David; Kim, Jinsuh; Oakes, Howard; Kureshi, Inam; Witt, Suzanne T

    2012-06-01

    Several reports show that traumatic brain injury (TBI) results in abnormalities in the coordinated activation among brain regions. Because most previous studies examined moderate/severe TBI, the extensiveness of functional connectivity abnormalities and their relationship to postconcussive complaints or white matter microstructural damage are unclear in mild TBI. This study characterized widespread injury effects on multiple integrated neural networks typically observed during a task-unconstrained "resting state" in mild TBI patients. Whole brain functional connectivity for twelve separate networks was identified using independent component analysis (ICA) of fMRI data collected from thirty mild TBI patients mostly free of macroscopic intracerebral injury and thirty demographically-matched healthy control participants. Voxelwise group comparisons found abnormal mild TBI functional connectivity in every brain network identified by ICA, including visual processing, motor, limbic, and numerous circuits believed to underlie executive cognition. Abnormalities not only included functional connectivity deficits, but also enhancements possibly reflecting compensatory neural processes. Postconcussive symptom severity was linked to abnormal regional connectivity within nearly every brain network identified, particularly anterior cingulate. A recently developed multivariate technique that identifies links between whole brain profiles of functional and anatomical connectivity identified several novel mild TBI abnormalities, and represents a potentially important new tool in the study of the complex neurobiological sequelae of TBI.

  15. Estimating time-varying brain connectivity networks from functional MRI time series.

    PubMed

    Monti, Ricardo Pio; Hellyer, Peter; Sharp, David; Leech, Robert; Anagnostopoulos, Christoforos; Montana, Giovanni

    2014-12-01

    At the forefront of neuroimaging is the understanding of the functional architecture of the human brain. In most applications functional networks are assumed to be stationary, resulting in a single network estimated for the entire time course. However recent results suggest that the connectivity between brain regions is highly non-stationary even at rest. As a result, there is a need for new brain imaging methodologies that comprehensively account for the dynamic nature of functional networks. In this work we propose the Smooth Incremental Graphical Lasso Estimation (SINGLE) algorithm which estimates dynamic brain networks from fMRI data. We apply the proposed algorithm to functional MRI data from 24 healthy patients performing a Choice Reaction Task to demonstrate the dynamic changes in network structure that accompany a simple but attentionally demanding cognitive task. Using graph theoretic measures we show that the properties of the Right Inferior Frontal Gyrus and the Right Inferior Parietal lobe dynamically change with the task. These regions are frequently reported as playing an important role in cognitive control. Our results suggest that both these regions play a key role in the attention and executive function during cognitively demanding tasks and may be fundamental in regulating the balance between other brain regions.

  16. Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications

    PubMed Central

    Tadić, Bosiljka; Andjelković, Miroslav; Boshkoska, Biljana Mileva; Levnajić, Zoran

    2016-01-01

    Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener’s concentration to the story, confirmed by self-rating, and closeness to the speaker’s brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener’s group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener’s rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli. PMID:27880802

  17. Combining functional and anatomical connectivity reveals brain networks for auditory language comprehension.

    PubMed

    Saur, Dorothee; Schelter, Björn; Schnell, Susanne; Kratochvil, David; Küpper, Hanna; Kellmeyer, Philipp; Kümmerer, Dorothee; Klöppel, Stefan; Glauche, Volkmar; Lange, Rüdiger; Mader, Wolfgang; Feess, David; Timmer, Jens; Weiller, Cornelius

    2010-02-15

    Cognitive functions are organized in distributed, overlapping, and interacting brain networks. Investigation of those large-scale brain networks is a major task in neuroimaging research. Here, we introduce a novel combination of functional and anatomical connectivity to study the network topology subserving a cognitive function of interest. (i) In a given network, direct interactions between network nodes are identified by analyzing functional MRI time series with the multivariate method of directed partial correlation (dPC). This method provides important improvements over shortcomings that are typical for ordinary (partial) correlation techniques. (ii) For directly interacting pairs of nodes, a region-to-region probabilistic fiber tracking on diffusion tensor imaging data is performed to identify the most probable anatomical white matter fiber tracts mediating the functional interactions. This combined approach is applied to the language domain to investigate the network topology of two levels of auditory comprehension: lower-level speech perception (i.e., phonological processing) and higher-level speech recognition (i.e., semantic processing). For both processing levels, dPC analyses revealed the functional network topology and identified central network nodes by the number of direct interactions with other nodes. Tractography showed that these interactions are mediated by distinct ventral (via the extreme capsule) and dorsal (via the arcuate/superior longitudinal fascicle fiber system) long- and short-distance association tracts as well as commissural fibers. Our findings demonstrate how both processing routines are segregated in the brain on a large-scale network level. Combining dPC with probabilistic tractography is a promising approach to unveil how cognitive functions emerge through interaction of functionally interacting and anatomically interconnected brain regions.

  18. 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).

  19. Reconfiguration of the Brain Functional Network Associated with Visual Task Demands.

    PubMed

    Wen, Xue; Zhang, Delong; Liang, Bishan; Zhang, Ruibin; Wang, Zengjian; Wang, Junjing; Liu, Ming; Huang, Ruiwang

    2015-01-01

    Neuroimaging studies have demonstrated that the topological properties of resting-state brain functional networks are modulated through task performances. However, the reconfiguration of functional networks associated with distinct degrees of task demands is not well understood. In the present study, we acquired fMRI data from 18 healthy adult volunteers during resting-state (RS) and two visual tasks (i.e., visual stimulus watching, VSW; and visual stimulus decision, VSD). Subsequently, we constructed the functional brain networks associated with these three conditions and analyzed the changes in the topological properties (e.g., network efficiency, wiring-cost, modularity, and robustness) among them. Although the small-world attributes were preserved qualitatively across the functional networks of the three conditions, changes in the topological properties were also observed. Compared with the resting-state, the functional networks associated with the visual tasks exhibited significantly increased network efficiency and wiring-cost, but decreased modularity and network robustness. The changes in the task-related topological properties were modulated according to the task complexity (i.e., from RS to VSW and VSD). Moreover, at the regional level, we observed that the increased nodal efficiencies in the visual and working memory regions were positively associated with the increase in task complexity. Together, these results suggest that the increased efficiency of the functional brain network and higher wiring-cost were observed to afford the demands of visual tasks. These observations provide further insights into the mechanisms underlying the reconfiguration of the brain network during task performance.

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

    PubMed

    Jalili, Mahdi

    2016-07-15

    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.

  1. 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.

  2. The discovery of population differences in network community structure: New methods and applications to brain functional networks in schizophrenia

    PubMed Central

    Alexander-Bloch, Aaron; Lambiotte, Renaud; Roberts, Ben; Giedd, Jay; Gogtay, Nitin; Bullmore, Ed

    2012-01-01

    The modular organization of the brain network can vary in two fundamental ways. The amount of interversus intra-modular connections between network nodes can be altered, or the community structure itself can be perturbed, in terms of which nodes belong to which modules (or communities). Alterations have previously been reported in modularity, which is a function of the proportion of intra-modular edges over all modules in the network. For example, we have reported that modularity is decreased in functional brain networks in schizophrenia: There are proportionally more inter-modular edges and fewer intra-modular edges. However, despite numerous and increasing studies of brain modular organization, it is not known how to test for differences in the community structure, i.e., the assignment of regional nodes to specific modules. Here, we introduce a method based on the normalized mutual information between pairs of modular networks to show that the community structure of the brain network is significantly altered in schizophrenia, using resting-state fMRI in 19 participants with childhood-onset schizophrenia and 20 healthy participants. We also develop tools to show which specific nodes (or brain regions) have significantly different modular communities between groups, a subset that includes right insular and perisylvian cortical regions. The methods that we propose are broadly applicable to other experimental contexts, both in neuroimaging and other areas of network science. PMID:22119652

  3. The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia.

    PubMed

    Alexander-Bloch, Aaron; Lambiotte, Renaud; Roberts, Ben; Giedd, Jay; Gogtay, Nitin; Bullmore, Ed

    2012-02-15

    The modular organization of the brain network can vary in two fundamental ways. The amount of inter- versus intra-modular connections between network nodes can be altered, or the community structure itself can be perturbed, in terms of which nodes belong to which modules (or communities). Alterations have previously been reported in modularity, which is a function of the proportion of intra-modular edges over all modules in the network. For example, we have reported that modularity is decreased in functional brain networks in schizophrenia: There are proportionally more inter-modular edges and fewer intra-modular edges. However, despite numerous and increasing studies of brain modular organization, it is not known how to test for differences in the community structure, i.e., the assignment of regional nodes to specific modules. Here, we introduce a method based on the normalized mutual information between pairs of modular networks to show that the community structure of the brain network is significantly altered in schizophrenia, using resting-state fMRI in 19 participants with childhood-onset schizophrenia and 20 healthy participants. We also develop tools to show which specific nodes (or brain regions) have significantly different modular communities between groups, a subset that includes right insular and perisylvian cortical regions. The methods that we propose are broadly applicable to other experimental contexts, both in neuroimaging and other areas of network science.

  4. Resting state functional connectivity changes induced by prior brain state are not network specific.

    PubMed

    Tailby, Chris; Masterton, Richard A J; Huang, Jenny Y; Jackson, Graeme D; Abbott, David F

    2015-02-01

    Resting state functional connectivity (rFC) is used to identify functionally related brain areas without requiring subjects to perform specific tasks. Previous work suggests that prior brain state, as determined by the activity engaged in immediately prior to collection of resting state data, can influence the networks recovered by rFC analyses. We determined the prevalence and network specificity of rFC changes induced by manipulations of prior state (including an unstructured (unconstrained) state, and language and motor tasks). Three blocks of rest data (one after each of the specified prior states) were acquired on each of 25 subjects. We hypothesised that prior state induced changes in rFC would be greatest within the networks most actively recruited by that prior state. Changes in rFC were greatest following the motor task and, contrary to our hypothesis, were not network specific. This was demonstrated by comparing (1) the timecourses within a set of ROIs selected on the basis of task-related de/activation, and (2) seed-based whole brain voxel-wise connectivity maps, seeded from local maxima in the task-related de/activation maps. Changes in connectivity strength tended to manifest as increases in rFC relative to that in the unstructured rest state, with change maps resembling partially complete maps of the primary sensory cortices and the cognitive control network. The majority of rFC changes occurred in areas moderately (but not weakly) connected to the seeds. Constrained prior states were associated with lower across-participant variance in rFC. This systematic investigation of the effect of prior brain state on rFC indicates that the rFC changes induced by prior brain state occur both in brain networks related to that brain activity and in networks nominally unrelated to that brain activity.

  5. Altered functional-structural coupling of large-scale brain networks in idiopathic generalized epilepsy.

    PubMed

    Zhang, Zhiqiang; Liao, Wei; Chen, Huafu; Mantini, Dante; Ding, Ju-Rong; Xu, Qiang; Wang, Zhengge; Yuan, Cuiping; Chen, Guanghui; Jiao, Qing; Lu, Guangming

    2011-10-01

    The human brain is a large-scale integrated network in the functional and structural domain. Graph theoretical analysis provides a novel framework for analysing such complex networks. While previous neuroimaging studies have uncovered abnormalities in several specific brain networks in patients with idiopathic generalized epilepsy characterized by tonic-clonic seizures, little is known about changes in whole-brain functional and structural connectivity networks. Regarding functional and structural connectivity, networks are intimately related and share common small-world topological features. We predict that patients with idiopathic generalized epilepsy would exhibit a decoupling between functional and structural networks. In this study, 26 patients with idiopathic generalized epilepsy characterized by tonic-clonic seizures and 26 age- and sex-matched healthy controls were recruited. Resting-state functional magnetic resonance imaging signal correlations and diffusion tensor image tractography were used to generate functional and structural connectivity networks. Graph theoretical analysis revealed that the patients lost optimal topological organization in both functional and structural connectivity networks. Moreover, the patients showed significant increases in nodal topological characteristics in several cortical and subcortical regions, including mesial frontal cortex, putamen, thalamus and amygdala relative to controls, supporting the hypothesis that regions playing important roles in the pathogenesis of epilepsy may display abnormal hub properties in network analysis. Relative to controls, patients showed further decreases in nodal topological characteristics in areas of the default mode network, such as the posterior cingulate gyrus and inferior temporal gyrus. Most importantly, the degree of coupling between functional and structural connectivity networks was decreased, and exhibited a negative correlation with epilepsy duration in patients. Our findings

  6. Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy.

    PubMed

    Zhang, Jie; Cheng, Wei; Wang, ZhengGe; Zhang, ZhiQiang; Lu, WenLian; Lu, GuangMing; Feng, Jianfeng

    2012-01-01

    The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology

  7. Acute functional reactivation of the language network during awake intraoperative brain mapping.

    PubMed

    Spena, Giannantonio; Costi, Emanuele; Panciani, Pier Paolo; Roca, Elena; Migliorati, Karol; Fontanella, Marco Maria

    2015-01-01

    Acute brain plasticity during resection of central lesions has been recently described. In the cases reported, perilesional latent networks, useful to preserve the neurological functions, were detected in asymptomatic patients. In this paper, we presented a case of acute functional reactivation (AFR) of the language network in a symptomatic patient. Tumor resection allowed to acutely restore the neurological deficit. Intraoperative direct cortical stimulation (DCS) and functional neuroimaging showed new epicentres of activation of the language network after tumor excision. DCS in awake surgery is mandatory to reveal AFR needful to improve the extent of resection preserving the quality of life.

  8. 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.

  9. Quantitative evaluation of simulated functional brain networks in graph theoretical analysis.

    PubMed

    Lee, Won Hee; Bullmore, Ed; Frangou, Sophia

    2017-02-01

    There is increasing interest in the potential of whole-brain computational models to provide mechanistic insights into resting-state brain networks. It is therefore important to determine the degree to which computational models reproduce the topological features of empirical functional brain networks. We used empirical connectivity data derived from diffusion spectrum and resting-state functional magnetic resonance imaging data from healthy individuals. Empirical and simulated functional networks, constrained by structural connectivity, were defined based on 66 brain anatomical regions (nodes). Simulated functional data were generated using the Kuramoto model in which each anatomical region acts as a phase oscillator. Network topology was studied using graph theory in the empirical and simulated data. The difference (relative error) between graph theory measures derived from empirical and simulated data was then estimated. We found that simulated data can be used with confidence to model graph measures of global network organization at different dynamic states and highlight the sensitive dependence of the solutions obtained in simulated data on the specified connection densities. This study provides a method for the quantitative evaluation and external validation of graph theory metrics derived from simulated data that can be used to inform future study designs.

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

    PubMed

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

    2013-10-01

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

  11. Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development.

    PubMed

    Uddin, Lucina Q; Supekar, Kaustubh S; Ryali, Srikanth; Menon, Vinod

    2011-12-14

    Brain structural and functional development, throughout childhood and into adulthood, underlies the maturation of increasingly sophisticated cognitive abilities. High-level attentional and cognitive control processes rely on the integrity of, and dynamic interactions between, core neurocognitive networks. The right fronto-insular cortex (rFIC) is a critical component of a salience network (SN) that mediates interactions between large-scale brain networks involved in externally oriented attention [central executive network (CEN)] and internally oriented cognition [default mode network (DMN)]. How these systems reconfigure and mature with development is a critical question for cognitive neuroscience, with implications for neurodevelopmental pathologies affecting brain connectivity. Using functional and effective connectivity measures applied to fMRI data, we examine interactions within and between the SN, CEN, and DMN. We find that functional coupling between key network nodes is stronger in adults than in children, as are causal links emanating from the rFIC. Specifically, the causal influence of the rFIC on nodes of the SN and CEN was significantly greater in adults compared with children. Notably, these results were entirely replicated on an independent dataset of matched children and adults. Developmental changes in functional and effective connectivity were related to structural connectivity along these links. Diffusion tensor imaging tractography revealed increased structural integrity in adults compared with children along both within- and between-network pathways associated with the rFIC. These results suggest that structural and functional maturation of rFIC pathways is a critical component of the process by which human brain networks mature during development to support complex, flexible cognitive processes in adulthood.

  12. 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

  13. 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

  14. Adaptive reconfiguration of fractal small-world human brain functional networks.

    PubMed

    Bassett, Danielle S; Meyer-Lindenberg, Andreas; Achard, Sophie; Duke, Thomas; Bullmore, Edward

    2006-12-19

    Brain function depends on adaptive self-organization of large-scale neural assemblies, but little is known about quantitative network parameters governing these processes in humans. Here, we describe the topology and synchronizability of frequency-specific brain functional networks using wavelet decomposition of magnetoencephalographic time series, followed by construction and analysis of undirected graphs. Magnetoencephalographic data were acquired from 22 subjects, half of whom performed a finger-tapping task, whereas the other half were studied at rest. We found that brain functional networks were characterized by small-world properties at all six wavelet scales considered, corresponding approximately to classical delta (low and high), , alpha, beta, and gamma frequency bands. Global topological parameters (path length, clustering) were conserved across scales, most consistently in the frequency range 2-37 Hz, implying a scale-invariant or fractal small-world organization. Dynamical analysis showed that networks were located close to the threshold of order/disorder transition in all frequency bands. The highest-frequency gamma network had greater synchronizability, greater clustering of connections, and shorter path length than networks in the scaling regime of (lower) frequencies. Behavioral state did not strongly influence global topology or synchronizability; however, motor task performance was associated with emergence of long-range connections in both beta and gamma networks. Long-range connectivity, e.g., between frontal and parietal cortex, at high frequencies during a motor task may facilitate sensorimotor binding. Human brain functional networks demonstrate a fractal small-world architecture that supports critical dynamics and task-related spatial reconfiguration while preserving global topological parameters.

  15. 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.

  16. Structural and functional brain network correlates of depressive symptoms in premanifest Huntington's disease.

    PubMed

    McColgan, Peter; Razi, Adeel; Gregory, Sarah; Seunarine, Kiran K; Durr, Alexandra; A C Roos, Raymund; Leavitt, Blair R; Scahill, Rachael I; Clark, Chris A; Langbehn, Doug R; Rees, Geraint; Tabrizi, Sarah J

    2017-03-15

    Depression is common in premanifest Huntington's disease (preHD) and results in significant morbidity. We sought to examine how variations in structural and functional brain networks relate to depressive symptoms in premanifest HD and healthy controls. Brain networks were constructed using diffusion tractography (70 preHD and 81 controls) and resting state fMRI (92 preHD and 94 controls) data. A sub-network associated with depression was identified in a data-driven fashion and network-based statistics was used to investigate which specific connections correlated with depression scores. A replication analysis was then performed using data from a separate study. Correlations between depressive symptoms with increased functional connectivity and decreased structural connectivity were seen for connections in the default mode network (DMN) and basal ganglia in preHD. This study reveals specific connections in the DMN and basal ganglia that are associated with depressive symptoms in preHD. Hum Brain Mapp, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  17. 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.

  18. Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities.

    PubMed

    Kepinska, Olga; de Rover, Mischa; Caspers, Johanneke; Schiller, Niels O

    2017-03-01

    In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca's region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners' differences. We conclude that in terms of functional connectivity, the engagement of brain's networks during grammar acquisition is coupled with one's language learning abilities.

  19. Multiscale topological properties of functional brain networks during motor imagery after stroke.

    PubMed

    De Vico Fallani, Fabrizio; Pichiorri, Floriana; Morone, Giovanni; Molinari, Marco; Babiloni, Fabio; Cincotti, Febo; Mattia, Donatella

    2013-12-01

    In recent years, network analyses have been used to evaluate brain reorganization following stroke. However, many studies have often focused on single topological scales, leading to an incomplete model of how focal brain lesions affect multiple network properties simultaneously and how changes on smaller scales influence those on larger scales. In an EEG-based experiment on the performance of hand motor imagery (MI) in 20 patients with unilateral stroke, we observed that the anatomic lesion affects the functional brain network on multiple levels. In the beta (13-30 Hz) frequency band, the MI of the affected hand (Ahand) elicited a significantly lower smallworldness and local efficiency (Eloc) versus the unaffected hand (Uhand). Notably, the abnormal reduction in Eloc significantly depended on the increase in interhemispheric connectivity, which was in turn determined primarily by the rise of regional connectivity in the parieto-occipital sites of the affected hemisphere. Further, in contrast to the Uhand MI, in which significantly high connectivity was observed for the contralateral sensorimotor regions of the unaffected hemisphere, the regions with increased connectivity during the Ahand MI lay in the frontal and parietal regions of the contralaterally affected hemisphere. Finally, the overall sensorimotor function of our patients, as measured by Fugl-Meyer Assessment (FMA) index, was significantly predicted by the connectivity of their affected hemisphere. These results improve on our understanding of stroke-induced alterations in functional brain networks.

