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Sample records for brain anatomical network

  1. Anatomical and Functional Brain Network Architecture in Schizophrenia

    NARCIS (Netherlands)

    Collin, G.; van den Heuvel, M. P.|info:eu-repo/dai/nl/304820466

    2016-01-01

    Dating back to the late 19th century, a longstanding hypothesis of schizophrenia is that it is a disorder of neural dissociation resulting from a disruption of the brain's anatomical association fibers. Corroborating this notion, a wealth of recent neuroimaging studies have demonstrated affected

  2. Brain anatomical networks in world class gymnasts: a DTI tractography study.

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    Wang, Bin; Fan, Yuanyuan; Lu, Min; Li, Shumei; Song, Zheng; Peng, Xiaoling; Zhang, Ruibin; Lin, Qixiang; He, Yong; Wang, Jun; Huang, Ruiwang

    2013-01-15

    The excellent motor skills of world class gymnasts amaze everyone. People marvel at the way they precisely control their movements and wonder how the brain structure and function of these elite athletes differ from those of non-athletes. In this study, we acquired diffusion images from thirteen world class gymnasts and fourteen matched controls, constructed their anatomical networks, and calculated the topological properties of each network based on graph theory. From a connectivity-based analysis, we found that most of the edges with increased connection density in the champions were linked to brain regions that are located in the sensorimotor, attentional, and default-mode systems. From graph-based metrics, we detected significantly greater global and local efficiency but shorter characteristic path length in the anatomical networks of the champions compared with the controls. Moreover, in the champions we found a significantly higher nodal degree and greater regional efficiency in several brain regions that correspond to motor and attention functions. These included the left precentral gyrus, left postcentral gyrus, right anterior cingulate gyrus and temporal lobes. In addition, we revealed an increase in the mean fractional anisotropy of the corticospinal tract in the champions, possibly in response to long-term gymnastic training. Our study indicates that neuroanatomical adaptations and plastic changes occur in gymnasts' brain anatomical networks either in response to long-term intensive gymnastic training or as an innate predisposition or both. Our findings may help to explain gymnastic skills at the highest levels of performance and aid in understanding the neural mechanisms that distinguish expert gymnasts from novices. Copyright © 2012 Elsevier Inc. All rights reserved.

  3. Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography.

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    Yu, Zhou; Tao, Ling; Qian, Zhiyu; Wu, Jiangfen; Liu, Hongyi; Yu, Yun; Song, Jiantai; Wang, Shaobo; Sun, Jinyang

    2016-11-01

    Brain tumor patients are usually accompanied by impairments in cognitive functions, and these dysfunctions arise from the altered diffusion tensor of water molecules and disrupted neuronal conduction in white matter. Diffusion tensor imaging (DTI) is a powerful noninvasive imaging technique that can reflect diffusion anisotropy of water and brain white matter neural connectivity in vivo. This study was aimed to analyze the topological properties and connection densities of the brain anatomical networks in brain tumor patients based on DTI and provide new insights into the investigation of the structural plasticity and compensatory mechanism of tumor patient's brain. In this study, the brain anatomical networks of tumor patients and healthy controls were constructed using the tracking of white matter fiber bundles based on DTI and the topological properties of these networks were described quantitatively. The statistical comparisons were performed between two groups with six DTI parameters: degree, regional efficiency, local efficiency, clustering coefficient, vulnerability, and betweenness centrality. In order to localize changes in structural connectivity to specific brain regions, a network-based statistic approach was utilized. By comparing the edge connection density of brain network between two groups, the edges with greater difference in connection density were associated with three functional systems. Compared with controls, tumor patients show a significant increase in small-world feature of cerebral structural network. Two-sample two-tailed t test indicates that the regional properties are altered in 17 regions ([Formula: see text]). Study reveals that the positive and negative changes in vulnerability take place in the 14 brain areas. In addition, tumor patients lose 3 hub regions and add 2 new hubs when compared to normal controls. Eleven edges show much significantly greater connection density in the patients than in the controls. Most of the edges with

  4. Sex differences in the influence of body mass index on anatomical architecture of brain networks.

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    Gupta, A; Mayer, E A; Hamadani, K; Bhatt, R; Fling, C; Alaverdyan, M; Torgerson, C; Ashe-McNalley, C; Van Horn, J D; Naliboff, B; Tillisch, K; Sanmiguel, C P; Labus, J S

    2017-08-01

    The brain has a central role in regulating ingestive behavior in obesity. Analogous to addiction behaviors, an imbalance in the processing of rewarding and salient stimuli results in maladaptive eating behaviors that override homeostatic needs. We performed network analysis based on graph theory to examine the association between body mass index (BMI) and network measures of integrity, information flow and global communication (centrality) in reward, salience and sensorimotor regions and to identify sex-related differences in these parameters. Structural and diffusion tensor imaging were obtained in a sample of 124 individuals (61 males and 63 females). Graph theory was applied to calculate anatomical network properties (centrality) for regions of the reward, salience and sensorimotor networks. General linear models with linear contrasts were performed to test for BMI and sex-related differences in measures of centrality, while controlling for age. In both males and females, individuals with high BMI (obese and overweight) had greater anatomical centrality (greater connectivity) of reward (putamen) and salience (anterior insula) network regions. Sex differences were observed both in individuals with normal and elevated BMI. In individuals with high BMI, females compared to males showed greater centrality in reward (amygdala, hippocampus and nucleus accumbens) and salience (anterior mid-cingulate cortex) regions, while males compared to females had greater centrality in reward (putamen) and sensorimotor (posterior insula) regions. In individuals with increased BMI, reward, salience and sensorimotor network regions are susceptible to topological restructuring in a sex-related manner. These findings highlight the influence of these regions on integrative processing of food-related stimuli and increased ingestive behavior in obesity, or in the influence of hedonic ingestion on brain topological restructuring. The observed sex differences emphasize the importance of

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

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

    2014-01-01

    Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease. PMID:25275860

  6. Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.

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    Christian Lohse

    2014-10-01

    Full Text Available Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.

  7. Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.

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

    2014-10-01

    Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.

  8. Exploring brain function from anatomical connectivity

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    Gorka eZamora-López

    2011-06-01

    Full Text Available The intrinsic relationship between the architecture of the brain and the range of sensory and behavioral phenomena it produces is a relevant question in neuroscience. Here, we review recent knowledge gained on the architecture of the anatomical connectivity by means of complex network analysis. It has been found that corticocortical networks display a few prominent characteristics: (i modular organization, (ii abundant alternative processing paths and (iii the presence of highly connected hubs. Additionally, we present a novel classification of cortical areas of the cat according to the role they play in multisensory connectivity. All these properties represent an ideal anatomical substrate supporting rich dynamical behaviors, as-well-as facilitating the capacity of the brain to process sensory information of different modalities segregated and to integrate them towards a comprehensive perception of the real world. The result here exposed are mainly based in anatomical data of cats’ brain, but we show how further observations suggest that, from worms to humans, the nervous system of all animals might share fundamental principles of organization.

  9. Altered anatomical network in early blindness revealed by diffusion tensor tractography.

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    Ni Shu

    Full Text Available The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. Diffusion MRI studies have revealed the efficient small-world properties and modular structure of the anatomical network in normal subjects. However, no previous study has used diffusion MRI to reveal changes in the brain anatomical network in early blindness. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 17 early blind subjects and 17 age- and gender-matched sighted controls. We established the existence of structural connections between any pair of the 90 cortical and sub-cortical regions using deterministic tractography. Compared with controls, early blind subjects showed a decreased degree of connectivity, a reduced global efficiency, and an increased characteristic path length in their brain anatomical network, especially in the visual cortex. Moreover, we revealed some regions with motor or somatosensory function have increased connections with other brain regions in the early blind, which suggested experience-dependent compensatory plasticity. This study is the first to show alterations in the topological properties of the anatomical network in early blindness. From the results, we suggest that analyzing the brain's anatomical network obtained using diffusion MRI data provides new insights into the understanding of the brain's re-organization in the specific population with early visual deprivation.

  10. Altered anatomical network in early blindness revealed by diffusion tensor tractography.

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    Shu, Ni; Liu, Yong; Li, Jun; Li, Yonghui; Yu, Chunshui; Jiang, Tianzi

    2009-09-28

    The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. Diffusion MRI studies have revealed the efficient small-world properties and modular structure of the anatomical network in normal subjects. However, no previous study has used diffusion MRI to reveal changes in the brain anatomical network in early blindness. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 17 early blind subjects and 17 age- and gender-matched sighted controls. We established the existence of structural connections between any pair of the 90 cortical and sub-cortical regions using deterministic tractography. Compared with controls, early blind subjects showed a decreased degree of connectivity, a reduced global efficiency, and an increased characteristic path length in their brain anatomical network, especially in the visual cortex. Moreover, we revealed some regions with motor or somatosensory function have increased connections with other brain regions in the early blind, which suggested experience-dependent compensatory plasticity. This study is the first to show alterations in the topological properties of the anatomical network in early blindness. From the results, we suggest that analyzing the brain's anatomical network obtained using diffusion MRI data provides new insights into the understanding of the brain's re-organization in the specific population with early visual deprivation.

  11. A propositional representation model of anatomical and functional brain data.

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    Maturana, Pablo; Batrancourt, Bénédicte

    2011-01-01

    Networks can represent a large number of systems. Recent advances in the domain of networks have been transferred to the field of neuroscience. For example, the graph model has been used in neuroscience research as a methodological tool to examine brain networks organization, topology and complex dynamics, as well as a framework to test the structure-function hypothesis using neuroimaging data. In the current work we propose a graph-theoretical framework to represent anatomical, functional and neuropsychological assessment instruments information. On the one hand, interrelationships between anatomic elements constitute an anatomical graph. On the other hand, a functional graph contains several cognitive functions and their more elementary cognitive processes. Finally, the neuropsychological assessment instruments graph includes several neuropsychological tests and scales linked with their different sub-tests and variables. The two last graphs are connected by relations of type "explore" linking a particular instrument with the cognitive function it explores. We applied this framework to a sample of patients with focal brain damage. Each patient was related to: (i) the cerebral entities injured (assessed with structural neuroimaging data) and (ii) the neusopsychological assessment tests carried out (weight by performance). Our model offers a suitable platform to visualize patients' relevant information, facilitating the representation, standardization and sharing of clinical data. At the same time, the integration of a large number of patients in this framework will make possible to explore relations between anatomy (injured entities) and function (performance in different tests assessing different cognitive functions) and the use of neurocomputational tools for graph analysis may help diagnostic and contribute to the comprehension of neural bases of cognitive functions. Copyright © 2011 Elsevier Ltd. All rights reserved.

  12. Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging.

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    Peng Fang

    Full Text Available Magnetic resonance imaging studies have reported significant functional and structural differences between depressed patients and controls. Little attention has been given, however, to the abnormalities in anatomical connectivity in depressed patients. In the present study, we aim to investigate the alterations in connectivity of whole-brain anatomical networks in those suffering from major depression by using machine learning approaches. Brain anatomical networks were extracted from diffusion magnetic resonance images obtained from both 22 first-episode, treatment-naive adults with major depressive disorder and 26 matched healthy controls. Using machine learning approaches, we differentiated depressed patients from healthy controls based on their whole-brain anatomical connectivity patterns and identified the most discriminating features that represent between-group differences. Classification results showed that 91.7% (patients=86.4%, controls=96.2%; permutation test, p<0.0001 of subjects were correctly classified via leave-one-out cross-validation. Moreover, the strengths of all the most discriminating connections were increased in depressed patients relative to the controls, and these connections were primarily located within the cortical-limbic network, especially the frontal-limbic network. These results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease.

  13. Multilayer motif analysis of brain networks

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

    2017-04-01

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

  14. Modular Brain Networks.

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    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.

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

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

    2016-03-01

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

  16. Brain Network Modelling

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther

    Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

  17. Nanotomography of brain networks

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    Saiga, Rino; Mizutani, Ryuta; Takekoshi, Susumu; Osawa, Motoki; Arai, Makoto; Takeuchi, Akihisa; Uesugi, Kentaro; Terada, Yasuko; Suzuki, Yoshio; de Andrade, Vincent; de Carlo, Francesco

    The first step to understanding how the brain functions is to analyze its 3D network. The brain network consists of neurons having micrometer to nanometer sized structures. Therefore, 3D analysis of brain tissue at the relevant resolution is essential for elucidating brain's functional mechanisms. Here, we report 3D structures of human and fly brain networks revealed with synchrotron radiation nanotomography, or nano-CT. Neurons were stained with high-Z elements to visualize their structures with X-rays. Nano-CT experiments were then performed at the 32-ID beamline of the Advanced Photon Source, Argonne National Laboratory and at the BL37XU and BL47XU beamlines of SPring-8. Reconstructed 3D images illustrated precise structures of human neurons, including dendritic spines responsible for synaptic connections. The network of the fly brain hemisphere was traced to build a skeletonized wire model. An article reviewing our study appeared in MIT Technology Review. Movies of the obtained structures can be found in our YouTube channel.

  18. Multilayer Brain Networks

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    Vaiana, Michael; Muldoon, Sarah Feldt

    2018-01-01

    The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.

  19. An adaptive complex network model for brain functional networks.

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    Ignacio J Gomez Portillo

    Full Text Available 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.

  20. Anatomical brain connectivity can assess cognitive dysfunction in multiple sclerosis.

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    Bozzali, M; Spanò, B; Parker, G J M; Giulietti, G; Castelli, M; Basile, B; Rossi, S; Serra, L; Magnani, G; Nocentini, U; Caltagirone, C; Centonze, D; Cercignani, M

    2013-08-01

    Brain disconnection plays a major role in determining cognitive disabilities in multiple sclerosis (MS). We recently developed a novel diffusion-weighted magnetic resonance imaging (DW-MRI) tractography approach, namely anatomical connectivitity mapping (ACM), that quantifies structural brain connectivity. Use of ACM to assess structural connectivity modifications in MS brains and ascertain their relationship with the patients' Paced-Auditory-Serial-Addition-Test (PASAT) scores. Relapsing-remitting MS (RRMS) patients (n = 25) and controls (n = 25) underwent MRI at 3T, including conventional images, T1-weighted volumes and DW-MRI. Volumetric scans were coregistered to fractional anisotropy (FA) images, to obtain parenchymal FA maps for both white and grey matter. We initiated probabilistic tractography from all parenchymal voxels, obtaining ACM maps by counting the number of streamlines passing through each voxel, then normalizing by the total number of streamlines initiated. The ACM maps were transformed into standard space, for statistical use. RRMS patients had reduced grey matter volume and FA, consistent with previous literature. Also, we showed reduced ACM in the thalamus and in the head of the caudate nucleus, bilaterally. In our RRMS patients, ACM was associated with PASAT scores in the corpus callosum, right hippocampus and cerebellum. ACM opens a new perspective, clarifying the contribution of anatomical brain disconnection to clinical disabilities in MS.

  1. How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure

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    Bettinardi, R. G.; Deco, G.; Karlaftis, V. M.; Van Hartevelt, T. J.; Fernandes, H. M.; Kourtzi, Z.; Kringelbach, M. L.; Zamora-López, G.

    2017-04-01

    Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.

  2. Visualizing the anatomical-functional correlation of the human brain

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    Chang, YuKuang; Rockwood, Alyn P.; Reiman, Eric M.

    1995-04-01

    Three-dimensional tomographic images obtained from different modalities or from the same modality at different times provide complementary information. For example, while PET shows brain function, images from MRI identify anatomical structures. In this paper, we investigate the problem of displaying available information about structures and function together. Several steps are described to achieve our goal. These include segmentation of the data, registration, resampling, and display. Segmentation is used to identify brain tissue from surrounding tissues, especially in the MRI data. Registration aligns the different modalities as closely as possible. Resampling arises from the registration since two data sets do not usually correspond and the rendering method is most easily achieved if the data correspond to the same grid used in display. We combine several techniques to display the data. MRI data is reconstructed from 2D slices into 3D structures from which isosurfaces are extracted and represented by approximating polygonalizations. These are then displayed using standard graphics pipelines including shaded and transparent images. PET data measures the qualitative rates of cerebral glucose utilization or oxygen consumption. PET image is best displayed as a volume of luminous particles. The combination of both display methods allows the viewer to compare the functional information contained in the PET data with the anatomically more precise MRI data.

  3. FROM BRAIN DRAIN TO BRAIN NETWORKING

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    Irina BONCEA

    2015-06-01

    Full Text Available Scientific networking is the most accessible way a country can turn the brain drain into brain gain. Diaspora’s members offer valuable information, advice or financial support from the destination country, without being necessary to return. This article aims to investigate Romania’s potential of turning brain drain into brain networking, using evidence from the medical sector. The main factors influencing the collaboration with the country of origin are investigated. The conclusions suggest that Romania could benefit from the diaspora option, through an active implication at institutional level and the implementation of a strategy in this area.

  4. Altered cortical anatomical networks in temporal lobe epilepsy

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    Lv, Bin; He, Huiguang; Lu, Jingjing; Li, Wenjing; Dai, Dai; Li, Meng; Jin, Zhengyu

    2011-03-01

    Temporal lobe epilepsy (TLE) is one of the most common epilepsy syndromes with focal seizures generated in the left or right temporal lobes. With the magnetic resonance imaging (MRI), many evidences have demonstrated that the abnormalities in hippocampal volume and the distributed atrophies in cortical cortex. However, few studies have investigated if TLE patients have the alternation in the structural networks. In the present study, we used the cortical thickness to establish the morphological connectivity networks, and investigated the network properties using the graph theoretical methods. We found that all the morphological networks exhibited the small-world efficiency in left TLE, right TLE and normal groups. And the betweenness centrality analysis revealed that there were statistical inter-group differences in the right uncus region. Since the right uncus located at the right temporal lobe, these preliminary evidences may suggest that there are topological alternations of the cortical anatomical networks in TLE, especially for the right TLE.

  5. Training brain networks and states.

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    Tang, Yi-Yuan; Posner, Michael I

    2014-07-01

    Brain training refers to practices that alter the brain in a way that improves cognition, and performance in domains beyond those involved in the training. We argue that brain training includes network training through repetitive practice that exercises specific brain networks and state training, which changes the brain state in a way that influences many networks. This opinion article considers two widely used methods - working memory training (WMT) and meditation training (MT) - to demonstrate the similarities and differences between network and state training. These two forms of training involve different areas of the brain and different forms of generalization. We propose a distinction between network and state training methods to improve understanding of the most effective brain training. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Network Theory and Effects of Transcranial Brain Stimulation Methods on the Brain Networks

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    Sema Demirci

    2014-12-01

    Full Text Available In recent years, there has been a shift from classic localizational approaches to new approaches where the brain is considered as a complex system. Therefore, there has been an increase in the number of studies involving collaborations with other areas of neurology in order to develop methods to understand the complex systems. One of the new approaches is graphic theory that has principles based on mathematics and physics. According to this theory, the functional-anatomical connections of the brain are defined as a network. Moreover, transcranial brain stimulation techniques are amongst the recent research and treatment methods that have been commonly used in recent years. Changes that occur as a result of applying brain stimulation techniques on physiological and pathological networks help better understand the normal and abnormal functions of the brain, especially when combined with techniques such as neuroimaging and electroencephalography. This review aims to provide an overview of the applications of graphic theory and related parameters, studies conducted on brain functions in neurology and neuroscience, and applications of brain stimulation systems in the changing treatment of brain network models and treatment of pathological networks defined on the basis of this theory.

  7. Comparison of brain perfusion SPECT abnormalities with anatomical imaging in mild traumatic brain injury

    Directory of Open Access Journals (Sweden)

    Majid Asadi

    2007-02-01

    Full Text Available Background: Trauma is the most common cause of morbidity and mortality in industrialized countries and also in Iran. Anatomical imaging (AI CT and MRI is helpful in the diagnosis of acute traumatic complications however it is not efficient in the diagnosis of disabling injury syndrome. In contrast, brain perfusion SPECT (Single Photon Emission Computed Tomography can be more useful for evaluation of microvascular structure. This study was designed to compare these two diagnostic methods. Methods: A total of 50 patients who had been suffering from traumatic brain injury for more than 1 year, and were followed as mild traumatic brain injury group according to “the Brain Injury Interdisciplinary Special Interest Group of the Ameri can Congress of Rehabilitation Medicine” criteria, were examined by brain perfusion SPECT and AI. The common anatomical classification of the lobes of brain was used. Results: The male to female ratio was 3:2. The mean age was 32.32±11.8 years and mean post-traumatic time was 1.48±0.65 years. The most common symptoms were headache (60%, agusia (36% and anosmia (32%. Among 400 examined brain lobes in this study, brain perfusion SPECT revealed remarkable abnormality in 76 lobes (19%, but AI determined abnormalities in 38 lobes (9.5% therefore, SPECT was twice sensitive than AI in mild traumatic brain injury (P<0.001. The correlation between SPECT and AI findings was 84%. SPECT was more sensitive than AI in demonstrating brain abnormalities in frontal lobe it was more obvious in the male group however, there was no significant difference between more and less than 30 years old groups. Conclusion: According to the findings of this study, we recommend using brain perfusion SPECT for all patients with chronic complications of head trauma, particularly those who have signs and symptoms of hypofrontalism, even though with some abnormalities in AI.

  8. An algorithm to estimate anatomical connectivity between brain regions using diffusion MRI.

    Science.gov (United States)

    Campanella, Martina; Molinari, Elisa; Baraldi, Patrizia; Nocetti, Luca; Porro, Carlo A; Alexander, Daniel C

    2013-04-01

    The study of anatomical connectivity is essential for interpreting functional MRI data and for establishing how brain areas are linked together into networks to support higher-order functions. Diffusion-weighted MR images (DWI) and tractography provide a unique noninvasive tool to explore the connectional architecture of the brain. The identification of anatomical circuits associated with a specific function can be better accomplished by the joint application of diffusion and functional MRI. In this article, we propose a simple algorithm to identify the set of pathways between two regions of interest. The method is based upon running deterministic tractography from all possible starting positions in the brain and selecting trajectories that intersect both regions. We compare results from single-fiber tractography using diffusion tensor imaging and from multi-fiber tractography using reduced-encoding persistent angular structure (PAS) MRI on standard DWI datasets from healthy human volunteers. Our results show that, in comparison with single-fiber tractography, the multi-fiber technique reveals additional putative routes of connection. We demonstrate highly consistent results of the proposed technique over a cohort of 16 healthy subjects. Copyright © 2013 Elsevier Inc. All rights reserved.

  9. A New Measure of Imagination Ability: Anatomical Brain Imaging Correlates.

    Science.gov (United States)

    Jung, Rex E; Flores, Ranee A; Hunter, Dan

    2016-01-01

    Imagination involves episodic memory retrieval, visualization, mental simulation, spatial navigation, and future thinking, making it a complex cognitive construct. Prior studies of imagination have attempted to study various elements of imagination (e.g., visualization), but none have attempted to capture the entirety of imagination ability in a single instrument. Here we describe the Hunter Imagination Questionnaire (HIQ), an instrument designed to assess imagination over an extended period of time, in a naturalistic manner. We hypothesized that the HIQ would be related to measures of creative achievement and to a network of brain regions previously identified to be important to imagination/creative abilities. Eighty subjects were administered the HIQ in an online format; all subjects were administered a broad battery of tests including measures of intelligence, personality, and aptitude, as well as structural Magnetic Resonance Imaging (sMRI). Responses of the HIQ were found to be normally distributed, and exploratory factor analysis yielded four factors. Internal consistency of the HIQ ranged from 0.76 to 0.79, and two factors ("Implementation" and "Learning") were significantly related to measures of Creative Achievement (Scientific-r = 0.26 and Writing-r = 0.31, respectively), suggesting concurrent validity. We found that the HIQ and its factors were related to a broad network of brain volumes including increased bilateral hippocampi, lingual gyrus, and caudal/rostral middle frontal lobe, and decreased volumes within the nucleus accumbens and regions within the default mode network (e.g., precuneus, posterior cingulate, transverse temporal lobe). The HIQ was found to be a reliable and valid measure of imagination in a cohort of normal human subjects, and was related to brain volumes previously identified as central to imagination including episodic memory retrieval (e.g., hippocampus). We also identified compelling evidence suggesting imagination ability

  10. A New Measure of Imagination Ability: Anatomical Brain Imaging Correlates

    Directory of Open Access Journals (Sweden)

    Rex Eugene Jung

    2016-04-01

    Full Text Available Imagination involves episodic memory retrieval, visualization, mental simulation, spatial navigation, and future thinking, making it a complex cognitive construct. Prior studies of imagination have attempted to study various elements of imagination (e.g., visualization, but none have attempted to capture the entirety of imagination ability in a single instrument. Here we describe the Hunter Imagination Questionnaire (HIQ, an instrument designed to assess imagination over an extended period of time, in a naturalistic manner. We hypothesized that the HIQ would be related to measures of creative achievement and to a network of brain regions previously identified to be important to imagination/creative abilities. Eighty subjects were administered the HIQ in an online format; all subjects were administered a broad battery of tests including measures of intelligence, personality, and aptitude, as well as structural Magnetic Resonance Imaging (sMR. Responses of the HIQ were found to be normally distributed, and exploratory factor analysis yielded four factors. Internal consistency of the HIQ ranged from .76 to .79, and two factors (Implementation and Learning were significantly related to measures of Creative Achievement (Scientifific - r = .26 and Writing - r = .31 respectively, suggesting concurrent validity. We found that the HIQ and its factors were related to a broad network of brain volumes including increased bilateral hippocampi, lingual gyrus, and caudal/rostral middle frontal lobe, and decreased volumes within the nucleus accumbens and regions within the default mode network (e.g., precuneus, posterior cingulate, transverse temporal lobe. The HIQ was found to be a reliable and valid measure of imagination in a cohort of normal human subjects, and was related to brain volumes previously identified as central to imagination including episodic memory retrieval (e.g., hippocampus. We also identified compelling evidence suggesting imagination

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

    Science.gov (United States)

    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].

  12. Anatomic brain disease in hemodialysis patients: a cross-sectional study

    Science.gov (United States)

    Although dialysis patients are at high risk of stroke and have a high burden of cognitive impairment, there are few reports of anatomic brain findings in the hemodialysis population. Using magnetic resonance imaging of the brain, we compared the prevalence of brain abnormalities in hemodialysis pati...

  13. Shapley ratings in brain networks

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    Rolf Kötter

    2007-11-01

    Full Text Available Recent applications of network theory to brain networks as well as the expanding empirical databases of brain architecture spawn an interest in novel techniques for analyzing connectivity patterns in the brain. Treating individual brain structures as nodes in a directed graph model permits the application of graph theoretical concepts to the analysis of these structures within their large-scale connectivity networks. In this paper, we explore the application of concepts from graph and game theory toward this end. Specifically, we utilize the Shapley value principle, which assigns a rank to players in a coalition based upon their individual contributions to the collective profit of that coalition, to assess the contributions of individual brain structures to the graph derived from the global connectivity network. We report Shapley values for variations of a prefrontal network, as well as for a visual cortical network, which had both been extensively investigated previously. This analysis highlights particular nodes as strong or weak contributors to global connectivity. To understand the nature of their contribution, we compare the Shapley values obtained from these networks and appropriate controls to other previously described nodal measures of structural connectivity. We find a strong correlation between Shapley values and both betweenness centrality and connection density. Moreover, a stepwise multiple linear regression analysis indicates that approximately 79% of the variance in Shapley values obtained from random networks can be explained by betweenness centrality alone. Finally, we investigate the effects of local lesions on the Shapley ratings, showing that the present networks have an immense structural resistance to degradation. We discuss our results highlighting the use of such measures for characterizing the organization and functional role of brain networks.

  14. Network of anatomical texts (NAnaTex), an open-source project for visualizing the interaction between anatomical terms.

    Science.gov (United States)

    Momota, Ryusuke; Ohtsuka, Aiji

    2018-01-01

    Anatomy is the science and art of understanding the structure of the body and its components in relation to the functions of the whole-body system. Medicine is based on a deep understanding of anatomy, but quite a few introductory-level learners are overwhelmed by the sheer amount of anatomical terminology that must be understood, so they regard anatomy as a dull and dense subject. To help them learn anatomical terms in a more contextual way, we started a new open-source project, the Network of Anatomical Texts (NAnaTex), which visualizes relationships of body components by integrating text-based anatomical information using Cytoscape, a network visualization software platform. Here, we present a network of bones and muscles produced from literature descriptions. As this network is primarily text-based and does not require any programming knowledge, it is easy to implement new functions or provide extra information by making changes to the original text files. To facilitate collaborations, we deposited the source code files for the network into the GitHub repository ( https://github.com/ryusukemomota/nanatex ) so that anybody can participate in the evolution of the network and use it for their own non-profit purposes. This project should help not only introductory-level learners but also professional medical practitioners, who could use it as a quick reference.

  15. Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects

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    Jason W Bohland

    2012-12-01

    Full Text Available Brain imaging methods have long held promise as diagnostic aids for neuropsychiatric conditions with complex behavioral phenotypes such as Attention-Deficit/Hyperactivity Disorder. This promise has largely been unrealized, at least partly due to the heterogeneity of clinical populations and the small sample size of many studies. A large, multi-center dataset provided by the ADHD-200 Consortium affords new opportunities to test methods for individual diagnosis based on MRI-observable structural brain attributes and functional interactions observable from resting state fMRI. In this study, we systematically calculated a large set of standard and new quantitative markers from individual subject datasets. These features (>12,000 per subject consisted of local anatomical attributes such as cortical thickness and structure volumes and both local and global resting state network measures. Three methods were used to compute graphs representing interdependencies between activations in different brain areas, and a full set of network features was derived from each. Of these, features derived from the inverse of the time series covariance matrix, under an L1-norm regularization penalty, proved most powerful. Anatomical and network feature sets were used individually, and combined with non-imaging phenotypic features from each subject. Machine learning algorithms were used to rank attributes, and performance was assessed under cross-validation and on a separate test set of 168 subjects for a variety of feature set combinations. While non-imaging features gave highest performance in cross-validation, the addition of imaging features in sufficient numbers led to improved generalization to new data. Stratification by gender also proved to be a fruitful strategy to improve classifier performance. We describe the overall approach used, compare the predictive power of different classes of features, and describe the most impactful features in relation to the

  16. An algebraic topological method for multimodal brain networks comparison

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    Tiago eSimas

    2015-07-01

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

  17. Andreas Vesalius and Thomas Willis: their anatomic brain illustrations and illustrators.

    Science.gov (United States)

    Scatliff, J H; Johnston, S

    2014-01-01

    The brain illustrations of Vesalius and Willis were the first in anatomic history with pictorial accuracy. Their illustrations, illustrators, and methods are discussed. Woodcut blocks were used for the prints of figures in the Vesalian anatomy. Figures of the brain appear to be done after external fixation in the work of Willis.

  18. A comprehensive study of the anatomical variations of the circle of willis in adult human brains.

    Science.gov (United States)

    Iqbal, S

    2013-11-01

    Cerebrovascular diseases such as stroke, aneurysms and arterio-venous malformations are very much prevalent in our country. Circle of Willis, as an anastomotic polygon at the base of the brain forms an important collateral network to maintain adequate cerebral perfusion. Changes in the normal morphology of the circle may condition the appearance and severity of symptoms of cerebrovascular disorders, such as aneurysms, infarctions and other vascular anomalies. A possible link between abnormalities of the circle of Willis and the mentally ill patients has been observed. The aim of the present study is to have an intimate knowledge of the variations in the cerebral arterial circle and to clarify the clinical importance of these variations in certain forms of cerebrovascular diseases. So an attempt was made to analyse the anatomical variations of the circle in a random population. The work was based on fifty adult brains from persons died of diverse causes. The materials were obtained during routine autopsy studies. The base of the brain including the brain stem with intact arterial circle was preserved in 10% formalin for 10 days. The circle of Willis and its major branches were carefully dissected under water using a magnifying lens. The variations were recorded and photographed. Majority of the circles (52%) showed anomalies. Hypoplasia was the most frequent anomaly and was found in 24% of the brains. Accessory vessels in the form of duplications/triplications of anterior communicating artery were seen in 12% of the circles. The embryonic origin of the posterior cerebral artery from the internal carotid persisted in 10% of the circles. An incomplete circle due to the absence of one or other posterior communicating artery was found in 6% of the specimens.Variations are more frequent in posterior half of the circle. The anatomical variations of the circle of Willis were probably genetically determined, develop in early embryonic stage and persist in post natal life

  19. Assortative mixing in functional brain networks during epileptic seizures

    Science.gov (United States)

    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.

  20. An anatomically comprehensive atlas of the adult human brain transcriptome

    NARCIS (Netherlands)

    Hawrylycz, M.J.; Beckmann, Christian

    2012-01-01

    Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising

  1. A high-resolution anatomical framework of the neonatal mouse brain for managing gene expression data

    Directory of Open Access Journals (Sweden)

    Jyl Boline

    2007-11-01

    Full Text Available This study aims to provide a high-resolution atlas and use it as an anatomical framework to localize the gene expression data for mouse brain on postnatal day 0 (P0. A color Nissl-stained volume with a resolution of 13.3×50×13.3 µm3 was constructed and co-registered to a standard anatomical space defined by an averaged geometry of C57BL/6J P0 mouse brains. A 145 anatomical structures were delineated based on the histological images. Anatomical relationships of delineated structures were established based on the hierarchical relations defined in the atlas of adult mouse brain (MacKenzie-Graham et al., 2004 so the P0 atlas can be related to the database associated with the adult atlas. The co-registered multimodal atlas as well as the original anatomical delineations is available for download at http://www.loni.ucla.edu/Atlases/. The region-specific anatomical framework based on the neonatal atlas allows for the analysis of gene activity within a high-resolution anatomical space at an early developmental stage. We demonstrated the potential application of this framework by incorporating gene expression data generated using in situ hybridization to the atlas space. By normalizing the gene expression patterns revealed by different images, experimental results from separate studies can be compared and summarized in an anatomical context. Co-displaying multiple registered datasets in the atlas space allows for 3D reconstruction of the co-expression patterns of the different genes in the atlas space, hence providing better insight into the relationship between the differentiated distribution pattern of gene products and specific anatomical systems.

  2. A local insult of okadaic acid in wild-type mice induces tau phosphorylation and protein aggregation in anatomically distinct brain regions

    OpenAIRE

    Baker, Si?n; G?tz, J?rgen

    2016-01-01

    In Alzheimer?s disease (AD), the distribution and density of neurofibrillary tangles, a histological hallmark comprised predominately of phosphorylated tau protein, follows a distinct pattern through anatomically connected brain regions. Studies in transgenic mice engineered to regionally confine tau expression have suggested spreading of tau within neural networks. Furthermore, injection of protein lysates isolated from brains of transgenic mice or patients with tauopathies, including AD, we...

  3. Risky Cerebrovascular Anatomic Orientation: Implications for Brain Revascularization.

    Science.gov (United States)

    Nagm, Alhusain; Horiuchi, Tetsuyoshi; Yanagawa, Takao; Hongo, Kazuhiro

    2016-12-01

    This study documents a risky vascular anatomic orientation that might play an important role in the postoperative hemodynamics following anterior cerebral artery (ACA) revascularization. A 71-year-old woman presented with uncontrollable frequent right lower limb transient ischemic attacks (TIAs) attributed to a left cerebral ischemic lesion due to severe left ACA stenosis. She underwent successful left-sided superficial temporal artery-ACA bypass using interposed vascular graft. The patient awoke satisfactory from anesthesia; however, on postoperative day 1, she developed right-sided hemiparesis. Extensive postoperative investigations disclosed that watershed shift infarction was considered the etiology for this neurologic deterioration. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Visual analytics of brain networks.

    Science.gov (United States)

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

    2012-05-15

    Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. Copyright © 2012 Elsevier Inc. All rights reserved.

  5. Graph analysis of the anatomical network organization of the hippocampal formation and parahippocampal region in the rat.

    Science.gov (United States)

    Binicewicz, F Z M; van Strien, N M; Wadman, W J; van den Heuvel, M P; Cappaert, N L M

    2016-04-01

    Graph theory was used to analyze the anatomical network of the rat hippocampal formation and the parahippocampal region (van Strien et al., Nat Rev Neurosci 10(4):272-282, 2009). For this analysis, the full network was decomposed along the three anatomical axes, resulting in three networks that describe the connectivity within the rostrocaudal, dorsoventral and laminar dimensions. The rostrocaudal network had a connection density of 12% and a path length of 2.4. The dorsoventral network had a high cluster coefficient (0.53), a relatively high path length (1.62) and a rich club was identified. The modularity analysis revealed three modules in the dorsoventral network. The laminar network contained most information. The laminar dimension revealed a network with high clustering coefficient (0.47), a relatively high path length (2.11) and four significantly increased characteristic network building blocks (structural motifs). Thirteen rich club nodes were identified, almost all of them situated in the parahippocampal region. Six connector hubs were detected and all of them were located in the entorhinal cortex. Three large modules were revealed, indicating a close relationship between the perirhinal and postrhinal cortex as well as between the lateral and medial entorhinal cortex. These results confirmed the central position of the entorhinal cortex in the (para)hippocampal network and this possibly explains why pathology in this region has such profound impact on cognitive function, as seen in several brain diseases. The results also have implications for the idea of strict separation of the "spatial" and the "non-spatial" information stream into the hippocampus. This two-stream memory model suggests that the information influx from, respectively, the postrhinal-medial entorhinal cortex and the perirhinal-lateral entorhinal cortex is separate, but the current analysis shows that this apparent separation is not determined by anatomical constraints.

  6. Brain Growth Across the Life Span in Autism: Age-Specific Changes in Anatomical Pathology

    Science.gov (United States)

    Courchesne, Eric; Campbell, Kathleen; Solso, Stephanie

    2014-01-01

    Autism is marked by overgrowth of the brain at the earliest ages but not at older ages when decreases in structural volumes and neuron numbers are observed instead. This has lead to the theory of age-specific anatomic abnormalities in autism. Here we report age-related changes in brain size in autistic and typical subjects from 12 months to 50 years of age based on analyses of 586 longitudinal and cross-sectional MRI scans. This dataset is several times larger than the largest autism study to date. Results demonstrate early brain overgrowth during infancy and the toddler years in autistic boys and girls, followed by an accelerated rate of decline in size and perhaps degeneration from adolescence to late middle age in this disorder. We theorize that underlying these age-specific changes in anatomic abnormalities in autism there may also be age-specific changes in gene expression, molecular, synaptic, cellular and circuit abnormalities. A peak age for detecting and studying the earliest fundamental biological underpinnings of autism is prenatal life and the first three postnatal years. Studies of the older autistic brain may not address original causes but are essential to discovering how best to help the older aging autistic person. Lastly, the theory of age-specific anatomic abnormalities in autism has broad implications for a wide range of work on the disorder including the design, validation and interpretation of animal model, lymphocyte gene expression, brain gene expression, and genotype/CNV-anatomic phenotype studies. PMID:20920490

  7. Multi-scale brain networks

    CERN Document Server

    Betzel, Richard F

    2016-01-01

    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 neuros...

  8. Multi-scale brain networks.

    Science.gov (United States)

    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. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  9. Dynamics of the anatomical changes that occur in the brains of schoolchildren as they learn to read.

    Directory of Open Access Journals (Sweden)

    Gregory Simon

    Full Text Available Although the functional brain network involved in reading for adults and children is now well documented, a critical lack of knowledge still exists about the structural development of these brain areas. To provide a better overview of the structural dynamics of the brain that sustain reading acquisition, we acquired anatomical MRI brain images from 55 children that were divided into two groups: one prior to the formal learning of reading (n = 33, 5-6 years old and the second a few years after formal learning (n = 22, 9-10 years old. Reading performances were collected based on the "Alouette-R" test, a standardized test for reading text in French. Voxel-based morphometry analysis of gray matter showed that only the right insula volume was different between the two groups. Moreover, the reading group showed that the volumes of the left fusiform gyrus (corresponding to the well-known visual word form area, VWFA, the anterior part of the left inferior occipital gyrus and the left thalamus were significantly modulated by reading performance. This study reinforces the crucial role of the Visual Word Form Area in reading and correlation analyses performed between ROIs volumes suggesting that the VWFA is fully connected with the traditional left-hemispheric language brain network.

  10. Brain networks of social comparison.

    Science.gov (United States)

    Kedia, Gayannée; Lindner, Michael; Mussweiler, Thomas; Ihssen, Niklas; Linden, David E J

    2013-03-27

    Social comparison, that is, the process of comparing oneself to other people, is a ubiquitous social cognitive mechanism; however, so far its neural correlates have remained unknown. The present study tested the hypothesis that social comparisons are supported by partly dissociated networks, depending on whether the dimension under comparison concerns a physical or a psychological attribute. We measured brain activity with functional MRI, whereas participants were comparing their own height or intelligence to that of individuals they personally know. Height comparisons were associated with higher activity in a frontoparietal network involved in spatial and numerical cognition. Conversely, intelligence comparisons recruited a network of midline areas that have been previously implicated in the attribution of mental states to oneself and others (Theory of mind). These findings suggest that social comparisons rely on diverse domain-specific mechanisms rather than on one unitary process.

  11. scMRI reveals large-scale brain network abnormalities in autism.

    Directory of Open Access Journals (Sweden)

    Brandon A Zielinski

    Full Text Available Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a 'posteriorization' of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI.

  12. THERMAL REGULATION OF THE BRAIN -AN ANATOMICAL AND PHYSIOLOGICAL REVIEW FOR CLINICAL NEUROSCIENTISTS

    Directory of Open Access Journals (Sweden)

    Huan (John eWang

    2016-01-01

    Full Text Available Humans, like all mammals and birds, maintain a nearly constant core body temperature (36 -37.5°C over a wide range of environmental conditions and are thus referred to as endotherms. The evolution of the brain and its supporting structures in mammals and birds coincided with this development of endothermy. Despite the recognition that a more evolved and complicated brain with all of its temperature-dependent cerebral circuitry and neuronal processes would require more sophisticated thermal control mechanisms, the current understanding of brain temperature regulation remains limited. To optimize the development and maintenance of the brain in health and to accelerate its healing and restoration in illness, focused and committed efforts are much needed to advance the fundamental understanding of brain temperature. In order to effectively study and examine brain temperature regulation, it is critical to first understand the relevant anatomical and physiological properties in the head-neck regions.

  13. Efficient Variational Approach to Multimodal Registration of Anatomical and Functional Intra-Patient Tumorous Brain Data.

    Science.gov (United States)

    Legaz-Aparicio, Alvar-Ginés; Verdú-Monedero, Rafael; Larrey-Ruiz, Jorge; Morales-Sánchez, Juan; López-Mir, Fernando; Naranjo, Valery; Bernabéu, Ángela

    2017-09-01

    This paper addresses the functional localization of intra-patient images of the brain. Functional images of the brain (fMRI and PET) provide information about brain function and metabolism whereas anatomical images (MRI and CT) supply the localization of structures with high spatial resolution. The goal is to find the geometric correspondence between functional and anatomical images in order to complement and fuse the information provided by each imaging modality. The proposed approach is based on a variational formulation of the image registration problem in the frequency domain. It has been implemented as a C/C[Formula: see text] library which is invoked from a GUI. This interface is routinely used in the clinical setting by physicians for research purposes (Inscanner, Alicante, Spain), and may be used as well for diagnosis and surgical planning. The registration of anatomic and functional intra-patient images of the brain makes it possible to obtain a geometric correspondence which allows for the localization of the functional processes that occur in the brain. Through 18 clinical experiments, it has been demonstrated how the proposed approach outperforms popular state-of-the-art registration methods in terms of efficiency, information theory-based measures (such as mutual information) and actual registration error (distance in space of corresponding landmarks).

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

    Science.gov (United States)

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

    2017-04-01

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

  15. Brain growth across the life span in autism: age-specific changes in anatomical pathology.

    Science.gov (United States)

    Courchesne, Eric; Campbell, Kathleen; Solso, Stephanie

    2011-03-22

    Autism is marked by overgrowth of the brain at the earliest ages but not at older ages when decreases in structural volumes and neuron numbers are observed instead. This has led to the theory of age-specific anatomic abnormalities in autism. Here we report age-related changes in brain size in autistic and typical subjects from 12 months to 50 years of age based on analyses of 586 longitudinal and cross-sectional MRI scans. This dataset is several times larger than the largest autism study to date. Results demonstrate early brain overgrowth during infancy and the toddler years in autistic boys and girls, followed by an accelerated rate of decline in size and perhaps degeneration from adolescence to late middle age in this disorder. We theorize that underlying these age-specific changes in anatomic abnormalities in autism, there may also be age-specific changes in gene expression, molecular, synaptic, cellular, and circuit abnormalities. A peak age for detecting and studying the earliest fundamental biological underpinnings of autism is prenatal life and the first three postnatal years. Studies of the older autistic brain may not address original causes but are essential to discovering how best to help the older aging autistic person. Lastly, the theory of age-specific anatomic abnormalities in autism has broad implications for a wide range of work on the disorder including the design, validation, and interpretation of animal model, lymphocyte gene expression, brain gene expression, and genotype/CNV-anatomic phenotype studies. Copyright © 2010 Elsevier B.V. All rights reserved.

  16. Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex.

    Science.gov (United States)

    Lang, Stefan; Dercksen, Vincent J; Sakmann, Bert; Oberlaender, Marcel

    2011-11-01

    The three-dimensional (3D) structure of neural circuits represents an essential constraint for information flow in the brain. Methods to directly monitor streams of excitation, at subcellular and millisecond resolution, are at present lacking. Here, we describe a pipeline of tools that allow investigating information flow by simulating electrical signals that propagate through anatomically realistic models of average neural networks. The pipeline comprises three blocks. First, we review tools that allow fast and automated acquisition of 3D anatomical data, such as neuron soma distributions or reconstructions of dendrites and axons from in vivo labeled cells. Second, we introduce NeuroNet, a tool for assembling the 3D structure and wiring of average neural networks. Finally, we introduce a simulation framework, NeuroDUNE, to investigate structure-function relationships within networks of full-compartmental neuron models at subcellular, cellular and network levels. We illustrate the pipeline by simulations of a reconstructed excitatory network formed between the thalamus and spiny stellate neurons in layer 4 (L4ss) of a cortical barrel column in rat vibrissal cortex. Exciting the ensemble of L4ss neurons with realistic input from an ensemble of thalamic neurons revealed that the location-specific thalamocortical connectivity may result in location-specific spiking of cortical cells. Specifically, a radial decay in spiking probability toward the column borders could be a general feature of signal flow in a barrel column. Our simulations provide insights of how anatomical parameters, such as the subcellular organization of synapses, may constrain spiking responses at the cellular and network levels. Copyright © 2011 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Damien A Fair

    2009-05-01

    Full Text Available 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

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

    Science.gov (United States)

    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

  19. Efficiency and cost of economical brain functional networks.

    Directory of Open Access Journals (Sweden)

    Sophie Achard

    2007-02-01

    Full Text Available Brain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years and healthy young (N = 15; mean age = 24.7 years volunteers were each scanned twice in a no-task or "resting" state following placebo or a single dose of a dopamine receptor antagonist (sulpiride 400 mg. Functional connectivity between 90 cortical and subcortical regions was estimated by wavelet correlation analysis, in the frequency interval 0.06-0.11 Hz, and thresholded to construct undirected graphs. These brain functional networks were small-world and economical in the sense of providing high global and local efficiency of parallel information processing for low connection cost. Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Dopamine antagonism also impaired global and local efficiency of the network, but this effect was differentially localised and did not interact with the effect of age. Brain functional networks have economical small-world properties-supporting efficient parallel information transfer at relatively low cost-which are differently impaired by normal aging and pharmacological blockade of dopamine transmission.

  20. Anatomical location of LPA1 activation and LPA phospholipid precursors in rodent and human brain.

    Science.gov (United States)

    González de San Román, Estibaliz; Manuel, Iván; Giralt, María Teresa; Chun, Jerold; Estivill-Torrús, Guillermo; Rodríguez de Fonseca, Fernando; Santín, Luis Javier; Ferrer, Isidro; Rodríguez-Puertas, Rafael

    2015-08-01

    Lysophosphatidic acid (LPA) is a signaling molecule that binds to six known G protein-coupled receptors: LPA1 -LPA6 . LPA evokes several responses in the CNS, including cortical development and folding, growth of the axonal cone and its retraction process. Those cell processes involve survival, migration, adhesion proliferation, differentiation, and myelination. The anatomical localization of LPA1 is incompletely understood, particularly with regard to LPA binding. Therefore, we have used functional [(35) S]GTPγS autoradiography to verify the anatomical distribution of LPA1 binding sites in adult rodent and human brain. The greatest activity was observed in myelinated areas of the white matter such as corpus callosum, internal capsule and cerebellum. MaLPA1 -null mice (a variant of LPA1 -null) lack [(35) S]GTPγS basal binding in white matter areas, where the LPA1 receptor is expressed at high levels, suggesting a relevant role of the activity of this receptor in the most myelinated brain areas. In addition, phospholipid precursors of LPA were localized by MALDI-IMS in both rodent and human brain slices identifying numerous species of phosphatides and phosphatidylcholines. Both phosphatides and phosphatidylcholines species represent potential LPA precursors. The anatomical distribution of these precursors in rodent and human brain may indicate a metabolic relationship between LPA and LPA1 receptors. Lysophosphatidic acid (LPA) is a signaling molecule that binds to six known G protein-coupled receptors (GPCR), LPA1 to LPA6 . LPA evokes several responses in the central nervous system (CNS), including cortical development and folding, growth of the axonal cone and its retraction process. We used functional [(35) S]GTPγS autoradiography to verify the anatomical distribution of LPA1 -binding sites in adult rodent and human brain. The distribution of LPA1 receptors in rat, mouse and human brains show the highest activity in white matter myelinated areas. The basal and

  1. Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging☆

    Science.gov (United States)

    Oishi, Kenichi; Faria, Andreia V.; Yoshida, Shoko; Chang, Linda; Mori, Susumu

    2013-01-01

    The development of the brain is structure-specific, and the growth rate of each structure differs depending on the age of the subject. Magnetic resonance imaging (MRI) is often used to evaluate brain development because of the high spatial resolution and contrast that enable the observation of structure-specific developmental status. Currently, most clinical MRIs are evaluated qualitatively to assist in the clinical decision-making and diagnosis. The clinical MRI report usually does not provide quantitative values that can be used to monitor developmental status. Recently, the importance of image quantification to detect and evaluate mild-to-moderate anatomical abnormalities has been emphasized because these alterations are possibly related to several psychiatric disorders and learning disabilities. In the research arena, structural MRI and diffusion tensor imaging (DTI) have been widely applied to quantify brain development of the pediatric population. To interpret the values from these MR modalities, a “growth percentile chart,” which describes the mean and standard deviation of the normal developmental curve for each anatomical structure, is required. Although efforts have been made to create such a growth percentile chart based on MRI and DTI, one of the greatest challenges is to standardize the anatomical boundaries of the measured anatomical structures. To avoid inter- and intra-reader variability about the anatomical boundary definition, and hence, to increase the precision of quantitative measurements, an automated structure parcellation method, customized for the neonatal and pediatric population, has been developed. This method enables quantification of multiple MR modalities using a common analytic framework. In this paper, the attempt to create an MRI- and a DTI-based growth percentile chart, followed by an application to investigate developmental abnormalities related to cerebral palsy, Williams syndrome, and Rett syndrome, have been introduced

  2. Graph analysis of the anatomical network organization of the hippocampal formation and parahippocampal region in the rat

    NARCIS (Netherlands)

    Binicewicz, F.Z.M.; van Strien, N.M.; Wadman, W.J.; van den Heuvel, M.P.; Cappaert, N.L.M.

    2016-01-01

    Graph theory was used to analyze the anatomical network of the rat hippocampal formation and the parahippocampal region (van Strien et al., Nat Rev Neurosci 10(4):272-282, 2009). For this analysis, the full network was decomposed along the three anatomical axes, resulting in three networks that

  3. Anatomical alterations of the visual motion processing network in migraine with and without aura.

    Directory of Open Access Journals (Sweden)

    Cristina Granziera

    2006-10-01

    Full Text Available Patients suffering from migraine with aura (MWA and migraine without aura (MWoA show abnormalities in visual motion perception during and between attacks. Whether this represents the consequences of structural changes in motion-processing networks in migraineurs is unknown. Moreover, the diagnosis of migraine relies on patient's history, and finding differences in the brain of migraineurs might help to contribute to basic research aimed at better understanding the pathophysiology of migraine.To investigate a common potential anatomical basis for these disturbances, we used high-resolution cortical thickness measurement and diffusion tensor imaging (DTI to examine the motion-processing network in 24 migraine patients (12 with MWA and 12 MWoA and 15 age-matched healthy controls (HCs. We found increased cortical thickness of motion-processing visual areas MT+ and V3A in migraineurs compared to HCs. Cortical thickness increases were accompanied by abnormalities of the subjacent white matter. In addition, DTI revealed that migraineurs have alterations in superior colliculus and the lateral geniculate nucleus, which are also involved in visual processing.A structural abnormality in the network of motion-processing areas could account for, or be the result of, the cortical hyperexcitability observed in migraineurs. The finding in patients with both MWA and MWoA of thickness abnormalities in area V3A, previously described as a source in spreading changes involved in visual aura, raises the question as to whether a "silent" cortical spreading depression develops as well in MWoA. In addition, these experimental data may provide clinicians and researchers with a noninvasively acquirable migraine biomarker.

  4. Brain network clustering with information flow motifs

    NARCIS (Netherlands)

    Märtens, M.; Meier, J.M.; Hillebrand, Arjan; Tewarie, Prejaas; Van Mieghem, P.F.A.

    2017-01-01

    Recent work has revealed frequency-dependent global patterns of information flow by a network analysis of magnetoencephalography data of the human brain. However, it is unknown which properties on a small subgraph-scale of those functional brain networks are dominant at different frequencies bands.

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

    Science.gov (United States)

    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.

  6. Flow distributions and spatial correlations in human brain capillary networks

    Science.gov (United States)

    Lorthois, Sylvie; Peyrounette, Myriam; Larue, Anne; Le Borgne, Tanguy

    2015-11-01

    The vascular system of the human brain cortex is composed of a space filling mesh-like capillary network connected upstream and downstream to branched quasi-fractal arterioles and venules. The distribution of blood flow rates in these networks may affect the efficiency of oxygen transfer processes. Here, we investigate the distribution and correlation properties of blood flow velocities from numerical simulations in large 3D human intra-cortical vascular network (10000 segments) obtained from an anatomical database. In each segment, flow is solved from a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation to deduce blood pressure, blood flow and red blood cell volume fraction distributions throughout the network. The network structural complexity is found to impart broad and spatially correlated Lagrangian velocity distributions, leading to power law transit time distributions. The origins of this behavior (existence of velocity correlations in capillary networks, influence of the coupling with the feeding arterioles and draining veins, topological disorder, complex blood rheology) are studied by comparison with results obtained in various model capillary networks of controlled disorder. ERC BrainMicroFlow GA615102, ERC ReactiveFronts GA648377.

  7. Dissociation of functional and anatomical brain abnormalities in unaffected siblings of schizophrenia patients.

    Science.gov (United States)

    Guo, Wenbin; Song, Yan; Liu, Feng; Zhang, Zhikun; Zhang, Jian; Yu, Miaoyu; Liu, Jianrong; Xiao, Changqing; Liu, Guiying; Zhao, Jingping

    2015-05-01

    Schizophrenia patients and their unaffected siblings share similar brain functional and structural abnormalities. However, no study is engaged to investigate whether and how functional abnormalities are related to structural abnormalities in unaffected siblings. This study was undertaken to examine the association between functional and anatomical abnormalities in unaffected siblings. Forty-six unaffected siblings of schizophrenia patients and 46 age-, sex-, and education-matched healthy controls underwent structural and resting-state functional magnetic resonance imaging scanning. Voxel-based morphometry (VBM), amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) were utilized to analyze imaging data. The VBM analysis showed gray matter volume decreases in the fronto-temporal regions (the left middle temporal gyrus and right inferior frontal gyrus, orbital part) and increases in basal ganglia system (the left putamen). Functional abnormalities measured by ALFF and fALFF mainly involved in the fronto-limbic-sensorimotor circuit (decreased ALFF in bilateral middle frontal gyrus and the right middle cingulate gyrus, and decreased fALFF in the right inferior frontal gyrus, orbital part; and increased ALFF in the left fusiform gyrus and left lingual gyrus, and increased fALFF in bilateral calcarine cortex). No significant correlation was found between functional and anatomical abnormalities in the sibling group. A dissociation pattern of brain regions with functional and anatomical abnormalities is observed in unaffected siblings. Our findings suggest that brain functional and anatomical abnormalities might be present independently in unaffected siblings of schizophrenia patients. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  8. Anatomical landmarks for registration of experimental image data to volumetric rodent brain atlasing templates.