  20. Interferon-α acutely impairs whole-brain functional connectivity network architecture - A preliminary study.

    PubMed

    Dipasquale, Ottavia; Cooper, Ella A; Tibble, Jeremy; Voon, Valerie; Baglio, Francesca; Baselli, Giuseppe; Cercignani, Mara; Harrison, Neil A

    2016-11-01

    Interferon-alpha (IFN-α) is a key mediator of antiviral immune responses used to treat Hepatitis C infection. Though clinically effective, IFN-α rapidly impairs mood, motivation and cognition, effects that can appear indistinguishable from major depression and provide powerful empirical support for the inflammation theory of depression. Though inflammation has been shown to modulate activity within discrete brain regions, how it affects distributed information processing and the architecture of whole brain functional connectivity networks have not previously been investigated. Here we use a graph theoretic analysis of resting state functional magnetic resonance imaging (rfMRI) to investigate acute effects of systemic interferon-alpha (IFN-α) on whole brain functional connectivity architecture and its relationship to IFN-α-induced mood change. Twenty-two patients with Hepatitis-C infection, initiating IFN-α-based therapy were scanned at baseline and 4h after their first IFN-α dose. The whole brain network was parcellated into 110 cortical and sub-cortical nodes based on the Oxford-Harvard Atlas and effects assessed on higher-level graph metrics, including node degree, betweenness centrality, global and local efficiency. IFN-α was associated with a significant reduction in global network connectivity (node degree) (p=0.033) and efficiency (p=0.013), indicating a global reduction of information transfer among the nodes forming the whole brain network. Effects were similar for highly connected (hub) and non-hub nodes, with no effect on betweenness centrality (p>0.1). At a local level, we identified regions with reduced efficiency of information exchange and a sub-network with decreased functional connectivity after IFN-α. Changes in local and particularly global functional connectivity correlated with associated changes in mood measured on the Profile of Mood States (POMS) questionnaire. IFN-α rapidly induced a profound shift in whole brain network structure

  1. Second language experience modulates functional brain network for the native language production in bimodal bilinguals.

    PubMed

    Zou, Lijuan; Abutalebi, Jubin; Zinszer, Benjamin; Yan, Xin; Shu, Hua; Peng, Danling; Ding, Guosheng

    2012-09-01

    The functional brain network of a bilingual's first language (L1) plays a crucial role in shaping that of his or her second language (L2). However, it is less clear how L2 acquisition changes the functional network of L1 processing in bilinguals. In this study, we demonstrate that in bimodal (Chinese spoken-sign) bilinguals, the functional network supporting L1 production (spoken language) has been reorganized to accommodate the network underlying L2 production (sign language). Using functional magnetic resonance imaging (fMRI) and a picture naming task, we find greater recruitment of the right supramarginal gyrus (RSMG), the right temporal gyrus (RSTG), and the right superior occipital gyrus (RSOG) for bilingual speakers versus monolingual speakers during L1 production. In addition, our second experiment reveals that these regions reflect either automatic activation of L2 (RSOG) or extra cognitive coordination (RSMG and RSTG) between both languages during L1 production. The functional connectivity between these regions, as well as between other regions that are L1- or L2-specific, is enhanced during L1 production in bimodal bilinguals as compared to their monolingual peers. These findings suggest that L1 production in bimodal bilinguals involves an interaction between L1 and L2, supporting the claim that learning a second language does, in fact, change the functional brain network of the first language.

  2. 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

  3. Analysis of a phase synchronized functional network based on the rhythm of brain activities

    NASA Astrophysics Data System (ADS)

    Li, Ling; Jin, Zhen-Lan; Li, Bin

    2011-03-01

    Rhythm of brain activities represents oscillations of postsynaptic potentials in neocortex, therefore it can serve as an indicator of the brain activity state. In order to check the connectivity of brain rhythm, this paper develops a new method of constructing functional network based on phase synchronization. Electroencephalogram (EEG) data were collected while subjects looking at a green cross in two states, performing an attention task and relaxing with eyes-open. The EEG from these two states was filtered by three band-pass filters to obtain signals of theta (4-7 Hz), alpha (8-13 Hz) and beta (14-30 Hz) bands. Mean resultant length was used to estimate strength of phase synchronization in three bands to construct networks of both states, and mean degree K and cluster coefficient C of networks were calculated as a function of threshold. The result shows higher cluster coefficient in the attention state than in the eyes-open state in all three bands, suggesting that cluster coefficient reflects brain state. In addition, an obvious fronto-parietal network is found in the attention state, which is a well-known attention network. These results indicate that attention modulates the fronto-parietal connectivity in different modes as compared with the eyes-open state. Taken together this method is an objective and important tool to study the properties of neural networks of brain rhythm. Project supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 30800242). yCorresponding author. E-mail: libin@uestc.edu.cn

  4. 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.

  5. Functional connectivity analysis using whole brain and regional network metrics in MS patients.

    PubMed

    Chirumamilla, V C; Fleischer, V; Droby, A; Anjum, T; Muthuraman, M; Zipp, F; Groppa, S

    2016-08-01

    In the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order to obtain information of differences in entire brain network, we focused on both, local and global network connectivity parameters. And the regional connectivity differences were assessed using regional network parameters. RRMS patients presented a significant increase of modularity in comparison to HC, pointing towards a network structure with densely interconnected nodes within one module, while the number of connections with other modules outside decreases. This higher decomposable network favours cost-efficient local information processing and promotes long-range disconnection. In addition, at the regional anatomical level, the network parameters clustering coefficient and local efficiency were increased in the insula, the superior parietal gyrus and the temporal pole. Our study indicates that modularity as derived from fMRI can be seen as a characteristic connectivity feature that is increased in MS patients compared to HC. Furthermore, specific anatomical regions linked to perception, motor function and cognition were mainly involved in the enhanced local information processing.

  6. Attentional load modulates large-scale functional brain connectivity beyond the core attention networks.

    PubMed

    Alnæs, Dag; Kaufmann, Tobias; Richard, Geneviève; Duff, Eugene P; Sneve, Markus H; Endestad, Tor; Nordvik, Jan Egil; Andreassen, Ole A; Smith, Stephen M; Westlye, Lars T

    2015-04-01

    In line with the notion of a continuously active and dynamic brain, functional networks identified during rest correspond with those revealed by task-fMRI. Characterizing the dynamic cross-talk between these network nodes is key to understanding the successful implementation of effortful cognitive processing in healthy individuals and its breakdown in a variety of conditions involving aberrant brain biology and cognitive dysfunction. We employed advanced network modeling on fMRI data collected during a task involving sustained attentive tracking of objects at two load levels and during rest. Using multivariate techniques, we demonstrate that attentional load levels can be significantly discriminated, and from a resting-state condition, the accuracy approaches 100%, by means of estimates of between-node functional connectivity. Several network edges were modulated during task engagement: The dorsal attention network increased connectivity with a visual node, while decreasing connectivity with motor and sensory nodes. Also, we observed a decoupling between left and right hemisphere dorsal visual streams. These results support the notion of dynamic network reconfigurations based on attentional effort. No simple correspondence between node signal amplitude change and node connectivity modulations was found, thus network modeling provides novel information beyond what is revealed by conventional task-fMRI analysis. The current decoding of attentional states confirms that edge connectivity contains highly predictive information about the mental state of the individual, and the approach shows promise for the utilization in clinical contexts.

  7. Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function

    PubMed Central

    Braun, Urs; Schäfer, Axel; Rausch, Franziska; Schweiger, Janina I.; Bilek, Edda; Erk, Susanne; Romanczuk-Seiferth, Nina; Grimm, Oliver; Geiger, Lena S.; Haddad, Leila; Otto, Kristina; Mohnke, Sebastian; Heinz, Andreas; Zink, Mathias; Walter, Henrik; Schwarz, Emanuel; Meyer-Lindenberg, Andreas; Tost, Heike

    2016-01-01

    Schizophrenia is increasingly recognized as a disorder of distributed neural dynamics, but the molecular and genetic contributions are poorly understood. Recent work highlights a role for altered N-methyl-d-aspartate (NMDA) receptor signaling and related impairments in the excitation–inhibitory balance and synchrony of large-scale neural networks. Here, we combined a pharmacological intervention with novel techniques from dynamic network neuroscience applied to functional magnetic resonance imaging (fMRI) to identify alterations in the dynamic reconfiguration of brain networks related to schizophrenia genetic risk and NMDA receptor hypofunction. We quantified “network flexibility,” a measure of the dynamic reconfiguration of the community structure of time-variant brain networks during working memory performance. Comparing 28 patients with schizophrenia, 37 unaffected first-degree relatives, and 139 healthy controls, we detected significant differences in network flexibility [F(2,196) = 6.541, P = 0.002] in a pattern consistent with the assumed genetic risk load of the groups (highest for patients, intermediate for relatives, and lowest for controls). In an observer-blinded, placebo-controlled, randomized, cross-over pharmacological challenge study in 37 healthy controls, we further detected a significant increase in network flexibility as a result of NMDA receptor antagonism with 120 mg dextromethorphan [F(1,34) = 5.291, P = 0.028]. Our results identify a potential dynamic network intermediate phenotype related to the genetic liability for schizophrenia that manifests as altered reconfiguration of brain networks during working memory. The phenotype appears to be influenced by NMDA receptor antagonism, consistent with a critical role for glutamate in the temporal coordination of neural networks and the pathophysiology of schizophrenia. PMID:27791105

  8. Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function.

    PubMed

    Braun, Urs; Schäfer, Axel; Bassett, Danielle S; Rausch, Franziska; Schweiger, Janina I; Bilek, Edda; Erk, Susanne; Romanczuk-Seiferth, Nina; Grimm, Oliver; Geiger, Lena S; Haddad, Leila; Otto, Kristina; Mohnke, Sebastian; Heinz, Andreas; Zink, Mathias; Walter, Henrik; Schwarz, Emanuel; Meyer-Lindenberg, Andreas; Tost, Heike

    2016-11-01

    Schizophrenia is increasingly recognized as a disorder of distributed neural dynamics, but the molecular and genetic contributions are poorly understood. Recent work highlights a role for altered N-methyl-d-aspartate (NMDA) receptor signaling and related impairments in the excitation-inhibitory balance and synchrony of large-scale neural networks. Here, we combined a pharmacological intervention with novel techniques from dynamic network neuroscience applied to functional magnetic resonance imaging (fMRI) to identify alterations in the dynamic reconfiguration of brain networks related to schizophrenia genetic risk and NMDA receptor hypofunction. We quantified "network flexibility," a measure of the dynamic reconfiguration of the community structure of time-variant brain networks during working memory performance. Comparing 28 patients with schizophrenia, 37 unaffected first-degree relatives, and 139 healthy controls, we detected significant differences in network flexibility [F(2,196) = 6.541, P = 0.002] in a pattern consistent with the assumed genetic risk load of the groups (highest for patients, intermediate for relatives, and lowest for controls). In an observer-blinded, placebo-controlled, randomized, cross-over pharmacological challenge study in 37 healthy controls, we further detected a significant increase in network flexibility as a result of NMDA receptor antagonism with 120 mg dextromethorphan [F(1,34) = 5.291, P = 0.028]. Our results identify a potential dynamic network intermediate phenotype related to the genetic liability for schizophrenia that manifests as altered reconfiguration of brain networks during working memory. The phenotype appears to be influenced by NMDA receptor antagonism, consistent with a critical role for glutamate in the temporal coordination of neural networks and the pathophysiology of schizophrenia.

  9. 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.

  10. 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

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

    PubMed

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

    2014-11-01

    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.

  12. Modulation of the brain's functional network architecture in the transition from wake to sleep.

    PubMed

    Larson-Prior, Linda J; Power, Jonathan D; Vincent, Justin L; Nolan, Tracy S; Coalson, Rebecca S; Zempel, John; Snyder, Abraham Z; Schlaggar, Bradley L; Raichle, Marcus E; Petersen, Steven E

    2011-01-01

    The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes.

  13. The social network-network: size is predicted by brain structure and function in the amygdala and paralimbic regions.

    PubMed

    Von Der Heide, Rebecca; Vyas, Govinda; Olson, Ingrid R

    2014-12-01

    The social brain hypothesis proposes that the large size of the primate neocortex evolved to support complex and demanding social interactions. Accordingly, recent studies have reported correlations between the size of an individual's social network and the density of gray matter (GM) in regions of the brain implicated in social cognition. However, the reported relationships between GM density and social group size are somewhat inconsistent with studies reporting correlations in different brain regions. One factor that might account for these discrepancies is the use of different measures of social network size (SNS). This study used several measures of SNS to assess the relationships SNS and GM density. The second goal of this study was to test the relationship between social network measures and functional brain activity. Participants performed a social closeness task using photos of their friends and unknown people. Across the VBM and functional magnetic resonance imaging analyses, individual differences in SNS were consistently related to structural and functional differences in three regions: the left amygdala, right amygdala and the right entorhinal/ventral anterior temporal cortex.

  14. 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

  15. 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.

  16. Changes of Functional Brain Networks in Major Depressive Disorder: A Graph Theoretical Analysis of Resting-State fMRI.

    PubMed

    Ye, Ming; Yang, Tianliang; Qing, Peng; Lei, Xu; Qiu, Jiang; Liu, Guangyuan

    2015-01-01

    Recent developments in graph theory have heightened the need for investigating the disruptions in the topological structure of functional brain network in major depressive disorder (MDD). In this study, we employed resting-state functional magnetic resonance imaging (fMRI) and graph theory to examine the whole-brain functional networks among 42 MDD patients and 42 healthy controls. Our results showed that compared with healthy controls, MDD patients showed higher local efficiency and modularity. Furthermore, MDD patients showed altered nodal centralities of many brain regions, including hippocampus, temporal cortex, anterior cingulate gyrus and dorsolateral prefrontal gyrus, mainly located in default mode network and cognitive control network. Together, our results suggested that MDD was associated with disruptions in the topological structure of functional brain networks, and provided new insights concerning the pathophysiological mechanisms of MDD.

  17. Tau Pathology Distribution in Alzheimer's disease Corresponds Differentially to Cognition-Relevant Functional Brain Networks.

    PubMed

    Hansson, Oskar; Grothe, Michel J; Strandberg, Tor Olof; Ohlsson, Tomas; Hägerström, Douglas; Jögi, Jonas; Smith, Ruben; Schöll, Michael

    2017-01-01

    Neuropathological studies have shown that the typical neurofibrillary pathology of hyperphosphorylated tau protein in Alzheimer's disease (AD) preferentially affects specific brain regions whereas others remain relatively spared. It has been suggested that the distinct regional distribution profile of tau pathology in AD may be a consequence of the intrinsic network structure of the human brain. The spatially distributed brain regions that are most affected by the spread of tau pathology may hence reflect an interconnected neuronal system. Here, we characterized the brain-wide regional distribution profile of tau pathology in AD using (18)F-AV 1451 tau-sensitive positron emission tomography (PET) imaging, and studied this pattern in relation to the functional network organization of the human brain. Specifically, we quantified the spatial correspondence of the regional distribution pattern of PET-evidenced tau pathology in AD with functional brain networks characterized by large-scale resting state functional magnetic resonance imaging (rs-fMRI) data in healthy subjects. Regional distribution patterns of increased PET-evidenced tau pathology in AD compared to controls were characterized in two independent samples of prodromal and manifest AD cases (the Swedish BioFINDER study, n = 44; the ADNI study, n = 35). In the BioFINDER study we found that the typical AD tau pattern involved predominantly inferior, medial, and lateral temporal cortical areas, as well as the precuneus/posterior cingulate, and lateral parts of the parietal and occipital cortex. This pattern overlapped primarily with the dorsal attention, and to some extent with higher visual, limbic and parts of the default-mode network. PET-evidenced tau pathology in the ADNI replication sample, which represented a more prodromal group of AD cases, was less pronounced but showed a highly similar spatial distribution profile, suggesting an earlier-stage snapshot of a consistently progressing regional pattern

  18. Tau Pathology Distribution in Alzheimer's disease Corresponds Differentially to Cognition-Relevant Functional Brain Networks

    PubMed Central

    Hansson, Oskar; Grothe, Michel J.; Strandberg, Tor Olof; Ohlsson, Tomas; Hägerström, Douglas; Jögi, Jonas; Smith, Ruben; Schöll, Michael

    2017-01-01

    Neuropathological studies have shown that the typical neurofibrillary pathology of hyperphosphorylated tau protein in Alzheimer's disease (AD) preferentially affects specific brain regions whereas others remain relatively spared. It has been suggested that the distinct regional distribution profile of tau pathology in AD may be a consequence of the intrinsic network structure of the human brain. The spatially distributed brain regions that are most affected by the spread of tau pathology may hence reflect an interconnected neuronal system. Here, we characterized the brain-wide regional distribution profile of tau pathology in AD using 18F-AV 1451 tau-sensitive positron emission tomography (PET) imaging, and studied this pattern in relation to the functional network organization of the human brain. Specifically, we quantified the spatial correspondence of the regional distribution pattern of PET-evidenced tau pathology in AD with functional brain networks characterized by large-scale resting state functional magnetic resonance imaging (rs-fMRI) data in healthy subjects. Regional distribution patterns of increased PET-evidenced tau pathology in AD compared to controls were characterized in two independent samples of prodromal and manifest AD cases (the Swedish BioFINDER study, n = 44; the ADNI study, n = 35). In the BioFINDER study we found that the typical AD tau pattern involved predominantly inferior, medial, and lateral temporal cortical areas, as well as the precuneus/posterior cingulate, and lateral parts of the parietal and occipital cortex. This pattern overlapped primarily with the dorsal attention, and to some extent with higher visual, limbic and parts of the default-mode network. PET-evidenced tau pathology in the ADNI replication sample, which represented a more prodromal group of AD cases, was less pronounced but showed a highly similar spatial distribution profile, suggesting an earlier-stage snapshot of a consistently progressing regional pattern. In

  19. 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.

  20. 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.

  1. Exploring Patterns of Alteration in Alzheimer's Disease Brain Networks: A Combined Structural and Functional Connectomics Analysis

    PubMed Central

    Palesi, Fulvia; Castellazzi, Gloria; Casiraghi, Letizia; Sinforiani, Elena; Vitali, Paolo; Gandini Wheeler-Kingshott, Claudia A. M.; D'Angelo, Egidio

    2016-01-01

    Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a severe derangement of cognitive functions, primarily memory, in elderly subjects. As far as the functional impairment is concerned, growing evidence supports the “disconnection syndrome” hypothesis. Recent investigations using fMRI have revealed a generalized alteration of resting state networks (RSNs) in patients affected by AD and mild cognitive impairment (MCI). However, it was unclear whether the changes in functional connectivity were accompanied by corresponding structural network changes. In this work, we have developed a novel structural/functional connectomic approach: resting state fMRI was used to identify the functional cortical network nodes and diffusion MRI to reconstruct the fiber tracts to give a weight to internodal subcortical connections. Then, local and global efficiency were determined for different networks, exploring specific alterations of integration and segregation patterns in AD and MCI patients compared to healthy controls (HC). In the default mode network (DMN), that was the most affected, axonal loss, and reduced axonal integrity appeared to compromise both local and global efficiency along posterior-anterior connections. In the basal ganglia network (BGN), disruption of white matter integrity implied that main alterations occurred in local microstructure. In the anterior insular network (AIN), neuronal loss probably subtended a compromised communication with the insular cortex. Cognitive performance, evaluated by neuropsychological examinations, revealed a dependency on integration and segregation of brain networks. These findings are indicative of the fact that cognitive deficits in AD could be associated not only with cortical alterations (revealed by fMRI) but also with subcortical alterations (revealed by diffusion MRI) that extend beyond the areas primarily damaged by neurodegeneration, toward the support of an emerging concept of AD as a

  2. Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection.

    PubMed

    Fornito, Alex; Harrison, Ben J; Zalesky, Andrew; Simons, Jon S

    2012-07-31

    Analyses of functional interactions between large-scale brain networks have identified two broad systems that operate in apparent competition or antagonism with each other. One system, termed the default mode network (DMN), is thought to support internally oriented processing. The other system acts as a generic external attention system (EAS) and mediates attention to exogenous stimuli. Reports that the DMN and EAS show anticorrelated activity across a range of experimental paradigms suggest that competition between these systems supports adaptive behavior. Here, we used functional MRI to characterize functional interactions between the DMN and different EAS components during performance of a recollection task known to coactivate regions of both networks. Using methods to isolate task-related, context-dependent changes in functional connectivity between these systems, we show that increased cooperation between the DMN and a specific right-lateralized frontoparietal component of the EAS is associated with more rapid memory recollection. We also show that these cooperative dynamics are facilitated by a dynamic reconfiguration of the functional architecture of the DMN into core and transitional modules, with the latter serving to enhance integration with frontoparietal regions. In particular, the right posterior cingulate cortex may act as a critical information-processing hub that provokes these context-dependent reconfigurations from an intrinsic or default state of antagonism. Our findings highlight the dynamic, context-dependent nature of large-scale brain dynamics and shed light on their contribution to individual differences in behavior.