    Science.gov (United States)

    Sergejeva, Marina; Papp, Eszter A; Bakker, Rembrandt; Gaudnek, Manuel A; Okamura-Oho, Yuko; Boline, Jyl; Bjaalie, Jan G; Hess, Andreas

    2015-01-30

    Assignment of anatomical reference is a key step in integration of the rapidly expanding collection of rodent brain data. Landmark-based registration facilitates spatial anchoring of diverse types of data not suitable for automated methods operating on voxel-based image information. Here we propose a standardized set of anatomical landmarks for registration of whole brain imaging datasets from the mouse and rat brain, and in particular for integration of experimental image data in Waxholm Space (WHS). Sixteen internal landmarks of the C57BL/6J mouse brain have been reliably identified: by different individuals, independent of their experience in anatomy; across different MRI contrasts (T1, T2, T2(*)) and other modalities (Nissl histology and block-face anatomy); in different specimens; in different slice acquisition angles; and in different image resolutions. We present a registration example between T1-weighted MRI and the mouse WHS template using these landmarks and reaching fairly high accuracy. Landmark positions identified in the mouse WHS template are shared through the Scalable Brain Atlas, accompanied by graphical and textual guidelines for locating each landmark. We identified 14 of the 16 landmarks in the WHS template for the Sprague Dawley rat. This landmark set can withstand substantial differences in acquisition angle, imaging modality, and is less vulnerable to subjectivity. This facilitates registration of multimodal 3D brain data to standard coordinate spaces for mouse and rat brain taking a step toward the creation of a common rodent reference system; raising data sharing to a qualitatively higher level. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Anatomical Characterization of Human Fetal Brain Development with Diffusion Tensor Magnetic Resonance Imaging

    Science.gov (United States)

    Huang, Hao; Xue, Rong; Zhang, Jiangyang; Ren, Tianbo; Richards, Linda J.; Yarowsky, Paul; Miller, Michael I.; Mori, Susumu

    2009-01-01

    The human brain is extraordinarily complex, and yet its origin is a simple tubular structure. Characterizing its anatomy at different stages of human fetal brain development not only aids in understanding this highly ordered process but also provides clues to detecting abnormalities caused by genetic or environmental factors. During the second trimester of human fetal development, neural structures in the brain undergo significant morphological changes. Diffusion tensor imaging (DTI), a novel method of magnetic resonance imaging, is capable of delineating anatomical components with high contrast and revealing structures at the microscopic level. In this study, high-resolution and high-signal-to-noise-ratio DTI data of fixed tissues of second-trimester human fetal brains were acquired and analyzed. DTI color maps and tractography revealed that important white matter tracts, such as the corpus callosum and uncinate and inferior longitudinal fasciculi, become apparent during this period. Three-dimensional reconstruction shows that major brain fissures appear while most of the cerebral surface remains smooth until the end of the second trimester. A dominant radial organization was identified at 15 gestational weeks, followed by both laminar and radial architectures in the cerebral wall throughout the remainder of the second trimester. Volumetric measurements of different structures indicate that the volumes of basal ganglia and ganglionic eminence increase along with that of the whole brain, while the ventricle size decreases in the later second trimester. The developing fetal brain DTI database presented can be used for education, as an anatomical research reference, and for data registration. PMID:19339620

  10. Neuroplasticity as a function of second language learning: anatomical changes in the human brain.

    Science.gov (United States)

    Li, Ping; Legault, Jennifer; Litcofsky, Kaitlyn A

    2014-09-01

    The brain has an extraordinary ability to functionally and physically change or reconfigure its structure in response to environmental stimulus, cognitive demand, or behavioral experience. This property, known as neuroplasticity, has been examined extensively in many domains. But how does neuroplasticity occur in the brain as a function of an individual's experience with a second language? It is not until recently that we have gained some understanding of this question by examining the anatomical changes as well as functional neural patterns that are induced by the learning and use of multiple languages. In this article we review emerging evidence regarding how structural neuroplasticity occurs in the brain as a result of one's bilingual experience. Our review aims at identifying the processes and mechanisms that drive experience-dependent anatomical changes, and integrating structural imaging evidence with current knowledge of functional neural plasticity of language and other cognitive skills. The evidence reviewed so far portrays a picture that is highly consistent with structural neuroplasticity observed for other domains: second language experience-induced brain changes, including increased gray matter (GM) density and white matter (WM) integrity, can be found in children, young adults, and the elderly; can occur rapidly with short-term language learning or training; and are sensitive to age, age of acquisition, proficiency or performance level, language-specific characteristics, and individual differences. We conclude with a theoretical perspective on neuroplasticity in language and bilingualism, and point to future directions for research. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. Discovering anatomical patterns with pathological meaning by clustering of visual primitives in structural brain MRI

    Science.gov (United States)

    Leon, Juan; Pulido, Andrea; Romero, Eduardo

    2015-01-01

    Computational anatomy is a subdiscipline of the anatomy that studies macroscopic details of the human body structure using a set of automatic techniques. Different reference systems have been developed for brain mapping and morphometry in functional and structural studies. Several models integrate particular anatomical regions to highlight pathological patterns in structural brain MRI, a really challenging task due to the complexity, variability, and nonlinearity of the human brain anatomy. In this paper, we present a strategy that aims to find anatomical regions with pathological meaning by using a probabilistic analysis. Our method starts by extracting visual primitives from brain MRI that are partitioned into small patches and which are then softly clustered, forming different regions not necessarily connected. Each of these regions is described by a co- occurrence histogram of visual features, upon which a probabilistic semantic analysis is used to find the underlying structure of the information, i.e., separated regions by their low level similarity. The proposed approach was tested with the OASIS data set which includes 69 Alzheimer's disease (AD) patients and 65 healthy subjects (NC).

  12. Anatomical predictors of aphasia recovery: a tractography study of bilateral perisylvian language networks.

    Science.gov (United States)

    Forkel, Stephanie J; Thiebaut de Schotten, Michel; Dell'Acqua, Flavio; Kalra, Lalit; Murphy, Declan G M; Williams, Steven C R; Catani, Marco

    2014-07-01

    Stroke-induced aphasia is associated with adverse effects on quality of life and the ability to return to work. For patients and clinicians the possibility of relying on valid predictors of recovery is an important asset in the clinical management of stroke-related impairment. Age, level of education, type and severity of initial symptoms are established predictors of recovery. However, anatomical predictors are still poorly understood. In this prospective longitudinal study, we intended to assess anatomical predictors of recovery derived from diffusion tractography of the perisylvian language networks. Our study focused on the arcuate fasciculus, a language pathway composed of three segments connecting Wernicke's to Broca's region (i.e. long segment), Wernicke's to Geschwind's region (i.e. posterior segment) and Broca's to Geschwind's region (i.e. anterior segment). In our study we were particularly interested in understanding how lateralization of the arcuate fasciculus impacts on severity of symptoms and their recovery. Sixteen patients (10 males; mean age 60 ± 17 years, range 28-87 years) underwent post stroke language assessment with the Revised Western Aphasia Battery and neuroimaging scanning within a fortnight from symptoms onset. Language assessment was repeated at 6 months. Backward elimination analysis identified a subset of predictor variables (age, sex, lesion size) to be introduced to further regression analyses. A hierarchical regression was conducted with the longitudinal aphasia severity as the dependent variable. The first model included the subset of variables as previously defined. The second model additionally introduced the left and right arcuate fasciculus (separate analysis for each segment). Lesion size was identified as the only independent predictor of longitudinal aphasia severity in the left hemisphere [beta = -0.630, t(-3.129), P = 0.011]. For the right hemisphere, age [beta = -0.678, t(-3.087), P = 0.010] and volume of the long

  13. Scaling in topological properties of brain networks

    NARCIS (Netherlands)

    Singh, S.S.; Khundrakpam, B.S.; Reid, A.T.; Lewis, J.D.; Evans, A.C.; Ishrat, R.; Sharma, B.I.; Singh, R.K.B.

    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.

  14. Anatomical location of effective deep brain stimulation electrodes in chronic cluster headache.

    Science.gov (United States)

    Fontaine, Denys; Lanteri-Minet, Michel; Ouchchane, Lemlih; Lazorthes, Yves; Mertens, Patrick; Blond, Serge; Geraud, G; Fabre, Nelly; Navez, Malou; Lucas, Christian; Dubois, Francois; Sol, Jean Christophe; Paquis, Philippe; Lemaire, Jean Jacques

    2010-04-01

    Deep brain stimulation of the posterior hypothalamus is a therapeutic approach to the treatment of refractory chronic cluster headache, but the precise anatomical location of the electrode contacts has not been clearly assessed. Our aim was to study the location of the contacts used for chronic stimulation, projecting each contact centre on anatomic atlases. Electrodes were implanted in a series of 10 patients (prospective controlled trial) in the so-called 'posteroinferior hypothalamus' according to previously described coordinates, i.e. 2 mm lateral, 3 mm posterior and 5 mm below the mid-commissural point. The coordinates of the centre of each stimulating contact were measured on postoperative computed tomography or magnetic resonance imaging scans, taking into account the artefact of the electrode. Each contact centre (n=10; left and right hemispheres pooled) was displayed on the Schaltenbrand atlas and a stereotactic three dimensional magnetic resonance imaging atlas (4.7 tesla) of the diencephalon-mesencephalic junction for accurate anatomical location. Of the 10 patients with 1-year follow-up, 5 responded to deep brain stimulation (weekly frequency of attacks decrease >50%). In responders, the mean (standard deviation) coordinates of the contacts were 2.98 (1.16) mm lateral, 3.53 (1.97) mm posterior and 3.31 (1.97) mm below the mid-commissural point. All the effective contacts were located posterior to the hypothalamus. In responders, structures located deep brain stimulation treatment in cluster headache may be due to factors unrelated to electrode misplacement. They also suggest that the therapeutic effect is probably not related to direct hypothalamic stimulation. Deep brain stimulation might modulate either a local cluster headache generator, located in the hypothalamus or in the mesencephalic grey substance, or non-specific anti-nocioceptive systems.

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

    Science.gov (United States)

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

    2017-06-06

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

  16. Pain: A Distributed Brain Information Network?

    Science.gov (United States)

    Mano, Hiroaki; Seymour, Ben

    2015-01-01

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

  17. Anatomical and spatial matching in imitation: Evidence from left and right brain-damaged patients.

    Science.gov (United States)

    Mengotti, Paola; Ripamonti, Enrico; Pesavento, Valentina; Rumiati, Raffaella Ida

    2015-12-01

    Imitation is a sensorimotor process whereby the visual information present in the model's movement has to be coupled with the activation of the motor system in the observer. This also implies that greater the similarity between the seen and the produced movement, the easier it will be to execute the movement, a process also known as ideomotor compatibility. Two components can influence the degree of similarity between two movements: the anatomical and the spatial component. The anatomical component is present when the model and imitator move the same body part (e.g., the right hand) while the spatial component is present when the movement of the model and that of the imitator occur at the same spatial position. Imitation can be achieved by relying on both components, but typically the model's and imitator's movements are matched either anatomically or spatially. The aim of this study was to ascertain the contribution of the left and right hemisphere to the imitation accomplished either with anatomical or spatial matching (or with both). Patients with unilateral left and right brain damage performed an ideomotor task and a gesture imitation task. Lesions in the left and right hemispheres gave rise to different performance deficits. Patients with lesions in the left hemisphere showed impaired imitation when anatomical matching was required, and patients with lesions in the right hemisphere showed impaired imitation when spatial matching was required. Lesion analysis further revealed a differential involvement of left and right hemispheric regions, such as the parietal opercula, in supporting imitation in the ideomotor task. Similarly, gesture imitation seemed to rely on different regions in the left and right hemisphere, such as parietal regions in the left hemisphere and premotor, somatosensory and subcortical regions in the right hemisphere. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Functional Brain Network Classification With Compact Representation of SICE Matrices.

    Science.gov (United States)

    Zhang, Jianjia; Zhou, Luping; Wang, Lei; Li, Wanqing

    2015-06-01

    Recently, a sparse inverse covariance estimation (SICE) technique has been employed to model functional brain connectivity. The inverse covariance matrix (SICE matrix in short) estimated for each subject is used as a representation of brain connectivity to discriminate Alzheimers disease from normal controls. However, we observed that direct use of the SICE matrix does not necessarily give satisfying discrimination, due to its high dimensionality and the scarcity of training subjects. Looking into this problem, we argue that the intrinsic dimensionality of these SICE matrices shall be much lower, considering 1) an SICE matrix resides on a Riemannian manifold of symmetric positive definiteness matrices, and 2) human brains share common patterns of connectivity across subjects. Therefore, we propose to employ manifold-based similarity measures and kernel-based PCA to extract principal connectivity components as a compact representation of brain network. Moreover, to cater for the requirement of both discrimination and interpretation in neuroimage analysis, we develop a novel preimage estimation algorithm to make the obtained connectivity components anatomically interpretable. To verify the efficacy of our method and gain insights into SICE-based brain networks, we conduct extensive experimental study on synthetic data and real rs-fMRI data from the ADNI dataset. Our method outperforms the comparable methods and improves the classification accuracy significantly.

  19. New insights into differences in brain organization between Neanderthals and anatomically modern humans

    Science.gov (United States)

    Pearce, Eiluned; Stringer, Chris; Dunbar, R. I. M.

    2013-01-01

    Previous research has identified morphological differences between the brains of Neanderthals and anatomically modern humans (AMHs). However, studies using endocasts or the cranium itself are limited to investigating external surface features and the overall size and shape of the brain. A complementary approach uses comparative primate data to estimate the size of internal brain areas. Previous attempts to do this have generally assumed that identical total brain volumes imply identical internal organization. Here, we argue that, in the case of Neanderthals and AMHs, differences in the size of the body and visual system imply differences in organization between the same-sized brains of these two taxa. We show that Neanderthals had significantly larger visual systems than contemporary AMHs (indexed by orbital volume) and that when this, along with their greater body mass, is taken into account, Neanderthals have significantly smaller adjusted endocranial capacities than contemporary AMHs. We discuss possible implications of differing brain organization in terms of social cognition, and consider these in the context of differing abilities to cope with fluctuating resources and cultural maintenance. PMID:23486442

  20. New insights into differences in brain organization between Neanderthals and anatomically modern humans.

    Science.gov (United States)

    Pearce, Eiluned; Stringer, Chris; Dunbar, R I M

    2013-05-07

    Previous research has identified morphological differences between the brains of Neanderthals and anatomically modern humans (AMHs). However, studies using endocasts or the cranium itself are limited to investigating external surface features and the overall size and shape of the brain. A complementary approach uses comparative primate data to estimate the size of internal brain areas. Previous attempts to do this have generally assumed that identical total brain volumes imply identical internal organization. Here, we argue that, in the case of Neanderthals and AMHs, differences in the size of the body and visual system imply differences in organization between the same-sized brains of these two taxa. We show that Neanderthals had significantly larger visual systems than contemporary AMHs (indexed by orbital volume) and that when this, along with their greater body mass, is taken into account, Neanderthals have significantly smaller adjusted endocranial capacities than contemporary AMHs. We discuss possible implications of differing brain organization in terms of social cognition, and consider these in the context of differing abilities to cope with fluctuating resources and cultural maintenance.

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

    Science.gov (United States)

    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.

  2. Effect of anatomical variability in brain on transcranial magnetic stimulation treatment

    Science.gov (United States)

    Syeda, F.; Magsood, H.; Lee, E. G.; El-Gendy, A. A.; Jiles, D. C.; Hadimani, R. L.

    2017-05-01

    Transcranial Magnetic Stimulation is a non-invasive clinical therapy used to treat depression and migraine, and shows further promise as treatment for Parkinson's disease, Alzheimer's disease, and other neurological disorders. However, it is yet unclear as to how anatomical differences may affect stimulation from this treatment. We use finite element analysis to model and analyze the results of Transcranial Magnetic Stimulation in various head models. A number of heterogeneous head models have been developed using MRI data of real patients, including healthy individuals as well as patients of Parkinson's disease. Simulations of Transcranial Magnetic Stimulation performed on 22 anatomically different models highlight the differences in induced stimulation. A standard Figure of 8 coil is used with frequency 2.5 kHz, placed 5 mm above the head. We compare cortical stimulation, volume of brain tissue stimulated, specificity, and maximum E-field induced in the brain for models ranging from ages 20 to 60. Results show that stimulation varies drastically between patients of the same age and health status depending upon brain-scalp distance, which is not necessarily a linear progression with age.

  3. Anatomical connectivity mapping: a new tool to assess brain disconnection in Alzheimer's disease.

    Science.gov (United States)

    Bozzali, Marco; Parker, Geoffrey J M; Serra, Laura; Embleton, Karl; Gili, Tommaso; Perri, Roberta; Caltagirone, Carlo; Cercignani, Mara

    2011-02-01

    Previous studies suggest that the clinical manifestations of Alzheimer's disease (AD) are not only associated with regional gray matter damage but also with abnormal functional integration of different brain regions by disconnection mechanisms. A measure of anatomical connectivity (anatomical connectivity mapping or ACM) can be obtained by initiating diffusion tractography streamlines from all parenchymal voxels and then counting the number of streamlines passing through each voxel of the brain. In order to assess the potential of this parameter for the study of disconnection in AD, we computed it in a group of patients with AD (N=9), in 16 patients with amnestic mild cognitive impairment (a-MCI, which is considered the prodromal stage of AD) and in 12 healthy volunteers. All subjects had an MRI scan at 3T, and diffusion MRI data were analyzed to obtain fractional anisotropy (FA) and ACM. Two types of ACM maps, absolute count (ac-ACM) and normalized by brain size count (nc-ACM), were obtained. No between group differences in FA surviving correction for multiple comparison were found, while areas of both decreased (in the supramarginal gyrus) and increased (in the putamen) ACM were found in patients with AD. Similar results were obtained with ac-ACM and nc-ACM. ACM of the supramarginal gyrus was strongly associated with measures of short-term memory in healthy subjects. This study shows that ACM provides information that is complementary to that offered by FA and appears to be more sensitive than FA to brain changes in patients with AD. The increased ACM in the putamen was unexpected. Given the nature of ACM, an increase of this parameter may reflect a change in any of the areas connected to it. One intriguing possibility is that this increase of ACM in AD patients might reflect processes of brain plasticity driven by cholinesterase inhibitors. Copyright © 2010 Elsevier Inc. All rights reserved.

  4. Nicotine increases brain functional network efficiency.

    Science.gov (United States)

    Wylie, Korey P; Rojas, Donald C; Tanabe, Jody; Martin, Laura F; Tregellas, Jason R

    2012-10-15

    Despite the use of cholinergic therapies in Alzheimer's disease and the development of cholinergic strategies for schizophrenia, relatively little is known about how the system modulates the connectivity and structure of large-scale brain networks. To better understand how nicotinic cholinergic systems alter these networks, this study examined the effects of nicotine on measures of whole-brain network communication efficiency. Resting state fMRI was acquired from fifteen healthy subjects before and after the application of nicotine or placebo transdermal patches in a single blind, crossover design. Data, which were previously examined for default network activity, were analyzed with network topology techniques to measure changes in the communication efficiency of whole-brain networks. Nicotine significantly increased local efficiency, a parameter that estimates the network's tolerance to local errors in communication. Nicotine also significantly enhanced the regional efficiency of limbic and paralimbic areas of the brain, areas which are especially altered in diseases such as Alzheimer's disease and schizophrenia. These changes in network topology may be one mechanism by which cholinergic therapies improve brain function. Published by Elsevier Inc.

  5. Reaction time as a stochastic process implemented by functional brain networks.

    Science.gov (United States)

    Siettos, Constantinos I; Smyrnis, Nikolaos

    2017-04-01

    Many studies focus on anatomical brain connectivity in an effort to explain the effect of practice on reaction time (RT) that is observed in many cognitive tasks. In this commentary, we suggest that RT reflects a stochastic process that varies in each single repetition of any cognitive task and cannot be attributed only to anatomical properties of the underlying neuronal circuit. Based on recent evidence from Magnetoencephalographic, Electroencephalographic, and fMRI studies, we further propose that the functional properties of key brain areas and their self-organization into functional connectivity networks contribute to the RT and could also explain the effects of training on the distribution of the RT.

  6. Brain rhythms reveal a hierarchical network organization.

    Directory of Open Access Journals (Sweden)

    G Karl Steinke

    2011-10-01

    Full Text Available 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

  7. Brain networks underlying novel metaphor production.

    Science.gov (United States)

    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. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Stable functional networks exhibit consistent timing in the human brain.

    Science.gov (United States)

    Chapeton, Julio I; Inati, Sara K; Zaghloul, Kareem A

    2017-03-01

    Despite many advances in the study of large-scale human functional networks, the question of timing, stability, and direction of communication between cortical regions has not been fully addressed. At the cellular level, neuronal communication occurs through axons and dendrites, and the time required for such communication is well defined and preserved. At larger spatial scales, however, the relationship between timing, direction, and communication between brain regions is less clear. Here, we use a measure of effective connectivity to identify connections between brain regions that exhibit communication with consistent timing. We hypothesized that if two brain regions are communicating, then knowledge of the activity in one region should allow an external observer to better predict activity in the other region, and that such communication involves a consistent time delay. We examine this question using intracranial electroencephalography captured from nine human participants with medically refractory epilepsy. We use a coupling measure based on time-lagged mutual information to identify effective connections between brain regions that exhibit a statistically significant increase in average mutual information at a consistent time delay. These identified connections result in sparse, directed functional networks that are stable over minutes, hours, and days. Notably, the time delays associated with these connections are also highly preserved over multiple time scales. We characterize the anatomic locations of these connections, and find that the propagation of activity exhibits a preferred posterior to anterior temporal lobe direction, consistent across participants. Moreover, networks constructed from connections that reliably exhibit consistent timing between anatomic regions demonstrate features of a small-world architecture, with many reliable connections between anatomically neighbouring regions and few long range connections. Together, our results demonstrate

  9. The development of brain network architecture

    NARCIS (Netherlands)

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

    2016-01-01

    Brain connectivity shows protracted development throughout childhood and adolescence, and, as such, the topology of brain networks changes during this period. The complexity of these changes with development is reflected by regional differences in maturation. This study explored age-related changes

  10. Brain networks modulated by subthalamic nucleus deep brain stimulation.

    Science.gov (United States)

    Accolla, Ettore A; Herrojo Ruiz, Maria; Horn, Andreas; Schneider, Gerd-Helge; Schmitz-Hübsch, Tanja; Draganski, Bogdan; Kühn, Andrea A

    2016-09-01

    Deep brain stimulation of the subthalamic nucleus is an established treatment for the motor symptoms of Parkinson's disease. Given the frequent occurrence of stimulation-induced affective and cognitive adverse effects, a better understanding about the role of the subthalamic nucleus in non-motor functions is needed. The main goal of this study is to characterize anatomical circuits modulated by subthalamic deep brain stimulation, and infer about the inner organization of the nucleus in terms of motor and non-motor areas. Given its small size and anatomical intersubject variability, functional organization of the subthalamic nucleus is difficult to investigate in vivo with current methods. Here, we used local field potential recordings obtained from 10 patients with Parkinson's disease to identify a subthalamic area with an analogous electrophysiological signature, namely a predominant beta oscillatory activity. The spatial accuracy was improved by identifying a single contact per macroelectrode for its vicinity to the electrophysiological source of the beta oscillation. We then conducted whole brain probabilistic tractography seeding from the previously identified contacts, and further described connectivity modifications along the macroelectrode's main axis. The designated subthalamic 'beta' area projected predominantly to motor and premotor cortical regions additional to connections to limbic and associative areas. More ventral subthalamic areas showed predominant connectivity to medial temporal regions including amygdala and hippocampus. We interpret our findings as evidence for the convergence of different functional circuits within subthalamic nucleus' portions deemed to be appropriate as deep brain stimulation target to treat motor symptoms in Parkinson's disease. Potential clinical implications of our study are illustrated by an index case where deep brain stimulation of estimated predominant non-motor subthalamic nucleus induced hypomanic behaviour. © The

  11. Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph

    Directory of Open Access Journals (Sweden)

    Stavros I. Dimitriadis

    2017-12-01

    Full Text Available Structural brain networks estimated from diffusion MRI (dMRI via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI from the Automated Anatomical Labeling (AAL template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN. Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a intra-class correlation coefficient (ICC of well-known network metrics, both node-wise and per network level; and (b the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node

  12. Structure and function of complex brain networks

    Science.gov (United States)

    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

  13. Hierarchical modularity in human brain functional networks

    CERN Document Server

    Meunier, D; Fornito, A; Ersche, K D; Bullmore, E T; 10.3389/neuro.11.037.2009

    2010-01-01

    The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or "modules-within-modules") decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at ...

  14. Dynamic Networks in the Emotional Brain.

    Science.gov (United States)

    Pessoa, Luiz; McMenamin, Brenton

    2016-10-25

    Research on the emotional brain has often focused on a few structures thought to be central to this type of processing-hypothalamus, amygdala, insula, and so on. Conceptual thinking about emotion has viewed this mental faculty as linked to broader brain circuits, too, including early ideas by Papez and others. In this article, we discuss research that embraces a distributed view of emotion circuits and efforts to unravel the impact on emotional manipulations on the processing of several large-scale brain networks that are chiefly important for mental operations traditionally labeled with terms such as "perception," "action," and "cognition." Furthermore, we describe networks as dynamic processes and how emotion-laden stimuli strongly affect network structure. As networks are not static entities, their organization unfolds temporally, such that specific brain regions affiliate with them in a time-varying fashion. Thus, at a specific moment, brain regions participate more strongly in some networks than others. In this dynamic view of brain function, emotion has broad, distributed effects on processing in a manner that transcends traditional boundaries and inflexible labels, such as "emotion" and "cognition." What matters is the coordinated action that supports behaviors. © The Author(s) 2016.

  15. Brain Networks of Explicit and Implicit Learning

    Science.gov (United States)

    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

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

    Science.gov (United States)

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

    2017-02-01

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

  17. Physiologic and anatomic characterization of the brain surface glia barrier of Drosophila.

    Science.gov (United States)

    DeSalvo, Michael K; Mayer, Nasima; Mayer, Fahima; Bainton, Roland J

    2011-09-01

    Central nervous system (CNS) physiology requires special chemical, metabolic, and cellular privileges for normal function, and blood-brain barrier (BBB) structures are the anatomic and physiologic constructs that arbitrate communication between the brain and body. In the vertebrate BBB, two primary cell types create CNS exclusion biology, a polarized vascular endothelium (VE), and a tightly associated single layer of astrocytic glia (AG). Examples of direct action by the BBB in CNS disease are constantly expanding, including key pathophysiologic roles in multiple sclerosis, stroke, and cancer. In addition, its role as a pharmacologic treatment obstacle to the brain is long standing; thus, molecular model systems that can parse BBB functions and understand the complex integration of sophisticated cellular anatomy and highly polarized chemical protection physiology are desperately needed. Compound barrier structures that use two primary cell types (i.e., functional bicellularity) are common to other humoral/CNS barrier structures. For example, invertebrates use two cell layers of glia, perineurial and subperineurial, to control chemical access to the brain, and analogous glial layers, fenestrated and pseudocartridge, to maintain the blood-eye barrier. In this article, we summarize our current understanding of brain-barrier glial anatomy in Drosophila, demonstrate the power of live imaging as a screening methodology for identifying physiologic characteristics of BBB glia, and compare the physiologies of Drosophila barrier layers to the VE/AG interface of vertebrates. We conclude that many unique BBB physiologies are conserved across phyla and suggest new methods for modeling CNS physiology and disease. Copyright © 2011 Wiley-Liss, Inc.

  18. Genetic control of functional brain network efficiency in children

    NARCIS (Netherlands)

    Heuvel, M.P.; van Soelen, I.L.C.; Stam, C.J.; Kahn, R.S.; Boomsma, D.I.; Hulshoff Pol, H.E.

    2013-01-01

    The human brain is a complex network of interconnected brain regions. In adulthood, the brain's network was recently found to be under genetic influence. However, the extent to which genes influence the functional brain network early in development is not yet known. We report on the heritability of

  19. Linking graph features of anatomical architecture to regional brain activity: A multi-modal MRI study.

    Science.gov (United States)

    Lee, Tien-Wen; Xue, Shao-Wei

    2017-06-09

    Previous empirical research has treated regional neural responses and network architecture separately. However, anecdotal reports have suggested a close relationship between the two. This study aims to investigate the influence of structural connectivity on regional spontaneous activities. Datasets of structural magnetic resonance imaging (sMRI), resting state functional MRI (rs-fMRI) and diffusion weighted imaging (DWI) of 36 right-handed healthy subjects (average age 27.4) were selected from the NKI Rockland sample. In the sMRI data, the cerebral cortex was parcellated into 70 regions of interest (ROIs) according to an anatomical atlas. Two indices were calculated from rs-fMRI for each ROI: the regional homogeneity (ReHo) and the amplitude of low frequency fluctuation (ALFF). Diffusion tensor imaging was computed from DWI and was converted to tractography. Four graph indices of structural connectivity were retrieved from the tractography results and the 70 ROIs, as follows: nodal degree, clustering coefficient, local efficiency and betweenness centrality. ReHo values were significantly correlated with all 4 graph features, whereas ALFF values were significantly correlated with nodal degrees and clustering coefficients. Both ReHo and ALFF tended to increase with segregation (clustering coefficient and local efficiency) and decrease with centrality (nodal degree and betweenness centrality). Though derived from local spontaneous activities, ReHo and ALFF may reflect the network properties of the underlying anatomical architecture. The results supported the hypothesis that the properties of the network structure may shape the regional neural response profiles. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Histological-subtypes and anatomical location correlated in meningeal brain tumors (meningiomas

    Directory of Open Access Journals (Sweden)

    Abdul Rashid Bhat

    2014-01-01

    Full Text Available Context: Not enough literature is available to suggest a link between the histological subtypes of intracranial meningeal brain tumors, called ′meningiomas′ and their location of origin. Aim: The evidence of correlation between the anatomical location of the intracranial meningiomas and the histopathological grades will facilitate specific diagnosis and accurate treatment. Materials and Methods: The retrospective study was conducted in a single high-patient-inflow Neurosurgical Center, under a standard and uniform medical protocol, over a period of 30 years from December 1982 to December 2012. The records of all the operated 729 meningiomas were analyzed from the patient files in the Medical Records Department. The biodata, x-rays, angiography, computed tomography (CT scans, imaging, histopathological reports, and mortality were evaluated and results drawn. Results: The uncommon histopathological types of meningiomas (16.88% had common locations of origin in the sphenoid ridge, posterior parafalcine, jugular foramen, peritorcular and intraventricular regions, cerebellopontine angle, and tentorial and petroclival areas. The histopathological World Health Organization (WHO Grade I (Benign Type meningiomas were noted in 89.30%, WHO Grade II (Atypical Type in 5.90%, and WHO Grade III (Malignant Type in 4.80% of all meningiomas. Meningiomas of 64.60% were found in females, 47.32% were in the age group of 41-50 years, and 3.43% meningiomas were found in children. An overall mortality of 6.04% was noted. WHO Grade III (malignant meningiomas carried a high mortality (25.71% and the most common sites of meningiomas with high mortality were: The cerebellopontine angles, intraventricular region, sphenoid ridge, tuberculum sellae, and the posterior parafalcine areas. Conclusion: The correlation between the histological subtypes and the anatomical location of intracranial meningeal brain tumors, called meningiomas, is evident, but further research is

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Katarzyna J Blinowska

    Full Text Available 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.

  3. Brain anatomy alterations associated with Social Networking Site (SNS) addiction

    Science.gov (United States)

    He, Qinghua; Turel, Ofir; Bechara, Antoine

    2017-01-01

    This study relies on knowledge regarding the neuroplasticity of dual-system components that govern addiction and excessive behavior and suggests that alterations in the grey matter volumes, i.e., brain morphology, of specific regions of interest are associated with technology-related addictions. Using voxel based morphometry (VBM) applied to structural Magnetic Resonance Imaging (MRI) scans of twenty social network site (SNS) users with varying degrees of SNS addiction, we show that SNS addiction is associated with a presumably more efficient impulsive brain system, manifested through reduced grey matter volumes in the amygdala bilaterally (but not with structural differences in the Nucleus Accumbens). In this regard, SNS addiction is similar in terms of brain anatomy alterations to other (substance, gambling etc.) addictions. We also show that in contrast to other addictions in which the anterior-/ mid- cingulate cortex is impaired and fails to support the needed inhibition, which manifests through reduced grey matter volumes, this region is presumed to be healthy in our sample and its grey matter volume is positively correlated with one’s level of SNS addiction. These findings portray an anatomical morphology model of SNS addiction and point to brain morphology similarities and differences between technology addictions and substance and gambling addictions. PMID:28332625

  4. Brain anatomy alterations associated with Social Networking Site (SNS) addiction.

    Science.gov (United States)

    He, Qinghua; Turel, Ofir; Bechara, Antoine

    2017-03-23

    This study relies on knowledge regarding the neuroplasticity of dual-system components that govern addiction and excessive behavior and suggests that alterations in the grey matter volumes, i.e., brain morphology, of specific regions of interest are associated with technology-related addictions. Using voxel based morphometry (VBM) applied to structural Magnetic Resonance Imaging (MRI) scans of twenty social network site (SNS) users with varying degrees of SNS addiction, we show that SNS addiction is associated with a presumably more efficient impulsive brain system, manifested through reduced grey matter volumes in the amygdala bilaterally (but not with structural differences in the Nucleus Accumbens). In this regard, SNS addiction is similar in terms of brain anatomy alterations to other (substance, gambling etc.) addictions. We also show that in contrast to other addictions in which the anterior-/ mid- cingulate cortex is impaired and fails to support the needed inhibition, which manifests through reduced grey matter volumes, this region is presumed to be healthy in our sample and its grey matter volume is positively correlated with one's level of SNS addiction. These findings portray an anatomical morphology model of SNS addiction and point to brain morphology similarities and differences between technology addictions and substance and gambling addictions.

  5. Stress Impact on Resting State Brain Networks.

    Science.gov (United States)

    Soares, José Miguel; Sampaio, Adriana; Ferreira, Luís Miguel; Santos, Nadine Correia; Marques, Paulo; Marques, Fernanda; Palha, Joana Almeida; Cerqueira, João José; Sousa, Nuno

    2013-01-01

    Resting state brain networks (RSNs) are spatially distributed large-scale networks, evidenced by resting state functional magnetic resonance imaging (fMRI) studies. Importantly, RSNs are implicated in several relevant brain functions and present abnormal functional patterns in many neuropsychiatric disorders, for which stress exposure is an established risk factor. Yet, so far, little is known about the effect of stress in the architecture of RSNs, both in resting state conditions or during shift to task performance. Herein we assessed the architecture of the RSNs using functional magnetic resonance imaging (fMRI) in a cohort of participants exposed to prolonged stress (participants that had just finished their long period of preparation for the medical residence selection exam), and respective gender- and age-matched controls (medical students under normal academic activities). Analysis focused on the pattern of activity in resting state conditions and after deactivation. A volumetric estimation of the RSNs was also performed. Data shows that stressed participants displayed greater activation of the default mode (DMN), dorsal attention (DAN), ventral attention (VAN), sensorimotor (SMN), and primary visual (VN) networks than controls. Importantly, stressed participants also evidenced impairments in the deactivation of resting state-networks when compared to controls. These functional changes are paralleled by a constriction of the DMN that is in line with the pattern of brain atrophy observed after stress exposure. These results reveal that stress impacts on activation-deactivation pattern of RSNs, a finding that may underlie stress-induced changes in several dimensions of brain activity.

  6. Generative adversarial networks for brain lesion detection

    Science.gov (United States)

    Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

    2017-02-01

    Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

  7. Changes in cognitive state alter human functional brain networks

    Directory of Open Access Journals (Sweden)

    Malaak Nasser Moussa

    2011-08-01

    Full Text Available The study of the brain as a whole system can be accomplished using network theory principles. Research has shown that human functional brain networks during a resting state exhibit small-world properties and high degree nodes, or hubs, localized to brain areas consistent with the default mode network (DMN. However, the study of brain networks across different tasks and or cognitive states has been inconclusive. Research in this field is important because the underpinnings of behavioral output are inherently dependent on whether or not brain networks are dynamic. This is the first comprehensive study to evaluate multiple network metrics at a voxel-wise resolution in the human brain at both the whole brain and regional level under various conditions: resting state, visual stimulation, and multisensory (auditory and visual stimulation. Our results show that despite global network stability, functional brain networks exhibit considerable task-induced changes in connectivity, efficiency, and community structure at the regional level.

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

    Science.gov (United States)

    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.

  9. Regional brain stem atrophy in idiopathic Parkinson's disease detected by anatomical MRI.

    Directory of Open Access Journals (Sweden)

    Thomas Jubault

    Full Text Available Idiopathic Parkinson's disease (PD is a neurodegenerative disorder characterized by the dysfunction of dopaminergic dependent cortico-basal ganglia loops and diagnosed on the basis of motor symptoms (tremors and/or rigidity and bradykinesia. Post-mortem studies tend to show that the destruction of dopaminergic neurons in the substantia nigra constitutes an intermediate step in a broader neurodegenerative process rather than a unique feature of Parkinson's disease, as a consistent pattern of progression would exist, originating from the medulla oblongata/pontine tegmentum. To date, neuroimaging techniques have been unable to characterize the pre-symptomatic stages of PD. However, if such a regular neurodegenerative pattern were to exist, consistent damages would be found in the brain stem, even at early stages of the disease. We recruited 23 PD patients at Hoenn and Yahr stages I to II of the disease and 18 healthy controls (HC matched for age. T1-weighted anatomical scans were acquired (MPRAGE, 1 mm3 resolution and analyzed using an optimized VBM protocol to detect white and grey matter volume reduction without spatial a priori. When the HC group was compared to the PD group, a single cluster exhibited statistical difference (p<0.05 corrected for false detection rate, 4287 mm3 in the brain stem, between the pons and the medulla oblongata. The present study provides in-vivo evidence that brain stem damage may be the first identifiable stage of PD neuropathology, and that the identification of this consistent damage along with other factors could help with earlier diagnosis in the future. This damage could also explain some non-motor symptoms in PD that often precede diagnosis, such as autonomic dysfunction and sleep disorders.

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

    Science.gov (United States)

    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. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  11. Spatial patterns of genome-wide expression profiles reflect anatomic and fiber connectivity architecture of healthy human brain.

    Science.gov (United States)

    Goel, Pragya; Kuceyeski, Amy; LoCastro, Eve; Raj, Ashish

    2014-08-01

    Unraveling the relationship between molecular signatures in the brain and their functional, architectonic, and anatomic correlates is an important neuroscientific goal. It is still not well understood whether the diversity demonstrated by histological studies in the human brain is reflected in the spatial patterning of whole brain transcriptional profiles. Using genome-wide maps of transcriptional distribution of the human brain by the Allen Brain Institute, we test the hypothesis that gene expression profiles are specific to anatomically described brain regions. In this work, we demonstrate that this is indeed the case by showing that gene similarity clusters appear to respect conventional basal-cortical and caudal-rostral gradients. To fully investigate the causes of this observed spatial clustering, we test a connectionist hypothesis that states that the spatial patterning of gene expression in the brain is simply reflective of the fiber tract connectivity between brain regions. We find that although gene expression and structural connectivity are not determined by each other, they do influence each other with a high statistical significance. This implies that spatial diversity of gene expressions is a result of mainly location-specific features but is influenced by neuronal connectivity, such that like cellular species preferentially connects with like cells. Copyright © 2014 Wiley Periodicals, Inc.

  12. Early adverse life events are associated with altered brain network architecture in a sex- dependent manner

    Directory of Open Access Journals (Sweden)

    Arpana Gupta, PhD

    2017-12-01

    Full Text Available Introduction: Early adverse life events (EALs increase the risk for chronic medical and psychiatric disorders by altering early neurodevelopment. The aim of this study was to examine associations between EALs and network properties of core brain regions in the emotion regulation and salience networks, and to test the influence of sex on these associations. Methods: Resting-state functional and diffusion tensor magnetic resonance imaging were obtained in healthy individuals (61 men, 63 women. Functional and anatomical network properties of centrality and segregation were calculated for the core regions of the two networks using graph theory. Moderator analyses were applied to test hypotheses. Results: The type of adversity experienced influences brain wiring differently, as higher general EALs were associated with decreased functional and anatomical centrality in salience and emotion regulation regions, while physical and emotional EALs were associated with increased anatomical centrality and segregation in emotion regulation regions. Sex moderated the associations between EALs and measures of centrality; with decreased centrality of salience and emotion regulation regions with increased general EALs in females, and increased centrality in salience regions with higher physical and emotional EALs in males. Increased segregation of salience regions was associated with increased general EALs in males. Centrality of the amygdala was associated with physical symptoms, and segregation of salience regions was correlated with higher somatization in men only. Conclusions: Emotion regulation and salience regions are susceptible to topological brain restructuring associated with EALs. The male and female brains appear to be differently affected by specific types of EALs. Keywords: Early adverse traumatic life events, Centrality, Segregation, Network metrics, Moderating effects of sex, Emotion regulation network, Salience network

  13. Neuroanatomical substrates of action perception and understanding: an anatomic likelihood estimation meta-analysis of lesion-symptom mapping studies in brain injured patients.

    Science.gov (United States)

    Urgesi, Cosimo; Candidi, Matteo; Avenanti, Alessio

    2014-01-01

    Several neurophysiologic and neuroimaging studies suggested that motor and perceptual systems are tightly linked along a continuum rather than providing segregated mechanisms supporting different functions. Using correlational approaches, these studies demonstrated that action observation activates not only visual but also motor brain regions. On the other hand, brain stimulation and brain lesion evidence allows tackling the critical question of whether our action representations are necessary to perceive and understand others' actions. In particular, recent neuropsychological studies have shown that patients with temporal, parietal, and frontal lesions exhibit a number of possible deficits in the visual perception and the understanding of others' actions. The specific anatomical substrates of such neuropsychological deficits however, are still a matter of debate. Here we review the existing literature on this issue and perform an anatomic likelihood estimation meta-analysis of studies using lesion-symptom mapping methods on the causal relation between brain lesions and non-linguistic action perception and understanding deficits. The meta-analysis encompassed data from 361 patients tested in 11 studies and identified regions in the inferior frontal cortex, the inferior parietal cortex and the middle/superior temporal cortex, whose damage is consistently associated with poor performance in action perception and understanding tasks across studies. Interestingly, these areas correspond to the three nodes of the action observation network that are strongly activated in response to visual action perception in neuroimaging research and that have been targeted in previous brain stimulation studies. Thus, brain lesion mapping research provides converging causal evidence that premotor, parietal and temporal regions play a crucial role in action recognition and understanding.

  14. The conundrum of functional brain networks: small-world efficiency or fractal modularity

    Directory of Open Access Journals (Sweden)

    Lazaros eGallos

    2012-05-01

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

  15. Network effects of deep brain stimulation.

    Science.gov (United States)

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

    2015-10-01

    The ability to differentially alter specific brain functions via deep brain stimulation (DBS) represents a monumental advance in clinical neuroscience, as well as within medicine as a whole. Despite the efficacy of DBS in the treatment of movement disorders, for which it is often the gold-standard therapy when medical management becomes inadequate, the mechanisms through which DBS in various brain targets produces therapeutic effects is still not well understood. This limited knowledge is a barrier to improving efficacy and reducing side effects in clinical brain stimulation. A field of study related to assessing the network effects of DBS is gradually emerging that promises to reveal aspects of the underlying pathophysiology of various brain disorders and their response to DBS that will be critical to advancing the field. This review summarizes the nascent literature related to network effects of DBS measured by cerebral blood flow and metabolic imaging, functional imaging, and electrophysiology (scalp and intracranial electroencephalography and magnetoencephalography) in order to establish a framework for future studies. Copyright © 2015 the American Physiological Society.

  16. Brain networks for integrative rhythm formation.

    Directory of Open Access Journals (Sweden)

    Michael H Thaut

    2008-05-01

    Full Text Available 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.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.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.

  17. Specification and estimation of sources of bias affecting neurological studies in PET/MR with an anatomical brain phantom

    Energy Technology Data Exchange (ETDEWEB)

    Teuho, J., E-mail: jarmo.teuho@tyks.fi [Turku PET Centre, Turku (Finland); Johansson, J. [Turku PET Centre, Turku (Finland); Linden, J. [Turku PET Centre, Turku (Finland); Department of Mathematics and Statistics, University of Turku, Turku (Finland); Saunavaara, V.; Tolvanen, T.; Teräs, M. [Turku PET Centre, Turku (Finland)

    2014-01-11

    Selection of reconstruction parameters has an effect on the image quantification in PET, with an additional contribution from a scanner-specific attenuation correction method. For achieving comparable results in inter- and intra-center comparisons, any existing quantitative differences should be identified and compensated for. In this study, a comparison between PET, PET/CT and PET/MR is performed by using an anatomical brain phantom, to identify and measure the amount of bias caused due to differences in reconstruction and attenuation correction methods especially in PET/MR. Differences were estimated by using visual, qualitative and quantitative analysis. The qualitative analysis consisted of a line profile analysis for measuring the reproduction of anatomical structures and the contribution of the amount of iterations to image contrast. The quantitative analysis consisted of measurement and comparison of 10 anatomical VOIs, where the HRRT was considered as the reference. All scanners reproduced the main anatomical structures of the phantom adequately, although the image contrast on the PET/MR was inferior when using a default clinical brain protocol. Image contrast was improved by increasing the amount of iterations from 2 to 5 while using 33 subsets. Furthermore, a PET/MR-specific bias was detected, which resulted in underestimation of the activity values in anatomical structures closest to the skull, due to the MR-derived attenuation map that ignores the bone. Thus, further improvements for the PET/MR reconstruction and attenuation correction could be achieved by optimization of RAMLA-specific reconstruction parameters and implementation of bone to the attenuation template. -- Highlights: • Comparison between PET, PET/CT and PET/MR was performed with a novel brain phantom. • The performance of reconstruction and attenuation correction in PET/MR was studied. • A recently developed brain phantom was found feasible for PET/MR imaging. • Contrast reduction

  18. Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency.

    Science.gov (United States)

    Baek, K; Morris, L S; Kundu, P; Voon, V

    2017-03-01

    The efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED. Multi-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%-25%. We also verified our findings using a separate parcellation, the Harvard-Oxford atlas parcellated into 470 regions. Obese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis. Using this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in

  19. Social networking sites use and the morphology of a social-semantic brain network.

    Science.gov (United States)

    Turel, Ofir; He, Qinghua; Brevers, Damien; Bechara, Antoine

    2017-09-30

    Social lives have shifted, at least in part, for large portions of the population to social networking sites. How such lifestyle changes may be associated with brain structures is still largely unknown. In this manuscript, we describe two preliminary studies aimed at exploring this issue. The first study (n = 276) showed that Facebook users reported on increased social-semantic and mentalizing demands, and that such increases were positively associated with people's level of Facebook use. The second study (n = 33) theorized on and examined likely anatomical correlates of such changes in demands on the brain. Findings indicated that the grey matter volumes of the posterior parts of the bilateral middle and superior temporal, and left fusiform gyri were positively associated with the level of Facebook use. These results provided preliminary evidence that grey matter volumes of brain structures involved in social-semantic and mentalizing tasks may be linked to the extent of social networking sites use.

  20. Brain networks and their origins. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

    Science.gov (United States)

    Cisek, Paul

    2014-09-01

    Nearly every textbook on psychology or neuroscience contains theories of function described with box and arrow diagrams. Sometimes, the boxes stand for purely theoretical constructs, such as attention or working memory, and sometimes they also correspond to specific brain regions or systems, such as parietal or prefrontal cortex, and the arrows between them to known anatomical pathways. It is common for scientists (present company included) to summarize their theories in this way and to think of the brain as a set of interacting modules with clearly distinguishable functions.

  1. An Anatomically Constrained Model for Path Integration in the Bee Brain.

    Science.gov (United States)

    Stone, Thomas; Webb, Barbara; Adden, Andrea; Weddig, Nicolai Ben; Honkanen, Anna; Templin, Rachel; Wcislo, William; Scimeca, Luca; Warrant, Eric; Heinze, Stanley

    2017-10-23

    Path integration is a widespread navigational strategy in which directional changes and distance covered are continuously integrated on an outward journey, enabling a straight-line return to home. Bees use vision for this task-a celestial-cue-based visual compass and an optic-flow-based visual odometer-but the underlying neural integration mechanisms are unknown. Using intracellular electrophysiology, we show that polarized-light-based compass neurons and optic-flow-based speed-encoding neurons converge in the central complex of the bee brain, and through block-face electron microscopy, we identify potential integrator cells. Based on plausible output targets for these cells, we propose a complete circuit for path integration and steering in the central complex, with anatomically identified neurons suggested for each processing step. The resulting model circuit is thus fully constrained biologically and provides a functional interpretation for many previously unexplained architectural features of the central complex. Moreover, we show that the receptive fields of the newly discovered speed neurons can support path integration for the holonomic motion (i.e., a ground velocity that is not precisely aligned with body orientation) typical of bee flight, a feature not captured in any previously proposed model of path integration. In a broader context, the model circuit presented provides a general mechanism for producing steering signals by comparing current and desired headings-suggesting a more basic function for central complex connectivity, from which path integration may have evolved. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.

    Science.gov (United States)

    Chen, Hao; Dou, Qi; Yu, Lequan; Qin, Jing; Heng, Pheng-Ann

    2017-04-23

    Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train such a deep network with limited training data for brain segmentation, we seamlessly integrate multi-modality and multi-level contextual information into our network, so that the complementary information of different modalities can be harnessed and features of different scales can be exploited. Furthermore, an auto-context version of the VoxResNet is proposed by combining the low-level image appearance features, implicit shape information, and high-level context together for further improving the segmentation performance. Extensive experiments on the well-known benchmark (i.e., MRBrainS) of brain segmentation from 3D magnetic resonance (MR) images corroborated the efficacy of the proposed VoxResNet. Our method achieved the first place in the challenge out of 37 competitors including several state-of-the-art brain segmentation methods. Our method is inherently general and can be readily applied as a powerful tool to many brain-related studies, where accurate segmentation of brain

  3. Identifying modular relations in complex brain networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Mørup, Morten; Siebner, Hartwig

    2012-01-01

    and obtains comparable reproducibility and predictability. For resting state functional magnetic resonance imaging data from 30 healthy controls the IRM model is also superior to the two simpler alternatives, suggesting that brain networks indeed exhibit universal complex relational structure......We evaluate the infinite relational model (IRM) against two simpler alternative nonparametric Bayesian models for identifying structures in multi subject brain networks. The models are evaluated for their ability to predict new data and infer reproducible structures. Prediction and reproducibility...... are measured within the data driven NPAIRS split-half framework. Using synthetic data drawn from each of the generative models we show that the IRM model outperforms the two competing models when data contain relational structure. For data drawn from the other two simpler models the IRM does not overfit...

  4. Topological correlations of structural and functional networks in patients with traumatic brain injury

    Directory of Open Access Journals (Sweden)

    Karen eCaeyenberghs

    2013-11-01

    Full Text Available Despite an increasing amount of specific correlation studies between structural and functional connectivity, there is still a need for combined studies, especially in pathological conditions. Impairments of brain white matter and diffuse axonal injuries are commonly suspected to be responsible for the disconnection hypothesis in traumatic brain injury (TBI patients. Moreover, our previous research on TBI patients shows a strong relationship between abnormalities in topological organization of brain networks and behavioral deficits. In this study, we combined task-related functional connectivity (using event-related fMRI with structural connectivity (derived from fiber tractography using diffusion MRI data estimates in the same participants (17 adults with TBI and 16 controls, allowing for direct comparison between graph metrics of the different imaging modalities. Connectivity matrices were computed covering the switching motor network, which includes the basal ganglia, anterior cingulate cortex/supplementary motor area, and anterior insula/inferior frontal gyrus. The edges constituting this network consisted of the partial correlations between the fMRI time series from each node of the switching motor network. The interregional anatomical connections between the switching-related areas were determined using the fiber tractography results. We found that graph metrics and hubs obtained showed no agreement in both groups. The topological properties of brain functional networks could not be solely accounted for the properties of the underlying structural networks. However, combining complementary information from both different imaging modalities could improve accuracy in prediction of switching performance. Direct comparison between functional task-related and anatomical structural connectivity, presented here for the first time in TBI patients, links two powerful approaches to map the patterns of brain connectivity that may underlie behavioral

  5. Altered brain structural networks in attention deficit/hyperactivity disorder children revealed by cortical thickness.

    Science.gov (United States)

    Liu, Tian; Chen, Yanni; Li, Chenxi; Li, Youjun; Wang, Jue

    2017-07-04

    This study investigated the cortical thickness and topological features of human brain anatomical networks related to attention deficit/hyperactivity disorder. Data were collected from 40 attention deficit/hyperactivity disorder children and 40 normal control children. Interregional correlation matrices were established by calculating the correlations of cortical thickness between all pairs of cortical regions (68 regions) of the whole brain. Further thresholds were applied to create binary matrices to construct a series of undirected and unweighted graphs, and global, local, and nodal efficiencies were computed as a function of the network cost. These experimental results revealed abnormal cortical thickness and correlations in attention deficit/hyperactivity disorder, and showed that the brain structural networks of attention deficit/hyperactivity disorder subjects had inefficient small-world topological features. Furthermore, their topological properties were altered abnormally. In particular, decreased global efficiency combined with increased local efficiency in attention deficit/hyperactivity disorder children led to a disorder-related shift of the network topological structure toward regular networks. In addition, nodal efficiency, cortical thickness, and correlation analyses revealed that several brain regions were altered in attention deficit/hyperactivity disorder patients. These findings are in accordance with a hypothesis of dysfunctional integration and segregation of the brain in patients with attention deficit/hyperactivity disorder and provide further evidence of brain dysfunction in attention deficit/hyperactivity disorder patients by observing cortical thickness on magnetic resonance imaging.

  6. Stress Impact on Resting State Brain Networks.

    Directory of Open Access Journals (Sweden)

    José Miguel Soares

    Full Text Available Resting state brain networks (RSNs are spatially distributed large-scale networks, evidenced by resting state functional magnetic resonance imaging (fMRI studies. Importantly, RSNs are implicated in several relevant brain functions and present abnormal functional patterns in many neuropsychiatric disorders, for which stress exposure is an established risk factor. Yet, so far, little is known about the effect of stress in the architecture of RSNs, both in resting state conditions or during shift to task performance. Herein we assessed the architecture of the RSNs using functional magnetic resonance imaging (fMRI in a cohort of participants exposed to prolonged stress (participants that had just finished their long period of preparation for the medical residence selection exam, and respective gender- and age-matched controls (medical students under normal academic activities. Analysis focused on the pattern of activity in resting state conditions and after deactivation. A volumetric estimation of the RSNs was also performed. Data shows that stressed participants displayed greater activation of the default mode (DMN, dorsal attention (DAN, ventral attention (VAN, sensorimotor (SMN, and primary visual (VN networks than controls. Importantly, stressed participants also evidenced impairments in the deactivation of resting state-networks when compared to controls. These functional changes are paralleled by a constriction of the DMN that is in line with the pattern of brain atrophy observed after stress exposure. These results reveal that stress impacts on activation-deactivation pattern of RSNs, a finding that may underlie stress-induced changes in several dimensions of brain activity.

  7. Brain networks, structural realism, and local approaches to the scientific realism debate.

    Science.gov (United States)

    Yan, Karen; Hricko, Jonathon

    2017-08-01

    We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what instantiates that structure (nonstructural knowledge). And we argue that this work provides structural knowledge of brain networks. We explore the significance of this conclusion for the scientific realism debate. We argue that our conclusion should not be understood as an instance of a global structural realist claim regarding the structure of the unobservable part of the world, but instead, as a local structural realist attitude towards brain networks in particular. And we argue that various local approaches to the realism debate, i.e., approaches that restrict realist commitments to particular theories and/or entities, are problematic insofar as they don't allow for the possibility of such a local structural realist attitude. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2016-10-01

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

  9. Relationship of Topology, Multiscale Phase Synchronization, and State Transitions in Human Brain Networks

    Directory of Open Access Journals (Sweden)

    Minkyung Kim

    2017-06-01

    Full Text Available How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are “progressive and earlier” or “abrupt but delayed” account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious

  10. The Virtual Brain: a simulator of primate brain network dynamics.

    Science.gov (United States)

    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.

  11. Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion.

    Science.gov (United States)

    Prentašić, Pavle; Lončarić, Sven

    2016-12-01

    Diabetic retinopathy is one of the leading disabling chronic diseases and one of the leading causes of preventable blindness in developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into automated population screening programs. Detection of exudates in color fundus photographs is very important for early diagnosis of diabetic retinopathy. We use deep convolutional neural networks for exudate detection. In order to incorporate high level anatomical knowledge about potential exudate locations, output of the convolutional neural network is combined with the output of the optic disc detection and vessel detection procedures. In the validation step using a manually segmented image database we obtain a maximum F1 measure of 0.78. As manually segmenting and counting exudate areas is a tedious task, having a reliable automated output, such as automated segmentation using convolutional neural networks in combination with other landmark detectors, is an important step in creating automated screening programs for early detection of diabetic retinopathy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  12. Spectral properties of the temporal evolution of brain network structure

    Science.gov (United States)

    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.

  13. Specification and estimation of sources of bias affecting neurological studies in PET/MR with an anatomical brain phantom

    Science.gov (United States)

    Teuho, J.; Johansson, J.; Linden, J.; Saunavaara, V.; Tolvanen, T.; Teräs, M.

    2014-01-01

    Selection of reconstruction parameters has an effect on the image quantification in PET, with an additional contribution from a scanner-specific attenuation correction method. For achieving comparable results in inter- and intra-center comparisons, any existing quantitative differences should be identified and compensated for. In this study, a comparison between PET, PET/CT and PET/MR is performed by using an anatomical brain phantom, to identify and measure the amount of bias caused due to differences in reconstruction and attenuation correction methods especially in PET/MR. Differences were estimated by using visual, qualitative and quantitative analysis. The qualitative analysis consisted of a line profile analysis for measuring the reproduction of anatomical structures and the contribution of the amount of iterations to image contrast. The quantitative analysis consisted of measurement and comparison of 10 anatomical VOIs, where the HRRT was considered as the reference. All scanners reproduced the main anatomical structures of the phantom adequately, although the image contrast on the PET/MR was inferior when using a default clinical brain protocol. Image contrast was improved by increasing the amount of iterations from 2 to 5 while using 33 subsets. Furthermore, a PET/MR-specific bias was detected, which resulted in underestimation of the activity values in anatomical structures closest to the skull, due to the MR-derived attenuation map that ignores the bone. Thus, further improvements for the PET/MR reconstruction and attenuation correction could be achieved by optimization of RAMLA-specific reconstruction parameters and implementation of bone to the attenuation template.

  14. The Relation Between Structure and Function in Brain Networks : A network science perspective

    NARCIS (Netherlands)

    Meier, J.M.