  3. Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

    NASA Astrophysics Data System (ADS)

    Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

    2016-10-01

    In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

  4. Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study.

    PubMed

    Tian, Lixia; Wang, Jinhui; Yan, Chaogan; He, Yong

    2011-01-01

    We employed resting-state functional MRI (R-fMRI) to investigate hemisphere- and gender-related differences in the topological organization of human brain functional networks. Brain networks were first constructed by measuring inter-regional temporal correlations of R-fMRI data within each hemisphere in 86 young, healthy, right-handed adults (38 males and 48 females) followed by a graph-theory analysis. The hemispheric networks exhibit small-world attributes (high clustering and short paths) that are compatible with previous results in the whole-brain functional networks. Furthermore, we found that compared with females, males have a higher normalized clustering coefficient in the right hemispheric network but a lower clustering coefficient in the left hemispheric network, suggesting a gender-hemisphere interaction. Moreover, we observed significant hemisphere-related differences in the regional nodal characteristics in various brain regions, such as the frontal and occipital regions (leftward asymmetry) and the temporal regions (rightward asymmetry), findings that are consistent with previous studies of brain structural and functional asymmetries. Together, our results suggest that the topological organization of human brain functional networks is associated with gender and hemispheres, and they provide insights into the understanding of functional substrates underlying individual differences in behaviors and cognition.

  5. Sustained NMDA receptor hypofunction induces compromised neural systems integration and schizophrenia-like alterations in functional brain networks.

    PubMed

    Dawson, Neil; Xiao, Xiaolin; McDonald, Martin; Higham, Desmond J; Morris, Brian J; Pratt, Judith A

    2014-02-01

    Compromised functional integration between cerebral subsystems and dysfunctional brain network organization may underlie the neurocognitive deficits seen in psychiatric disorders. Applying topological measures from network science to brain imaging data allows the quantification of complex brain network connectivity. While this approach has recently been used to further elucidate the nature of brain dysfunction in schizophrenia, the value of applying this approach in preclinical models of psychiatric disease has not been recognized. For the first time, we apply both established and recently derived algorithms from network science (graph theory) to functional brain imaging data from rats treated subchronically with the N-methyl-D-aspartic acid (NMDA) receptor antagonist phencyclidine (PCP). We show that subchronic PCP treatment induces alterations in the global properties of functional brain networks akin to those reported in schizophrenia. Furthermore, we show that subchronic PCP treatment induces compromised functional integration between distributed neural systems, including between the prefrontal cortex and hippocampus, that have established roles in cognition through, in part, the promotion of thalamic dysconnectivity. We also show that subchronic PCP treatment promotes the functional disintegration of discrete cerebral subsystems and also alters the connectivity of neurotransmitter systems strongly implicated in schizophrenia. Therefore, we propose that sustained NMDA receptor hypofunction contributes to the pathophysiology of dysfunctional brain network organization in schizophrenia.

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

    PubMed

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

    2015-05-01

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

  7. Handedness- and Brain Size-Related Efficiency Differences in Small-World Brain Networks: A Resting-State Functional Magnetic Resonance Imaging Study

    PubMed Central

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

    2015-01-01

    Abstract The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size. PMID:25535788

  8. Alterations of resting state functional network connectivity in the brain of nicotine and alcohol users.

    PubMed

    Vergara, Victor M; Liu, Jingyu; Claus, Eric D; Hutchison, Kent; Calhoun, Vince

    2016-11-15

    Alcohol and nicotine intake result in neurological alterations at the circuit level. Resting state functional connectivity has shown great potential in identifying these alterations. However, current studies focus on specific seeds and leave out many brain regions where effects might exist. The present study uses a data driven technique for brain segmentation covering the whole brain. Functional magnetic-resonance-imaging (fMRI) data were collected from 188 subjects:51 non-substance consumption controls (CTR), 36 smoking-and-drinking subjects (SAD), 28 drinkers (DRN), and 73 smokers (SMK). Data were processed using group independent component analysis to derive resting state networks (RSN). The resting state functional network connectivity (rsFNC) was then calculated through correlation between time courses. One-way ANOVA tests were used to detect rsFNC differences among the four groups. A total of 50 ANOVA tests were significant after multi-comparison correction. Results delineate a general pattern of hypo-connectivity in the substance consumers. Precuneus, postcentral gyrus, insula and visual cortex were the main brain areas with rsFNC reduction suggesting reduced interoceptive awareness in drinkers. In addition, connectivity reduction between postcentral and one RSN covering right fusiform and lingual gyri showed significant association with severity of hazardous drinking. In smokers, connectivity changes agreed with the idea of a shift towards endogenous information processing, represented by the DMN. Hypo-connectivity between thalamus and putamen was observed in smokers. In contrast, the angular gyrus showed hyper-connectivity with the precuneus linked to smoking and significantly correlated with nicotine dependence severity. In spite of the presence of common effects, our results suggest that particular effects of alcohol and nicotine can be separated and identified. Results also suggest that concurrent use of both substances affects brain connectivity in a

  9. 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

    2014-08-28

    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.

  10. 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

  11. 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.

  12. 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.

  13. Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks.

    PubMed

    Petersen, Alexander; Zhao, Jianyang; Carmichael, Owen; Müller, Hans-Georg

    2016-09-01

    In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.

  14. 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

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

    PubMed Central

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

    2013-01-01

    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

  16. Moment to moment variability in functional brain networks during cognitive activity in EEG data.

    PubMed

    Dasari, Naga M; Nandagopal, Nanda D; Ramasamy, Vijayalaxmi; Cocks, Bernadine; Thomas, Bruce H; Dahal, Nabaraj; Gaertner, Paul

    2015-09-01

    Functional brain networks (FBNs) are gaining increasing attention in computational neuroscience due to their ability to reveal dynamic interdependencies between brain regions. The dynamics of such networks during cognitive activity between stimulus and response using multi-channel electroencephalogram (EEG), recorded from 16 healthy human participants are explored in this research. Successive EEG segments of 500[Formula: see text]ms duration starting from the onset of cognitive stimulation have been used to analyze and understand the cognitive dynamics. The approach employs a combination of signal processing techniques, nonlinear statistical measures and graph-theoretical analysis. The efficacy of this approach in detecting and tracking cognitive load induced changes in EEG data is clearly demonstrated using graph metrics. It is revealed that most cognitive activity occurs within approximately 500[Formula: see text]ms of the stimulus presentation in addition to temporal variability in the FBNs. It is shown that mutual information (MI), a nonlinear measure, produces good correlations between the EEG channels thus enabling the construction of FBNs which are sensitive to cognitive load induced changes in EEG. Analyses of the dynamics of FBNs and the visualization approach reveal hard to detect subtle changes in cognitive function and hence may lead to a better understanding of cognitive processing in the brain. The techniques exploited have the potential to detect human cognitive dysfunction (impairments).

  17. Comparison of Local Information Indices Applied in Resting State Functional Brain Network Connectivity Prediction

    PubMed Central

    Cheng, Chen; Chen, Junjie; Cao, Xiaohua; Guo, Hao

    2016-01-01

    Anatomical distance has been widely used to predict functional connectivity because of the potential relationship between structural connectivity and functional connectivity. The basic implicit assumption of this method is “distance penalization.” But studies have shown that one-parameter model (anatomical distance) cannot account for the small-worldness, modularity, and degree distribution of normal human brain functional networks. Two local information indices–common neighbor (CN) and preferential attachment index (PA), are introduced into the prediction model as another parameter to emulate many key topological of brain functional networks in the previous study. In addition to these two indices, many other local information indices can be chosen for investigation. Different indices evaluate local similarity from different perspectives. Currently, we still have no idea about how to select local information indices to achieve higher predicted accuracy of functional connectivity. Here, seven local information indices are chosen, including CN, hub depressed index (HDI), hub promoted index (HPI), Leicht-Holme-Newman index (LHN-I), Sørensen index (SI), PA, and resource allocation index (RA). Statistical analyses were performed on eight network topological properties to evaluate the predictions. Analysis shows that different prediction models have different performances in terms of simulating topological properties and most of the predicted network properties are close to the real data. There are four topological properties whose average relative error is less than 5%, including characteristic path length, clustering coefficient, global efficiency, and local efficiency. CN model shows the most accurate predictions. Statistical analysis reveals that five properties within the CN-predicted network do not differ significantly from the real data (P > 0.05, false-discovery rate method corrected for seven comparisons). PA model shows the worst prediction performance

  18. Theoretical model for mesoscopic-level scale-free self-organization of functional brain networks.

    PubMed

    Piersa, Jaroslaw; Piekniewski, Filip; Schreiber, Tomasz

    2010-11-01

    In this paper, we provide theoretical and numerical analysis of a geometric activity flow network model which is aimed at explaining mathematically the scale-free functional graph self-organization phenomena emerging in complex nervous systems at a mesoscale level. In our model, each unit corresponds to a large number of neurons and may be roughly seen as abstracting the functional behavior exhibited by a single voxel under functional magnetic resonance imaging (fMRI). In the course of the dynamics, the units exchange portions of formal charge, which correspond to waves of activity in the underlying microscale neuronal circuit. The geometric model abstracts away the neuronal complexity and is mathematically tractable, which allows us to establish explicit results on its ground states and the resulting charge transfer graph modeling functional graph of the network. We show that, for a wide choice of parameters and geometrical setups, our model yields a scale-free functional connectivity with the exponent approaching 2, which is in agreement with previous empirical studies based on fMRI. The level of universality of the presented theory allows us to claim that the model does shed light on mesoscale functional self-organization phenomena of the nervous system, even without resorting to closer details of brain connectivity geometry which often remain unknown. The material presented here significantly extends our previous work where a simplified mean-field model in a similar spirit was constructed, ignoring the underlying network geometry.

  19. Selective development of anticorrelated networks in the intrinsic functional organization of the human brain.

    PubMed

    Chai, Xiaoqian J; Ofen, Noa; Gabrieli, John D E; Whitfield-Gabrieli, Susan

    2014-03-01

    We examined the normal development of intrinsic functional connectivity of the default network (brain regions typically deactivated for attention-demanding tasks) as measured by resting-state fMRI in children, adolescents, and young adults ages 8-24 years. We investigated both positive and negative correlations and employed analysis methods that allowed for valid interpretation of negative correlations and that also minimized the influence of motion artifacts that are often confounds in developmental neuroimaging. As age increased, there were robust developmental increases in negative correlations, including those between medial pFC (MPFC) and dorsolateral pFC (DLPFC) and between lateral parietal cortices and brain regions associated with the dorsal attention network. Between multiple regions, these correlations reversed from being positive in children to negative in adults. Age-related changes in positive correlations within the default network were below statistical threshold after controlling for motion. Given evidence in adults that greater negative correlation between MPFC and DLPFC is associated with superior cognitive performance, the development of an intrinsic anticorrelation between MPFC and DLPFC may be a marker of the large growth of working memory and executive functions that occurs from childhood to young adulthood.

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

    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.

  1. 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

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

    PubMed

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

    2016-08-26

    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.

  3. Typical and Atypical Development of Functional Human Brain Networks: Insights from Resting-State fMRI

    PubMed Central

    Uddin, Lucina Q.; Supekar, Kaustubh; Menon, Vinod

    2010-01-01

    Over the past several decades, structural MRI studies have provided remarkable insights into human brain development by revealing the trajectory of gray and white matter maturation from childhood to adolescence and adulthood. In parallel, functional MRI studies have demonstrated changes in brain activation patterns accompanying cognitive development. Despite these advances, studying the maturation of functional brain networks underlying brain development continues to present unique scientific and methodological challenges. Resting-state fMRI (rsfMRI) has emerged as a novel method for investigating the development of large-scale functional brain networks in infants and young children. We review existing rsfMRI developmental studies and discuss how this method has begun to make significant contributions to our understanding of maturing brain organization. In particular, rsfMRI has been used to complement studies in other modalities investigating the emergence of functional segregation and integration across short and long-range connections spanning the entire brain. We show that rsfMRI studies help to clarify and reveal important principles of functional brain development, including a shift from diffuse to focal activation patterns, and simultaneous pruning of local connectivity and strengthening of long-range connectivity with age. The insights gained from these studies also shed light on potentially disrupted functional networks underlying atypical cognitive development associated with neurodevelopmental disorders. We conclude by identifying critical gaps in the current literature, discussing methodological issues, and suggesting avenues for future research. PMID:20577585

  4. 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

  5. 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.

  6. Small-World Brain Functional Networks in Children With Attention-Deficit/Hyperactivity Disorder Revealed by EEG Synchrony.

    PubMed

    Liu, Tian; Chen, Yanni; Lin, Pan; Wang, Jue

    2015-07-01

    We investigated the topologic properties of human brain attention-related functional networks associated with Multi-Source Interference Task (MSIT) performance using electroencephalography (EEG). Data were obtained from 13 children diagnosed with attention-deficit/hyperactivity disorder (ADHD) and 13 normal control children. Functional connectivity between all pairwise combinations of EEG channels was established by calculating synchronization likelihood (SL). The cluster coefficients and path lengths were computed as a function of degree K. The results showed that brain attention functional networks of normal control subjects had efficient small-world topologic properties, whereas these topologic properties were altered in ADHD. In particular, increased local characteristics combined with decreased global characteristics in ADHD led to a disorder-related shift of the network topologic structure toward ordered networks. These findings are consistent with a hypothesis of dysfunctional segregation and integration of the brain in ADHD, and enhance our understanding of the underlying pathophysiologic mechanism of this illness.

  7. 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

  8. 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

  9. 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.

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

    PubMed

    Ionta, Silvio; Martuzzi, Roberto; Salomon, Roy; Blanke, Olaf

    2014-12-01

    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.

  11. Structural brain network constrained neuroimaging marker identification for predicting cognitive functions.

    PubMed

    De, Wang; Nie, Feiping; Huang, Heng; Yan, Jingwen; Risacher, Shannon L; Saykin, Andrew J; Shen, Li

    2013-01-01

    Neuroimaging markers have been widely used to predict the cognitive functions relevant to the progression of Alzheimer's disease (AD). Most previous studies identify the imaging markers without considering the brain structural correlations between neuroimaging measures. However, many neuroimaging markers interrelate and work together to reveal the cognitive functions, such that these relevant markers should be selected together as the phenotypic markers. To solve this problem, in this paper, we propose a novel network constrained feature selection (NCFS) model to identify the neuroimaging markers guided by the structural brain network, which is constructed by the sparse representation method such that the interrelations between neuroimaging features are encoded into probabilities. Our new methods are evaluated by the MRI and AV45-PET data from ADNI-GO and ADNI-2 (Alzheimer's Disease Neuroimaging Initiative). In all cognitive function prediction tasks, our new NCFS method outperforms other state-of-the-art regression approaches. Meanwhile, we show that the new method can select the correlated imaging markers, which are ignored by the competing approaches.

  12. 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

  13. 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.

  14. 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 brain

  15. Network Analysis of Functional Brain Connectivity Driven by Gamma-Band Auditory Steady-State Response in Auditory Hallucinations.

    PubMed

    Ying, Jun; Zhou, Dan; Lin, Ke; Gao, Xiaorong

    The auditory steady-state response (ASSR) may reflect activity from different regions of the brain. Particularly, it was reported that the gamma-band ASSR plays an important role in working memory, speech understanding, and recognition. Traditionally, the ASSR has been determined by power spectral density analysis, which cannot detect the exact overall distributed properties of the ASSR. Functional network analysis has recently been applied in electroencephalography studies. Previous studies on resting or working state found a small-world organization of the brain network. Some researchers have studied dysfunctional networks caused by diseases. The present study investigates the brain connection networks of schizophrenia patients with auditory hallucinations during an ASSR task. A directed transfer function is utilized to estimate the brain connectivity patterns. Moreover, the structures of brain networks are analyzed by converting the connectivity matrices into graphs. It is found that for normal subjects, network connections are mainly distributed at the central and frontal-temporal regions. This indicates that the central regions act as transmission hubs of information under ASSR stimulation. For patients, network connections seem unordered. The finding that the path length was larger in patients compared to that in normal subjects under most thresholds provides insight into the structures of connectivity patterns. The results suggest that there are more synchronous oscillations that cover a long distance on the cortex but a less efficient network for patients with auditory hallucinations.

  16. 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

  17. Brain networks for visual creativity: a functional connectivity study of planning a visual artwork

    PubMed Central

    De Pisapia, Nicola; Bacci, Francesca; Parrott, Danielle; Melcher, David

    2016-01-01

    Throughout recorded history, and across cultures, humans have made visual art. In recent years, the neural bases of creativity, including artistic creativity, have become a topic of interest. In this study we investigated the neural bases of the visual creative process with both professional artists and a group of control participants. We tested the idea that creativity (planning an artwork) would influence the functional connectivity between regions involved in the default mode network (DMN), implicated in divergent thinking and generating novel ideas, and the executive control network (EN), implicated in evaluating and selecting ideas. We measured functional connectivity with functional Magnetic Resonance Imaging (fMRI) during three different conditions: rest, visual imagery of the alphabet and planning an artwork to be executed immediately after the scanning session. Consistent with our hypothesis, we found stronger connectivity between areas of the DMN and EN during the creative task, and this difference was enhanced in professional artists. These findings suggest that creativity involves an expert balance of two brain networks typically viewed as being in opposition. PMID:27991592

  18. 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

  19. Evaluation of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Large-Scale Network Analysis Using Network-Based Statistic.

    PubMed

    Kaushal, Mayank; Oni-Orisan, Akinwunmi; Chen, Gang; Li, Wenjun; Leschke, Jack; Ward, B Douglas; Kalinosky, Benjamin; Budde, Matthew D; Schmit, Brian D; Li, Shi-Jiang; Muqeet, Vaishnavi; Kurpad, Shekar N

    2017-03-15

    Large-scale network analysis characterizes the brain as a complex network of nodes and edges to evaluate functional connectivity patterns. The utility of graph-based techniques has been demonstrated in an increasing number of resting-state functional MRI (rs-fMRI) studies in the normal and diseased brain. However, to our knowledge, graph theory has not been used to study the reorganization pattern of resting-state brain networks in patients with traumatic complete spinal cord injury (SCI). In the present analysis, we applied a graph-theoretical approach to explore changes to global brain network architecture as a result of SCI. Fifteen subjects with chronic (> 2 years) complete (American Spinal Injury Association [ASIA] A) cervical SCI and 15 neurologically intact controls were scanned using rs-fMRI. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI) or nodes. The average time series was extracted at each node, and correlation analysis was performed between every pair of nodes. A functional connectivity matrix for each subject was then generated. Subsequently, the matrices were averaged across groups, and network changes were evaluated between groups using the network-based statistic (NBS) method. Our results showed decreased connectivity in a subnetwork of the whole brain in SCI compared with control subjects. Upon further examination, increased connectivity was observed in a subnetwork of the sensorimotor cortex and cerebellum network in SCI. In conclusion, our findings emphasize the applicability of NBS to study functional connectivity architecture in diseased brain states. Further, we show reorganization of large-scale resting-state brain networks in traumatic SCI, with potential prognostic and therapeutic implications.

  20. Resting State fMRI in Mice Reveals Anesthesia Specific Signatures of Brain Functional Networks and Their Interactions

    PubMed Central

    Bukhari, Qasim; Schroeter, Aileen; Cole, David M.; Rudin, Markus

    2017-01-01

    fMRI studies in mice typically require the use of anesthetics. Yet, it is known that anesthesia alters responses to stimuli or functional networks at rest. In this work, we have used Dual Regression analysis Network Modeling to investigate the effects of two commonly used anesthetics, isoflurane and medetomidine, on rs-fMRI derived functional networks, and in particular to what extent anesthesia affected the interaction within and between these networks. Experimental data have been used from a previous study (Grandjean et al., 2014). We applied multivariate ICA analysis and Dual Regression to infer the differences in functional connectivity between isoflurane- and medetomidine-anesthetized mice. Further network analysis was performed to investigate within- and between-network connectivity differences between these anesthetic regimens. The results revealed five major networks in the mouse brain: lateral cortical, associative cortical, default mode, subcortical, and thalamic network. The anesthesia regime had a profound effect both on within- and between-network interactions. Under isoflurane anesthesia predominantly intra- and inter-cortical interactions have been observed, with only minor interactions involving subcortical structures and in particular attenuated cortico-thalamic connectivity. In contrast, medetomidine-anesthetized mice displayed subcortical functional connectivity including interactions between cortical and thalamic ICA components. Combining the two anesthetics at low dose resulted in network interaction that constituted the superposition of the interaction observed for each anesthetic alone. The study demonstrated that network modeling is a promising tool for analyzing the brain functional architecture in mice and comparing alterations therein caused by different physiological or pathological states. Understanding the differential effects of anesthetics on brain networks and their interaction is essential when interpreting fMRI data recorded under

  1. Resting State fMRI in Mice Reveals Anesthesia Specific Signatures of Brain Functional Networks and Their Interactions.

    PubMed

    Bukhari, Qasim; Schroeter, Aileen; Cole, David M; Rudin, Markus

    2017-01-01

    fMRI studies in mice typically require the use of anesthetics. Yet, it is known that anesthesia alters responses to stimuli or functional networks at rest. In this work, we have used Dual Regression analysis Network Modeling to investigate the effects of two commonly used anesthetics, isoflurane and medetomidine, on rs-fMRI derived functional networks, and in particular to what extent anesthesia affected the interaction within and between these networks. Experimental data have been used from a previous study (Grandjean et al., 2014). We applied multivariate ICA analysis and Dual Regression to infer the differences in functional connectivity between isoflurane- and medetomidine-anesthetized mice. Further network analysis was performed to investigate within- and between-network connectivity differences between these anesthetic regimens. The results revealed five major networks in the mouse brain: lateral cortical, associative cortical, default mode, subcortical, and thalamic network. The anesthesia regime had a profound effect both on within- and between-network interactions. Under isoflurane anesthesia predominantly intra- and inter-cortical interactions have been observed, with only minor interactions involving subcortical structures and in particular attenuated cortico-thalamic connectivity. In contrast, medetomidine-anesthetized mice displayed subcortical functional connectivity including interactions between cortical and thalamic ICA components. Combining the two anesthetics at low dose resulted in network interaction that constituted the superposition of the interaction observed for each anesthetic alone. The study demonstrated that network modeling is a promising tool for analyzing the brain functional architecture in mice and comparing alterations therein caused by different physiological or pathological states. Understanding the differential effects of anesthetics on brain networks and their interaction is essential when interpreting fMRI data recorded under

  2. Subanesthetic Ketamine Treatment Promotes Abnormal Interactions between Neural Subsystems and Alters the Properties of Functional Brain Networks

    PubMed Central

    Dawson, Neil; McDonald, Martin; Higham, Desmond J; Morris, Brian J; Pratt, Judith A

    2014-01-01

    Acute treatment with subanesthetic ketamine, a non-competitive N-methyl-D-aspartic acid (NMDA) receptor antagonist, is widely utilized as a translational model for schizophrenia. However, how acute NMDA receptor blockade impacts on brain functioning at a systems level, to elicit translationally relevant symptomatology and behavioral deficits, has not yet been determined. Here, for the first time, we apply established and recently validated topological measures from network science to brain imaging data gained from ketamine-treated mice to elucidate how acute NMDA receptor blockade impacts on the properties of functional brain networks. We show that the effects of acute ketamine treatment on the global properties of these networks are divergent from those widely reported in schizophrenia. Where acute NMDA receptor blockade promotes hyperconnectivity in functional brain networks, pronounced dysconnectivity is found in schizophrenia. We also show that acute ketamine treatment increases the connectivity and importance of prefrontal and thalamic brain regions in brain networks, a finding also divergent to alterations seen in schizophrenia. In addition, we characterize how ketamine impacts on bipartite functional interactions between neural subsystems. A key feature includes the enhancement of prefrontal cortex (PFC)-neuromodulatory subsystem connectivity in ketamine-treated animals, a finding consistent with the known effects of ketamine on PFC neurotransmitter levels. Overall, our data suggest that, at a systems level, acute ketamine-induced alterations in brain network connectivity do not parallel those seen in chronic schizophrenia. Hence, the mechanisms through which acute ketamine treatment induces translationally relevant symptomatology may differ from those in chronic schizophrenia. Future effort should therefore be dedicated to resolve the conflicting observations between this putative translational model and schizophrenia. PMID:24492765

  3. Obesity is marked by distinct functional connectivity in brain networks involved in food reward and salience.

    PubMed

    Wijngaarden, M A; Veer, I M; Rombouts, S A R B; van Buchem, M A; Willems van Dijk, K; Pijl, H; van der Grond, J

    2015-01-01

    We hypothesized that brain circuits involved in reward and salience respond differently to fasting in obese versus lean individuals. We compared functional connectivity networks related to food reward and saliency after an overnight fast (baseline) and after a prolonged fast of 48 h in lean versus obese subjects. We included 13 obese (2 males, 11 females, BMI 35.4 ± 1.2 kg/m(2), age 31 ± 3 years) and 11 lean subjects (2 males, 9 females, BMI 23.2 ± 0.5 kg/m(2), age 28 ± 3 years). Resting-state functional magnetic resonance imaging scans were made after an overnight fast (baseline) and after a prolonged 48 h fast. Functional connectivity of the amygdala, hypothalamus and posterior cingulate cortex (default-mode) networks was assessed using seed-based correlations. At baseline, we found a stronger connectivity between hypothalamus and left insula in the obese subjects. This effect diminished upon the prolonged fast. After prolonged fasting, connectivity of the hypothalamus with the dorsal anterior cingulate cortex (dACC) increased in lean subjects and decreased in obese subjects. Amygdala connectivity with the ventromedial prefrontal cortex was stronger in lean subjects at baseline, which did not change upon the prolonged fast. No differences in posterior cingulate cortex connectivity were observed. In conclusion, obesity is marked by alterations in functional connectivity networks involved in food reward and salience. Prolonged fasting differentially affected hypothalamic connections with the dACC and the insula between obese and lean subjects. Our data support the idea that food reward and nutrient deprivation are differently perceived and/or processed in obesity.

  4. The effects of cognitive-behavioral therapy on intrinsic functional brain networks in adults with attention-deficit/hyperactivity disorder.

    PubMed

    Wang, Xiaoli; Cao, Qingjiu; Wang, Jinhui; Wu, Zhaomin; Wang, Peng; Sun, Li; Cai, Taisheng; Wang, Yufeng

    2016-01-01

    Cognitive-behavioral therapy (CBT) is an efficacious psychological treatment for adults with attention-deficit/hyperactivity disorder (ADHD), but the neural processes underlying the benefits of CBT are not well understood. This study aims to unravel psychosocial mechanisms for treatment ADHD by exploring the effects of CBT on functional brain networks. Ten adults with ADHD were enrolled and resting-state functional magnetic resonance imaging scans were acquired before and after a 12-session CBT. Twelve age- and gender-matched healthy controls were also scanned. We constructed whole-brain functional connectivity networks using graph-theory approaches and further computed the changes of regional functional connectivity strength (rFCS) between pre- and post-CBT in ADHD for measuring the effects of CBT. The results showed that rFCS was increased in the fronto-parietal network and cerebellum, the brain regions that were most often affected by medication, in adults with ADHD following CBT. Furthermore, the enhanced functional coupling between bilateral superior parietal gyrus was positively correlated with the improvement of ADHD symptoms following CBT. Together, these findings provide evidence that CBT can selectively modulate the intrinsic network connectivity in the fronto-parietal network and cerebellum and suggest that the CBT may share common brain mechanism with the pharmacology in adults with ADHD.

  5. 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.

  6. Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity.

    PubMed

    Dong, Debo; Wang, Yulin; Chang, Xuebin; Luo, Cheng; Yao, Dezhong

    2017-03-11

    Schizophrenia is a complex mental disorder with disorganized communication among large-scale brain networks, as demonstrated by impaired resting-state functional connectivity (rsFC). Individual rsFC studies, however, vary greatly in their methods and findings. We searched for consistent patterns of network dysfunction in schizophrenia by using a coordinate-based meta-analysis. Fifty-six seed-based voxel-wise rsFC datasets from 52 publications (2115 patients and 2297 healthy controls) were included in this meta-analysis. Then, coordinates of seed regions of interest (ROI) and between-group effects were extracted and coded. Seed ROIs were categorized into seed networks by their location within an a priori template. Multilevel kernel density analysis was used to identify brain networks in which schizophrenia was linked to hyper-connectivity or hypo-connectivity with each a priori network. Our results showed that schizophrenia was characterized by hypo-connectivity within the default network (DN, self-related thought), affective network (AN, emotion processing), ventral attention network (VAN, processing of salience), thalamus network (TN, gating information) and somatosensory network (SS, involved in sensory and auditory perception). Additionally, hypo-connectivity between the VAN and TN, VAN and DN, VAN and frontoparietal network (FN, external goal-directed regulation), FN and TN, and FN and DN were found in schizophrenia. Finally, the only instance of hyper-connectivity in schizophrenia was observed between the AN and VAN. Our meta-analysis motivates an empirical foundation for a disconnected large-scale brain networks model of schizophrenia in which the salience processing network (VAN) plays the core role, and its imbalanced communication with other functional networks may underlie the core difficulty of patients to differentiate self-representation (inner world) and environmental salience processing (outside world).

  7. 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.

  8. Broad integration of expression maps and co-expression networks compassing novel gene functions in the brain.

    PubMed

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

    2014-11-10

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

  9. 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…

  10. A neural network that links brain function, white-matter structure and risky behavior.

    PubMed

    Kohno, Milky; Morales, Angelica M; Guttman, Zoe; London, Edythe D

    2017-04-01

    The ability to evaluate the balance between risk and reward and to adjust behavior accordingly is fundamental to adaptive decision-making. Although brain-imaging studies consistently have shown involvement of the dorsolateral prefrontal cortex, anterior insula and striatum during risky decision-making, activation in a neural network formed by these regions has not been linked to structural connectivity. Therefore, in this study, white-matter connectivity was measured with diffusion-weighted imaging in 40 healthy research participants who performed the Balloon Analogue Risk Task, a test of risky decision-making, during fMRI. Fractional anisotropy within a network that includes white-matter pathways connecting four regions (the prefrontal cortex, insula and midbrain to the striatum) was positively correlated with the number of risky choices and total amount earned on the task, and with the parametric modulation of activation in regions within the network to the level of risk during choice selection. Furthermore, analysis using a mixed model demonstrated how relationships of the parametric modulation of activation in each of the four aforementioned regions are related to risk probabilities, and how previous trial outcomes and task progression influence the choice to take risk. The present findings provide the first direct evidence that white-matter integrity is linked to function within previously identified components of a network that is activated during risky decision-making, and demonstrate that the integrity of white-matter tracts is critical in consolidating and processing signals between cortical and striatal circuits during the decision-making process.

  11. MRI Study on the Functional and Spatial Consistency of Resting State-Related Independent Components of the Brain Network

    PubMed Central

    Jeong, Bumseok; Kim, Ji-Woong

    2012-01-01

    Objective Resting-state networks (RSNs), including the default mode network (DMN), have been considered as markers of brain status such as consciousness, developmental change, and treatment effects. The consistency of functional connectivity among RSNs has not been fully explored, especially among resting-state-related independent components (RSICs). Materials and Methods This resting-state fMRI study addressed the consistency of functional connectivity among RSICs as well as their spatial consistency between 'at day 1' and 'after 4 weeks' in 13 healthy volunteers. Results We found that most RSICs, especially the DMN, are reproducible across time, whereas some RSICs were variable in either their spatial characteristics or their functional connectivity. Relatively low spatial consistency was found in the basal ganglia, a parietal region of left frontoparietal network, and the supplementary motor area. The functional connectivity between two independent components, the bilateral angular/supramarginal gyri/intraparietal lobule and bilateral middle temporal/occipital gyri, was decreased across time regardless of the correlation analysis method employed, (Pearson's or partial correlation). Conclusion RSICs showing variable consistency are different between spatial characteristics and functional connectivity. To understand the brain as a dynamic network, we recommend further investigation of both changes in the activation of specific regions and the modulation of functional connectivity in the brain network. PMID:22563263

  12. 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.

  13. Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory.

    PubMed

    Zhang, Gaoyan; Yao, Li; Shen, Jiahui; Yang, Yihong; Zhao, Xiaojie

    2015-05-01

    Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks.

  14. 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

  15. Study of brain functional network based on sample entropy of EEG under magnetic stimulation at PC6 acupoint.

    PubMed

    Guo, Lei; Wang, Yao; Yu, Hongli; Yin, Ning; Li, Ying

    2014-01-01

    Acupuncture is based on the theory of traditional Chinese medicine. Its therapeutic effectiveness has been proved by clinical practice. However, its mechanism of action is still unclear. Magnetic stimulation at acupuncture point provides a new means for studying the theory of acupuncture. Based on the Graph Theory, the construction and analysis method of complex network can help to investigate the topology of brain functional network and understand the working mechanism of brain. In this study, magnetic stimulation was used to stimulate Neiguan (PC6) acupoint and the EEG (Electroencephalograph) signal was recorded. Using non-linear method (Sample Entropy) and complex network theory, brain functional network based on EEG signal under magnetic stimulation at PC6 acupoint was constructed and analyzed. In addition, the features of complex network were comparatively analyzed between the quiescent and stimulated states. Our experimental results show the topology of the network is changed, the connection of the network is enhanced, the efficiency of information transmission is improved and the small-world property is strengthened through stimulating the PC6 acupoint.

  16. Bursty properties revealed in large-scale brain networks with a point-based method for dynamic functional connectivity

    PubMed Central

    Thompson, William Hedley; Fransson, Peter

    2016-01-01

    The brain is organized into large scale spatial networks that can be detected during periods of rest using fMRI. The brain is also a dynamic organ with activity that changes over time. We developed a method and investigated properties where the connections as a function of time are derived and quantified. The point based method (PBM) presented here derives covariance matrices after clustering individual time points based upon their global spatial pattern. This method achieved increased temporal sensitivity, together with temporal network theory, allowed us to study functional integration between resting-state networks. Our results show that functional integrations between two resting-state networks predominately occurs in bursts of activity. This is followed by varying intermittent periods of less connectivity. The described point-based method of dynamic resting-state functional connectivity allows for a detailed and expanded view on the temporal dynamics of resting-state connectivity that provides novel insights into how neuronal information processing is integrated in the human brain at the level of large-scale networks. PMID:27991540

  17. 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…

  18. Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain.

    PubMed

    Liang, Xia; Zou, Qihong; He, Yong; Yang, Yihong

    2013-01-29

    Human brain functional networks contain a few densely connected hubs that play a vital role in transferring information across regions during resting and task states. However, the relationship of these functional hubs to measures of brain physiology, such as regional cerebral blood flow (rCBF), remains incompletely understood. Here, we used functional MRI data of blood-oxygenation-level-dependent and arterial-spin-labeling perfusion contrasts to investigate the relationship between functional connectivity strength (FCS) and rCBF during resting and an N-back working-memory task. During resting state, functional brain hubs with higher FCS were identified, primarily in the default-mode, insula, and visual regions. The FCS showed a striking spatial correlation with rCBF, and the correlation was stronger in the default-mode network (DMN; including medial frontal-parietal cortices) and executive control network (ECN; including lateral frontal-parietal cortices) compared with visual and sensorimotor networks. Moreover, the relationship was connection-distance dependent; i.e., rCBF correlated stronger with long-range hubs than short-range ones. It is notable that several DMN and ECN regions exhibited higher rCBF per unit connectivity strength (rCBF/FCS ratio); whereas, this index was lower in posterior visual areas. During the working-memory experiment, both FCS-rCBF coupling and rCBF/FCS ratio were modulated by task load in the ECN and/or DMN regions. Finally, task-induced changes of FCS and rCBF in the lateral-parietal lobe positively correlated with behavioral performance. Together, our results indicate a tight coupling between blood supply and brain functional topology during rest and its modulation in response to task demands, which may shed light on the physiological basis of human brain functional connectome.

  19. Functional brain networks in healthy subjects under acupuncture stimulation: An EEG study based on nonlinear synchronization likelihood analysis

    NASA Astrophysics Data System (ADS)

    Yu, Haitao; Liu, Jing; Cai, Lihui; Wang, Jiang; Cao, Yibin; Hao, Chongqing

    2017-02-01

    Electroencephalogram (EEG) signal evoked by acupuncture stimulation at "Zusanli" acupoint is analyzed to investigate the modulatory effect of manual acupuncture on the functional brain activity. Power spectral density of EEG signal is first calculated based on the autoregressive Burg method. It is shown that the EEG power is significantly increased during and after acupuncture in delta and theta bands, but decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear correlation between each pairwise EEG signals. By applying a threshold to resulting synchronization matrices, functional networks for each band are reconstructed and further quantitatively analyzed to study the impact of acupuncture on network structure. Graph theoretical analysis demonstrates that the functional connectivity of the brain undergoes obvious change under different conditions: pre-acupuncture, acupuncture, and post-acupuncture. The minimum path length is largely decreased and the clustering coefficient keeps increasing during and after acupuncture in delta and theta bands. It is indicated that acupuncture can significantly modulate the functional activity of the brain, and facilitate the information transmission within different brain areas. The obtained results may facilitate our understanding of the long-lasting effect of acupuncture on the brain function.

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

    PubMed Central

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  2. 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

  3. 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.

  4. 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.

  5. Modulating brain oscillations to drive brain function.

    PubMed

    Thut, Gregor

    2014-12-01

    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.

  6. 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

  7. Brain network science needs to become predictive. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

    NASA Astrophysics Data System (ADS)

    Hilgetag, Claus C.; von Luxburg, Ulrike

    2014-09-01

    In his thought-provoking review of current concepts in neuroscience, Pessoa [1] addresses the ongoing paradigm shift of the field, in which the perspective has moved from individual nodes to distributed networks in order to account for distributed brain function. Within this perspective, Pessoa describes diverse aspects and topological features of brain networks that are potentially relevant for brain function. As he notes, however, the shift to networks does not solve all problems of linking brain function to structure.

  8. Small-world brain networks.

    PubMed

    Bassett, Danielle Smith; Bullmore, Ed

    2006-12-01

    Many complex networks have a small-world topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of brain anatomical and functional networks because a small-world topology can support both segregated/specialized and distributed/integrated information processing. Moreover, small-world networks are economical, tending to minimize wiring costs while supporting high dynamical complexity. The authors introduce some of the key mathematical concepts in graph theory required for small-world analysis and review how these methods have been applied to quantification of cortical connectivity matrices derived from anatomical tract-tracing studies in the macaque monkey and the cat. The evolution of small-world networks is discussed in terms of a selection pressure to deliver cost-effective information-processing systems. The authors illustrate how these techniques and concepts are increasingly being applied to the analysis of human brain functional networks derived from electroencephalography/magnetoencephalography and fMRI experiments. Finally, the authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development. They conclude that small-world models provide a powerful and versatile approach to understanding the structure and function of human brain systems.

  9. Test-retest reliability of graph metrics in functional brain networks: a resting-state fNIRS study.

    PubMed

    Niu, Haijing; Li, Zhen; Liao, Xuhong; Wang, Jinhui; Zhao, Tengda; Shu, Ni; Zhao, Xiaohu; He, Yong

    2013-01-01

    Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC <0.74). For global and nodal measures, reliability was generally threshold-sensitive and varied among both network metrics and hemoglobin concentration signals. Specifically, the majority of global metrics exhibited fair to excellent reliability, with notably higher ICC values for the clustering coefficient (HbO: 0.76; HbR: 0.78; HbT: 0.53) and global efficiency (HbO: 0.76; HbR: 0.70; HbT: 0.78). Similarly, both nodal degree and efficiency measures also showed fair to excellent reliability across nodes (degree: 0.52∼0.84; efficiency: 0.50∼0.84); reliability was concordant across HbO, HbR and HbT and was significantly higher than that of nodal betweenness (0.28∼0.68). Together, our results suggest that most graph-theoretical network metrics derived from fNIRS are TRT reliable and can be used effectively for brain network

  10. 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

  11. Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials

    PubMed Central

    Gao, Jun-feng; Yang, Yong; Huang, Wen-tao; Lin, Pan; Ge, Sheng; Zheng, Hong-mei; Gu, Ling-yun; Zhou, Hui; Li, Chen-hong; Rao, Ni-ni

    2016-01-01

    To better characterize the cognitive processes and mechanisms that are associated with deception, wavelet coherence was employed to evaluate functional connectivity between different brain regions. Two groups of subjects were evaluated for this purpose: 32 participants were required to either tell the truth or to lie when facing certain stimuli, and their electroencephalogram signals on 12 electrodes were recorded. The experimental results revealed that deceptive responses elicited greater connectivity strength than truthful responses, particularly in the θ band on specific electrode pairs primarily involving connections between the prefrontal/frontal and central regions and between the prefrontal/frontal and left parietal regions. These results indicate that these brain regions play an important role in executing lying responses. Additionally, three time- and frequency-dependent functional connectivity networks were proposed to thoroughly reflect the functional coupling of brain regions that occurs during lying. Furthermore, the wavelet coherence values for the connections shown in the networks were extracted as features for support vector machine training. High classification accuracy suggested that the proposed network effectively characterized differences in functional connectivity between the two groups of subjects over a specific time-frequency area and hence could be a sensitive measurement for identifying deception. PMID:27833159

  12. Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials

    NASA Astrophysics Data System (ADS)

    Gao, Jun-Feng; Yang, Yong; Huang, Wen-Tao; Lin, Pan; Ge, Sheng; Zheng, Hong-Mei; Gu, Ling-Yun; Zhou, Hui; Li, Chen-Hong; Rao, Ni-Ni

    2016-11-01

    To better characterize the cognitive processes and mechanisms that are associated with deception, wavelet coherence was employed to evaluate functional connectivity between different brain regions. Two groups of subjects were evaluated for this purpose: 32 participants were required to either tell the truth or to lie when facing certain stimuli, and their electroencephalogram signals on 12 electrodes were recorded. The experimental results revealed that deceptive responses elicited greater connectivity strength than truthful responses, particularly in the θ band on specific electrode pairs primarily involving connections between the prefrontal/frontal and central regions and between the prefrontal/frontal and left parietal regions. These results indicate that these brain regions play an important role in executing lying responses. Additionally, three time- and frequency-dependent functional connectivity networks were proposed to thoroughly reflect the functional coupling of brain regions that occurs during lying. Furthermore, the wavelet coherence values for the connections shown in the networks were extracted as features for support vector machine training. High classification accuracy suggested that the proposed network effectively characterized differences in functional connectivity between the two groups of subjects over a specific time-frequency area and hence could be a sensitive measurement for identifying deception.