    2017-01-01

    Over the last two decades the field of network science has been evolving fast. Many useful applications in a wide variety of disciplines have been found. The application of network science to the brain initiated the interdisciplinary field of complex brain networks. On a macroscopic level, brain

  15. Estimating functional brain networks by incorporating a modularity prior.

    Science.gov (United States)

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

    2016-11-01

    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. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. Brain networks that track musical structure.

    Science.gov (United States)

    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.

  17. Lifespan Development of the Human Brain Revealed by Large-Scale Network Eigen-Entropy

    Directory of Open Access Journals (Sweden)

    Yiming Fan

    2017-09-01

    Full Text Available Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying functional connectivity patterns of the developing and aging brain. Normal brain development is characterized by continuous and significant network evolution through infancy, childhood, and adolescence, following specific maturational patterns. Normal aging is related to some resting state brain networks disruption, which are associated with certain cognitive decline. It is a big challenge to design an integral metric to track connectome evolution patterns across the lifespan, which is to understand the principles of network organization in the human brain. In this study, we first defined a brain network eigen-entropy (NEE based on the energy probability (EP of each brain node. Next, we used the NEE to characterize the lifespan orderness trajectory of the whole-brain functional connectivity of 173 healthy individuals ranging in age from 7 to 85 years. The results revealed that during the lifespan, the whole-brain NEE exhibited a significant non-linear decrease and that the EP distribution shifted from concentration to wide dispersion, implying orderness enhancement of functional connectome over age. Furthermore, brain regions with significant EP changes from the flourishing (7–20 years to the youth period (23–38 years were mainly located in the right prefrontal cortex and basal ganglia, and were involved in emotion regulation and executive function in coordination with the action of the sensory system, implying that self-awareness and voluntary control performance significantly changed during neurodevelopment. However, the changes from the youth period to middle age (40–59 years were located in the mesial temporal lobe and caudate, which are associated with long-term memory, implying that the memory of the human brain begins to decline with age during this period. Overall, the findings suggested that the human connectome

  18. Task-Based Cohesive Evolution of Dynamic Brain Networks

    Science.gov (United States)

    Davison, Elizabeth

    2014-03-01

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

  19. Brain Networks Implicated in Seasonal Affective Disorder

    DEFF Research Database (Denmark)

    Nørgaard, Martin; Ganz, Melanie; Svarer, Claus

    2017-01-01

    Background: Seasonal Affective Disorder (SAD) is a subtype of Major Depressive Disorder characterized by seasonally occurring depression that often presents with atypical vegetative symptoms such as hypersomnia and carbohydrate craving. It has recently been shown that unlike healthy people......, patients with SAD fail to globally downregulate their cerebral serotonin transporter (5-HTT) in winter, and that this effect seemed to be particularly pronounced in female S-carriers of the 5-HTTLPR genotype. The purpose of this study was to identify a 5-HTT brain network that accounts for the adaption...

  20. Brain tumor segmentation with Deep Neural Networks.

    Science.gov (United States)

    Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

    2017-01-01

    In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Incidence trends in the anatomic location of primary malignant brain tumors in the United States: 1992-2006.

    Science.gov (United States)

    Zada, Gabriel; Bond, Aaron E; Wang, Ya-Ping; Giannotta, Steven L; Deapen, Dennis

    2012-01-01

    This study sought to determine incidence trends of the anatomical origin of primary malignant brain tumors. Incidence data for histologically confirmed brain tumors were obtained from the Los Angeles County Cancer Surveillance Program (LAC), the California Cancer Registry (CCR), and the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program for 1992 to 2006. Age-adjusted incidence rates (AAIR) and annual percent changes (APC) were calculated by histologic subtypes and anatomic subsites. Statistical analyses were performed using the SEER*Stat analytic software and SAS statistical software. Increased AAIRs of frontal (APC +2.4% to +3.0%, P ≤ 0.001) and temporal (APC +1.3% to +2.3%, P ≤ 0.027) lobe glioblastoma multiforme (GBM) tumors were observed across all registries, accompanied by decreased AAIRs in overlapping region GBMs (-2.0% to -2.8% APC, P ≤ 0.015). The AAIRs of GBMs in the parietal and occipital lobes remained stable. The AAIR of cerebellar GBMs increased according to CCR (APC +11.9%, P AAIR of all gliomas, which includes all anatomical subsites, decreased (-0.5% to -0.8% APC, P ≤ 0.034). Low-grade and anaplastic astrocytomas demonstrated decreased AAIRs in the majority of brain regions. Data from 3 major cancer registries demonstrate increased incidences of GBMs in the frontal lobe, temporal lobe, and cerebellum, despite decreased incidences in other brain regions. Although this may represent an effect of diagnostic bias, the incidence of both large and small tumors increased in these regions. The cause of these observed trends is unknown. Copyright © 2012 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    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.

  3. Resting-state brain organization revealed by functional covariance networks.

    Directory of Open Access Journals (Sweden)

    Zhiqiang Zhang

    Full Text Available BACKGROUND: Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN and structural covariance network (SCN have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization. METHODOLOGY AND PRINCIPAL FINDINGS: We proposed a functional covariance network (FCN method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network. CONCLUSION: The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale.

  4. HIVBrainSeqDB: a database of annotated HIV envelope sequences from brain and other anatomical sites

    Directory of Open Access Journals (Sweden)

    O'Connor Niall

    2010-12-01

    Full Text Available Abstract Background The population of HIV replicating within a host consists of independently evolving and interacting sub-populations that can be genetically distinct within anatomical compartments. HIV replicating within the brain causes neurocognitive disorders in up to 20-30% of infected individuals and is a viral sanctuary site for the development of drug resistance. The primary determinant of HIV neurotropism is macrophage tropism, which is primarily determined by the viral envelope (env gene. However, studies of genetic aspects of HIV replicating in the brain are hindered because existing repositories of HIV sequences are not focused on neurotropic virus nor annotated with neurocognitive and neuropathological status. To address this need, we constructed the HIV Brain Sequence Database. Results The HIV Brain Sequence Database is a public database of HIV envelope sequences, directly sequenced from brain and other tissues from the same patients. Sequences are annotated with clinical data including viral load, CD4 count, antiretroviral status, neurocognitive impairment, and neuropathological diagnosis, all curated from the original publication. Tissue source is coded using an anatomical ontology, the Foundational Model of Anatomy, to capture the maximum level of detail available, while maintaining ontological relationships between tissues and their subparts. 44 tissue types are represented within the database, grouped into 4 categories: (i brain, brainstem, and spinal cord; (ii meninges, choroid plexus, and CSF; (iii blood and lymphoid; and (iv other (bone marrow, colon, lung, liver, etc. Patient coding is correlated across studies, allowing sequences from the same patient to be grouped to increase statistical power. Using Cytoscape, we visualized relationships between studies, patients and sequences, illustrating interconnections between studies and the varying depth of sequencing, patient number, and tissue representation across studies

  5. Anatomical Brain Magnetic Resonance Imaging of Typically Developing Children and Adolescents

    Science.gov (United States)

    Giedd, Jay N.; Lalonde, Francois M.; Celano, Mark J.; White, Samantha L.; Wallace, Gregory L.; Lee, Nancy R.; Lenroot, Rhoshel K.

    2009-01-01

    Methodological issues relevant to magnetic resonance imaging studies of brain anatomy are discussed along with the findings on the neuroanatomic changes during childhood and adolescence. The development of the brain is also discussed.

  6. Anatomical variants of brain structure: confused spatial relationship of the fornix to the corpus callosum and anterior commissure.

    Science.gov (United States)

    Hori, A

    1997-12-01

    How the developing nerve fibers are guided to and able to find their target is currently a matter of research. As examples of the false guidance of axons, anatomical variants of the spatial relationship of the columna fornicis to the corpus callosum and the anterior commissure are demonstrated. In a 60 year-old female patient, some of the fibers of the genu corporis callosi were found to be entrapped by a fornix fiber bundle. The brain of a 20 year-old man showed that the unilateral anterior commissure ran posterior to the columna fornicis. These changes were clinically insignificant.

  7. Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation.

    Science.gov (United States)

    Fakhry, Ahmed; Zeng, Tao; Ji, Shuiwang

    2017-02-01

    Accurate reconstruction of anatomical connections between neurons in the brain using electron microscopy (EM) images is considered to be the gold standard for circuit mapping. A key step in obtaining the reconstruction is the ability to automatically segment neurons with a precision close to human-level performance. Despite the recent technical advances in EM image segmentation, most of them rely on hand-crafted features to some extent that are specific to the data, limiting their ability to generalize. Here, we propose a simple yet powerful technique for EM image segmentation that is trained end-to-end and does not rely on prior knowledge of the data. Our proposed residual deconvolutional network consists of two information pathways that capture full-resolution features and contextual information, respectively. We showed that the proposed model is very effective in achieving the conflicting goals in dense output prediction; namely preserving full-resolution predictions and including sufficient contextual information. We applied our method to the ongoing open challenge of 3D neurite segmentation in EM images. Our method achieved one of the top results on this open challenge. We demonstrated the generality of our technique by evaluating it on the 2D neurite segmentation challenge dataset where consistently high performance was obtained. We thus expect our method to generalize well to other dense output prediction problems.

  8. Complex brain networks: From topological communities to clustered ...

    Indian Academy of Sciences (India)

    Abstract. Recent research has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. A challenging task is to understand the implications of such network structures on the functional organisation of the brain activ- ities. We investigate synchronisation dynamics on the ...

  9. Mapping human whole-brain structural networks with diffusion MRI.

    Directory of Open Access Journals (Sweden)

    Patric Hagmann

    Full Text Available Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.

  10. Insight and Psychosis : Functional and Anatomical Brain Connectivity and Self-Reflection in Schizophrenia

    NARCIS (Netherlands)

    Curcic-Blake, Branisalava; van der Meer, Lisette; Pijnenborg, Gerdina H. M.; David, Anthony S.; Aleman, Andre

    2015-01-01

    Impaired insight into illness, associated with worse treatment outcome, is common in schizophrenia. Insight has been related to the self-reflective processing, centred on the medial frontal cortex. We hypothesized that anatomical and functional routes to and from the ventromedial prefrontal cortex

  11. Anatomical characterization of cytoglobin and neuroglobin mRNA and protein expression in the mouse brain

    DEFF Research Database (Denmark)

    Hundahl, Christian Ansgar; Allen, Gregg C; Hannibal, Jens

    2010-01-01

    -localize with almost all orexin neurons in the LH. In conclusion the distribution of Cygb and Ngb seems much more restricted and coherent than previously reported. We believe other functions than pure oxygen buffers and neuroprotectants should be considered. The anatomical data indicate a role in NO signalling...... for Cygb and involvement in sleep-wake cycling for Cygb and Ngb....

  12. The Brain Network Underpinning Novel Melody Creation.

    Science.gov (United States)

    Adhikari, Bhim M; Norgaard, Martin; Quinn, Kristen M; Ampudia, Jenine; Squirek, Justin; Dhamala, Mukesh

    2016-12-01

    Musical improvisation offers an excellent experimental paradigm for the study of real-time human creativity. It involves moment-to-moment decision-making, monitoring of one's performance, and utilizing external feedback to spontaneously create new melodies or variations on a melody. Recent neuroimaging studies have begun to study the brain activity during musical improvisation, aiming to unlock the mystery of human creativity. What brain resources come together and how these are utilized during musical improvisation are not well understood. To help answer these questions, we recorded electroencephalography (EEG) signals from 19 experienced musicians while they played or imagined short isochronous learned melodies and improvised on those learned melodies. These four conditions (Play-Prelearned, Play-Improvised, Imagine-Prelearned, Imagine-Improvised) were randomly interspersed in a total of 300 trials per participant. From the sensor-level EEG, we found that there were power differences in the alpha (8-12 Hz) and beta (13-30 Hz) bands in separate clusters of frontal, parietal, temporal, and occipital electrodes. Using EEG source localization and dipole modeling methods for task-related signals, we identified the locations and network activities of five sources: the left superior frontal gyrus (L SFG), supplementary motor area (SMA), left inferior parietal lobule (L IPL), right dorsolateral prefrontal cortex, and right superior temporal gyrus. During improvisation, the network activity between L SFG, SMA, and L IPL was significantly less than during the prelearned conditions. Our results support the general idea that attenuated cognitive control facilitates the production of creative output.

  13. Brain-on-a-chip integrated neuronal networks

    NARCIS (Netherlands)

    Xie, Sijia

    2016-01-01

    The brain-on-a-chip technology aims to provide an efficient and economic in vitro platform for brain disease study. In the well-known literature on brain-on-a-chip systems, nonstructured surfaces were conventionally used for the cell attachment in a culture chamber, therefore the neuronal networks

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

    Science.gov (United States)

    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. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  15. Scholastic performance and functional connectivity of brain networks in children.

    Directory of Open Access Journals (Sweden)

    Laura Chaddock-Heyman

    Full Text Available One of the keys to understanding scholastic success is to determine the neural processes involved in school performance. The present study is the first to use a whole-brain connectivity approach to explore whether functional connectivity of resting state brain networks is associated with scholastic performance in seventy-four 7- to 9-year-old children. We demonstrate that children with higher scholastic performance across reading, math and language have more integrated and interconnected resting state networks, specifically the default mode network, salience network, and frontoparietal network. To add specificity, core regions of the dorsal attention and visual networks did not relate to scholastic performance. The results extend the cognitive role of brain networks in children as well as suggest the importance of network connectivity in scholastic success.

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

    Science.gov (United States)

    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.

  17. Disrupted functional brain networks in autistic toddlers

    NARCIS (Netherlands)

    Boersma, M.; Kemner, C.; Reus, M.A. de; Collin, G; Snijders, T.M.; Hofman, D.; Buitelaar, J.K.; Stam, C.J.; Heuvel, M.P. van den

    2013-01-01

    Communication and integration of information between brain regions plays a key role in healthy brain function. Conversely, disruption in brain communication may lead to cognitive and behavioral problems. Autism is a neurodevelopmental disorder that is characterized by impaired social interactions

  18. Driving the brain towards creativity and intelligence: A network control theory analysis.

    Science.gov (United States)

    Kenett, Yoed N; Medaglia, John D; Beaty, Roger E; Chen, Qunlin; Betzel, Richard F; Thompson-Schill, Sharon L; Qiu, Jiang

    2018-01-04

    High-level cognitive constructs, such as creativity and intelligence, entail complex and multiple processes, including cognitive control processes. Recent neurocognitive research on these constructs highlight the importance of dynamic interaction across neural network systems and the role of cognitive control processes in guiding such a dynamic interaction. How can we quantitatively examine the extent and ways in which cognitive control contributes to creativity and intelligence? To address this question, we apply a computational network control theory (NCT) approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how NCT relates to individual differences in distinct measures of creative ability and intelligence. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that intelligence is related to the ability to "drive" the brain system into easy to reach neural states by the right inferior parietal lobe and lower integration abilities in the left retrosplenial cortex. We also find that creativity is related to the ability to "drive" the brain system into difficult to reach states by the right dorsolateral prefrontal cortex (inferior frontal junction) and higher integration abilities in sensorimotor areas. Furthermore, we found that different facets of creativity-fluency, flexibility, and originality-relate to generally similar but not identical network controllability processes. We relate our findings to general theories on intelligence and creativity. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Qing eGao

    2013-06-01

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

  20. An improved anatomical MRI technique with suppression of fixative fluid artifacts for the investigation of human postmortem brain phantoms.

    Science.gov (United States)

    Droby, Amgad; Yuen, Kenneth Sung Lai; Schänzer, Anne; Spiwoks-Becker, Isabella; Acker, Till; Lienerth, Bianca; Zipp, Frauke; Deichmann, Ralf

    2017-03-01

    Phantoms are often used to assess MR system stability in multicenter studies. Postmortem brain phantoms best replicate human brain anatomy, allowing for a combined assessment of the MR system and software chain for data analysis. However, a wash-out of fixative fluid affecting T1 values and thus T1-weighted sequences such as magnetization-prepared 180 degrees radiofrequency pulses and rapid gradient-echo (MP-RAGE) has been reported for brain phantoms, hampering their immediate use. The purpose of this study was the creation of anatomical data that provide the characteristics of conventional data while avoiding this artifact. Two brain phantoms were scanned at several time points, acquiring conventional MP-RAGE data and quantitative T1 and proton density (PD) maps. Assuming a suitable cutoff value T1cut , synthetic MP-RAGE data were created from these maps, being T1-weighted for T1 > T1cut to reduce fluid signal in the sulci, but PD-weighted for T1 phantoms, avoiding artifacts induced by the wash-out of fixative fluid, and thus achieving high signal stability shortly after fixation. Magn Reson Med 77:1115-1123, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

  1. Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.

    Science.gov (United States)

    Qin, Yuan-Yuan; Hsu, Johnny T; Yoshida, Shoko; Faria, Andreia V; Oishi, Kumiko; Unschuld, Paul G; Redgrave, Graham W; Ying, Sarah H; Ross, Christopher A; van Zijl, Peter C M; Hillis, Argye E; Albert, Marilyn S; Lyketsos, Constantine G; Miller, Michael I; Mori, Susumu; Oishi, Kenichi

    2013-01-01

    We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

  2. Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability

    Science.gov (United States)

    Xie, Long; Pluta, John B.; Das, Sandhitsu R.; Wisse, Laura E.M.; Wang, Hongzhi; Mancuso, Lauren; Kliot, Dasha; Avants, Brian B.; Ding, Song-Lin; Manjón, José V.; Wolk, David A.; Yushkevich, Paul A.

    2016-01-01

    Rational The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimer's disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in ~97% of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single-template approaches, which may not be able to accurately model these discrete variants Methods A multi-template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2-weighted MRI scans. The pipeline uses multi-atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single-template and multi-template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age-matched controls (n=44). Results The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single-template approach. The largest reduction in thickness associated with aMCI, in absolute terms

  3. Resting state brain networks and their implications in neurodegenerative disease

    Science.gov (United States)

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

    2012-10-01

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

  4. A voxelwise approach to determine consensus regions-of-interest for the study of brain network plasticity.

    Science.gov (United States)

    Rajtmajer, Sarah M; Roy, Arnab; Albert, Reka; Molenaar, Peter C M; Hillary, Frank G

    2015-01-01

    Despite exciting advances in the functional imaging of the brain, it remains a challenge to define regions of interest (ROIs) that do not require investigator supervision and permit examination of change in networks over time (or plasticity). Plasticity is most readily examined by maintaining ROIs constant via seed-based and anatomical-atlas based techniques, but these approaches are not data-driven, requiring definition based on prior experience (e.g., choice of seed-region, anatomical landmarks). These approaches are limiting especially when functional connectivity may evolve over time in areas that are finer than known anatomical landmarks or in areas outside predetermined seeded regions. An ideal method would permit investigators to study network plasticity due to learning, maturation effects, or clinical recovery via multiple time point data that can be compared to one another in the same ROI while also preserving the voxel-level data in those ROIs at each time point. Data-driven approaches (e.g., whole-brain voxelwise approaches) ameliorate concerns regarding investigator bias, but the fundamental problem of comparing the results between distinct data sets remains. In this paper we propose an approach, aggregate-initialized label propagation (AILP), which allows for data at separate time points to be compared for examining developmental processes resulting in network change (plasticity). To do so, we use a whole-brain modularity approach to parcellate the brain into anatomically constrained functional modules at separate time points and then apply the AILP algorithm to form a consensus set of ROIs for examining change over time. To demonstrate its utility, we make use of a known dataset of individuals with traumatic brain injury sampled at two time points during the first year of recovery and show how the AILP procedure can be applied to select regions of interest to be used in a graph theoretical analysis of plasticity.

  5. A voxelwise approach to determine consensus regions-of-interest for the study of brain network plasticity

    Directory of Open Access Journals (Sweden)

    Sarah M. Rajtmajer

    2015-07-01

    Full Text Available Despite exciting advances in the functional imaging of the brain, it remains a challenge to define regions of interest (ROIs that do not require investigator supervision and permit examination of change in networks over time (or plasticity. Plasticity is most readily examined by maintaining ROIs constant via seed-based and anatomical-atlas based techniques, but these approaches are not data-driven, requiring definition based on prior experience (e.g. choice of seed-region, anatomical landmarks. These approaches are limiting especially when functional connectivity may evolve over time in areas that are finer than known anatomical landmarks or in areas outside predetermined seeded regions. An ideal method would permit investigators to study network plasticity due to learning, maturation effects, or clinical recovery via multiple time point data that can be compared to one another in the same ROI while also preserving the voxel-level data in those ROIs at each time point. Data-driven approaches (e.g., whole-brain voxelwise approaches ameliorate concerns regarding investigator bias, but the fundamental problem of comparing the results between distinct data sets remains. In this paper we propose an approach, aggregate-initialized label propagation (AILP, which allows for data at separate time points to be compared for examining developmental processes resulting in network change (plasticity. To do so, we use a whole-brain modularity approach to parcellate the brain into anatomically constrained functional modules at separate time points and then apply the AILP algorithm to form a consensus set of ROIs for examining change over time. To demonstrate its utility, we make use of a known dataset of individuals with traumatic brain injury sampled at two time points during the first year of recovery and show how the AILP procedure can be applied to select regions of interest to be used in a graph theoretical analysis of plasticity.

  6. Macroscopic networks in the human brain: mapping connectivity in healthy and damaged brains

    NARCIS (Netherlands)

    Nijhuis, E.H.J.

    2013-01-01

    The human brain contains a network of interconnected neurons. Recent advances in functional and structural in-vivo magnetic resonance neuroimaging (MRI) techniques have provided opportunities to model the networks of the human brain on a macroscopic scale. This dissertation investigates the

  7. EEG-based research on brain functional networks in cognition.

    Science.gov (United States)

    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.

  8. Brain and Social Networks: Fundamental Building Blocks of Human Experience.

    Science.gov (United States)

    Falk, Emily B; Bassett, Danielle S

    2017-09-01

    How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Role of physical and mental training in brain network configuration

    Directory of Open Access Journals (Sweden)

    Philip P. Foster

    2015-06-01

    Full Text Available 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 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 produces a reconfiguration of brain networks into greater small-worldness. Creation 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 playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.. However, mental training, meditation or virtual reality (films, games require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brainbrain com. molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g. amyotrophic lateral sclerosis, traumatism also achieve successful cognitive enhancement albeit they may only elicit mental practice

  10. Radial and tangential neuronal migration pathways in the human fetal brain: anatomically distinct patterns of diffusion MRI coherence

    Science.gov (United States)

    Kolasinski, James; Takahashi, Emi; Stevens, Allison A.; Benner, Thomas; Fischl, Bruce; Zöllei, Lilla; Grant, P. Ellen

    2014-01-01

    Corticogenesis is underpinned by a complex process of subcortical neuroproliferation, followed by highly orchestrated cellular migration. A greater appreciation of the processes involved in human fetal corticogenesis is vital to gaining an understanding of how developmental disturbances originating in gestation could establish a variety of complex neuropathology manifesting in childhood, or even in adult life. Magnetic resonance imaging modalities offer a unique insight into anatomical structure, and increasingly infer information regarding underlying microstructure in the human brain. In this study we applied a combination of high-resolution structural and diffusion-weighted magnetic resonance imaging to a unique cohort of three post-mortem fetal brain specimens, aged between 19 and 22 post-conceptual weeks. Specifically, we sought to assess patterns of diffusion coherence associated with subcortical neuroproliferative structures: the pallial ventricular/subventricular zone and subpallial ganglionic eminence. Two distinct three-dimensional patterns of diffusion coherence were evident: a clear radial pattern originating in ventricular/subventricular zone, and a tangentio-radial patterns originating in ganglionic eminence. These patterns appeared to regress in a caudo-rostral and lateral-ventral to medial-dorsal direction across the short period of fetal development under study. Our findings demonstrate for the first time distinct patterns of diffusion coherence associated with known anatomical proliferative structures. The radial pattern associated with dorsopallial ventricular/subventricular zone and the tangentio-radial pattern associated with subpallial ganglionic eminence are consistent with reports of radial-glial mediated neuronal migration pathways identified during human corticogenesis, supported by our prior studies of comparative fetal diffusion MRI and histology. The ability to assess such pathways in the fetal brain using MR imaging offers a unique

  11. Screening for fetal spina bifida at the 11-13-week scan using three anatomical features of the posterior brain.

    Science.gov (United States)

    Mangione, R; Dhombres, F; Lelong, N; Amat, S; Atoub, F; Friszer, S; Khoshnood, B; Jouannic, J-M

    2013-10-01

    To evaluate the contribution of examination of specific anatomical features of the fetal posterior brain on mid-sagittal first-trimester ultrasound examination to the early detection of open spina bifida. Four independent observers reviewed a series of 260 mid-sagittal first-trimester ultrasound images from 52 cases of open spina bifida and 208 normal fetuses. The following analysis was performed by each reviewer for each image: Herman score calculation, intracranial translucency score (CFEF-IT) calculation and determination of presence or absence of three anatomical criteria: intracranial translucency (IT), caudal displacement of the brainstem and cisterna magna. The sensitivity and the false-positive rate for spina bifida detection were calculated for each of the latter three criteria. A secondary analysis was performed on the subset of images achieving a Herman score ≥ 7. The highest detection rate for spina bifida was achieved by non-visualization of the cisterna magna, with associated sensitivity of 50-73% and 39-76%, respectively, for all images and for the subset of images achieving a Herman score ≥ 7. Posterior shift of the brainstem achieved the highest detection rate (86%), but for a single reviewer only. The level of variation in performance between observers was also greatest for this sign. Absence of IT was associated with a lower detection rate for all observers. Overall, an abnormal posterior brain presenting at least one of these three criteria was associated with a detection rate ranging from 50 to 90%. In the detection of spina bifida, non-visualization of the cisterna magna achieved the best screening performance. Both non-visualization of the IT and posterior shift of the brainstem were associated with acceptable but lower detection rates. A prospective evaluation of changes in the posterior brain is needed to allow assessment of the most pertinent criteria for first-trimester screening for spina bifida. Copyright © 2013 ISUOG. Published by

  12. Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition.

    Directory of Open Access Journals (Sweden)

    Timothy N Rubin

    2017-10-01

    Full Text Available A central goal of cognitive neuroscience is to decode human brain activity-that is, to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive-that is, capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a probabilistic decoding framework based on a novel topic model-Generalized Correspondence Latent Dirichlet Allocation-that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to "seed" decoder priors with arbitrary images and text-enabling researchers, for the first time, to generate quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

  13. Scalable brain network construction on white matter fibers

    Science.gov (United States)

    Chung, Moo K.; Adluru, Nagesh; Dalton, Kim M.; Alexander, Andrew L.; Davidson, Richard J.

    2011-03-01

    DTI offers a unique opportunity to characterize the structural connectivity of the human brain non-invasively by tracing white matter fiber tracts. Whole brain tractography studies routinely generate up to half million tracts per brain, which serves as edges in an extremely large 3D graph with up to half million edges. Currently there is no agreed-upon method for constructing the brain structural network graphs out of large number of white matter tracts. In this paper, we present a scalable iterative framework called the ɛ-neighbor method for building a network graph and apply it to testing abnormal connectivity in autism.

  14. The brain of René Descartes (1650): A neuro-anatomical analysis.

    Science.gov (United States)

    Philippe, Charlier; Isabelle, Huynh-Charlier; Philippe, Froesch; Russell, Shorto; Nadia, Benmoussa; Alain, Froment; Dominique, Grimaud-Hervé; Saudamini, Deo; Anaïs, Augias; Lou, Albessard; Antoine, Balzeau

    2017-07-15

    The skull of René Descartes is held in the National Museum of Natural History since the 19th c. Up to date, only anthropological examinations were carried out, focusing on the cranial capacity and phrenological interpretation of the skull morphology. Using CT-scan based 3D technology, a reconstruction of the endocast was performed, allowing for its first complete description and inter-disciplinary analysis: assessment of metrical and non-metrical features, retrospective diagnosis of anatomical anomalies, and confrontation with neuro-psychological abilities of this well-identified individual. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Development of large-scale functional brain networks in children.

    Directory of Open Access Journals (Sweden)

    Kaustubh Supekar

    2009-07-01

    Full Text Available 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.