  13. 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.

  14. 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.

  15. 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.

  16. Neurological Soft Signs Are Not “Soft” in Brain Structure and Functional Networks: Evidence From ALE Meta-Analysis

    PubMed Central

    Chan, Raymond C. K.

    2014-01-01

    Background: Neurological soft signs (NSS) are associated with schizophrenia and related psychotic disorders. NSS have been conventionally considered as clinical neurological signs without localized brain regions. However, recent brain imaging studies suggest that NSS are partly localizable and may be associated with deficits in specific brain areas. Method: We conducted an activation likelihood estimation meta-analysis to quantitatively review structural and functional imaging studies that evaluated the brain correlates of NSS in patients with schizophrenia and other psychotic disorders. Six structural magnetic resonance imaging (sMRI) and 15 functional magnetic resonance imaging (fMRI) studies were included. Results: The results from meta-analysis of the sMRI studies indicated that NSS were associated with atrophy of the precentral gyrus, the cerebellum, the inferior frontal gyrus, and the thalamus. The results from meta-analysis of the fMRI studies demonstrated that the NSS-related task was significantly associated with altered brain activation in the inferior frontal gyrus, bilateral putamen, the cerebellum, and the superior temporal gyrus. Conclusions: Our findings from both sMRI and fMRI meta-analyses further support the conceptualization of NSS as a manifestation of the “cerebello-thalamo-prefrontal” brain network model of schizophrenia and related psychotic disorders. PMID:23671197

  17. The brain's code and its canonical computational motifs. From sensory cortex to the default mode network: A multi-scale model of brain function in health and disease.

    PubMed

    Turkheimer, Federico E; Leech, Robert; Expert, Paul; Lord, Louis-David; Vernon, Anthony C

    2015-08-01

    A variety of anatomical and physiological evidence suggests that the brain performs computations using motifs that are repeated across species, brain areas, and modalities. The computational architecture of cortex, for example, is very similar from one area to another and the types, arrangements, and connections of cortical neurons are highly stereotyped. This supports the idea that each cortical area conducts calculations using similarly structured neuronal modules: what we term canonical computational motifs. In addition, the remarkable self-similarity of the brain observables at the micro-, meso- and macro-scale further suggests that these motifs are repeated at increasing spatial and temporal scales supporting brain activity from primary motor and sensory processing to higher-level behaviour and cognition. Here, we briefly review the biological bases of canonical brain circuits and the role of inhibitory interneurons in these computational elements. We then elucidate how canonical computational motifs can be repeated across spatial and temporal scales to build a multiplexing information system able to encode and transmit information of increasing complexity. We point to the similarities between the patterns of activation observed in primary sensory cortices by use of electrophysiology and those observed in large scale networks measured with fMRI. We then employ the canonical model of brain function to unify seemingly disparate evidence on the pathophysiology of schizophrenia in a single explanatory framework. We hypothesise that such a framework may also be extended to cover multiple brain disorders which are grounded in dysfunction of GABA interneurons and/or these computational motifs.

  18. 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…

  19. Antidepressant Effects of Electroconvulsive Therapy Unrelated to the Brain's Functional Network Connectivity alterations at an Individual Level

    PubMed Central

    Chen, Guang-Dong; Ji, Feng; Li, Gong-Ying; Lyu, Bo-Xuan; Hu, Wei; Zhuo, Chuan-Jun

    2017-01-01

    Background: Electroconvulsive therapy (ECT) can alleviate the symptoms of treatment-resistant depression (TRD). Functional network connectivity (FNC) is a newly developed method to investigate the brain's functional connectivity patterns. The first aim of this study was to investigate FNC alterations between TRD patients and healthy controls. The second aim was to explore the relationship between the ECT treatment response and pre-ECT treatment FNC alterations in individual TRD patients. Methods: This study included 82 TRD patients and 41 controls. Patients were screened at baseline and after 2 weeks of treatment with a combination of ECT and antidepressants. Group information guided-independent component analysis (GIG-ICA) was used to compute subject-specific functional networks (FNs). Grassmann manifold and step-wise forward component selection using support vector machines were adopted to perform the FNC measure and extract the functional networks' connectivity patterns (FCP). Pearson's correlation analysis was used to calculate the correlations between the FCP and ECT response. Results: A total of 82 TRD patients in the ECT group were successfully treated. On an average, 8.50 ± 2.00 ECT sessions were conducted. After ECT treatment, only 42 TRD patients had an improved response to ECT (the Hamilton scores reduction rate was more than 50%), response rate 51%. 8 FNs (anterior and posterior default mode network, bilateral frontoparietal network, audio network, visual network, dorsal attention network, and sensorimotor network) were obtained using GIG-ICA. We did not found that FCPs were significantly different between TRD patients and healthy controls. Moreover, the baseline FCP was unrelated to the ECT treatment response. Conclusions: The FNC was not significantly different between the TRD patients and healthy controls, and the baseline FCP was unrelated to the ECT treatment response. These findings will necessitate that we modify the experimental scheme to

  20. 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

  1. Large-Scale Brain Networks of the Human Left Temporal Pole: A Functional Connectivity MRI Study

    PubMed Central

    Pascual, Belen; Masdeu, Joseph C.; Hollenbeck, Mark; Makris, Nikos; Insausti, Ricardo; Ding, Song-Lin; Dickerson, Bradford C.

    2015-01-01

    The most rostral portion of the human temporal cortex, the temporal pole (TP), has been described as “enigmatic” because its functional neuroanatomy remains unclear. Comparative anatomy studies are only partially helpful, because the human TP is larger and cytoarchitectonically more complex than in nonhuman primates. Considered by Brodmann as a single area (BA 38), the human TP has been recently parceled into an array of cytoarchitectonic subfields. In order to clarify the functional connectivity of subregions of the TP, we undertook a study of 172 healthy adults using resting-state functional connectivity MRI. Remarkably, a hierarchical cluster analysis performed to group the seeds into distinct subsystems according to their large-scale functional connectivity grouped 87.5% of the seeds according to the recently described cytoarchitectonic subregions of the TP. Based on large-scale functional connectivity, there appear to be 4 major subregions of the TP: 1) dorsal, with predominant connectivity to auditory/somatosensory and language networks; 2) ventromedial, predominantly connected to visual networks; 3) medial, connected to paralimbic structures; and 4) anterolateral, connected to the default-semantic network. The functional connectivity of the human TP, far more complex than its known anatomic connectivity in monkey, is concordant with its hypothesized role as a cortical convergence zone. PMID:24068551

  2. 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

  3. Altered Topological Properties of Brain Networks in Social Anxiety Disorder: A Resting-state Functional MRI Study

    PubMed Central

    Zhu, Hongru; Qiu, Changjian; Meng, Yajing; Yuan, Minlan; Zhang, Yan; Ren, Zhengjia; Li, Yuchen; Huang, Xiaoqi; Gong, Qiyong; Lui, Su; Zhang, Wei

    2017-01-01

    Recent studies involving connectome analysis including graph theory have yielded potential biomarkers for mental disorders. In this study, we aimed to investigate the differences of resting-state network between patients with social anxiety disorder (SAD) and healthy controls (HCs), as well as to distinguish between individual subjects using topological properties. In total, 42 SAD patients and the same number of HCs underwent resting functional MRI, and the topological organization of the whole-brain functional network was calculated using graph theory. Compared with the controls, the patients showed a decrease in 49 positive connections. In the topological analysis, the patients showed an increase in the area under the curve (AUC) of the global shortest path length of the network (Lp) and a decrease in the AUC of the global clustering coefficient of the network (Cp). Furthermore, the AUCs of Lp and Cp were used to effectively discriminate the individual SAD patients from the HCs with high accuracy. This study revealed that the neural networks of the SAD patients showed changes in topological characteristics, and these changes were prominent not only in both groups but also at the individual level. This study provides a new perspective for the identification of patients with SAD. PMID:28266518

  4. Simulating the Evolution of Functional Brain Networks in Alzheimer’s Disease: Exploring Disease Dynamics from the Perspective of Global Activity

    PubMed Central

    Li, Wei; Wang, Miao; Zhu, Wenzhen; Qin, Yuanyuan; Huang, Yue; Chen, Xi

    2016-01-01

    Functional brain connectivity is altered during the pathological processes of Alzheimer’s disease (AD), but the specific evolutional rules are insufficiently understood. Resting-state functional magnetic resonance imaging indicates that the functional brain networks of individuals with AD tend to be disrupted in hub-like nodes, shifting from a small world architecture to a random profile. Here, we proposed a novel evolution model based on computational experiments to simulate the transition of functional brain networks from normal to AD. Specifically, we simulated the rearrangement of edges in a pathological process by a high probability of disconnecting edges between hub-like nodes, and by generating edges between random pair of nodes. Subsequently, four topological properties and a nodal distribution were used to evaluate our model. Compared with random evolution as a null model, our model captured well the topological alteration of functional brain networks during the pathological process. Moreover, we implemented two kinds of network attack to imitate the damage incurred by the brain in AD. Topological changes were better explained by ‘hub attacks’ than by ‘random attacks’, indicating the fragility of hubs in individuals with AD. This model clarifies the disruption of functional brain networks in AD, providing a new perspective on topological alterations. PMID:27677360

  5. Functional magnetic resonance imaging of chronic dysarthric speech after childhood brain injury: reliance on a left-hemisphere compensatory network.

    PubMed

    Morgan, Angela T; Masterton, Richard; Pigdon, Lauren; Connelly, Alan; Liégeois, Frédérique J

    2013-02-01

    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 after injury. Here, we examine the structural and functional neural correlates associated with chronic dysarthria after childhood-onset traumatic brain injury. Forty-nine participants aged 12 years 3 months to 24 years 11 months were recruited to the study: (i) a group with chronic dysarthria (n = 17); matched for age and sex with two control groups of (ii) healthy control subjects (n = 17); and (iii) individuals without dysarthria after traumatic brain injury (n = 15). A high-resolution 3D T(1)-weighted whole-brain data set was acquired for voxel-based morphometry analyses of group differences in grey matter. Functional magnetic resonance imaging was used to localize activation associated with speaking single words (baseline: listening to words). Group differences on voxel-based morphometry revealed widespread grey matter reductions in the dysarthric group compared with healthy control subjects, including in numerous speech motor regions bilaterally, such as the cerebellum, the basal ganglia and primary motor cortex representation of the articulators. Relative to the non-dysarthric traumatic brain injury group, individuals with dysarthria showed reduced grey matter bilaterally in the ventral sensorimotor cortex, but this reduction was concomitant with increased functional activation only in the left-hemisphere cluster during speech. Finally, increased recruitment of Broca's area (Brodmann area 45, pars triangularis) but not its right homologue, correlated with better speech outcome, suggesting that this 'higher-level' area may be more critically involved with production when associated motor speech regions are damaged. We

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

    PubMed Central

    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

  7. 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.

  8. A longitudinal study of the effect of short-term meditation training on functional network organization of the aging brain.

    PubMed

    Cotier, Francesca A; Zhang, Ruibin; Lee, Tatia M C

    2017-04-04

    The beneficial effects of meditation on preserving age-related changes in cognitive functioning are well established. Yet, the neural underpinnings of these positive effects have not been fully unveiled. This study employed a prospective longitudinal design, and graph-based analysis, to study how an eight-week meditation training vs. relaxation training shaped network configuration at global, intermediate, and local levels using graph theory in the elderly. At the intermediate level, meditation training lead to decreased intra-connectivity in the default mode network (DMN), salience network (SAN) and somatomotor network (SMN) modules post training. Also, there was decreased connectivity strength between the DMN and other modules. At a local level, meditation training lowered nodal strength in the left posterior cingulate gryus, bilateral paracentral lobule, and middle cingulate gyrus. According to previous literature, the direction of these changes is consistent with a movement towards a more self-detached viewpoint, as well as more efficient processing. Furthermore, our findings highlight the importance of considering brain network changes across organizational levels, as well as the pace at which these changes may occur. Overall, this study provides further support for short-term meditation as a potentially beneficial method of mental training for the elderly that warrants further investigation.

  9. 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.

  10. Schizophrenia and abnormal brain network hubs.

    PubMed

    Rubinov, Mikail; Bullmore, Ed

    2013-09-01

    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.

  11. Alteration of brain functional network at rest and in response to YMCA physical stress test in concussed athletes: RsFMRI study.

    PubMed

    Slobounov, S M; Gay, M; Zhang, K; Johnson, B; Pennell, D; Sebastianelli, W; Horovitz, S; Hallett, M

    2011-04-15

    There is still controversy in the literature whether a single episode of mild traumatic brain injury (mTBI) results in short- and/or long-term functional and structural deficits in the concussed brain. With the inability of traditional brain imaging techniques to properly assess the severity of brain damage induced by a concussive blow, there is hope that more advanced applications such as resting state functional magnetic resonance imaging (rsFMRI) will be more specific in accurately diagnosing mTBI. In this rsFMRI study, we examined 17 subjects 10±2 days post-sports-related mTBI and 17 age-matched normal volunteers (NVs) to investigate the possibility that the integrity of the resting state brain network is disrupted following a single concussive blow. We hypothesized that advanced brain imaging techniques may reveal subtle alterations of functional brain connections in asymptomatic mTBI subjects. There are several findings of interest. All mTBI subjects were asymptomatic based upon clinical evaluation and neuropsychological (NP) assessments prior to the MRI session. The mTBI subjects revealed a disrupted functional network both at rest and in response to the YMCA physical stress test. Specifically, interhemispheric connectivity was significantly reduced in the primary visual cortex, hippocampal and dorsolateral prefrontal cortex networks (p<0.05). The YMCA physical stress induced nonspecific and similar changes in brain network connectivity patterns in both the mTBI and NV groups. These major findings are discussed in relation to underlying mechanisms, clinical assessment of mTBI, and current debate regarding functional brain connectivity in a clinical population. Overall, our major findings clearly indicate that functional brain alterations in the acute phase of injury are overlooked when conventional clinical and neuropsychological examinations are used.

  12. Large Scale Functional Brain Networks Underlying Temporal Integration of Audio-Visual Speech Perception: An EEG Study.

    PubMed

    Kumar, G Vinodh; Halder, Tamesh; Jaiswal, Amit K; Mukherjee, Abhishek; Roy, Dipanjan; Banerjee, Arpan

    2016-01-01

    Observable lip movements of the speaker influence perception of auditory speech. A classical example of this influence is reported by listeners who perceive an illusory (cross-modal) speech sound (McGurk-effect) when presented with incongruent audio-visual (AV) speech stimuli. Recent neuroimaging studies of AV speech perception accentuate the role of frontal, parietal, and the integrative brain sites in the vicinity of the superior temporal sulcus (STS) for multisensory speech perception. However, if and how does the network across the whole brain participates during multisensory perception processing remains an open question. We posit that a large-scale functional connectivity among the neural population situated in distributed brain sites may provide valuable insights involved in processing and fusing of AV speech. Varying the psychophysical parameters in tandem with electroencephalogram (EEG) recordings, we exploited the trial-by-trial perceptual variability of incongruent audio-visual (AV) speech stimuli to identify the characteristics of the large-scale cortical network that facilitates multisensory perception during synchronous and asynchronous AV speech. We evaluated the spectral landscape of EEG signals during multisensory speech perception at varying AV lags. Functional connectivity dynamics for all sensor pairs was computed using the time-frequency global coherence, the vector sum of pairwise coherence changes over time. During synchronous AV speech, we observed enhanced global gamma-band coherence and decreased alpha and beta-band coherence underlying cross-modal (illusory) perception compared to unisensory perception around a temporal window of 300-600 ms following onset of stimuli. During asynchronous speech stimuli, a global broadband coherence was observed during cross-modal perception at earlier times along with pre-stimulus decreases of lower frequency power, e.g., alpha rhythms for positive AV lags and theta rhythms for negative AV lags. Thus, our

  13. Large Scale Functional Brain Networks Underlying Temporal Integration of Audio-Visual Speech Perception: An EEG Study

    PubMed Central

    Kumar, G. Vinodh; Halder, Tamesh; Jaiswal, Amit K.; Mukherjee, Abhishek; Roy, Dipanjan; Banerjee, Arpan

    2016-01-01

    Observable lip movements of the speaker influence perception of auditory speech. A classical example of this influence is reported by listeners who perceive an illusory (cross-modal) speech sound (McGurk-effect) when presented with incongruent audio-visual (AV) speech stimuli. Recent neuroimaging studies of AV speech perception accentuate the role of frontal, parietal, and the integrative brain sites in the vicinity of the superior temporal sulcus (STS) for multisensory speech perception. However, if and how does the network across the whole brain participates during multisensory perception processing remains an open question. We posit that a large-scale functional connectivity among the neural population situated in distributed brain sites may provide valuable insights involved in processing and fusing of AV speech. Varying the psychophysical parameters in tandem with electroencephalogram (EEG) recordings, we exploited the trial-by-trial perceptual variability of incongruent audio-visual (AV) speech stimuli to identify the characteristics of the large-scale cortical network that facilitates multisensory perception during synchronous and asynchronous AV speech. We evaluated the spectral landscape of EEG signals during multisensory speech perception at varying AV lags. Functional connectivity dynamics for all sensor pairs was computed using the time-frequency global coherence, the vector sum of pairwise coherence changes over time. During synchronous AV speech, we observed enhanced global gamma-band coherence and decreased alpha and beta-band coherence underlying cross-modal (illusory) perception compared to unisensory perception around a temporal window of 300–600 ms following onset of stimuli. During asynchronous speech stimuli, a global broadband coherence was observed during cross-modal perception at earlier times along with pre-stimulus decreases of lower frequency power, e.g., alpha rhythms for positive AV lags and theta rhythms for negative AV lags. Thus

  14. The Effect of Souvenaid on Functional Brain Network Organisation in Patients with Mild Alzheimer’s Disease: A Randomised Controlled Study

    PubMed Central

    de Waal, Hanneke; Stam, Cornelis J.; Lansbergen, Marieke M.; Wieggers, Rico L.; Kamphuis, Patrick J. G. H.; Scheltens, Philip; Maestú, Fernando; van Straaten, Elisabeth C. W.

    2014-01-01

    Background Synaptic loss is a major hallmark of Alzheimer’s disease (AD). Disturbed organisation of large-scale functional brain networks in AD might reflect synaptic loss and disrupted neuronal communication. The medical food Souvenaid, containing the specific nutrient combination Fortasyn Connect, is designed to enhance synapse formation and function and has been shown to improve memory performance in patients with mild AD in two randomised controlled trials. Objective To explore the effect of Souvenaid compared to control product on brain activity-based networks, as a derivative of underlying synaptic function, in patients with mild AD. Design A 24-week randomised, controlled, double-blind, parallel-group, multi-country study. Participants 179 drug-naïve mild AD patients who participated in the Souvenir II study. Intervention Patients were randomised 1∶1 to receive Souvenaid or an iso-caloric control product once daily for 24 weeks. Outcome In a secondary analysis of the Souvenir II study, electroencephalography (EEG) brain networks were constructed and graph theory was used to quantify complex brain structure. Local brain network connectivity (normalised clustering coefficient gamma) and global network integration (normalised characteristic path length lambda) were compared between study groups, and related to memory performance. Results The network measures in the beta band were significantly different between groups: they decreased in the control group, but remained relatively unchanged in the active group. No consistent relationship was found between these network measures and memory performance. Conclusions The current results suggest that Souvenaid preserves the organisation of brain networks in patients with mild AD within 24 weeks, hypothetically counteracting the progressive network disruption over time in AD. The results strengthen the hypothesis that Souvenaid affects synaptic integrity and function. Secondly, we conclude that advanced EEG

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

    PubMed

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

    2016-10-01

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

  16. 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.