  16. Role of physical and mental training in brain network configuration.

    Science.gov (United States)

    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

  17. The accuracy of image registration for the brain and the nasopharynx using external anatomical landmarks

    Energy Technology Data Exchange (ETDEWEB)

    Peters, Anton R.; Herk, Marcel van [Department of Radiotherapy, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Huis (NKI/AVL), Plesmanlaan 121, 1066 CX Amsterdam (Netherlands); Muller, Sara H. [Department of Radiotherapy, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Huis (NKI/AVL), Plesmanlaan 121, 1066 CX Amsterdam (Netherlands); Department of Radiology, NKI/AVL, Amsterdam (Netherlands); Munck, Jan C. de [MEG Centrum KNAW, Free University Hospital, De Boelelaan 1117, 1081 HV Amsterdam (Netherlands)

    2000-08-01

    We investigated the accuracy of 3D image registration using markers that are repeatedly applied to external anatomical landmarks on the head. The purpose of this study is to establish a lower limit of the errors that would occur in, for instance, MRI-SPECT matching, which in some situations can only be achieved using external landmarks. Marker matching was compared with (single-modality) volume matching for 20 MRI scans. The results were compared with a published expression for the target registration error (TRE) which gives the 3D distribution of the mismatch between both scans. It was found that the main error source is reapplying the external markers on the anatomical landmarks. The published expression describes the relative distribution of the TRE in space well, but tends to underestimate the actual registration error. This deviation is due to anisotropy in the error distribution of the marker position (errors in the direction perpendicular to the skin surface are in general much smaller than errors in other directions). A simulation of marker matching with anisotropy in the errors confirmed this finding. With four reapplied markers, the TRE is 6 mm or smaller in most regions of the head. (author)

  18. Nutritional status, brain network organization, and general intelligence.

    Science.gov (United States)

    Zamroziewicz, Marta K; Talukdar, M Tanveer; Zwilling, Chris E; Barbey, Aron K

    2017-11-01

    The high energy demands of the brain underscore the importance of nutrition in maintaining brain health and further indicate that aspects of nutrition may optimize brain health, in turn enhancing cognitive performance. General intelligence represents a critical cognitive ability that has been well characterized by cognitive neuroscientists and psychologists alike, but the extent to which a driver of brain health, namely nutritional status, impacts the neural mechanisms that underlie general intelligence is not understood. This study therefore examined the relationship between the intrinsic connectivity networks supporting general intelligence and nutritional status, focusing on nutrients known to impact the metabolic processes that drive brain function. We measured general intelligence, favorable connective architecture of seven intrinsic connectivity networks, and seventeen plasma phospholipid monounsaturated and saturated fatty acids in a sample of 99 healthy, older adults. A mediation analysis was implemented to investigate the relationship between empirically derived patterns of fatty acids, general intelligence, and underlying intrinsic connectivity networks. The mediation analysis revealed that small world propensity within one intrinsic connectivity network supporting general intelligence, the dorsal attention network, was promoted by a pattern of monounsaturated fatty acids. These results suggest that the efficiency of functional organization within a core network underlying general intelligence is influenced by nutritional status. This report provides a novel connection between nutritional status and functional network efficiency, and further supports the promise and utility of functional connectivity metrics in studying the impact of nutrition on cognitive and brain health. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Shangjie [Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin (China); Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States); Hara, Wendy; Wang, Lei; Buyyounouski, Mark K.; Le, Quynh-Thu; Xing, Lei [Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States); Li, Ruijiang, E-mail: rli2@stanford.edu [Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States)

    2017-03-15

    Purpose: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. Methods and Materials: We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. Results: The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). Conclusion: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.

  20. Episodic memory in aspects of large-scale brain networks

    Directory of Open Access Journals (Sweden)

    Woorim eJeong

    2015-08-01

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

  1. Episodic memory in aspects of large-scale brain networks

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

    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.

  3. Disrupted functional brain networks in autistic toddlers

    OpenAIRE

    Boersma, M.; Kemner, C.; M. de Reus; Collin, G; Snijders, T.; Hofman, D.; Buitelaar, J.; Stam, C.; van den Heuvel, M

    2013-01-01

    Communication and integration of information between brain regions plays a key role in healthy brain function. Conversely, disruption in brain communication may lead to cognitive and behavioral problems. Autism is a neurodevelopmental disorder that is characterized by impaired social interactions and aberrant basic information processing. Aberrant brain connectivity patterns have indeed been hypothesized to be a key neural underpinning of autism. In this study, graph analytical tools are used...

  4. WHOLE BRAIN GROUP NETWORK ANALYSIS USING NETWORK BIAS AND VARIANCE PARAMETERS.

    Science.gov (United States)

    Akhondi-Asl, Alireza; Hans, Arne; Scherrer, Benoit; Peters, Jurriaan M; Warfield, Simon K

    2012-05-01

    The disruption of normal function and connectivity of neural circuits is common across many diseases and disorders of the brain. This disruptive effect can be studied and analyzed using the brain's complex functional and structural connectivity network. Complex network measures from the field of graph theory have been used for this purpose in the literature. In this paper we have introduced a new approach for analyzing the brain connectivity network. In our approach the true connectivity network and each subject's bias and variance are estimated using a population of patients and healthy controls. These parameters can then be used to compare two groups of brain networks. We have used this approach for the comparison of the resting state functional MRI network of pediatric Tuberous Sclerosis Complex (TSC) patients and healthy subjects. We have shown that a significant difference between the two groups can be found. For validation, we have compared our findings with three well known complex network measures.

  5. Smart Brain Hemorrhage Diagnosis Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Santosh H. Suryawanshi

    2015-08-01

    Full Text Available Abstract The fundamental motivation behind this study is to identify the brain hemorrhage and to give accurate treatment so that death rate because of brain hemorrhage can be reduced. This project investigates the possibility of diagnosing brain hemorrhage using an image segmentation of CT scan images using watershed method and feeding of the appropriate inputs extracted from the brain CT image to an artificial neural network for classification. The output generated as the type of brain hemorrhages can be used to verify expert diagnosis and also as learning tool for trainee radiologists to minimize errors in current methods.

  6. Three-dimensional network of Drosophila brain hemisphere

    CERN Document Server

    Mizutani, Ryuta; Takeuchi, Akihisa; Uesugi, Kentaro; Suzuki, Yoshio

    2016-01-01

    The first step to understanding brain function is to determine the brain's network structure. We report a three-dimensional analysis of the brain network of the fruit fly Drosophila melanogaster by synchrotron-radiation tomographic microscopy. A skeletonized wire model of the left half of the brain network was built by tracing the three-dimensional distribution of X-ray absorption coefficients. The obtained models of neuronal processes were classified into groups on the basis of their three-dimensional structures. These classified groups correspond to neuronal tracts that send long-range projections or repeated structures of the optic lobe. The skeletonized model is also composed of neuronal processes that could not be classified into the groups. The distribution of these unclassified structures correlates with the distribution of contacts between neuronal processes. This suggests that neurons that cannot be classified into typical structures should play important roles in brain functions. The quantitative de...

  7. Brain connectivity dynamics during social interaction reflect social network structure.

    Science.gov (United States)

    Schmälzle, Ralf; Brook O'Donnell, Matthew; Garcia, Javier O; Cascio, Christopher N; Bayer, Joseph; Bassett, Danielle S; Vettel, Jean M; Falk, Emily B

    2017-05-16

    Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants' friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics.

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

    National Research Council Canada - National Science Library

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

    2015-01-01

    ... 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...

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

    Science.gov (United States)

    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. The hippocampus: hub of brain network communication for memory.

    NARCIS (Netherlands)

    Battaglia, F.P.; Benchenane, K.; Sirota, A.; Pennartz, C.M.A.; Wiener, S.I.

    2011-01-01

    A complex brain network, centered on the hippocampus, supports episodic memories throughout their lifetimes. Classically, upon memory encoding during active behavior, hippocampal activity is dominated by theta oscillations (6-10Hz). During inactivity, hippocampal neurons burst synchronously,

  11. A Multimodal Approach for Determining Brain Networks by Jointly Modeling Functional and Structural Connectivity

    Directory of Open Access Journals (Sweden)

    Wenqiong eXue

    2015-02-01

    Full Text Available Recent innovations in neuroimaging technology have provided opportunities for researchers to investigate connectivity in the human brain by examining the anatomical circuitry as well as functional relationships between brain regions. Existing statistical approaches for connectivity generally examine resting-state or task-related functional connectivity (FC between brain regions or separately examine structural linkages. As a means to determine brain networks, we present a unified Bayesian framework for analyzing FC utilizing the knowledge of associated structural connections, which extends an approach by Patel et al.(2006a that considers only functional data. We introduce an FC measure that rests upon assessments of functional coherence between regional brain activity identified from functional magnetic resonance imaging (fMRI data. Our structural connectivity (SC information is drawn from diffusion tensor imaging (DTI data, which is used to quantify probabilities of SC between brain regions. We formulate a prior distribution for FC that depends upon the probability of SC between brain regions, with this dependence adhering to structural-functional links revealed by our fMRI and DTI data. We further characterize the functional hierarchy of functionally connected brain regions by defining an ascendancy measure that compares the marginal probabilities of elevated activity between regions. In addition, we describe topological properties of the network, which is composed of connected region pairs, by performing graph theoretic analyses. We demonstrate the use of our Bayesian model using fMRI and DTI data from a study of auditory processing. We further illustrate the advantages of our method by comparisons to methods that only incorporate functional information.

  12. A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity.

    Science.gov (United States)

    Xue, Wenqiong; Bowman, F DuBois; Pileggi, Anthony V; Mayer, Andrew R

    2015-01-01

    Recent innovations in neuroimaging technology have provided opportunities for researchers to investigate connectivity in the human brain by examining the anatomical circuitry as well as functional relationships between brain regions. Existing statistical approaches for connectivity generally examine resting-state or task-related functional connectivity (FC) between brain regions or separately examine structural linkages. As a means to determine brain networks, we present a unified Bayesian framework for analyzing FC utilizing the knowledge of associated structural connections, which extends an approach by Patel et al. (2006a) that considers only functional data. We introduce an FC measure that rests upon assessments of functional coherence between regional brain activity identified from functional magnetic resonance imaging (fMRI) data. Our structural connectivity (SC) information is drawn from diffusion tensor imaging (DTI) data, which is used to quantify probabilities of SC between brain regions. We formulate a prior distribution for FC that depends upon the probability of SC between brain regions, with this dependence adhering to structural-functional links revealed by our fMRI and DTI data. We further characterize the functional hierarchy of functionally connected brain regions by defining an ascendancy measure that compares the marginal probabilities of elevated activity between regions. In addition, we describe topological properties of the network, which is composed of connected region pairs, by performing graph theoretic analyses. We demonstrate the use of our Bayesian model using fMRI and DTI data from a study of auditory processing. We further illustrate the advantages of our method by comparisons to methods that only incorporate functional information.

  13. Altered Brain Network in Amyotrophic Lateral Sclerosis: A Resting Graph Theory-Based Network Study at Voxel-Wise Level.

    Science.gov (United States)

    Zhou, Chaoyang; Hu, Xiaofei; Hu, Jun; Liang, Minglong; Yin, Xuntao; Chen, Lin; Zhang, Jiuquan; Wang, Jian

    2016-01-01

    Amyotrophic lateral sclerosis (ALS) is a rare degenerative disorder characterized by loss of upper and lower motor neurons. Neuroimaging has provided noticeable evidence that ALS is a complex disease, and shown that anatomical and functional lesions extend beyond precentral cortices and corticospinal tracts, to include the corpus callosum; frontal, sensory, and premotor cortices; thalamus; and midbrain. The aim of this study is to investigate graph theory-based functional network abnormalities at voxel-wise level in ALS patients on a whole brain scale. Forty-three ALS patients and 44 age- and sex-matched healthy volunteers were enrolled. The voxel-wise network degree centrality (DC), a commonly employed graph-based measure of network organization, was used to characterize the alteration of whole brain functional network. Compared with the controls, the ALS patients showed significant increase of DC in the left cerebellum posterior lobes, bilateral cerebellum crus, bilateral occipital poles, right orbital frontal lobe, and bilateral prefrontal lobes; significant decrease of DC in the bilateral primary motor cortex, bilateral sensory motor region, right prefrontal lobe, left bilateral precuneus, bilateral lateral temporal lobes, left cingulate cortex, and bilateral visual processing cortex. The DC's z-scores of right inferior occipital gyrus were significant negative correlated with the ALSFRS-r scores. Our findings confirm that the regions with abnormal network DC in ALS patients were located in multiple brain regions including primary motor, somatosensory and extra-motor areas, supporting the concept that ALS is a multisystem disorder. Specifically, our study found that DC in the visual areas was altered and ALS patients with higher DC in right inferior occipital gyrus have more severity of disease. The result demonstrated that the altered DC value in this region can probably be used to assess severity of ALS.

  14. Mnemonic Training Reshapes Brain Networks to Support Superior Memory

    NARCIS (Netherlands)

    Dresler, M.; Shirer, W.R.; Konrad, B.N.; Muller, N.C.J.; Wagner, I.; Fernandez, G.S.E.; Czisch, M.; Greicius, M.D.

    2017-01-01

    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

  15. Anatomical differences determine distribution of adenovirus after convection-enhanced delivery to the rat brain

    NARCIS (Netherlands)

    Idema, Sander; Caretti, Viola; Lamfers, Martine L. M.; van Beusechem, Victor W.; Noske, David P.; Vandertop, W. Peter; Dirven, Clemens M. F.

    2011-01-01

    Convection-enhanced delivery (CED) of adenoviruses offers the potential of widespread virus distribution in the brain. In CED, the volume of distribution (Vd) should be related to the volume of infusion (Vi) and not to dose, but when using adenoviruses contrasting results have been reported. As the

  16. Anatomical differences determine distribution of adenovirus after convection-enhanced delivery to the rat brain

    NARCIS (Netherlands)

    S. Idema (Sander); V. Caretti (Viola); M.L.M. Lamfers (Martine); V.W. Beusechem (Victor); D.P. Noske (David); W.P. Vandertop (Peter); C.M.F. Dirven (Clemens)

    2011-01-01

    textabstractBackground: Convection-enhanced delivery (CED) of adenoviruses offers the potential of widespread virus distribution in the brain. In CED, the volume of distribution (Vd) should be related to the volume of infusion (Vi) and not to dose, but when using adenoviruses contrasting results

  17. Anatomical and functional changes in the brain after simultaneous interpreting training : A longitudinal study

    NARCIS (Netherlands)

    Van De Putte, Eowyn; De Baene, W.; García-Pentón, Lorna; Woumans, Evy; Dijkgraaf, Aster; Duyck, Wouter

    In the recent literature on bilingualism, a lively debate has arisen about the long-term effects of bilingualism on cognition and the brain. These studies yield inconsistent results, in part because they rely on comparisons between bilingual and monolingual control groups that may also differ on

  18. Distribution of RF energy emitted by mobile phones in anatomical structures of the brain

    Energy Technology Data Exchange (ETDEWEB)

    Cardis, E; Deltour, I; Moissonnier, M [Radiation Group, International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon 69372 (France); Mann, S [EMF Dosimetry Group, Health Protection Agency, Centre for Radiation, Chemical and Environmental Hazards, Didcot, OX11 0RQ (United Kingdom); Taki, M; Varsier, N [Department of Electrical and Electronic Engineering, Tokyo Metropolitan University, 1-1, Minami-Osawa, Hachioji, Tokyo 192-0397 (Japan); Wake, K [EMC Group, Applied Electromagnetic Research Center, National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei 184-8795, Tokyo (Japan); Wiart, J [France Telecom RD 38-40, rue du General Leclerc, 92794 Issy-les-Moulineaux Cedex 9 (France)

    2008-06-07

    The rapid worldwide increase in mobile phone use in the last decade has generated considerable interest in possible carcinogenic effects of radio frequency (RF). Because exposure to RF from phones is localized, if a risk exists it is likely to be greatest for tumours in regions with greatest energy absorption. The objective of the current paper was to characterize the spatial distribution of RF energy in the brain, using results of measurements made in two laboratories on 110 phones used in Europe or Japan. Most (97-99% depending on frequency) appears to be absorbed in the brain hemisphere on the side where the phone is used, mainly (50-60%) in the temporal lobe. The average relative SAR is highest in the temporal lobe (6-15%, depending on frequency, of the spatial peak SAR in the most exposed region of the brain) and the cerebellum (2-10%) and decreases very rapidly with increasing depth, particularly at higher frequencies. The SAR distribution appears to be fairly similar across phone models, between older and newer phones and between phones with different antenna types and positions. Analyses of risk by location of tumour are therefore important for the interpretation of results of studies of brain tumours in relation to mobile phone use.

  19. Galen on the brain: anatomical knowledge and physiological speculation in the second century AD

    National Research Council Canada - National Science Library

    Rocca, Julius

    2003-01-01

    ... physiology ... 2.4 Galen's empirical substrate: animal material ... 2.5 Conclusion ... 49 50 59 67 77viii  PART TWO THE ARCHITECTURE OF THE BRAIN Chapter Three: The exposure and disse...

  20. Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia.

    Science.gov (United States)

    Alexander-Bloch, Aaron F; Gogtay, Nitin; Meunier, David; Birn, Rasmus; Clasen, Liv; Lalonde, Francois; Lenroot, Rhoshel; Giedd, Jay; Bullmore, Edward T

    2010-01-01

    Modularity is a fundamental concept in systems neuroscience, referring to the formation of local cliques or modules of densely intra-connected nodes that are sparsely inter-connected with nodes in other modules. Topological modularity of brain functional networks can quantify theoretically anticipated abnormality of brain network community structure - so-called dysmodularity - in developmental disorders such as childhood-onset schizophrenia (COS). We used graph theory to investigate topology of networks derived from resting-state fMRI data on 13 COS patients and 19 healthy volunteers. We measured functional connectivity between each pair of 100 regional nodes, focusing on wavelet correlation in the frequency interval 0.05-0.1 Hz, then applied global and local thresholding rules to construct graphs from each individual association matrix over the full range of possible connection densities. We show how local thresholding based on the minimum spanning tree facilitates group comparisons of networks by forcing the connectedness of sparse graphs. Threshold-dependent graph theoretical results are compatible with the results of a k-means unsupervised learning algorithm and a multi-resolution (spin glass) approach to modularity, both of which also find community structure but do not require thresholding of the association matrix. In general modularity of brain functional networks was significantly reduced in COS, due to a relatively reduced density of intra-modular connections between neighboring regions. Other network measures of local organization such as clustering were also decreased, while complementary measures of global efficiency and robustness were increased, in the COS group. The group differences in complex network properties were mirrored by differences in simpler statistical properties of the data, such as the variability of the global time series and the internal homogeneity of the time series within anatomical regions of interest.

  1. Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia

    Directory of Open Access Journals (Sweden)

    Aaron F Alexander-Bloch

    2010-10-01

    Full Text Available Modularity is a fundamental concept in systems neuroscience, referring to the formation of local cliques or modules of densely intra-connected nodes that are sparsely inter-connected with nodes in other modules. Topological modularity of brain functional networks can quantify theoretically anticipated abnormality of brain network community structure--so called dysmodularity--in developmental disorders such as childhood-onset schizophrenia (COS. We used graph theory to investigate topology of networks derived from resting-state fMRI data on 13 COS patients and 19 healthy volunteers. We measured functional connectivity between each pair of 100 regional nodes, focusing on wavelet correlation in the frequency interval 0.05-0.1 Hz, then applied global and local thresholding rules to construct graphs from each individual association matrix over the full range of possible connection densities. We show how local thresholding based on the minimum spanning tree facilitates group comparisons of networks by forcing the connectedness of sparse graphs. Threshold-dependent graph theoretical results are compatible with the results of a k-means unsupervised learning algorithm and a multi-resolution (spin glass approach to modularity, both of which also find community structure but do not require thresholding of the association matrix. In general modularity of brain functional networks was significantly reduced in COS, due to a relatively reduced density of intra-modular connections between neighboring regions. Other network measures of local organization such as clustering were also decreased, while complementary measures of global efficiency and robustness were increased, in the COS group. The group differences in complex network properties were mirrored by differences in simpler statistical properties of the data, such as the variability of the global time series and the internal homogeneity of the time series within anatomical regions of interest.

  2. Gender differences in brain networks supporting empathy.

    Science.gov (United States)

    Schulte-Rüther, Martin; Markowitsch, Hans J; Shah, N Jon; Fink, Gereon R; Piefke, Martina

    2008-08-01

    Females frequently score higher on standard tests of empathy, social sensitivity, and emotion recognition than do males. It remains to be clarified, however, whether these gender differences are associated with gender specific neural mechanisms of emotional social cognition. We investigated gender differences in an emotion attribution task using functional magnetic resonance imaging. Subjects either focused on their own emotional response to emotion expressing faces (SELF-task) or evaluated the emotional state expressed by the faces (OTHER-task). Behaviorally, females rated SELF-related emotions significantly stronger than males. Across the sexes, SELF- and OTHER-related processing of facial expressions activated a network of medial and lateral prefrontal, temporal, and parietal brain regions involved in emotional perspective taking. During SELF-related processing, females recruited the right inferior frontal cortex and superior temporal sulcus stronger than males. In contrast, there was increased neural activity in the left temporoparietal junction in males (relative to females). When performing the OTHER-task, females showed increased activation of the right inferior frontal cortex while there were no differential activations in males. The data suggest that females recruit areas containing mirror neurons to a higher degree than males during both SELF- and OTHER-related processing in empathic face-to-face interactions. This may underlie facilitated emotional "contagion" in females. Together with the observation that males differentially rely on the left temporoparietal junction (an area mediating the distinction between the SELF and OTHERS) the data suggest that females and males rely on different strategies when assessing their own emotions in response to other people.

  3. A Multi-Atlas Based Method for Automated Anatomical Rat Brain MRI Segmentation and Extraction of PET Activity

    Science.gov (United States)

    Lancelot, Sophie; Roche, Roxane; Slimen, Afifa; Bouillot, Caroline; Levigoureux, Elise; Langlois, Jean-Baptiste; Zimmer, Luc; Costes, Nicolas

    2014-01-01

    Introduction Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies. Methods High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i.e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created. Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [18F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures). Results Only the SA method showed volume bias. Dice indices were significantly different between methods (PF>MP>SA). PET regional measures were more accurate with multi-atlas methods than with SA method. Conclusions Multi-atlas methods outperform SA for automated anatomical brain segmentation and PET measure’s extraction. They perform comparably to manual segmentation for FDG-PET quantification. Multi-atlas methods are suitable for rapid reproducible VOI analyses. PMID:25330005

  4. Task-linked Diurnal Brain Network Reorganization in Older Adults: A Graph Theoretical Approach.

    Science.gov (United States)

    Anderson, John A E; Sarraf, Saman; Amer, Tarek; Bellana, Buddhika; Man, Vincent; Campbell, Karen L; Hasher, Lynn; Grady, Cheryl L

    2017-03-01

    Testing older adults in the morning generally improves behavioral performance relative to afternoon testing. Morning testing is also associated with brain activity similar to that of young adults. Here, we used graph theory to explore how time of day (TOD) affects the organization of brain networks in older adults across rest and task states. We used nodes from the automated anatomical labeling atlas to construct participant-specific correlation matrices of fMRI data obtained during 1-back tasks with interference and rest. We computed pairwise group differences for key graph metrics, including small-worldness and modularity. We found that older adults tested in the morning and young adults did not differ on any graph metric. Both of these groups differed from older adults tested in the afternoon during the tasks-but not rest. Specifically, the latter group had lower modularity and small-worldness (indices of more efficient network organization). Across all groups, higher modularity and small-worldness strongly correlated with reduced distractibility on an implicit priming task. Increasingly, TOD is seen as important for interpreting and reproducing neuroimaging results. Our study emphasizes how TOD affects brain network organization and executive control in older adults.

  5. Optimal Brain Surgeon on Artificial Neural Networks in

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine

    2012-01-01

    It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

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

    Science.gov (United States)

    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. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2015-04-14

    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. 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. 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. 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. © 2015 American Academy of Neurology.

  8. Assessment of variability in cerebral vasculature for neuro-anatomical surgery planning in rodent brain

    Science.gov (United States)

    Rangarajan, J. R.; Van Kuyck, K.; Himmelreich, U.; Nuttin, B.; Maes, F.; Suetens, P.

    2011-03-01

    Clinical and pre-clinical studies show that deep brain stimulation (DBS) of targeted brain regions by neurosurgical techniques ameliorate psychiatric disorder such as anorexia nervosa. Neurosurgical interventions in preclinical rodent brain are mostly accomplished manually with a 2D atlas. Considering both the large number of animals subjected to stereotactic surgical experiments and the associated imaging cost, feasibility of sophisticated pre-operative imaging based surgical path planning and/or robotic guidance is limited. Here, we spatially normalize vasculature information and assess the intra-strain variability in cerebral vasculature for a neurosurgery planning. By co-registering and subsequently building a probabilistic vasculature template in a standard space, we evaluate the risk of a user defined electrode trajectory damaging a blood vessel on its path. The use of such a method may not only be confined to DBS therapy in small animals, but also could be readily applicable to a wide range of stereotactic small animal surgeries like targeted injection of contrast agents and cell labeling applications.

  9. Altered resting state brain networks in Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Martin Göttlich

    Full Text Available 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

  10. [Comparative anatomical study of the ventral brain arteries of the Pudu pudu (Molina, 1782) with those of the cow].

    Science.gov (United States)

    Schweitzer-Delaunoy, W

    1997-06-01

    Comparative anatomical study of the ventral brain arteries of the Pudú pudu (Molina, 1782) with those of the cow. A comparison using the corrosion method was made between Pudú pudu (Molina, 1782) ventral brain arteries and those of the cow. The Pudú's Rete mirabile epidurale rostrale (Nomina Anatomica Veterinaria, 1994) is ventrally formed by branches of the A. maxillaris, and caudally formed by the A. vertebralis. The Hypophysis is surrounded by the Rete mirabile rostrale. The lateral parts are rostrally joined to that gland by a thin vascular bridge and caudally by thick arteries. The Pudú's Circulus arteriosus cerebri asymmetrical, that is, on the right side the A. cerebri rostralis ends in the A. cerebri media. The left-side A. cerebri rostralis irrigates every rostral portion of the encephalon. In the cow, practically the same arteries come out of the Circulus arteriosus cerebri, which is not asymmetrical. The A. cerebri caudalis comes first out of the A. communicans caudalis and then the branches for the Pons, and finally the A. cerebelli rostralis. In this species, there are arterial blocks that are not present in Pudú.

  11. Facephenes and rainbows: Causal evidence for functional and anatomical specificity of face and color processing in the human brain.

    Science.gov (United States)

    Schalk, Gerwin; Kapeller, Christoph; Guger, Christoph; Ogawa, Hiroshi; Hiroshima, Satoru; Lafer-Sousa, Rosa; Saygin, Zeynep M; Kamada, Kyousuke; Kanwisher, Nancy

    2017-11-14

    Neuroscientists have long debated whether some regions of the human brain are exclusively engaged in a single specific mental process. Consistent with this view, fMRI has revealed cortical regions that respond selectively to certain stimulus classes such as faces. However, results from multivoxel pattern analyses (MVPA) challenge this view by demonstrating that category-selective regions often contain information about "nonpreferred" stimulus dimensions. But is this nonpreferred information causally relevant to behavior? Here we report a rare opportunity to test this question in a neurosurgical patient implanted for clinical reasons with strips of electrodes along his fusiform gyri. Broadband gamma electrocorticographic responses in multiple adjacent electrodes showed strong selectivity for faces in a region corresponding to the fusiform face area (FFA), and preferential responses to color in a nearby site, replicating earlier reports. To test the causal role of these regions in the perception of nonpreferred dimensions, we then electrically stimulated individual sites while the patient viewed various objects. When stimulated in the FFA, the patient reported seeing an illusory face (or "facephene"), independent of the object viewed. Similarly, stimulation of color-preferring sites produced illusory "rainbows." Crucially, the patient reported no change in the object viewed, apart from the facephenes and rainbows apparently superimposed on them. The functional and anatomical specificity of these effects indicate that some cortical regions are exclusively causally engaged in a single specific mental process, and prompt caution about the widespread assumption that any information scientists can decode from the brain is causally relevant to behavior.

  12. Alkali metals levels in the human brain tissue: Anatomical region differences and age-related changes.

    Science.gov (United States)

    Ramos, Patrícia; Santos, Agostinho; Pinto, Edgar; Pinto, Nair Rosas; Mendes, Ricardo; Magalhães, Teresa; Almeida, Agostinho

    2016-12-01

    The link between trace elements imbalances (both "toxic" and "essential") in the human brain and neurodegenerative disease has been subject of extensive research. More recently, some studies have highlighted the potential role of the homeostasis deregulation of alkali metals in specific brain regions as key factor in the pathogenesis of neurodegenerative diseases such as multiple sclerosis and Alzheimer's disease. Using flame atomic emission spectrometry and inductively coupled plasma-mass spectrometry after microwave-assisted acid digestion of the samples, alkali metals (Na, K, Li, Rb and Cs) were determined in 14 different areas of the human brain (frontal cortex, superior and middle temporal gyri, caudate nucleus, putamen, globus pallidus, cingulated gyrus, hippocampus, inferior parietal lobule, visual cortex of the occipital lobe, midbrain, pons, medulla and cerebellum) of adult individuals (n=42; 71±12, range: 50-101 years old) with no known history and evidence of neurodegenerative, neurological or psychiatric disorder. Potassium was found as the most abundant alkali metal, followed by Na, Rb, Cs and Li. Lithium, K and Cs distribution showed to be quite heterogeneous. On the contrary, Rb and Na appeared quite homogeneously distributed within the human brain tissue. The lowest levels of Na, K, Rb and Li were found in the brainstem (midbrain, medulla and pons) and cerebellum, while the lowest levels of Cs were found in the frontal cortex. The highest levels of K (mean±sd; range 15.5±2.5; 8.9-21.8mg/g) Rb (17.2±6.1; 3.9-32.4μg/g and Cs (83.4±48.6; 17.3-220.5ng/g) were found in putamen. The highest levels of Na and Li were found in the frontal cortex (11.6±2.4; 6.6-17.1mg/g) and caudate nucleus (7.6±4.6 2.2-21.3ng/g), respectively. Although K, Cs and Li levels appear to remain largely unchanged with age, some age-related changes were observed for Na and Rb levels in particular brain regions (namely in the hippocampus). Copyright © 2016 Elsevier GmbH. All

  13. Morphometric brain characterization of refractory obsessive-compulsive disorder: diffeomorphic anatomic registration using exponentiated Lie algebra.

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    Tang, Wanjie; Li, Bin; Huang, Xiaoqi; Jiang, Xiaoyu; Li, Fei; Wang, Lijuan; Chen, Taolin; Wang, Jinhui; Gong, Qiyong; Yang, Yanchun

    2013-10-01

    Few studies have used neuroimaging to characterize treatment-refractory obsessive-compulsive disorder (OCD). This study sought to explore gray matter structure in patients with treatment-refractory OCD and compare it with that of healthy controls. A total of 18 subjects with treatment-refractory OCD and 26 healthy volunteers were analyzed by MRI using a 3.0-T scanner and voxel-based morphometry (VBM). Diffeomorphic anatomical registration using exponentiated Lie algebra (DARTEL) was used to identify structural changes in gray matter associated with treatment-refractory OCD. A partial correlation model was used to analyze whether morphometric changes were associated with Yale-Brown Obsessive-Compulsive Scale scores and illness duration. Gray matter volume did not differ significantly between the two groups. Treatment-refractory OCD patients showed significantly lower gray matter density than healthy subjects in the left posterior cingulate cortex (PCC) and mediodorsal thalamus (MD) and significantly higher gray matter density in the left dorsal striatum (putamen). These changes did not correlate with symptom severity or illness duration. Our findings provide new evidence of deficits in gray matter density in treatment-refractory OCD patients. These patients may show characteristic density abnormalities in the left PCC, MD and dorsal striatum (putamen), which should be verified in longitudinal studies. © 2013. Published by Elsevier Inc. All rights reserved.

  14. Network attributes underlying intellectual giftedness in the developing brain.

    Science.gov (United States)

    Ma, Jiyoung; Kang, Hee Jin; Kim, Jung Yoon; Jeong, Hyeonseok S; Im, Jooyeon Jamie; Namgung, Eun; Kim, Myeong Ju; Lee, Suji; Kim, Tammy D; Oh, Jin Kyoung; Chung, Yong-An; Lyoo, In Kyoon; Lim, Soo Mee; Yoon, Sujung

    2017-09-12

    Brain network is organized to maximize the efficiency of both segregated and integrated information processing that may be related to human intelligence. However, there have been surprisingly few studies that focus on the topological characteristics of brain network underlying extremely high intelligence that is intellectual giftedness, particularly in adolescents. Here, we examined the network topology in 25 adolescents with superior intelligence (SI-Adol), 25 adolescents with average intelligence (AI-Adol), and 27 young adults with AI (AI-Adult). We found that SI-Adol had network topological properties of high global efficiency as well as high clustering with a low wiring cost, relative to AI-Adol. However, contrary to the suggested role that brain hub regions play in general intelligence, the network efficiency of rich club connection matrix, which represents connections among brain hubs, was low in SI-Adol in comparison to AI-Adol. Rather, a higher level of local connection density was observed in SI-Adol than in AI-Adol. The highly intelligent brain may not follow this efficient yet somewhat stereotypical process of information integration entirely. Taken together, our results suggest that a highly intelligent brain may communicate more extensively, while being less dependent on rich club communications during adolescence.

  15. Information dynamics of brain-heart physiological networks during sleep

    Science.gov (United States)

    Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.

    2014-10-01

    This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

  16. THE IMPACT OF POVERTY ON THE DEVELOPMENT OF BRAIN NETWORKS

    Directory of Open Access Journals (Sweden)

    Sebastian J Lipina

    2012-08-01

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

  17. Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients

    Directory of Open Access Journals (Sweden)

    Douw Linda

    2010-08-01

    Full Text Available Abstract Background Both epilepsy patients and brain tumor patients show altered functional connectivity and less optimal brain network topology when compared to healthy controls, particularly in the theta band. Furthermore, the duration and characteristics of epilepsy may also influence functional interactions in brain networks. However, the specific features of connectivity and networks in tumor-related epilepsy have not been investigated yet. We hypothesize that epilepsy characteristics are related to (theta band connectivity and network architecture in operated glioma patients suffering from epileptic seizures. Included patients participated in a clinical study investigating the effect of levetiracetam monotherapy on seizure frequency in glioma patients, and were assessed at two time points: directly after neurosurgery (t1, and six months later (t2. At these time points, magnetoencephalography (MEG was recorded and information regarding clinical status and epilepsy history was collected. Functional connectivity was calculated in six frequency bands, as were a number of network measures such as normalized clustering coefficient and path length. Results At the two time points, MEG registrations were performed in respectively 17 and 12 patients. No changes in connectivity or network topology occurred over time. Increased theta band connectivity at t1 and t2 was related to a higher total number of seizures. Furthermore, higher number of seizures was related to a less optimal, more random brain network topology. Other factors were not significantly related to functional connectivity or network topology. Conclusions These results indicate that (pathologically increased theta band connectivity is related to a higher number of epileptic seizures in brain tumor patients, suggesting that theta band connectivity changes are a hallmark of tumor-related epilepsy. Furthermore, a more random brain network topology is related to greater vulnerability to

  18. Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging.

    Science.gov (United States)

    Matulewicz, Lukasz; Jansen, Jacobus F A; Bokacheva, Louisa; Vargas, Hebert Alberto; Akin, Oguz; Fine, Samson W; Shukla-Dave, Amita; Eastham, James A; Hricak, Hedvig; Koutcher, Jason A; Zakian, Kristen L

    2014-12-01

    To assess whether an artificial neural network (ANN) model is a useful tool for automatic detection of cancerous voxels in the prostate from (1)H-MRSI datasets and whether the addition of information about anatomical segmentation improves the detection of cancer. The Institutional Review Board approved this HIPAA-compliant study and waived informed consent. Eighteen men with prostate cancer (median age, 55 years; range, 36-71 years) who underwent endorectal MRI/MRSI before radical prostatectomy were included in this study. These patients had at least one cancer area on whole-mount histopathological map and at least one matching MRSI voxel suspicious for cancer detected. Two ANN models for automatic classification of MRSI voxels in the prostate were implemented and compared: model 1, which used only spectra as input, and model 2, which used the spectra plus information from anatomical segmentation. The models were trained, tested and validated using spectra from voxels that the spectroscopist had designated as cancer and that were verified on histopathological maps. At ROC analysis, model 2 (AUC = 0.968) provided significantly better (P = 0.03) classification of cancerous voxels than did model 1 (AUC = 0.949). Automatic analysis of prostate MRSI to detect cancer using ANN model is feasible. Application of anatomical segmentation from MRI as an additional input to ANN improves the accuracy of detecting cancerous voxels from MRSI. © 2013 Wiley Periodicals, Inc.

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

    Science.gov (United States)

    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.

  20. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Kaustubh Supekar

    2008-06-01

    Full Text Available 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.

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

    Science.gov (United States)

    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. Copyright © 2013 Elsevier Inc. All rights reserved.

  2. Joint Modelling of Structural and Functional Brain Networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten

    Functional and structural magnetic resonance imaging have become the most important noninvasive windows to the human brain. A major challenge in the analysis of brain networks is to establish the similarities and dissimilarities between functional and structural connectivity. We formulate a non......-parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...... significant structures that are consistently shared across subjects and data splits. This provides an unsupervised approach for modeling of structure-function relations in the brain and provides a general framework for multimodal integration....

  3. Deficiency of brain structural sub-network underlying post-ischaemic stroke apathy.

    Science.gov (United States)

    Yang, S; Hua, P; Shang, X; Cui, Z; Zhong, S; Gong, G; William Humphreys, G

    2015-02-01

    This study aimed to reveal the structural basis of post-ischaemic stroke apathy, especially in relation to disruptions in structural connectivity. Eighty-eight participants were included. The Apathy Evaluation Scale, clinician version, was used to characterize the severity of apathy. Diffusion tensor imaging tractography was used to examine white matter integrity and to reconstruct white matter networks using 90 nodes based on the automated anatomical labeling atlas. The degree for each node was extracted to determine the relationship to the severity of apathy. Apathy was not significantly associated with damage to any single brain region. The degrees of 24 nodes (limbic system, three nodes; frontal lobe, six; basal ganglia, two; temporal lobe, three; parietal lobe, three; insula, two; occipital lobe, five) were significantly correlated to the Apathy Evaluation Scale scores. These 24 nodes constituted an apathy-related sub-network and its global and local efficiencies were negatively correlated with apathy levels (global, r = -0.54, P apathy (odds ratio 0.03, 95% confidence interval 0.01-0.04, P = 0.007). Efficiencies of the non-apathy-related sub-network (the remaining 66 nodes) did not correlate or predict the presence of apathy. Post-stroke apathy is not due to the dysfunction of a single region or circuit. Rather, it results from disconnection of a complex sub-network of brain regions. This provides new insights into the neuroanatomical basis of post-stroke apathy. © 2014 EAN.

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

    Science.gov (United States)

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

    2014-06-01

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

  5. Spreading dynamics on spatially constrained complex brain networks.

    Science.gov (United States)

    O'Dea, Reuben; Crofts, Jonathan J; Kaiser, Marcus

    2013-04-06

    The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way.

  6. Brains of verbal memory specialists show anatomical differences in language, memory and visual systems.

    Science.gov (United States)

    Hartzell, James F; Davis, Ben; Melcher, David; Miceli, Gabriele; Jovicich, Jorge; Nath, Tanmay; Singh, Nandini Chatterjee; Hasson, Uri

    2016-05-01

    We studied a group of verbal memory specialists to determine whether intensive oral text memory is associated with structural features of hippocampal and lateral-temporal regions implicated in language processing. Professional Vedic Sanskrit Pandits in India train from childhood for around 10years in an ancient, formalized tradition of oral Sanskrit text memorization and recitation, mastering the exact pronunciation and invariant content of multiple 40,000-100,000 word oral texts. We conducted structural analysis of gray matter density, cortical thickness, local gyrification, and white matter structure, relative to matched controls. We found massive gray matter density and cortical thickness increases in Pandit brains in language, memory and visual systems, including i) bilateral lateral temporal cortices and ii) the anterior cingulate cortex and the hippocampus, regions associated with long and short-term memory. Differences in hippocampal morphometry matched those previously documented for expert spatial navigators and individuals with good verbal working memory. The findings provide unique insight into the brain organization implementing formalized oral knowledge systems. Copyright © 2015. Published by Elsevier Inc.

  7. Network-level structural covariance in the developing brain.

    Science.gov (United States)

    Zielinski, Brandon A; Gennatas, Efstathios D; Zhou, Juan; Seeley, William W

    2010-10-19

    Intrinsic or resting state functional connectivity MRI and structural covariance MRI have begun to reveal the adult human brain's multiple network architectures. How and when these networks emerge during development remains unclear, but understanding ontogeny could shed light on network function and dysfunction. In this study, we applied structural covariance MRI techniques to 300 children in four age categories (early childhood, 5-8 y; late childhood, 8.5-11 y; early adolescence, 12-14 y; late adolescence, 16-18 y) to characterize gray matter structural relationships between cortical nodes that make up large-scale functional networks. Network nodes identified from eight widely replicated functional intrinsic connectivity networks served as seed regions to map whole-brain structural covariance patterns in each age group. In general, structural covariance in the youngest age group was limited to seed and contralateral homologous regions. Networks derived using primary sensory and motor cortex seeds were already well-developed in early childhood but expanded in early adolescence before pruning to a more restricted topology resembling adult intrinsic connectivity network patterns. In contrast, language, social-emotional, and other cognitive networks were relatively undeveloped in younger age groups and showed increasingly distributed topology in older children. The so-called default-mode network provided a notable exception, following a developmental trajectory more similar to the primary sensorimotor systems. Relationships between functional maturation and structural covariance networks topology warrant future exploration.

  8. The Virtual Brain: a simulator of primate brain network dynamics

    Directory of Open Access Journals (Sweden)

    Paula eSanz Leon

    2013-06-01

    Full Text Available We present TheVirtualBrain (TVB, a neuroinformatics platform for full brainnetwork simulations using biologically realistic connectivity. This simulationenvironment enables the model-based inference of neurophysiological mechanismsacross different brain scales that underlie the generation of macroscopicneuroimaging signals including functional MRI (fMRI, EEG and MEG. Researchersfrom different backgrounds can benefit from an integrative software platformincluding a supporting framework for data management (generation,organization, storage, integration and sharing and a simulation core writtenin Python. TVB allows the reproduction and evaluation of personalizedconfigurations of the brain by using individual subject data. Thispersonalization facilitates an exploration of the consequences of pathologicalchanges in the system, permitting to investigate potential ways to counteractsuch unfavorable processes. The architecture of TVB supports interaction withMATLAB packages, for example, the well known Brain Connectivity Toolbox. TVBcan be used in a client-server configuration, such that it can be remotelyaccessed through the Internet thanks to its web-basedHTML5, JS and WebGL graphical user interface. TVB is alsoaccessible as a standalone cross-platform Python library and application, andusers can interact with the scientific core through the scripting interfaceIDLE, enabling easy modeling, development and debugging of the scientifickernel. This second interface makes TVB extensible by combining it with otherlibraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to thedevelopment of TVB, the architecture and features of its major softwarecomponents as well as potential neuroscience applications.

  9. Abnormal whole-brain functional networks in homogeneous acute mild traumatic brain injury.

    NARCIS (Netherlands)

    Shumskaya, E.; Andriessen, T.; Norris, David Gordon; Vos, P.E.

    2012-01-01

    Objectives: To evaluate the whole-brain resting-state networks in a homogeneous group of patients with acute mild traumatic brain injury (MTBI) and to identify alterations in functional connectivity induced by MTBI. Methods: Thirty-five patients with acute MTBI and 35 healthy control subjects,

  10. Brain network dysregulation, emotion, and complaints after mild traumatic brain injury

    NARCIS (Netherlands)

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

    ObjectivesTo assess the role of brain networks in emotion regulation and post-traumatic complaints in the sub-acute phase after non-complicated mild traumatic brain injury (mTBI). Experimental designFifty-four patients with mTBI (34 with and 20 without complaints) and 20 healthy controls

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-26

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

  13. Identifying topological motif patterns of human brain functional networks.

    Science.gov (United States)

    Wei, Yongbin; Liao, Xuhong; Yan, Chaogan; He, Yong; Xia, Mingrui

    2017-05-01

    Recent imaging connectome studies demonstrated that the human functional brain network follows an efficient small-world topology with cohesive functional modules and highly connected hubs. However, the functional motif patterns that represent the underlying information flow remain largely unknown. Here, we investigated motif patterns within directed human functional brain networks, which were derived from resting-state functional magnetic resonance imaging data with controlled confounding hemodynamic latencies. We found several significantly recurring motifs within the network, including the two-node reciprocal motif and five classes of three-node motifs. These recurring motifs were distributed in distinct patterns to support intra- and inter-module functional connectivity, which also promoted integration and segregation in network organization. Moreover, the significant participation of several functional hubs in the recurring motifs exhibited their critical role in global integration. Collectively, our findings highlight the basic architecture governing brain network organization and provide insight into the information flow mechanism underlying intrinsic brain activities. Hum Brain Mapp 38:2734-2750, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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

    Science.gov (United States)

    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/).

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

    Directory of Open Access Journals (Sweden)

    Mingrui Xia

    Full Text Available 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/.

  16. Disruption of functional networks in dyslexia: A whole-brain, data-driven analysis of connectivity

    Science.gov (United States)

    Finn, Emily S.; Shen, Xilin; Holahan, John M.; Scheinost, Dustin; Lacadie, Cheryl; Papademetris, Xenophon; Shaywitz, Sally E.; Shaywitz, Bennett A.; Constable, R. Todd

    2013-01-01

    Background Functional connectivity analyses of fMRI data are a powerful tool for characterizing brain networks and how they are disrupted in neural disorders. However, many such analyses examine only one or a small number of a priori seed regions. Studies that consider the whole brain frequently rely on anatomic atlases to define network nodes, which may result in mixing distinct activation timecourses within a single node. Here, we improve upon previous methods by using a data-driven brain parcellation to compare connectivity profiles of dyslexic (DYS) versus non-impaired (NI) readers in the first whole-brain functional connectivity analysis of dyslexia. Methods Whole-brain connectivity was assessed in children (n = 75; 43 NI, 32 DYS) and adult (n = 104; 64 NI, 40 DYS) readers. Results Compared to NI readers, DYS readers showed divergent connectivity within the visual pathway and between visual association areas and prefrontal attention areas; increased right-hemisphere connectivity; reduced connectivity in the visual word-form area (part of the left fusiform gyrus specialized for printed words); and persistent connectivity to anterior language regions around the inferior frontal gyrus. Conclusions Together, findings suggest that NI readers are better able to integrate visual information and modulate their attention to visual stimuli, allowing them to recognize words based on their visual properties, while DYS readers recruit altered reading circuits and rely on laborious phonology-based “sounding out” strategies into adulthood. These results deepen our understanding of the neural basis of dyslexia and highlight the importance of synchrony between diverse brain regions for successful reading. PMID:24124929

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

    Science.gov (United States)

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

    2015-12-01

    For efficient information processing during cognitive activity, functional brain networks have to rapidly and dynamically reorganize on a sub-second time scale. Tracking the spatiotemporal dynamics of large scale networks over this short time duration is a very challenging issue. Here, we tackle this problem by using dense electroencephalography (EEG) recorded during a picture naming task. We found that (i) the picture naming task can be divided into six brain network states (BNSs) characterized by significantly high synchronization of gamma (30-45 Hz) oscillations, (ii) fast transitions occur between these BNSs that last from 30 msec to 160 msec, (iii) based on the state of the art of the picture naming task, we consider that the spatial location of their nodes and edges, as well as the timing of transitions, indicate that each network can be associated with one or several specific function (from visual processing to articulation) and (iv) the comparison with previously-used approach aimed at localizing the sources showed that the network-based approach reveals networks that are more specific to the performed task. We speculate that the persistence of several brain regions in successive BNSs participates to fast and efficient information processing in the brain. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Leveraging anatomical information to improve transfer learning in brain-computer interfaces

    Science.gov (United States)

    Wronkiewicz, Mark; Larson, Eric; Lee, Adrian K. C.

    2015-08-01

    Objective. Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. Approach. We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Main results. Based on simulated and measured electroencephalography activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. Significance. These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.

  19. Neural murmurations. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

    Science.gov (United States)

    Laurienti, Paul J.

    2014-09-01

    If not the last frontier, understanding the human brain is certainly one of the last. Over the past decade there has been a shift in the focus of neuroscience. The concept of the brain as a network is gaining traction and is rapidly becoming a dominant perspective [1]. In the target article [2], Luiz Pessoa discusses major conceptual shifts that must accompany the methodological changes associated with network science applications to the brain. The software, algorithms, and computational power needed to perform network analyses are now at the fingertips of all neuroscientists. But, this places us at a fork in the road. Will these tools be used to substantiate what has already been discovered, or will we seek a totally new and improved understanding of the brain?

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

    OpenAIRE

    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 \\emph{multi-step} cognitive task involving with consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed base on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based ...

  1. Sleeping of a Complex Brain Networks with Hierarchical Organization

    Science.gov (United States)

    Zhang, Ying-Yue; Yang, Qiu-Ying; Chen, Tian-Lun

    2009-01-01

    The dynamical behavior in the cortical brain network of macaque is studied by modeling each cortical area with a subnetwork of interacting excitable neurons. We characterize the system by studying how to perform the transition, which is now topology-dependent, from the active state to that with no activity. This could be a naive model for the wakening and sleeping of a brain-like system, i.e., a multi-component system with two different dynamical behavior.

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

    Science.gov (United States)

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

    2016-01-01

    Rich, spontaneous brain activity has been observed across a range of different temporal and spatial scales. These dynamics are thought to be important for efficient neural functioning. A range of experimental evidence suggests that these neural dynamics are maintained across a variety of different cognitive states, in response to alterations of the environment and to changes in brain configuration (e.g., across individuals, development and in many neurological disorders). This suggests that the brain has evolved mechanisms to maintain rich dynamics across a broad range of situations. Several mechanisms based around homeostatic plasticity have been proposed to explain how these dynamics emerge from networks of neurons at the microscopic scale. Here we explore how a homeostatic mechanism may operate at the macroscopic scale: in particular, focusing on how it interacts with the underlying structural network topology and how it gives rise to well-described functional connectivity networks. We use a simple mean-field model of the brain, constrained by empirical white matter structural connectivity where each region of the brain is simulated using a pool of excitatory and inhibitory neurons. We show, as with the microscopic work, that homeostatic plasticity regulates network activity and allows for the emergence of rich, spontaneous dynamics across a range of brain configurations, which otherwise show a very limited range of dynamic regimes. In addition, the simulated functional connectivity of the homeostatic model better resembles empirical functional connectivity network. To accomplish this, we show how the inhibitory weights adapt over time to capture important graph theoretic properties of the underlying structural network. Therefore, this work presents suggests how inhibitory homeostatic mechanisms facilitate stable macroscopic dynamics to emerge in the brain, aiding the formation of functional connectivity networks. Copyright © 2015 Elsevier Inc. All rights

  3. Small-world human brain networks: Perspectives and challenges.

    Science.gov (United States)

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

    2017-06-01

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

  4. Electrophysiological signatures of atypical intrinsic brain connectivity networks in autism

    Science.gov (United States)

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

    2017-08-01

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

  5. Hierarchical multi-resolution mesh networks for brain decoding.

    Science.gov (United States)

    Onal Ertugrul, Itir; Ozay, Mete; Yarman Vural, Fatos T

    2017-10-04

    Human brain is supposed to process information in multiple frequency bands. Therefore, we can extract diverse information from functional Magnetic Resonance Imaging (fMRI) data by processing it at multiple resolutions. We propose a framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs), which establishes a set of brain networks at multiple resolutions of fMRI signal to represent the underlying cognitive process. Our framework, first, decomposes the fMRI signal into various frequency subbands using wavelet transform. Then, a brain network is formed at each subband by ensembling a set of local meshes. Arc weights of each local mesh are estimated by ridge regression. Finally, adjacency matrices of mesh networks obtained at different subbands are used to train classifiers in an ensemble learning architecture, called fuzzy stacked generalization (FSG). Our decoding performances on Human Connectome Project task-fMRI dataset reflect that HMMNs can successfully discriminate tasks with 99% accuracy, across 808 subjects. Diversity of information embedded in mesh networks of multiple subbands enables the ensemble of classifiers to collaborate with each other for brain decoding. The suggested HMMNs decode the cognitive tasks better than a single classifier applied to any subband. Also mesh networks have a better representation power compared to pairwise correlations or average voxel time series. Moreover, fusion of diverse information using FSG outperforms fusion with majority voting. We conclude that, fMRI data, recorded during a cognitive task, provide diverse information in multi-resolution mesh networks. Our framework fuses this complementary information and boosts the brain decoding performances obtained at individual subbands.

  6. Association of brain network efficiency with aging, depression, and cognition.

    Science.gov (United States)

    Ajilore, Olusola; Lamar, Melissa; Kumar, Anand

    2014-02-01

    Newly developed techniques for understanding brain connectivity have emerged with the application of graph theory-based measures to neuroimaging modalities. However, the cognitive correlates of these measures, particularly in the context of clinical diagnoses like major depression, are still poorly understood. The purpose of this study was to compare four measures of network efficiency derived from novel techniques for understanding white matter connectivity on their associations with aging, depression, and cognition. In a cross-sectional neuroimaging study, we recruited from the general community 43 healthy comparison subjects and 40 subjects with major depressive disorder who volunteered in response to advertisements. Brain network efficiency measures were generated from diffusion tensor imaging-derived structural connectivity matrices using the Brain Connectivity Toolbox. Information processing speed and decision making were assessed with the Trail Making Test and the Object Alternation task, respectively. All four network efficiency measures correlated negatively with age. In the depressed group, normalized global efficiency was negatively correlated with depression severity, whereas increasing global efficiency was associated with poorer performance on Object Alternation. Brain network efficiency measures may represent different aspects of underlying network organization depending on the population and behaviors in question. Copyright © 2014 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

  7. Graph theoretical analysis reveals the reorganization of the brain network pattern in primary open angle glaucoma patients

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jieqiong [Chinese Academy of Sciences, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing (China); Li, Ting; Xian, Junfang [Capital Medical University, Department of Radiology, Beijing Tongren Hospital, Beijing (China); Wang, Ningli [Capital Medical University, Department of Ophthalmology, Beijing Tongren Hospital, Beijing (China); He, Huiguang [Chinese Academy of Sciences, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing (China); Chinese Academy of Sciences, Research Center for Brain-Inspired Intelligence, Institute of Automation, Beijing (China)

    2016-11-15

    Most previous glaucoma studies with resting-state fMRI have focused on the neuronal activity in the individual structure of the brain, yet ignored the functional communication of anatomically separated structures. The purpose of this study is to investigate the efficiency of the functional communication change or not in glaucoma patients. We applied the resting-state fMRI data to construct the connectivity network of 25 normal controls and 25 age-gender-matched primary open angle glaucoma patients. Graph theoretical analysis was performed to assess brain network pattern differences between the two groups. No significant differences of the global network measures were found between the two groups. However, the local measures were radically reorganized in glaucoma patients. Comparing with the hub regions in normal controls' network, we found that six hub regions disappeared and nine hub regions appeared in the network of patients. In addition, the betweenness centralities of two altered hub regions, right fusiform gyrus and right lingual gyrus, were significantly correlated with the visual field mean deviation. Although the efficiency of functional communication is preserved in the brain network of the glaucoma at the global level, the efficiency of functional communication is altered in some specialized regions of the glaucoma. (orig.)

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

    Science.gov (United States)

    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.

  9. Cell diversity and network dynamics in photosensitive human brain organoids

    Science.gov (United States)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Fabrizio De Vico Fallani

    2017-01-01

    Full Text Available 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.

  11. Common and distinct brain networks underlying verbal and visual creativity.

    Science.gov (United States)

    Zhu, Wenfeng; Chen, Qunlin; Xia, Lingxiang; Beaty, Roger E; Yang, Wenjing; Tian, Fang; Sun, Jiangzhou; Cao, Guikang; Zhang, Qinglin; Chen, Xu; Qiu, Jiang

    2017-04-01

    Creativity is imperative to the progression of human civilization, prosperity, and well-being. Past creative researches tends to emphasize the default mode network (DMN) or the frontoparietal network (FPN) somewhat exclusively. However, little is known about how these networks interact to contribute to creativity and whether common or distinct brain networks are responsible for visual and verbal creativity. Here, we use functional connectivity analysis of resting-state functional magnetic resonance imaging data to investigate visual and verbal creativity-related regions and networks in 282 healthy subjects. We found that functional connectivity within the bilateral superior parietal cortex of the FPN was negatively associated with visual and verbal creativity. The strength of connectivity between the DMN and FPN was positively related to both creative domains. Visual creativity was negatively correlated with functional connectivity within the precuneus of the pDMN and right middle frontal gyrus of the FPN, and verbal creativity was negatively correlated with functional connectivity within the medial prefrontal cortex of the aDMN. Critically, the FPN mediated the relationship between the aDMN and verbal creativity, and it also mediated the relationship between the pDMN and visual creativity. Taken together, decreased within-network connectivity of the FPN and DMN may allow for flexible between-network coupling in the highly creative brain. These findings provide indirect evidence for the cooperative role of the default and executive control networks in creativity, extending past research by revealing common and distinct brain systems underlying verbal and visual creative cognition. Hum Brain Mapp 38:2094-2111, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  12. Dynamic Network Centrality Summarizes Learning in the Human Brain

    OpenAIRE

    Mantzaris, Alexander V.; Bassett, Danielle S.; Wymbs, Nicholas F.; Estrada, Ernesto; Porter, Mason A.; Mucha, Peter J; Grafton, Scott T.; Higham, Desmond J.

    2012-01-01

    We study functional activity in the human brain using functional Magnetic Resonance Imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over three days of practice produces significant evidence of `learning', in the sense that subjects typically move between clusters (of subjects whose dynamics are similar) as time ...

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

    Science.gov (United States)

    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 network overlap in cortical areas previously implicated in symptom expression (P brain regions involved in symptom expression; and (ii) publically available human connectome data can be used to incorporate these network effects into traditional lesion mapping approaches. Because the current technique

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

    Science.gov (United States)

    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.