  17. 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.

  18. The brain functional networks associated to human and animal suffering differ among omnivores, vegetarians and vegans.

    PubMed

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

    2010-05-26

    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.

  19. Balancing the Brain: Resting State Networks and Deep Brain Stimulation

    PubMed Central

    Kringelbach, Morten L.; Green, Alexander L.; Aziz, Tipu Z.

    2011-01-01

    Over the last three decades, large numbers of patients with otherwise treatment-resistant disorders have been helped by deep brain stimulation (DBS), yet a full scientific understanding of the underlying neural mechanisms is still missing. We have previously proposed that efficacious DBS works by restoring the balance of the brain's resting state networks. Here, we extend this proposal by reviewing how detailed investigations of the highly coherent functional and structural brain networks in health and disease (such as Parkinson's) have the potential not only to increase our understanding of fundamental brain function but of how best to modulate the balance. In particular, some of the newly identified hubs and connectors within and between resting state networks could become important new targets for DBS, including potentially in neuropsychiatric disorders. At the same time, it is of essence to consider the ethical implications of this perspective. PMID:21577250

  20. 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

  1. Brain rhythms reveal a hierarchical network organization.

    PubMed

    Steinke, G Karl; Galán, Roberto F

    2011-10-01

    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

  2. DWI and complex brain network analysis predicts vascular cognitive impairment in spontaneous hypertensive rats undergoing executive function tests

    PubMed Central

    López-Gil, Xavier; Amat-Roldan, Iván; Tudela, Raúl; Castañé, Anna; Prats-Galino, Alberto; Planas, Anna M.; Farr, Tracy D.; Soria, Guadalupe

    2014-01-01

    The identification of biomarkers of vascular cognitive impairment is urgent for its early diagnosis. The aim of this study was to detect and monitor changes in brain structure and connectivity, and to correlate them with the decline in executive function. We examined the feasibility of early diagnostic magnetic resonance imaging (MRI) to predict cognitive impairment before onset in an animal model of chronic hypertension: Spontaneously Hypertensive Rats. Cognitive performance was tested in an operant conditioning paradigm that evaluated learning, memory, and behavioral flexibility skills. Behavioral tests were coupled with longitudinal diffusion weighted imaging acquired with 126 diffusion gradient directions and 0.3 mm3 isometric resolution at 10, 14, 18, 22, 26, and 40 weeks after birth. Diffusion weighted imaging was analyzed in two different ways, by regional characterization of diffusion tensor imaging (DTI) indices, and by assessing changes in structural brain network organization based on Q-Ball tractography. Already at the first evaluated times, DTI scalar maps revealed significant differences in many regions, suggesting loss of integrity in white and gray matter of spontaneously hypertensive rats when compared to normotensive control rats. In addition, graph theory analysis of the structural brain network demonstrated a significant decrease of hierarchical modularity, global and local efficacy, with predictive value as shown by regional three-fold cross validation study. Moreover, these decreases were significantly correlated with the behavioral performance deficits observed at subsequent time points, suggesting that the diffusion weighted imaging and connectivity studies can unravel neuroimaging alterations even overt signs of cognitive impairment become apparent. PMID:25100993

  3. 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

  4. Differential effects of L-tryptophan and L-leucine administration on brain resting state functional networks and plasma hormone levels

    PubMed Central

    Zanchi, Davide; Meyer-Gerspach, Anne Christin; Suenderhauf, Claudia; Janach, Katharina; le Roux, Carel W.; Haller, Sven; Drewe, Jürgen; Beglinger, Christoph; Wölnerhanssen, Bettina K.; Borgwardt, Stefan

    2016-01-01

    Depending on their protein content, single meals can rapidly influence the uptake of amino acids into the brain and thereby modify brain functions. The current study investigates the effects of two different amino acids on the human gut-brain system, using a multimodal approach, integrating physiological and neuroimaging data. In a randomized, placebo-controlled trial, L-tryptophan, L-leucine, glucose and water were administered directly into the gut of 20 healthy subjects. Functional MRI (fMRI) in a resting state paradigm (RS), combined with the assessment of insulin and glucose blood concentration, was performed before and after treatment. Independent component analysis with dual regression technique was applied to RS-fMRI data. Results were corrected for multiple comparisons. In comparison to glucose and water, L-tryptophan consistently modifies the connectivity of the cingulate cortex in the default mode network, of the insula in the saliency network and of the sensory cortex in the somatosensory network. L-leucine has lesser effects on these functional networks. L-tryptophan and L-leucine also modified plasma insulin concentration. Finally, significant correlations were found between brain modifications after L-tryptophan administration and insulin plasma levels. This study shows that acute L-tryptophan and L-leucine intake directly influence the brain networks underpinning the food-reward system and appetite regulation. PMID:27760995

  5. 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.

  6. Brain networks underlying novel metaphor production.

    PubMed

    Beaty, Roger E; Silvia, Paul J; Benedek, Mathias

    2017-02-01

    Metaphors are widely used to convey abstract concepts and emotions in the arts and everyday life. Neuroimaging research suggests that dynamic interactions among large-scale brain networks, including the default and executive control networks, support the production of such creative ideas. However, the extent to which these networks interact to support other forms of creative language production such as metaphor remains unknown. Using functional magnetic resonance imaging (fMRI), we explored this question by assessing functional interactions between brain regions during novel metaphor production. Whole-brain functional connectivity analysis revealed a distributed network associated with metaphor production, including several nodes of the default (precuneus and left angular gyrus; AG) and executive control (right intraparietal sulcus; IPS) networks. Seed-based analyses showed increased connectivity between these network hubs, and temporal connectivity analysis found early coupling of default (left AG) and salience (right anterior insula) regions that preceded later coupling of the left AG and left DLPFC, pointing to a potential switching mechanism underlying default and executive network interaction. The results extend recent work on the cooperative role of large-scale networks in creative cognition, and suggest that metaphor production involves similar brain network dynamics as other forms of goal-directed, self-generated cognition.

  7. 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)

  8. Whole brain high-resolution functional imaging at ultra high magnetic fields: an application to the analysis of resting state networks.

    PubMed

    De Martino, Federico; Esposito, Fabrizio; van de Moortele, Pierre-Francois; Harel, Noam; Formisano, Elia; Goebel, Rainer; Ugurbil, Kamil; Yacoub, Essa

    2011-08-01

    Whole-brain functional magnetic resonance imaging (fMRI) allows measuring brain dynamics at all brain regions simultaneously and is widely used in research and clinical neuroscience to observe both stimulus-related and spontaneous neural activity. Ultrahigh magnetic fields (7T and above) allow functional imaging with high contrast-to-noise ratios and improved spatial resolution and specificity compared to clinical fields (1.5T and 3T). High-resolution 7T fMRI, however, has been mostly limited to partial brain coverage with previous whole-brain applications sacrificing either the spatial or temporal resolution. Here we present whole-brain high-resolution (1, 1.5 and 2mm isotropic voxels) resting state fMRI at 7T, obtained with parallel imaging technology, without sacrificing temporal resolution or brain coverage, over what is typically achieved at 3T with several fold larger voxel volumes. Using Independent Component Analysis we demonstrate that high resolution images acquired at 7T retain enough sensitivity for the reliable extraction of typical resting state brain networks and illustrate the added value of obtaining both single subject and group maps, using cortex based alignment, of the default-mode network (DMN) with high native resolution. By comparing results between multiple resolutions we show that smaller voxels volumes (1 and 1.5mm isotropic) data result in reduced partial volume effects, permitting separations of detailed spatial features within the DMN patterns as well as a better function to anatomy correspondence.

  9. Abnormal regional activity and functional connectivity in resting-state brain networks associated with etiology confirmed unilateral pulsatile tinnitus in the early stage of disease.

    PubMed

    Lv, Han; Zhao, Pengfei; Liu, Zhaohui; Li, Rui; Zhang, Ling; Wang, Peng; Yan, Fei; Liu, Liheng; Wang, Guopeng; Zeng, Rong; Li, Ting; Dong, Cheng; Gong, Shusheng; Wang, Zhenchang

    2017-03-01

    Abnormal neural activities can be revealed by resting-state functional magnetic resonance imaging (rs-fMRI) using analyses of the regional activity and functional connectivity (FC) of the networks in the brain. This study was designed to demonstrate the functional network alterations in the patients with pulsatile tinnitus (PT). In this study, we recruited 45 patients with unilateral PT in the early stage of disease (less than 48 months of disease duration) and 45 normal controls. We used regional homogeneity (ReHo) and seed-based FC computational methods to reveal resting-state brain activity features associated with pulsatile tinnitus. Compared with healthy controls, PT patients showed regional abnormalities mainly in the left middle occipital gyrus (MOG), posterior cingulate gyrus (PCC), precuneus and right anterior insula (AI). When these regions were defined as seeds, we demonstrated widespread modification of interaction between the auditory and non-auditory networks. The auditory network was positively connected with the cognitive control network (CCN), which may associate with tinnitus related distress. Both altered regional activity and changed FC were found in the visual network. The modification of interactions of higher order networks were mainly found in the DMN, CCN and limbic networks. Functional connectivity between the left MOG and left parahippocampal gyrus could also be an index to reflect the disease duration. This study helped us gain a better understanding of the characteristics of neural network modifications in patients with pulsatile tinnitus.

  10. 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.

  11. 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.

  12. 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

    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.

  13. 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

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

    PubMed

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

    2017-02-01

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

  15. Detection of Mild Traumatic Brain Injury by Machine Learning Classification Using Resting State Functional Network Connectivity and Fractional Anisotropy.

    PubMed

    Vergara, Victor M; Mayer, Andrew R; Damaraju, Eswar; Kiehl, Kent A; Calhoun, Vince

    2017-03-01

    Traumatic brain injury (TBI) may adversely affect a person's thinking, memory, personality, and behavior. While mild TBI (mTBI) diagnosis is challenging, there is a risk for long-term psychiatric, neurologic, and psychosocial problems in some patients that motivates the search for new and better biomarkers. Recently, diffusion magnetic resonance imaging (dMRI) has shown promise in detecting mTBI, but its validity is still being investigated. Resting state functional network connectivity (rsFNC) is another approach that is emerging as a promising option for the diagnosis of mTBI. The present work investigated the use of rsFNC for mTBI detection compared with dMRI results on the same cohort. Fifty patients with mTBI (25 males) and age-sex matched healthy controls were recruited. Features from dMRI were obtained using all voxels, the enhanced Z-score microstructural assessment for pathology, and the distribution corrected Z-score. Features based on rsFNC were obtained through group independent component analysis and correlation between pairs of resting state networks. A linear support vector machine was used for classification and validated using leave-one-out cross validation. Classification achieved a maximum accuracy of 84.1% for rsFNC and 75.5% for dMRI and 74.5% for both combined. A t test analysis revealed significant increase in rsFNC between cerebellum versus sensorimotor networks and between left angular gyrus versus precuneus in subjects with mTBI. These outcomes suggest that inclusion of both common and unique information is important for classification of mTBI. Results also suggest that rsFNC can yield viable biomarkers that might outperform dMRI and points to connectivity to the cerebellum as an important region for the detection of mTBI.

  16. 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

  17. Functional brain imaging across development.

    PubMed

    Rubia, Katya

    2013-12-01

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

  18. Scientific Accomplishments for ARL Brain Structure-Function Couplings Research on Large-Scale Brain Networks from FY11-FY13 (DSI Final Report)

    DTIC Science & Technology

    2014-03-01

    changes in blood flow as a proxy measure of brain activity and electroencephalography (EEG) data that records electrical activity on the scalp that...neuroimaging technologies: Magnetic resonance imaging (MRI) and Electroencephalography (EEG). The first can provide both structural and functional brain data...Michigan, Ann Arbor, and the University of California, San Diego. Electroencephalography (EEG) records the synchronous electrical activity of

  19. 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…

  20. Brain Networks of Explicit and Implicit Learning

    PubMed Central

    Yang, Jing; Li, Ping

    2012-01-01

    Are explicit versus implicit learning mechanisms reflected in the brain as distinct neural structures, as previous research indicates, or are they distinguished by brain networks that involve overlapping systems with differential connectivity? In this functional MRI study we examined the neural correlates of explicit and implicit learning of artificial grammar sequences. Using effective connectivity analyses we found that brain networks of different connectivity underlie the two types of learning: while both processes involve activation in a set of cortical and subcortical structures, explicit learners engage a network that uses the insula as a key mediator whereas implicit learners evoke a direct frontal-striatal network. Individual differences in working memory also differentially impact the two types of sequence learning. PMID:22952624

  1. Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

    PubMed Central

    Hanawa, Kenya; Tamura, Ryota; Hachisuka, Keisuke

    2016-01-01

    Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN) analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI) including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups. PMID:27872636

  2. 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.

  3. Network-based analysis reveals stronger local diffusion-based connectivity and different correlations with oral language skills in brains of children with high functioning autism spectrum disorders.

    PubMed

    Li, Hai; Xue, Zhong; Ellmore, Timothy M; Frye, Richard E; Wong, Stephen T C

    2014-02-01

    Neuroimaging has uncovered both long-range and short-range connectivity abnormalities in the brains of individuals with autism spectrum disorders (ASD). However, the precise connectivity abnormalities and the relationship between these abnormalities and cognition and ASD symptoms have been inconsistent across studies. Indeed, studies find both increases and decreases in connectivity, suggesting that connectivity changes in the ASD brain are not merely due to abnormalities in specific connections, but rather, due to changes in the structure of the network in which the brain areas interact (i.e., network topology). In this study, we examined the differences in the network topology between high-functioning ASD patients and age and gender matched typically developing (TD) controls. After quantitatively characterizing the whole-brain connectivity network using diffusion tensor imaging (DTI) data, we searched for brain regions with different connectivity between ASD and TD. A measure of oral language ability was then correlated with the connectivity changes to determine the functional significance of such changes. Whole-brain connectivity measures demonstrated greater local connectivity and shorter path length in ASD as compared to TD. Stronger local connectivity was found in ASD, especially in regions such as the left superior parietal lobule, the precuneus and angular gyrus, and the right supramarginal gyrus. The relationship between oral language ability and local connectivity within these regions was significantly different between ASD and TD. Stronger local connectivity was associated with better performance in ASD and poorer performance in TD. This study supports the notion that increased local connectivity is compensatory for supporting cognitive function in ASD.

  4. Functional Brain Networks and White Matter Underlying Theory-of-Mind in Autism

    PubMed Central

    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

  5. Levodopa changes brain motor network function during ankle movements in Parkinson's disease.

    PubMed

    Schwingenschuh, Petra; Katschnig, Petra; Jehna, Margit; Koegl-Wallner, Mariella; Seiler, Stephan; Wenzel, Karoline; Ropele, Stefan; Langkammer, Christian; Gattringer, Thomas; Svehlík, Martin; Ott, Erwin; Fazekas, Franz; Schmidt, Reinhold; Enzinger, Christian

    2013-03-01

    Bradykinesia-the cardinal symptom in Parkinson's disease (PD)-affects both upper and lower limbs. While several functional imaging studies investigated the impact of levodopa on movement-related neural activity in Parkinson's disease during upper limb movements, analogue studies on lower limb movements are rare. We studied 20 patients with PD (mean age 66.8 ± 7.2 years) after at least 12 h drug withdrawal (OFF-state) and a second time approximately 40 min after oral administration of 200 mg levodopa (ON-state) behaviourally and by functional magnetic resonance imaging (fMRI) at 3 T during externally cued active ankle movements of the more affected foot at fixed rate. Results were compared with that obtained in ten healthy controls (HC) to separate pure pharmacological from disease-related levodopa-induced effects and to allow for interaction analyses. Behaviourally, all patients improved by at least 20 % regarding the motor score of the Unified Parkinson's disease rating scale after levodopa-challenge (mean scores OFF-state: 38.4 ± 10.1; ON-state: 25.5 ± 8.1). On fMRI, levodopa application elicited increased activity in subcortical structures (contralateral putamen and thalamus) in the patients. In contrast, no significant levodopa-induced activation changes were found in HC. The interaction between "PD/HC group factor" and "levodopa OFF/ON" did not show significant results. Given the levodopa-induced activation increases in the putamen and thalamus with unilateral ankle movements in patients with PD but not in HC, we speculate that these regions show the most prominent response to levodopa within the cortico-subcortical motor-circuit in the context of nigrostriatal dysfunction.

  6. The hierarchical brain network for face recognition.

    PubMed

    Zhen, Zonglei; Fang, Huizhen; Liu, Jia

    2013-01-01

    Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

  7. Neural Network Function Classifier

    DTIC Science & Technology

    2003-02-07

    neural network sets. Each of the neural networks in a particular set is trained to recognize a particular data set type. The best function representation of the data set is determined from the neural network output. The system comprises sets of trained neural networks having neural networks trained to identify different types of data. The number of neural networks within each neural network set will depend on the number of function types that are represented. The system further comprises

  8. 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.

  9. 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.

  10. Brain Networks Shaping Religious Belief

    PubMed Central

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

    2014-01-01

    Abstract 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

  11. 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.

  12. 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.

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

    PubMed Central

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

    2015-01-01

    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

  14. Multi-modal analysis of functional connectivity and cerebral blood flow reveals shared and unique effects of propofol in large-scale brain networks.

    PubMed

    Qiu, Maolin; Scheinost, Dustin; Ramani, Ramachandran; Constable, R Todd

    2017-03-01

    Anesthesia-induced changes in functional connectivity and cerebral blow flow (CBF) in large-scale brain networks have emerged as key markers of reduced consciousness. However, studies of functional connectivity disagree on which large-scale networks are altered or preserved during anesthesia, making it difficult to find a consensus amount studies. Additionally, pharmacological alterations in CBF could amplify or occlude changes in connectivity due to the shared variance between CBF and connectivity. Here, we used data-driven connectivity methods and multi-modal imaging to investigate shared and unique neural correlates of reduced consciousness for connectivity in large-scale brain networks. Rs-fMRI and CBF data were collected from the same subjects during an awake and deep sedation condition induced by propofol. We measured whole-brain connectivity using the intrinsic connectivity distribution (ICD), a method not reliant on pre-defined seed regions, networks of interest, or connectivity thresholds. The shared and unique variance between connectivity and CBF were investigated. Finally, to account for shared variance, we present a novel extension to ICD that incorporates cerebral blood flow (CBF) as a scaling factor in the calculation of global connectivity, labeled CBF-adjusted ICD). We observed altered connectivity in multiple large-scale brain networks including the default mode (DMN), salience, visual, and motor networks and reduced CBF in the DMN, frontoparietal network, and thalamus. Regional connectivity and CBF were significantly correlated during both the awake and propofol condition. Nevertheless changes in connectivity and CBF between the awake and deep sedation condition were only significantly correlated in a subsystem of the DMN, suggesting that, while there is significant shared variance between the modalities, changes due to propofol are relatively unique. Similar, but less significant, results were observed in the CBF-adjusted ICD analysis, providing

  15. 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…

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

    PubMed

    Hari, Riitta; Parkkonen, Lauri

    2015-05-19

    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.

  17. 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

  18. Brain and cognitive reserve: Translation via network control theory.

    PubMed

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

    2017-04-01

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

  19. Functional brain network organisation of children between 2 and 5 years derived from reconstructed activity of cortical sources of high-density EEG recordings.

    PubMed

    Bathelt, Joe; O'Reilly, Helen; Clayden, Jonathan D; Cross, J Helen; de Haan, Michelle

    2013-11-15

    There is increasing interest in applying connectivity analysis to brain measures (Rubinov and Sporns, 2010), but most studies have relied on fMRI, which substantially limits the participant groups and numbers that can be studied. High-density EEG recordings offer a comparatively inexpensive easy-to-use alternative, but require channel-level connectivity analysis which currently lacks a common analytic framework and is very limited in spatial resolution. To address this problem, we have developed a new technique for studies of network development that overcomes the spatial constraint and obtains functional networks of cortical areas by using EEG source reconstruction with age-matched average MRI templates (He et al., 1999). In contrast to previously reported channel-level analysis, this approach provides information about the cortical areas most likely to be involved in the network as well as their functional relationship (Babiloni et al., 2005; De Vico Fallani et al., 2007). In this study, we applied source reconstruction with age-matched templates to task-free high-density EEG recordings in typically-developing children between 2 and 6 years of age (O'Reilly, 2012). Graph theory was then applied to the association strengths of 68 cortical regions of interest based on the Desikan-Killiany atlas. We found linear increases of mean node degree, mean clustering coefficient and maximum betweenness centrality between 2 years and 6 years of age. Characteristic path length was negatively correlated with age. The correlation of the network measures with age indicates network development towards more closely integrated networks similar to reports from other imaging modalities (Fair et al., 2008; Power et al., 2010). We also applied eigenvalue decomposition to obtain functional modules (Clayden et al., 2013). Connection strength within these modules did not change with age, and the modules resembled hub networks previously described for MRI (Hagmann et al., 2010; Power et al

  20. 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.

  1. Structure and function of large-scale brain systems.

    PubMed

    Koziol, Leonard F; Barker, Lauren A; Joyce, Arthur W; Hrin, Skip

    2014-01-01

    This article introduces the functional neuroanatomy of large-scale brain systems. Both the structure and functions of these brain networks are presented. All human behavior is the result of interactions within and between these brain systems. This system of brain function completely changes our understanding of how cognition and behavior are organized within the brain, replacing the traditional lesion model. Understanding behavior within the context of brain network interactions has profound implications for modifying abstract constructs such as attention, learning, and memory. These constructs also must be understood within the framework of a paradigm shift, which emphasizes ongoing interactions within a dynamically changing environment.

  2. Brain Networks for Integrative Rhythm Formation

    PubMed Central

    Thaut, Michael H.; Demartin, Martina; Sanes, Jerome N.

    2008-01-01

    Background Performance of externally paced rhythmic movements requires brain and behavioral integration of sensory stimuli with motor commands. The underlying brain mechanisms to elaborate beat-synchronized rhythm and polyrhythms that musicians readily perform may differ. Given known roles in perceiving time and repetitive movements, we hypothesized that basal ganglia and cerebellar structures would have greater activation for polyrhythms than for on-the-beat rhythms. Methodology/Principal Findings Using functional MRI methods, we investigated brain networks for performing rhythmic movements paced by auditory cues. Musically trained participants performed rhythmic movements at 2 and 3 Hz either at a 1∶1 on-the-beat or with a 3∶2 or a 2∶3 stimulus-movement structure. Due to their prior musical experience, participants performed the 3∶2 or 2∶3 rhythmic movements automatically. Both the isorhythmic 1∶1 and the polyrhythmic 3∶2 or 2∶3 movements yielded the expected activation in contralateral primary motor cortex and related motor areas and ipsilateral cerebellum. Direct comparison of functional MRI signals obtained during 3∶2 or 2∶3 and on-the-beat rhythms indicated activation differences bilaterally in the supplementary motor area, ipsilaterally in the supramarginal gyrus and caudate-putamen and contralaterally in the cerebellum. Conclusions/Significance The activated brain areas suggest the existence of an interconnected brain network specific for complex sensory-motor rhythmic integration that might have specificity for elaboration of musical abilities. PMID:18509462

  3. Network effects of deep brain stimulation

    PubMed Central

    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.

    2015-01-01

    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

  4. Dynamic Default Mode Network across Different Brain States

    PubMed Central

    Lin, Pan; Yang, Yong; Gao, Junfeng; De Pisapia, Nicola; Ge, Sheng; Wang, Xiang; Zuo, Chun S.; Jonathan Levitt, James; Niu, Chen

    2017-01-01

    The default mode network (DMN) is a complex dynamic network that is critical for understanding cognitive function. However, whether dynamic topological reconfiguration of the DMN occurs across different brain states, and whether this potential reorganization is associated with prior learning or experience is unclear. To better understand the temporally changing topology of the DMN, we investigated both nodal and global dynamic DMN-topology metrics across different brain states. We found that DMN topology changes over time and those different patterns are associated with different brain states. Further, the nodal and global topological organization can be rebuilt by different brain states. These results indicate that the post-task, resting-state topology of the brain network is dynamically altered as a function of immediately prior cognitive experience, and that these modulated networks are assembled in the subsequent state. Together, these findings suggest that the changing topology of the DMN may play an important role in characterizing brain states. PMID:28382944

  5. 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

    2016-03-29

    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.

  6. 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

  7. Multi-scale brain networks.

    PubMed

    Betzel, Richard F; Bassett, Danielle S

    2016-11-11

    The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales-of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure-irrespective of species, imaging modality, or spatial resolution.

  8. How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network

    PubMed Central

    Toppi, J.; De Vico Fallani, F.; Vecchiato, G.; Maglione, A. G.; Cincotti, F.; Mattia, D.; Salinari, S.; Babiloni, F.; Astolfi, L.

    2012-01-01

    The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes. PMID:22919427

  9. Factors affecting the cerebral network in brain tumor patients.

    PubMed

    Heimans, Jan J; Reijneveld, Jaap C

    2012-06-01

    Brain functions, including cognitive functions, are frequently disturbed in brain tumor patients. These disturbances may result from the tumor itself, but also from the treatment directed against the tumor. Surgery, radiotherapy and chemotherapy all may affect cerebral functioning, both in a positive as well as in a negative way. Apart from the anti-tumor treatment, glioma patients often receive glucocorticoids and anti-epileptic drugs, which both also have influence on brain functioning. The effect of a brain tumor on cerebral functioning is often more global than should be expected on the basis of the local character of the disease, and this is thought to be a consequence of disturbance of the cerebral network as a whole. Any network, whether it be a neural, a social or an electronic network, can be described in parameters assessing the topological characteristics of that particular network. Repeated assessment of neural network characteristics in brain tumor patients during their disease course enables study of the dynamics of neural networks and provides more insight into the plasticity of the diseased brain. Functional MRI, electroencephalography and especially magnetoencephalography are used to measure brain function and the signals that are being registered with these techniques can be analyzed with respect to network characteristics such as "synchronization" and "clustering". Evidence accumulates that loss of optimal neural network architecture negatively impacts complex cerebral functioning and also decreases the threshold to develop epileptic seizures. Future research should be focused on both plasticity of neural networks and the factors that have impact on that plasticity as well as the possible role of assessment of neural network characteristics in the determination of cerebral function during the disease course.

  10. 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.

  11. 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.

  12. Activating Developmental Reserve Capacity Via Cognitive Training or Non-invasive Brain Stimulation: Potentials for Promoting Fronto-Parietal and Hippocampal-Striatal Network Functions in Old Age

    PubMed Central

    Passow, Susanne; Thurm, Franka; Li, Shu-Chen

    2017-01-01

    Existing neurocomputational and empirical data link deficient neuromodulation of the fronto-parietal and hippocampal-striatal circuitries with aging-related increase in processing noise and declines in various cognitive functions. Specifically, the theory of aging neuronal gain control postulates that aging-related suboptimal neuromodulation may attenuate neuronal gain control, which yields computational consequences on reducing the signal-to-noise-ratio of synaptic signal transmission and hampering information processing within and between cortical networks. Intervention methods such as cognitive training and non-invasive brain stimulation, e.g., transcranial direct current stimulation (tDCS), have been considered as means to buffer cognitive functions or delay cognitive decline in old age. However, to date the reported effect sizes of immediate training gains and maintenance effects of a variety of cognitive trainings are small to moderate at best; moreover, training-related transfer effects to non-trained but closely related (i.e., near-transfer) or other (i.e., far-transfer) cognitive functions are inconsistent or lacking. Similarly, although applying different tDCS protocols to reduce aging-related cognitive impairments by inducing temporary changes in cortical excitability seem somewhat promising, evidence of effects on short- and long-term plasticity is still equivocal. In this article, we will review and critically discuss existing findings of cognitive training- and stimulation-related behavioral and neural plasticity effects in the context of cognitive aging, focusing specifically on working memory and episodic memory functions, which are subserved by the fronto-parietal and hippocampal-striatal networks, respectively. Furthermore, in line with the theory of aging neuronal gain control we will highlight that developing age-specific brain stimulation protocols and the concurrent applications of tDCS during cognitive training may potentially facilitate

  13. Activating Developmental Reserve Capacity Via Cognitive Training or Non-invasive Brain Stimulation: Potentials for Promoting Fronto-Parietal and Hippocampal-Striatal Network Functions in Old Age.

    PubMed

    Passow, Susanne; Thurm, Franka; Li, Shu-Chen

    2017-01-01

    Existing neurocomputational and empirical data link deficient neuromodulation of the fronto-parietal and hippocampal-striatal circuitries with aging-related increase in processing noise and declines in various cognitive functions. Specifically, the theory of aging neuronal gain control postulates that aging-related suboptimal neuromodulation may attenuate neuronal gain control, which yields computational consequences on reducing the signal-to-noise-ratio of synaptic signal transmission and hampering information processing within and between cortical networks. Intervention methods such as cognitive training and non-invasive brain stimulation, e.g., transcranial direct current stimulation (tDCS), have been considered as means to buffer cognitive functions or delay cognitive decline in old age. However, to date the reported effect sizes of immediate training gains and maintenance effects of a variety of cognitive trainings are small to moderate at best; moreover, training-related transfer effects to non-trained but closely related (i.e., near-transfer) or other (i.e., far-transfer) cognitive functions are inconsistent or lacking. Similarly, although applying different tDCS protocols to reduce aging-related cognitive impairments by inducing temporary changes in cortical excitability seem somewhat promising, evidence of effects on short- and long-term plasticity is still equivocal. In this article, we will review and critically discuss existing findings of cognitive training- and stimulation-related behavioral and neural plasticity effects in the context of cognitive aging, focusing specifically on working memory and episodic memory functions, which are subserved by the fronto-parietal and hippocampal-striatal networks, respectively. Furthermore, in line with the theory of aging neuronal gain control we will highlight that developing age-specific brain stimulation protocols and the concurrent applications of tDCS during cognitive training may potentially facilitate

  14. Network-dependent modulation of brain activity during sleep.

    PubMed

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

    2014-09-01

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

  15. Brain Functioning Models for Learning.

    ERIC Educational Resources Information Center

    Tipps, Steve; And Others

    This paper describes three models of brain function, each of which contributes to an integrated understanding of human learning. The first model, the up-and-down model, emphasizes the interconnection between brain structures and functions, and argues that since physiological, emotional, and cognitive responses are inseparable, the learning context…

  16. Small-World Brain Networks Revisited.

    PubMed

    Bassett, Danielle S; Bullmore, Edward T

    2016-09-21

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

  17. 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.

  18. Functional brain mapping using specific sensory-circuit stimulation and a theoretical graph network analysis in mice with neuropathic allodynia

    PubMed Central

    Komaki, Yuji; Hikishima, Keigo; Shibata, Shinsuke; Konomi, Tsunehiko; Seki, Fumiko; Yamada, Masayuki; Miyasaka, Naoyuki; Fujiyoshi, Kanehiro; Okano, Hirotaka J.; Nakamura, Masaya; Okano, Hideyuki

    2016-01-01

    Allodynia, a form of neuropathic pain, is defined as pain in response to a non-nociceptive stimulus. The brain regions responsible for pain, which are not normally activated, can be activated in allodynic mice by providing a suitable stimulus to Aβ-fibers, which transmit signals from tactile sensory fibers. Functional MRI (fMRI) can be used to objectively observe abnormal brain activation. In the present study, fMRI was conducted to investigate allodynia in mice; allodynia was generated by surgical injury at the L4 spinal nerve root, thus selectively stimulating sensory nerve fibers. In intact mice, only the primary somatosensory cortex (S1) was activated by stimulation of Aβ-fibers. Meanwhile, allodynic mice showed significantly higher BOLD signals in the anterior cingulate area (ACA) and thalamus. Using resting state fMRI, both degree and eigenvector centrality were significantly decreased in the contralateral S1, clustering coefficient and local efficiency were significantly increased in the ACA, and betweenness centrality was significantly higher in the ventral posterolateral nucleus of the thalamus. These results suggest that the observed abnormal BOLD activation is associated with defects in Aβ-fibers when Aβ-fibers in allodynic mice are selectively stimulated. The objective approach enabled by fMRI can improve our understanding of pathophysiological mechanisms and therapeutic efficacy. PMID:27898057

  19. Two views of brain function.

    PubMed

    Raichle, Marcus E

    2010-04-01

    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. Although such an approach has been remarkably productive, it ignores the alternative possibility that brain functions are mainly intrinsic, involving information processing for interpreting, responding to and predicting environmental demands. Here I argue 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. Recognizing the importance of 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.

  20. 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

  1. [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.

  2. Mnemonic Training Reshapes Brain Networks to Support Superior Memory.

    PubMed

    Dresler, Martin; Shirer, William R; Konrad, Boris N; Müller, Nils C J; Wagner, Isabella C; Fernández, Guillén; Czisch, Michael; Greicius, Michael D

    2017-03-08

    Memory skills strongly differ across the general population; however, little is known about the brain characteristics supporting superior memory performance. Here we assess functional brain network organization of 23 of the world's most successful memory athletes and matched controls with fMRI during both task-free resting state baseline and active memory encoding. We demonstrate that, in a group of naive controls, functional connectivity changes induced by 6 weeks of mnemonic training were correlated with the network organization that distinguishes athletes from controls. During rest, this effect was mainly driven by connections between rather than within the visual, medial temporal lobe and default mode networks, whereas during task it was driven by connectivity within these networks. Similarity with memory athlete connectivity patterns predicted memory improvements up to 4 months after training. In conclusion, mnemonic training drives distributed rather than regional changes, reorganizing the brain's functional network organization to enable superior memory performance.

  3. 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.

  4. Emerging concepts of brain function.

    PubMed

    Bach-Y-Rita, Paul

    2005-06-01

    For over 40 years, since I first obtained evidence for nonsynaptic diffusion neurotransmission (most scientists call it Volume Transmission), I have been convinced that we scientists were ignoring organizational dynamics other than the mechanistic synaptic organization of the brain. For many years it was an uneasy feeling, since I was aware there are so many avenues to explore in brain function. I have wondered how much we scientists have ignored, in our quest to understand how the brain really works, due to our efforts to "be scientific". In addition to the difficulty of understanding how the brain functions, how could we even begin to explore the human experience? In this paper I will first discuss some emerging concepts of brain function. I will then comment on the development of concepts that have been a part of my own research experience.

  5. 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…

  6. [Physical activity and brain function].

    PubMed

    Kempermann, G

    2012-06-01

    Physical activity has direct and indirect effects on brain function in health and disease. Findings demonstrating that physical activity improves cognitive and non-cognitive functions and is preventive for several neuropsychiatric disorders have attracted particular interest. This short review focuses on sports and physical exercise in normal brain function and summarizes which mechanisms might underlie the observed effects, which methodological problems exist, which relationships exist to concepts of plasticity and neural reserves and what evolutionary relevance the initially surprising finding that physical exercise is good for the brain has.

  7. Graph theoretical analysis of complex networks in the brain.

    PubMed

    Stam, Cornelis J; Reijneveld, Jaap C

    2007-07-05

    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.

  8. 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

  9. Dysfunction and Dysconnection in Cortical–Striatal Networks during Sustained Attention: Genetic Risk for Schizophrenia or Bipolar Disorder and its Impact on Brain Network Function

    PubMed Central

    Diwadkar, Vaibhav A.; Bakshi, Neil; Gupta, Gita; Pruitt, Patrick; White, Richard; Eickhoff, Simon B.

    2014-01-01

    Abnormalities in the brain’s attention network may represent early identifiable neurobiological impairments in individuals at increased risk for schizophrenia or bipolar disorder. Here, we provide evidence of dysfunctional regional and network function in adolescents at higher genetic risk for schizophrenia or bipolar disorder [henceforth higher risk (HGR)]. During fMRI, participants engaged in a sustained attention task with variable demands. The task alternated between attention (120 s), visual control (passive viewing; 120 s), and rest (20 s) epochs. Low and high demand attention conditions were created using the rapid presentation of two- or three-digit numbers. Subjects were required to detect repeated presentation of numbers. We demonstrate that the recruitment of cortical and striatal regions are disordered in HGR: relative to typical controls (TC), HGR showed lower recruitment of the dorsal prefrontal cortex, but higher recruitment of the superior parietal cortex. This imbalance was more dramatic in the basal ganglia. There, a group by task demand interaction was observed, such that increased attention demand led to increased engagement in TC, but disengagement in HGR. These activation studies were complemented by network analyses using dynamic causal modeling. Competing model architectures were assessed across a network of cortical–striatal regions, distinguished at a second level using random-effects Bayesian model selection. In the winning architecture, HGR were characterized by significant reductions in coupling across both frontal–striatal and frontal–parietal pathways. The effective connectivity analyses indicate emergent network dysconnection, consistent with findings in patients with schizophrenia. Emergent patterns of regional dysfunction and dysconnection in cortical–striatal pathways may provide functional biological signatures in the adolescent risk-state for psychiatric illness. PMID:24847286

  10. 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.

  11. Higher Brain Function.

    ERIC Educational Resources Information Center

    Chiaia, N.L.; Teyler, T.J.

    1983-01-01

    Focuses on how learning works. Discusses three major components related to processing information--sensory and perceptual systems, integration of information, and output of information--and developmental and environmental factors affecting brain information in each of these areas. Concludes with discussion of biological bases for cognitive and…

  12. 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

  13. Caffeine modulates attention network function.

    PubMed

    Brunyé, Tad T; Mahoney, Caroline R; Lieberman, Harris R; Taylor, Holly A

    2010-03-01

    The present work investigated the effects of caffeine (0mg, 100mg, 200mg, 400mg) on a flanker task designed to test Posner's three visual attention network functions: alerting, orienting, and executive control [Posner, M. I. (2004). Cognitive neuroscience of attention. New York, NY: Guilford Press]. In a placebo-controlled, double-blind study using a repeated-measures design, we found that the effects of caffeine on visual attention vary as a function of dose and the attention network under examination. Caffeine improved alerting and executive control function in a dose-response manner, asymptoting at 200mg; this effect is congruent with caffeine's adenosine-mediated effects on dopamine-rich areas of brain, and the involvement of these areas in alerting and the executive control of visual attention. Higher doses of caffeine also led to a marginally less efficient allocation of visual attention towards cued regions during task performance (i.e., orienting). Taken together, results of this study demonstrate that caffeine has differential effects on visual attention networks as a function of dose, and such effects have implications for hypothesized interactions of caffeine, adenosine and dopamine in brain areas mediating visual attention.

  14. Brain function, disease and dementia.

    PubMed

    Sandilyan, Malarvizhi Babu; Dening, Tom

    2015-05-27

    Dementia is a consequence of brain disease. This article, the second in this series on dementia, discusses normal brain function and how certain functions are localised to different areas of the brain. This is important in determining the symptoms of dementia, depending on which parts of the brain are most directly involved. The most common types of dementia - Alzheimer's disease, vascular dementia, dementia with Lewy bodies and frontotemporal dementia - affect the brain in different ways and cause different changes at the microscopic level. Dementia is affected by genetics, and recent advances in molecular techniques have improved our understanding of some of the mechanisms involved, which in turns suggests possibilities for new treatments in the future.

  15. 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

  16. Small-World Propensity and Weighted Brain Networks.

    PubMed

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

    2016-02-25

    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.

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. Brain Network Analysis from High-Resolution EEG Signals

    NASA Astrophysics Data System (ADS)

    de Vico Fallani, Fabrizio; Babiloni, Fabio

    Over the last decade, there has been a growing interest in the detection of the functional connectivity in the brain from different neuroelectromagnetic and hemodynamic signals recorded by several neuro-imaging devices such as the functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalography (EEG) and magnetoencephalography (MEG) apparatus. Many methods have been proposed and discussed in the literature with the aim of estimating the functional relationships among different cerebral structures. However, the necessity of an objective comprehension of the network composed by the functional links of different brain regions is assuming an essential role in the Neuroscience. Consequently, there is a wide interest in the development and validation of mathematical tools that are appropriate to spot significant features that could describe concisely the structure of the estimated cerebral networks. The extraction of salient characteristics from brain connectivity patterns is an open challenging topic, since often the estimated cerebral networks have a relative large size and complex structure. Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach seems relevant and useful as firstly demonstrated on a set of anatomical brain networks. In those studies, the authors have employed two characteristic measures, the average shortest path L and the clustering index C, to extract respectively the global and local properties of the network structure. They have found that anatomical brain networks exhibit many local connections (i.e. a high C) and few random long distance connections (i.e. a low L). These values identify a particular model that interpolate between a regular

  2. Modular and Hierarchically Modular Organization of Brain Networks

    PubMed Central

    Meunier, David; Lambiotte, Renaud; Bullmore, Edward T.

    2010-01-01

    Brain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. A module is topologically defined as a subset of highly inter-connected nodes which are relatively sparsely connected to nodes in other modules. In brain networks, topological modules are often made up of anatomically neighboring and/or functionally related cortical regions, and inter-modular connections tend to be relatively long distance. Moreover, brain networks and many other complex systems demonstrate the property of hierarchical modularity, or modularity on several topological scales: within each module there will be a set of sub-modules, and within each sub-module a set of sub-sub-modules, etc. There are several general advantages to modular and hierarchically modular network organization, including greater robustness, adaptivity, and evolvability of network function. In this context, we review some of the mathematical concepts available for quantitative analysis of (hierarchical) modularity in brain networks and we summarize some of the recent work investigating modularity of structural and functional brain networks derived from analysis of human neuroimaging data. PMID:21151783

  3. 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

  4. 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

  5. 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

  6. Brain network activity in monolingual and bilingual older adults.

    PubMed

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

    2015-01-01

    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.

  7. 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

  8. Alteration of default mode network in high school football athletes due to repetitive subconcussive mild traumatic brain injury: a resting-state functional magnetic resonance imaging study.

    PubMed

    Abbas, Kausar; Shenk, Trey E; Poole, Victoria N; Breedlove, Evan L; Leverenz, Larry J; Nauman, Eric A; Talavage, Thomas M; Robinson, Meghan E

    2015-03-01

    Long-term neurological damage as a result of head trauma while playing sports is a major concern for football athletes today. Repetitive concussions have been linked to many neurological disorders. Recently, it has been reported that repetitive subconcussive events can be a significant source of accrued damage. Since football athletes can experience hundreds of subconcussive hits during a single season, it is of utmost importance to understand their effect on brain health in the short and long term. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) was used to study changes in the default mode network (DMN) after repetitive subconcussive mild traumatic brain injury. Twenty-two high school American football athletes, clinically asymptomatic, were scanned using the rs-fMRI for a single season. Baseline scans were acquired before the start of the season, and follow-up scans were obtained during and after the season to track the potential changes in the DMN as a result of experienced trauma. Ten noncollision-sport athletes were scanned over two sessions as controls. Overall, football athletes had significantly different functional connectivity measures than controls for most of the year. The presence of this deviation of football athletes from their healthy peers even before the start of the season suggests a neurological change that has accumulated over the years of playing the sport. Football athletes also demonstrate short-term changes relative to their own baseline at the start of the season. Football athletes exhibited hyperconnectivity in the DMN compared to controls for most of the sessions, which indicates that, despite the absence of symptoms typically associated with concussion, the repetitive trauma accrued produced long-term brain changes compared to their healthy peers.

  9. Vitamin K and brain function.

    PubMed

    Ferland, Guylaine

    2013-11-01

    One of the fat-soluble vitamins, vitamin K was initially discovered for its role in blood coagulation. Although several vitamin K-dependent hemostatic proteins are particularly important for the brain, other vitamin K-dependent proteins (VKDPs), not associated with blood coagulation, also contribute to the brain function. In addition to the VKDPs, vitamin K participates in the nervous system through its involvement in sphingolipid metabolism, a class of lipids widely present in brain cell membranes. Classically known for their structural role, sphingolipids are biologically potent molecules involved in a wide range of cellular actions. Also, there is growing evidence that the K vitamer, menaquinone-4, has anti-inflammatory activity and offers protection against oxidative stress. Finally, although limited in numbers, reports point to a modulatory role of vitamin K in cognition. This short review presents an overview of the known role of vitamin K in brain function to date.

  10. 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.

  11. Magnetic Resonance, Functional (fMRI) -- Brain

    MedlinePlus

    ... thought, speech, movement and sensation, which is called brain mapping. help assess the effects of stroke, trauma or degenerative disease (such as Alzheimer's) on brain function. monitor the growth and function of brain ...

  12. 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

  13. Brain Connectivity Inference under Network Spatial Subsampling

    NASA Astrophysics Data System (ADS)

    da Rocha Amaral, Selene; Vieira, Gilson; Baccala, Luiz

    2013-03-01

    Neurophysiological time series analysis using functional Magnetic Resonance Magnetic Imaging (fMRI) data can be seen as tool to investigate how the complex networks of neuronal populations interact naturally leading to brain connectivity description issues where it is desirable to process as many simultaneous structures as possible to avoid misleading interaction inferences. Here we systematically use simulations to gauge how connectivity inference is affected when only subsets of network structures are considered through exploratory tools like Partial Directed Coherence (PDC) and confirmatory methods like Dynamic Causal Modeling (DCM). PDC is based on Granger causality and uses autoregressive models to expose the direction of information flow whereas DCM was proposed to characterize neural fMRI connectivity using prior knowlegde of possible connectivity structures. SPM software was used to simulate the full network fMRI data which was subject to realistic noise levels prior to analysis of network structure subsets. This work has been financially supported by FAPESP/CINAPCE 2011/0150-4

  14. 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

  15. 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

  16. 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.

  17. Development of human brain structural networks through infancy and childhood.

    PubMed

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

    2015-05-01

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

  18. Resting-state brain networks: literature review and clinical applications.

    PubMed

    Rosazza, Cristina; Minati, Ludovico

    2011-10-01

    This review focuses on resting-state functional connectivity, a functional MRI technique which allows the study of spontaneous brain activity generated under resting conditions. This approach is useful to explore the brain's functional organization and to examine if it is altered in neurological or psychiatric diseases. Resting-state functional connectivity has revealed a number of networks which are consistently found in healthy subjects and represent specific patterns of synchronous activity. In this review, we examine the behavioral, physiological and neurological evidences relevant to this coherent brain activity and, in particular, to each network. The investigation of functional connectivity appears promising from a clinical perspective, considering the amount of evidence regarding the importance of spontaneous activity and that resting-state paradigms are inherently simple to implement. We also discuss some examples of existing clinical applications, such as in Alzheimer's disease, and emerging possibilities such as in pre-operative mapping and disorders of consciousness.

  19. Mapping functional brain development: Building a social brain through interactive specialization.

    PubMed

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

    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 cognition and mentalizing computations in the brain. Second, they extend the implications of the IS view from the emergence of specialized functions within a cortical region to a focus on how different cortical regions with complementary functions become orchestrated into networks during human postnatal development.

  20. On mind wandering, attention, brain networks, and meditation.

    PubMed

    Sood, Amit; Jones, David T

    2013-01-01

    Human attention selectively focuses on aspects of experience that are threatening, pleasant, or novel. The physical threats of the ancient times have largely been replaced by chronic psychological worries and hurts. The mind gets drawn to these worries and hurts, mostly in the domain of the past and future, leading to mind wandering. In the brain, a network of neurons called the default mode network has been associated with mind wandering. Abnormal activity in the default mode network may predispose to depression, anxiety, attention deficit, and posttraumatic stress disorder. Several studies show that meditation can reverse some of these abnormalities, producing salutary functional and structural changes in the brain. This narrative review presents a mechanistic understanding of meditation in the context of recent advances in neurosciences about mind wandering, attention, and the brain networks.

  1. 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

  2. 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.

  3. Mutated Genes in Schizophrenia Map to Brain Networks

    MedlinePlus

    ... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the prefrontal ... Vasculitis Therapy as Effective as Standard Care Mutated Genes in Schizophrenia Map to Brain Networks Connect with Us Subscribe to ...

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

    PubMed

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

    2015-06-01

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

  5. 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.

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

    PubMed

    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.

  7. 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

  8. Scale-Free Brain Networks Based on the Event-Related Potential during Visual Spatial Attention

    NASA Astrophysics Data System (ADS)

    Li, Ling; Jin, Zhen-Lan

    2011-04-01

    The human brain is thought of as one of the most complex dynamical systems in the universe. The network view of the dynamical system has emerged since the discovery of scale-free networks. Brain functional networks, which represent functional associations among brain regions, are extracted by measuring the temporal correlations from electroencephalogram data. We measure the topological properties of the brain functional network, including degree distribution, average degree, clustering coefficient and the shortest path length, to compare the networks of multi-channel event-related potential activity between visual spatial attention and unattention conditions. It is found that the degree distribution of the brain functional networks under both the conditions is a power law distribution, which reflects a scale-free property. Moreover, the scaling exponent of the attention condition is significantly smaller than that of the unattention condition. However, the degree distribution of equivalent random networks does not follow the power law distribution. In addition, the clustering coefficient of these random networks is smaller than those of brain networks, and the shortest path length of these random networks is large and comparable with those of brain networks. Our results, typical of scale-free networks, indicate that the scaling exponent of brain activity could reflect different cognitive processes.

  9. [Brain function and white matter].

    PubMed

    Wake, Hiroaki; Kato, Daisuke

    2015-04-01

    Accumulated evidence shows that neural information processing takes place in superficial layers of the brain called the gray matter. Synapses, which connect different neurons reside in the gray matter and are considered the major components of information processing and plasticity. On the other hand, myelinated axons lie beneath the gray matter. These bundles of cables connect neurons in the different brain regions to form functional neural circuits. Myelinated axons were of little of interest to neuroscientists and have long been ignored in the formation of functional neuronal circuits. Recent evidence shows that myelin formed by oligodendrocytes shows plastic changes depending on neuronal activity. In this issue, we discuss the plastic changes of myelin and its functional role in learning and training.

  10. 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…

  11. Rat brains also have a default mode network

    PubMed Central

    Lu, Hanbing; Zou, Qihong; Gu, Hong; Raichle, Marcus E.; Stein, Elliot A.; Yang, Yihong

    2012-01-01

    The default mode network (DMN) in humans has been suggested to support a variety of cognitive functions and has been implicated in an array of neuropsychological disorders. However, its function(s) remains poorly understood. We show that rats possess a DMN that is broadly similar to the DMNs of nonhuman primates and humans. Our data suggest that, despite the distinct evolutionary paths between rodent and primate brain, a well-organized, intrinsically coherent DMN appears to be a fundamental feature in the mammalian brain whose primary functions might be to integrate multimodal sensory and affective information to guide behavior in anticipation of changing environmental contingencies. PMID:22355129

  12. 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.

  13. 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/).

  14. 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

  15. Sleep Deprivation Makes the Young Brain Resemble the Elderly Brain: A Large-Scale Brain Networks Study.

    PubMed

    Zhou, Xinqi; Wu, Taoyu; Yu, Jing; Lei, Xu

    2017-02-01

    Decreased cognition performance and impaired brain function are similar results of sleep deprivation (SD) and aging, according to mounted supporting evidence. Some investigators even proposed SD as a model of aging. However, few direct comparisons were ever explored between the effects of SD and aging by network module analysis with the resting-state functional magnetic resonance imaging. In this study, both within-module and between-module (BT) connectivities were calculated in the whole brain to describe a complete picture of brain networks' functional connectivity among three groups (young normal sleep, young SD, and old group). The results showed that the BT connectivities in subcortical and cerebellar networks were significantly declined in both the young SD group and old group. There were six other networks, that is, ventral attention, dorsal attention, default mode, auditory, cingulo-opercular, and memory retrieval networks, significantly influenced by aging. Therefore, we speculated that the effects of SD on the young group can be regarded as a simplified model of aging. Moreover, this provided a possible explanation, that is, the old were more tolerable for SD than the young. However, SD may not be a considerable model for aging when discussing the brain regions related to those SD-uninfluenced networks.

  16. 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

  17. Parcellating cortical functional networks in individuals.

    PubMed

    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

    2015-12-01

    The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (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 potential for use in clinical applications.

  18. 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

  19. 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.

  20. 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.

  1. Creative Cognition and Brain Network Dynamics

    PubMed Central

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

    2015-01-01

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

  2. Small-World Human Brain Networks: Perspectives and Challenges.

    PubMed

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

    2017-04-04

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

  3. The brain as a complex system: using network science as a tool for understanding the brain.

    PubMed

    Telesford, Qawi K; Simpson, Sean L; Burdette, Jonathan H; Hayasaka, Satoru; Laurienti, Paul J

    2011-01-01

    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.

  4. Evidence for Two Independent Factors that Modify Brain Networks to Meet Task Goals.

    PubMed

    Gratton, Caterina; Laumann, Timothy O; Gordon, Evan M; Adeyemo, Babatunde; Petersen, Steven E

    2016-10-25

    Humans easily and flexibly complete a wide variety of tasks. To accomplish this feat, the brain appears to subtly adjust stable brain networks. Here, we investigate what regional factors underlie these modifications, asking whether networks are either altered at (1) regions activated by a given task or (2) hubs that interconnect different networks. We used fMRI "functional connectivity" (FC) to compare networks during rest and three distinct tasks requiring semantic judgments, mental rotation, and visual coherence. We found that network modifications during these tasks were independently associated with both regional activation and network hubs. Furthermore, active and hub regions were associated with distinct patterns of network modification (differing in their localization, topography of FC changes, and variability across tasks), with activated hubs exhibiting patterns consistent with task control. These findings indicate that task goals modify brain networks through two separate processes linked to local brain function and network hubs.

  5. Directed Progression Brain Networks in Alzheimer's Disease: Properties and Classification

    PubMed Central

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

    2014-01-01

    Abstract 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

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

    PubMed

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

    2017-02-02

    Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1 -norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

  7. 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

  8. Connectomic Analysis of Brain Networks: Novel Techniques and Future Directions

    PubMed Central

    Cazemier, J. Leonie; Clascá, Francisco; Tiesinga, Paul H. E.

    2016-01-01

    Brain networks, localized or brain-wide, exist only at the cellular level, i.e., between specific pre- and post-synaptic neurons, which are connected through functionally diverse synapses located at specific points of their cell membranes. “Connectomics” is the emerging subfield of neuroanatomy explicitly aimed at elucidating the wiring of brain networks with cellular resolution and a quantified accuracy. Such data are indispensable for realistic modeling of brain circuitry and function. A connectomic analysis, therefore, needs to identify and measure the soma, dendrites, axonal path, and branching patterns together with the synapses and gap junctions of the neurons involved in any given brain circuit or network. However, because of the submicron caliber, 3D complexity, and high packing density of most such structures, as well as the fact that axons frequently extend over long distances to make synapses in remote brain regions, creating connectomic maps is technically challenging and requires multi-scale approaches, Such approaches involve the combination of the most sensitive cell labeling and analysis methods available, as well as the development of new ones able to resolve individual cells and synapses with increasing high-throughput. In this review, we provide an overview of recently introduced high-resolution methods, which researchers wanting to enter the field of connectomics may consider. It includes several molecular labeling tools, some of which specifically label synapses, and covers a number of novel imaging tools such as brain clearing protocols and microscopy approaches. Apart from describing the tools, we also provide an assessment of their qualities. The criteria we use assess the qualities that tools need in order to contribute to deciphering the key levels of circuit organization. We conclude with a brief future outlook for neuroanatomic research, computational methods, and network modeling, where we also point out several outstanding issues

  9. 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

  10. Thermodynamic laws apply to brain function.

    PubMed

    Salerian, Alen J

    2010-02-01

    Thermodynamic laws and complex system dynamics govern brain function. Thus, any change in brain homeostasis by an alteration in brain temperature, neurotransmission or content may cause region-specific brain dysfunction. This is the premise for the Salerian Theory of Brain built upon a new paradigm for neuropsychiatric disorders: the governing influence of neuroanatomy, neurophysiology, thermodynamic laws. The principles of region-specific brain function thermodynamics are reviewed. The clinical and supporting evidence including the paradoxical effects of various agents that alter brain homeostasis is demonstrated.

  11. Superbinding in Integrative Brain Function and Memory

    DTIC Science & Technology

    2007-11-02

    A:\\basar.doc 1 SUPERBINDING IN INTEGRATIVE BRAIN FUNCTION AND MEMORY E. Basar1,2, M. Özgören1,2, S. Karakas1,3 1TUBITAK Brain Dynamics...percepts and integrative brain function. Keywords- superbinding, oscillations, binding, coherence 1 Aim of the report The present report aims to...introduce the superbinding theory to describe the machineries of integrative brain function instead of single neuron doctrine. This concept is based on

  12. 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

  13. Topological isomorphisms of human brain and financial market networks.

    PubMed

    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.

  14. Insulin action in brain regulates systemic metabolism and brain function.

    PubMed

    Kleinridders, André; Ferris, Heather A; Cai, Weikang; Kahn, C Ronald

    2014-07-01

    Insulin receptors, as well as IGF-1 receptors and their postreceptor signaling partners, are distributed throughout the brain. Insulin acts on these receptors to modulate peripheral metabolism, including regulation of appetite, reproductive function, body temperature, white fat mass, hepatic glucose output, and response to hypoglycemia. Insulin signaling also modulates neurotransmitter channel activity, brain cholesterol synthesis, and mitochondrial function. Disruption of insulin action in the brain leads to impairment of neuronal function and synaptogenesis. In addition, insulin signaling modulates phosphorylation of tau protein, an early component in the development of Alzheimer disease. Thus, alterations in insulin action in the brain can contribute to metabolic syndrome, and the development of mood disorders and neurodegenerative diseases.

  15. On Shapley Ratings in Brain Networks

    PubMed Central

    Musegaas, Marieke; Dietzenbacher, Bas J.; Borm, Peter E. M.

    2016-01-01

    We consider the problem of computing the influence of a neuronal structure in a brain network. Abraham et al. (2006) computed this influence by using the Shapley value of a coalitional game corresponding to a directed network as a rating. Kötter et al. (2007) applied this rating to large-scale brain networks, in particular to the macaque visual cortex and the macaque prefrontal cortex. Our aim is to improve upon the above technique by measuring the importance of subgroups of neuronal structures in a different way. This new modeling technique not only leads to a more intuitive coalitional game, but also allows for specifying the relative influence of neuronal structures and a direct extension to a setting with missing information on the existence of certain connections. PMID:27965566

  16. 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

  17. The study of brain functional connectivity in Parkinson's disease.

    PubMed

    Gao, Lin-Lin; Wu, Tao

    2016-01-01

    Parkinson's disease (PD) is a neurodegenerative disorder primarily affecting the aging population. The neurophysiological mechanisms underlying parkinsonian symptoms remain unclear. PD affects extensive neural networks and a more thorough understanding of network disruption will help bridge the gap between known pathological changes and observed clinical presentations in PD. Development of neuroimaging techniques, especially functional magnetic resonance imaging, allows for detection of the functional connectivity of neural networks in patients with PD. This review aims to provide an overview of current research involving functional network disruption in PD relating to motor and non-motor symptoms. Investigations into functional network connectivity will further our understanding of the mechanisms underlying the effectiveness of clinical interventions, such as levodopa and deep brain stimulation treatment. In addition, identification of PD-specific neural network patterns has the potential to aid in the development of a definitive diagnosis of PD.

  18. Network localization of neurological symptoms from focal brain lesions.

    PubMed

    Boes, Aaron D; Prasad, Sashank; Liu, Hesheng; Liu, Qi; Pascual-Leone, Alvaro; Caviness, Verne S; Fox, Michael D

    2015-10-01

    A traditional and widely used approach for linking neurological symptoms to specific brain regions involves identifying overlap in lesion location across patients with similar symptoms, termed lesion mapping. This approach is powerful and broadly applicable, but has limitations when symptoms do not localize to a single region or stem from dysfunction in regions connected to the lesion site rather than the site itself. A newer approach sensitive to such network effects involves functional neuroimaging of patients, but this requires specialized brain scans beyond routine clinical data, making it less versatile and difficult to apply when symptoms are rare or transient. In this article we show that the traditional approach to lesion mapping can be expanded to incorporate network effects into symptom localization without the need for specialized neuroimaging of patients. Our approach involves three steps: (i) transferring the three-dimensional volume of a brain lesion onto a reference brain; (ii) assessing the intrinsic functional connectivity of the lesion volume with the rest of the brain using normative connectome data; and (iii) overlapping lesion-associated networks to identify regions common to a clinical syndrome. We first tested our approach in peduncular hallucinosis, a syndrome of visual hallucinations following subcortical lesions long hypothesized to be due to network effects on extrastriate visual cortex. While the lesions themselves were heterogeneously distributed with little overlap in lesion location, 22 of 23 lesions were negatively correlated with extrastriate visual cortex. This network overlap was specific compared to other subcortical lesions (P < 10(-5)) and relative to other cortical regions (P < 0.01). Next, we tested for generalizability of our technique by applying it to three additional lesion syndromes: central post-stroke pain, auditory hallucinosis, and subcortical aphasia. In each syndrome, heterogeneous lesions that themselves had

  19. 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

  20. A Topological Criterion for Filtering Information in Complex Brain Networks

    PubMed Central

    Latora, Vito; Chavez, Mario

    2017-01-01

    In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way. PMID:28076353