  15. Preserved modular network organization in the sedated rat brain.

    Directory of Open Access Journals (Sweden)

    Dany V D'Souza

    Full Text Available Translation of resting-state functional connectivity (FC magnetic resonance imaging (rs-fMRI applications from human to rodents has experienced growing interest, and bears a great potential in pre-clinical imaging as it enables assessing non-invasively the topological organization of complex FC networks (FCNs in rodent models under normal and various pathophysiological conditions. However, to date, little is known about the organizational architecture of FCNs in rodents in a mentally healthy state, although an understanding of the same is of paramount importance before investigating networks under compromised states. In this study, we characterized the properties of resting-state FCN in an extensive number of Sprague-Dawley rats (n = 40 under medetomidine sedation by evaluating its modular organization and centrality of brain regions and tested for reproducibility. Fully-connected large-scale complex networks of positively and negatively weighted connections were constructed based on Pearson partial correlation analysis between the time courses of 36 brain regions encompassing almost the entire brain. Applying recently proposed complex network analysis measures, we show that the rat FCN exhibits a modular architecture, comprising six modules with a high between subject reproducibility. In addition, we identified network hubs with strong connections to diverse brain regions. Overall our results obtained under a straight medetomidine protocol show for the first time that the community structure of the rat brain is preserved under pharmacologically induced sedation with a network modularity contrasting from the one reported for deep anesthesia but closely resembles the organization described for the rat in conscious state.

  16. Foundational perspectives on causality in large-scale brain networks

    Science.gov (United States)

    Mannino, Michael; Bressler, Steven L.

    2015-12-01

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

  17. Anatomical network analysis shows decoupling of modular lability and complexity in the evolution of the primate skull.

    Directory of Open Access Journals (Sweden)

    Borja Esteve-Altava

    Full Text Available Modularity and complexity go hand in hand in the evolution of the skull of primates. Because analyses of these two parameters often use different approaches, we do not know yet how modularity evolves within, or as a consequence of, an also-evolving complex organization. Here we use a novel network theory-based approach (Anatomical Network Analysis to assess how the organization of skull bones constrains the co-evolution of modularity and complexity among primates. We used the pattern of bone contacts modeled as networks to identify connectivity modules and quantify morphological complexity. We analyzed whether modularity and complexity evolved coordinately in the skull of primates. Specifically, we tested Herbert Simon's general theory of near-decomposability, which states that modularity promotes the evolution of complexity. We found that the skulls of extant primates divide into one conserved cranial module and up to three labile facial modules, whose composition varies among primates. Despite changes in modularity, statistical analyses reject a positive feedback between modularity and complexity. Our results suggest a decoupling of complexity and modularity that translates to varying levels of constraint on the morphological evolvability of the primate skull. This study has methodological and conceptual implications for grasping the constraints that underlie the developmental and functional integration of the skull of humans and other primates.

  18. Anatomical network analysis shows decoupling of modular lability and complexity in the evolution of the primate skull.

    Science.gov (United States)

    Esteve-Altava, Borja; Boughner, Julia C; Diogo, Rui; Villmoare, Brian A; Rasskin-Gutman, Diego

    2015-01-01

    Modularity and complexity go hand in hand in the evolution of the skull of primates. Because analyses of these two parameters often use different approaches, we do not know yet how modularity evolves within, or as a consequence of, an also-evolving complex organization. Here we use a novel network theory-based approach (Anatomical Network Analysis) to assess how the organization of skull bones constrains the co-evolution of modularity and complexity among primates. We used the pattern of bone contacts modeled as networks to identify connectivity modules and quantify morphological complexity. We analyzed whether modularity and complexity evolved coordinately in the skull of primates. Specifically, we tested Herbert Simon's general theory of near-decomposability, which states that modularity promotes the evolution of complexity. We found that the skulls of extant primates divide into one conserved cranial module and up to three labile facial modules, whose composition varies among primates. Despite changes in modularity, statistical analyses reject a positive feedback between modularity and complexity. Our results suggest a decoupling of complexity and modularity that translates to varying levels of constraint on the morphological evolvability of the primate skull. This study has methodological and conceptual implications for grasping the constraints that underlie the developmental and functional integration of the skull of humans and other primates.

  19. Resilience of developing brain networks to interictal epileptiform discharges is associated with cognitive outcome

    National Research Council Canada - National Science Library

    Ibrahim, George M; Cassel, Daniel; Morgan, Benjamin R; Smith, Mary Lou; Otsubo, Hiroshi; Ochi, Ayako; Taylor, Margot; Rutka, James T; Snead, 3rd, O Carter; Doesburg, Sam

    2014-01-01

    .... Such discharges may have a deleterious effect on the brain's intrinsic connectivity networks, which reflect the organization of functional networks at rest, and in turn on neurocognitive development...

  20. Patterns recognition of electric brain activity using artificial neural networks

    Science.gov (United States)

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

    2017-04-01

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

  1. Supervised dictionary learning for inferring concurrent brain networks.

    Science.gov (United States)

    Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

    2015-10-01

    Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

  2. Rounding of abrupt phase transitions in brain networks

    Science.gov (United States)

    Villa Martín, Paula; Moretti, Paolo; Muñoz, Miguel A.

    2015-01-01

    The observation of critical-like behavior in cortical networks represents a major step forward in elucidating how the brain manages information. Understanding the origin and functionality of critical-like dynamics, as well as its robustness, is a major challenge in contemporary neuroscience. Here, we present an extensive numerical study of a family of simple dynamical models, which describe activity propagation in brain networks through the integration of different neighboring spiking potentials, mimicking basic neural interactions. The requirement of signal integration may lead to discontinuous phase transitions in networks that are well described by the mean-field approximation, thus preventing the emergence of critical points in such systems. Brain networks, however, are finite dimensional and exhibit a heterogeneous hierarchical structure that cannot be encoded in mean-field models. Here we propose that, as a consequence of the presence of such a heterogeneous substrate with its concomitant structural disorder, critical-like features may emerge even in the presence of integration. These conclusions may prove significant in explaining the observation of traits of critical behavior in large-scale measurements of brain activity.

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

    Directory of Open Access Journals (Sweden)

    Shuntaro eSasai

    2014-12-01

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

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    Geier, Christian; Lehnertz, Klaus

    2017-04-01

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

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

    Science.gov (United States)

    Geier, Christian; Lehnertz, Klaus

    2017-04-01

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

  7. Evidence of a Christmas spirit network in the brain

    DEFF Research Database (Denmark)

    Hougaard, Anders; Lindberg, Ulrich; Arngrim, Nanna

    2015-01-01

    OBJECTIVE: To detect and localise the Christmas spirit in the human brain. DESIGN: Single blinded, cross cultural group study with functional magnetic resonance imaging (fMRI). SETTING: Functional imaging unit and department of clinical physiology, nuclear medicine and PET in Denmark. PARTICIPANTS...... theme. METHODS: Functional brain scans optimised for detection of the blood oxygen level dependent (BOLD) response were performed while participants viewed a series of images with Christmas themes interleaved with neutral images having similar characteristics but containing nothing that symbolises...... spirit network" in the human brain comprising several cortical areas. This network had a significantly higher activation in a people who celebrate Christmas with positive associations as opposed to a people who have no Christmas traditions and neutral associations. Further research is necessary...

  8. Sex differences in normal age trajectories of functional brain networks.

    Science.gov (United States)

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

    2015-04-01

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

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

    Science.gov (United States)

    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

  10. Cortical network architecture for context processing in primate brain.

    Science.gov (United States)

    Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka

    2015-09-29

    Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition.

  11. Online social network size is reflected in human brain structure.

    Science.gov (United States)

    Kanai, R; Bahrami, B; Roylance, R; Rees, G

    2012-04-07

    The increasing ubiquity of web-based social networking services is a striking feature of modern human society. The degree to which individuals participate in these networks varies substantially for reasons that are unclear. Here, we show a biological basis for such variability by demonstrating that quantitative variation in the number of friends an individual declares on a web-based social networking service reliably predicted grey matter density in the right superior temporal sulcus, left middle temporal gyrus and entorhinal cortex. Such regions have been previously implicated in social perception and associative memory, respectively. We further show that variability in the size of such online friendship networks was significantly correlated with the size of more intimate real-world social groups. However, the brain regions we identified were specifically associated with online social network size, whereas the grey matter density of the amygdala was correlated both with online and real-world social network sizes. Taken together, our findings demonstrate that the size of an individual's online social network is closely linked to focal brain structure implicated in social cognition.

  12. Structural brain network: What is the effect of LiFE optimization of whole brain tractography?

    Directory of Open Access Journals (Sweden)

    Shouliang eQi

    2016-02-01

    Full Text Available Structural brain networks constructed based on diffusion-weighted MRI (dMRI have provided a systems perspective to explore the organization of the human brain. Some redundant and nonexistent fibers, however, are inevitably generated in whole brain tractography. We propose to add one critical step while constructing the networks to remove these fibers using the linear fascicle evaluation (LiFE method, and study the differences between the networks with and without LiFE optimization. For a cohort of 9 healthy adults and for 9 out of the 35 subjects from Human Connectome Project, the T1-weighted images and dMRI data are analyzed. Each brain is parcellated into 90 regions-of-interest, whilst a probabilistic tractography algorithm is applied to generate the original connectome. The elimination of redundant and nonexistent fibers from the original connectome by LiFE creates the optimized connectome, and the random selection of the same number of fibers as the optimized connectome creates the non-optimized connectome. The combination of parcellations and these connectomes leads to the optimized and non-optimized networks, respectively. The optimized networks are constructed with six weighting schemes, and the correlations of different weighting methods are analyzed. The fiber length distributions of the non-optimized and optimized connectomes are compared. The optimized and non-optimized networks are compared with regard to edges, nodes and networks, within a sparsity range of 0.75-0.95. It has been found that relatively more short fibers exist in the optimized connectome. About 24.0% edges of the optimized network are significantly different from those in the non-optimized network at a sparsity of 0.75. About 13.2% of edges are classified as false positives or the possible missing edges. The strength and betweenness centrality of some nodes are significantly different for the non-optimized and optimized networks, but not the node efficiency. The

  13. Whole brain and brain regional coexpression network interactions associated with predisposition to alcohol consumption.

    Directory of Open Access Journals (Sweden)

    Lauren A Vanderlinden

    Full Text Available To identify brain transcriptional networks that may predispose an animal to consume alcohol, we used weighted gene coexpression network analysis (WGCNA. Candidate coexpression modules are those with an eigengene expression level that correlates significantly with the level of alcohol consumption across a panel of BXD recombinant inbred mouse strains, and that share a genomic region that regulates the module transcript expression levels (mQTL with a genomic region that regulates alcohol consumption (bQTL. To address a controversy regarding utility of gene expression profiles from whole brain, vs specific brain regions, as indicators of the relationship of gene expression to phenotype, we compared candidate coexpression modules from whole brain gene expression data (gathered with Affymetrix 430 v2 arrays in the Colorado laboratories and from gene expression data from 6 brain regions (nucleus accumbens (NA; prefrontal cortex (PFC; ventral tegmental area (VTA; striatum (ST; hippocampus (HP; cerebellum (CB available from GeneNetwork. The candidate modules were used to construct candidate eigengene networks across brain regions, resulting in three "meta-modules", composed of candidate modules from two or more brain regions (NA, PFC, ST, VTA and whole brain. To mitigate the potential influence of chromosomal location of transcripts and cis-eQTLs in linkage disequilibrium, we calculated a semi-partial correlation of the transcripts in the meta-modules with alcohol consumption conditional on the transcripts' cis-eQTLs. The function of transcripts that retained the correlation with the phenotype after correction for the strong genetic influence, implicates processes of protein metabolism in the ER and Golgi as influencing susceptibility to variation in alcohol consumption. Integration of these data with human GWAS provides further information on the function of polymorphisms associated with alcohol-related traits.

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

    Science.gov (United States)

    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. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. Meeting the memory challenges of brain-scale network simulation

    Directory of Open Access Journals (Sweden)

    Susanne eKunkel

    2012-01-01

    Full Text Available The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10^5 neurons with up to 10^9 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are one or two orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been studied in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Bluegene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of a neuronal simulator as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place.

  16. Lectures in Supercomputational Neurosciences Dynamics in Complex Brain Networks

    CERN Document Server

    Graben, Peter beim; Thiel, Marco; Kurths, Jürgen

    2008-01-01

    Computational Neuroscience is a burgeoning field of research where only the combined effort of neuroscientists, biologists, psychologists, physicists, mathematicians, computer scientists, engineers and other specialists, e.g. from linguistics and medicine, seem to be able to expand the limits of our knowledge. The present volume is an introduction, largely from the physicists' perspective, to the subject matter with in-depth contributions by system neuroscientists. A conceptual model for complex networks of neurons is introduced that incorporates many important features of the real brain, such as various types of neurons, various brain areas, inhibitory and excitatory coupling and the plasticity of the network. The computational implementation on supercomputers, which is introduced and discussed in detail in this book, will enable the readers to modify and adapt the algortihm for their own research. Worked-out examples of applications are presented for networks of Morris-Lecar neurons to model the cortical co...

  17. Frequency-specific directed interactions in the human brain network for language

    NARCIS (Netherlands)

    Schoffelen, J.M.; Hultén, A.H.; Lam, N.H.L.; Marquand, A.F.; Uddén, J.U.; Hagoort, P.

    2017-01-01

    The brain's remarkable capacity for language requires bidirectional interactions between functionally specialized brain regions. We used magnetoencephalography to investigate interregional interactions in the brain network for language while 102 participants were reading sentences. Using Granger

  18. Disrupted Brain Functional Network Architecture in Chronic Tinnitus Patients.

    Science.gov (United States)

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

    2016-01-01

    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. 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. 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. 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.

  19. Comparing brain networks of different size and connectivity density using graph theory.

    Directory of Open Access Journals (Sweden)

    Bernadette C M van Wijk

    Full Text Available Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N and the average degree (k of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring non-significant (significant connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others.

  20. Comparing brain networks of different size and connectivity density using graph theory.

    Science.gov (United States)

    van Wijk, Bernadette C M; Stam, Cornelis J; Daffertshofer, Andreas

    2010-10-28

    Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring) non-significant (significant) connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others.

  1. Automatic Segmentation of MR Brain Images With a Convolutional Neural Network.

    Science.gov (United States)

    Moeskops, Pim; Viergever, Max A; Mendrik, Adrienne M; de Vries, Linda S; Benders, Manon J N L; Isgum, Ivana

    2016-05-01

    Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2-weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

  2. Software for Brain Network Simulations: A Comparative Study

    Science.gov (United States)

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

    2017-01-01

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

  3. Visual analysis of transcriptome data in the context of anatomical structures and biological networks

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    Astrid eJunker

    2012-11-01

    Full Text Available The complexity and temporal as well as spatial resolution of transcriptome datasets is constantly increasing due to extensive technological developments. Here we present methods for advanced visualization and intuitive exploration of transcriptomics data as necessary prerequisites in order to facilitate the gain of biological knowledge. Color-coding of structural images based on the expression level enables a fast visual data analysis in the background of the examined biological system. The network-based exploration of these visualizations allows for comparative analysis of genes with specific transcript patterns and supports the extraction of functional relationships even from large datasets. In order to illustrate the presented methods, the tool HIVE was applied for visualization and exploration of database-retrieved expression data for master regulators of Arabidopsis thaliana flower and seed development in the context of corresponding tissue-specific regulatory networks.

  4. Visual analysis of transcriptome data in the context of anatomical structures and biological networks.

    Science.gov (United States)

    Junker, Astrid; Rohn, Hendrik; Schreiber, Falk

    2012-01-01

    The complexity and temporal as well as spatial resolution of transcriptome datasets is constantly increasing due to extensive technological developments. Here we present methods for advanced visualization and intuitive exploration of transcriptomics data as necessary prerequisites in order to facilitate the gain of biological knowledge. Color-coding of structural images based on the expression level enables a fast visual data analysis in the background of the examined biological system. The network-based exploration of these visualizations allows for comparative analysis of genes with specific transcript patterns and supports the extraction of functional relationships even from large datasets. In order to illustrate the presented methods, the tool HIVE was applied for visualization and exploration of database-retrieved expression data for master regulators of Arabidopsis thaliana flower and seed development in the context of corresponding tissue-specific regulatory networks.

  5. Loss of brain graph network efficiency in alcohol dependence.

    Science.gov (United States)

    Sjoerds, Zsuzsika; Stufflebeam, Steven M; Veltman, Dick J; Van den Brink, Wim; Penninx, Brenda W J H; Douw, Linda

    2017-03-01

    Alcohol dependence (AD) is characterized by corticostriatal impairments in individual brain areas such as the striatum. As yet however, complex brain network topology in AD and its association with disease progression are unknown. We applied graph theory to resting-state functional magnetic resonance imaging (RS-fMRI) to examine weighted global efficiency and local (clustering coefficient, degree and eigenvector centrality) network topology and the functional role of the striatum in 24 AD patients compared with 20 matched healthy controls (HCs), and their association with dependence characteristics. Graph analyses were performed based on Pearson's correlations between RS-fMRI time series, while correcting for age, gender and head motion. We found no significant group differences between AD patients and HCs in network topology. Notably, within the patient group, but not in HCs, the whole-brain network showed reduced average cluster coefficient with more severe alcohol use, whereas longer AD duration within the patient group was associated with a global decrease in efficiency, degree and clustering coefficient. Additionally, within four a-priori chosen bilateral striatal nodes, alcohol use severity was associated with lower clustering coefficient in the left caudate. Longer AD duration was associated with reduced clustering coefficient in caudate and putamen, and reduced degree in bilateral caudate, but with increased eigenvector centrality in left posterior putamen. Especially changes in global network topology and clustering coefficient in anterior striatum remained strikingly robust after exploratory variations in network weight. Our results show adverse effects of AD on overall network integration and possibly on striatal efficiency, putatively contributing to the increasing behavioral impairments seen in chronically addicted patients. © 2015 Society for the Study of Addiction.

  6. Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks.

    Science.gov (United States)

    Vértes, Petra E; Alexander-Bloch, Aaron; Bullmore, Edward T

    2014-10-05

    Rich clubs arise when nodes that are 'rich' in connections also form an elite, densely connected 'club'. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  7. Emergence of small-world anatomical networks in self-organizing clustered neuronal cultures.

    Directory of Open Access Journals (Sweden)

    Daniel de Santos-Sierra

    Full Text Available In vitro primary cultures of dissociated invertebrate neurons from locust ganglia are used to experimentally investigate the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. At all the different stages of the culture's development, identification of neurons' and neurites' location by means of a dedicated software allows to ultimately extract an adjacency matrix from each image of the culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main network's characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graph's micro- and meso-scale properties emerge. Finally, we identify the main physical processes ruling the culture's morphological transformations, and embed them into a simplified growth model qualitatively reproducing the overall set of experimental observations.

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

    Science.gov (United States)

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

    2014-05-01

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

  9. Youthful Brains in Older Adults: Preserved Neuroanatomy in the Default Mode and Salience Networks Contributes to Youthful Memory in Superaging.

    Science.gov (United States)

    Sun, Felicia W; Stepanovic, Michael R; Andreano, Joseph; Barrett, Lisa Feldman; Touroutoglou, Alexandra; Dickerson, Bradford C

    2016-09-14

    Decline in cognitive skills, especially in memory, is often viewed as part of "normal" aging. Yet some individuals "age better" than others. Building on prior research showing that cortical thickness in one brain region, the anterior midcingulate cortex, is preserved in older adults with memory performance abilities equal to or better than those of people 20-30 years younger (i.e., "superagers"), we examined the structural integrity of two large-scale intrinsic brain networks in superaging: the default mode network, typically engaged during memory encoding and retrieval tasks, and the salience network, typically engaged during attention, motivation, and executive function tasks. We predicted that superagers would have preserved cortical thickness in critical nodes in these networks. We defined superagers (60-80 years old) based on their performance compared to young adults (18-32 years old) on the California Verbal Learning Test Long Delay Free Recall test. We found regions within the networks of interest where the cerebral cortex of superagers was thicker than that of typical older adults, and where superagers were anatomically indistinguishable from young adults; hippocampal volume was also preserved in superagers. Within the full group of older adults, thickness of a number of regions, including the anterior temporal cortex, rostral medial prefrontal cortex, and anterior midcingulate cortex, correlated with memory performance, as did the volume of the hippocampus. These results indicate older adults with youthful memory abilities have youthful brain regions in key paralimbic and limbic nodes of the default mode and salience networks that support attentional, executive, and mnemonic processes subserving memory function. Memory performance typically declines with age, as does cortical structural integrity, yet some older adults maintain youthful memory. We tested the hypothesis that superagers (older individuals with youthful memory performance) would exhibit

  10. Brief Mental Training Reorganizes Large-Scale Brain Networks.

    Science.gov (United States)

    Tang, Yi-Yuan; Tang, Yan; Tang, Rongxiang; Lewis-Peacock, Jarrod A

    2017-01-01

    Emerging evidences have shown that one form of mental training-mindfulness meditation, can improve attention, emotion regulation and cognitive performance through changing brain activity and structural connectivity. However, whether and how the short-term mindfulness meditation alters large-scale brain networks are not well understood. Here, we applied a novel data-driven technique, the multivariate pattern analysis (MVPA) to resting-state fMRI (rsfMRI) data to identify changes in brain activity patterns and assess the neural mechanisms induced by a brief mindfulness training-integrative body-mind training (IBMT), which was previously reported in our series of randomized studies. Whole brain rsfMRI was performed on an undergraduate group who received 2 weeks of IBMT with 30 min per session (5 h training in total). Classifiers were trained on measures of functional connectivity in this fMRI data, and they were able to reliably differentiate (with 72% accuracy) patterns of connectivity from before vs. after the IBMT training. After training, an increase in positive functional connections (60 connections) were detected, primarily involving bilateral superior/middle occipital gyrus, bilateral frontale operculum, bilateral superior temporal gyrus, right superior temporal pole, bilateral insula, caudate and cerebellum. These results suggest that brief mental training alters the functional connectivity of large-scale brain networks at rest that may involve a portion of the neural circuitry supporting attention, cognitive and affective processing, awareness and sensory integration and reward processing.

  11. Abnormal Brain Network Organization in Body Dysmorphic Disorder

    Science.gov (United States)

    Arienzo, Donatello; Leow, Alex; Brown, Jesse A; Zhan, Liang; GadElkarim, Johnson; Hovav, Sarit; Feusner, Jamie D

    2013-01-01

    Body dysmorphic disorder (BDD) is characterized by preoccupation with misperceived defects of appearance, causing significant distress and disability. Previous studies suggest abnormalities in information processing characterized by greater local relative to global processing. The purpose of this study was to probe whole-brain and regional white matter network organization in BDD, and to relate this to specific metrics of symptomatology. We acquired diffusion-weighted 34-direction MR images from 14 unmedicated participants with DSM-IV BDD and 16 healthy controls, from which we conducted whole-brain deterministic diffusion tensor imaging tractography. We then constructed white matter structural connectivity matrices to derive whole-brain and regional graph theory metrics, which we compared between groups. Within the BDD group, we additionally correlated these metrics with scores on psychometric measures of BDD symptom severity as well as poor insight/delusionality. The BDD group showed higher whole-brain mean clustering coefficient than controls. Global efficiency negatively correlated with BDD symptom severity. The BDD group demonstrated greater edge betweenness centrality for connections between the anterior temporal lobe and the occipital cortex, and between bilateral occipital poles. This represents the first brain network analysis in BDD. Results suggest disturbances in whole brain structural topological organization in BDD, in addition to correlations between clinical symptoms and network organization. There is also evidence of abnormal connectivity between regions involved in lower-order visual processing and higher-order visual and emotional processing, as well as interhemispheric visual information transfer. These findings may relate to disturbances in information processing found in previous studies. PMID:23322186

  12. Enhanced structural connectivity within a brain sub-network supporting working memory and engagement processes after cognitive training.

    Science.gov (United States)

    Román, Francisco J; Iturria-Medina, Yasser; Martínez, Kenia; Karama, Sherif; Burgaleta, Miguel; Evans, Alan C; Jaeggi, Susanne M; Colom, Roberto

    2017-05-01

    The structural connectome provides relevant information about experience and training-related changes in the brain. Here, we used network-based statistics (NBS) and graph theoretical analyses to study structural changes in the brain as a function of cognitive training. Fifty-six young women were divided in two groups (experimental and control). We assessed their cognitive function before and after completing a working memory intervention using a comprehensive battery that included fluid and crystallized abilities, working memory and attention control, and we also obtained structural MRI images. We acquired and analyzed diffusion-weighted images to reconstruct the anatomical connectome and we computed standardized changes in connectivity as well as group differences across time using NBS. We also compared group differences relying on a variety of graph-theory indices (clustering, characteristic path length, global and local efficiency and strength) for the whole network as well as for the sub-network derived from NBS analyses. Finally, we calculated correlations between these graph indices and training performance as well as the behavioral changes in cognitive function. Our results revealed enhanced connectivity for the training group within one specific network comprised of nodes/regions supporting cognitive processes required by the training (working memory, interference resolution, inhibition, and task engagement). Significant group differences were also observed for strength and global efficiency indices in the sub-network detected by NBS. Therefore, the connectome approach is a valuable method for tracking the effects of cognitive training interventions across specific sub-networks. Moreover, this approach allowsfor the computation of graph theoretical network metricstoquantifythetopological architecture of the brain networkdetected. The observed structural brain changes support the behavioral results reported earlier (see Colom, Román, et al., 2013

  13. Construction of Individual Morphological Brain Networks with Multiple Morphometric Features

    Directory of Open Access Journals (Sweden)

    Chunlan Yang

    2017-04-01

    Full Text Available In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28 each scanned twice, and reproducibility was evaluated through test–retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20–40%, and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78 indicates

  14. The brain's router: a cortical network model of serial processing in the primate brain

    NARCIS (Netherlands)

    Zylberberg, Ariel; Fernández Slezak, Diego; Roelfsema, Pieter R.; Dehaene, Stanislas; Sigman, Mariano

    2010-01-01

    The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different

  15. Evidence for Functional Networks within the Human Brain's White Matter.

    Science.gov (United States)

    Peer, Michael; Nitzan, Mor; Bick, Atira S; Levin, Netta; Arzy, Shahar

    2017-07-05

    brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders. Copyright © 2017 the authors 0270-6474/17/376394-14$15.00/0.

  16. A new measure of centrality for brain networks.

    Directory of Open Access Journals (Sweden)

    Karen E Joyce

    2010-08-01

    Full Text Available Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood. Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network.

  17. A new measure of centrality for brain networks.

    Science.gov (United States)

    Joyce, Karen E; Laurienti, Paul J; Burdette, Jonathan H; Hayasaka, Satoru

    2010-08-16

    Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network.

  18. Plasticity of resting state brain networks in recovery from stress

    Science.gov (United States)

    Soares, José M.; Sampaio, Adriana; Marques, Paulo; Ferreira, Luís M.; Santos, Nadine C.; Marques, Fernanda; Palha, Joana A.; Cerqueira, João J.; Sousa, Nuno

    2013-01-01

    Chronic stress has been widely reported to have deleterious impact in multiple biological systems. Specifically, structural and functional remodeling of several brain regions following prolonged stress exposure have been described; importantly, some of these changes are eventually reversible. Recently, we showed the impact of stress on resting state networks (RSNs), but nothing is known about the plasticity of RSNs after recovery from stress. Herein, we examined the “plasticity” of RSNs, both at functional and structural levels, by comparing the same individuals before and after recovery from the exposure to chronic stress; results were also contrasted with a control group. Here we show that the stressed individuals after recovery displayed a decreased resting functional connectivity in the default mode network (DMN), ventral attention network (VAN), and sensorimotor network (SMN) when compared to themselves immediately after stress; however, this functional plastic recovery was only partial as when compared with the control group, as there were still areas of increased connectivity in dorsal attention network (DAN), SMN and primary visual network (VN) in participants recovered from stress. Data also shows that participants after recovery from stress displayed increased deactivations in DMN, SMN, and auditory network (AN), to levels similar to those of controls, showing a normalization of the deactivation pattern in RSNs after recovery from stress. In contrast, structural changes (volumetry) of the brain areas involving these networks are absent after the recovery period. These results reveal plastic phenomena in specific RSNs and a functional remodeling of the activation-deactivation pattern following recovery from chronic-stress, which is not accompanied by significant structural plasticity. PMID:24416009

  19. Energy landscapes of resting-state brain networks

    Directory of Open Access Journals (Sweden)

    Takamitsu eWatanabe

    2014-02-01

    Full Text Available During rest, the human brain performs essential functions such as memory maintenance, which are associated with resting-state brain networks (RSNs including the default-mode network (DMN and frontoparietal network (FPN. Previous studies based on spiking-neuron network models and their reduced models, as well as those based on imaging data, suggest that resting-state network activity can be captured as attractor dynamics, i.e., dynamics of the brain state toward an attractive state and transitions between different attractors. Here, we analyze the energy landscapes of the RSNs by applying the maximum entropy model, or equivalently the Ising spin model, to human RSN data. We use the previously estimated parameter values to define the energy landscape, and the disconnectivity graph method to estimate the number of local energy minima (equivalent to attractors in attractor dynamics, the basin size, and hierarchical relationships among the different local minima. In both of the DMN and FPN, low-energy local minima tended to have large basins. A majority of the network states belonged to a basin of one of a few local minima. Therefore, a small number of local minima constituted the backbone of each RSN. In the DMN, the energy landscape consisted of two groups of low-energy local minima that are separated by a relatively high energy barrier. Within each group, the activity patterns of the local minima were similar, and different minima were connected by relatively low energy barriers. In the FPN, all dominant energy were separated by relatively low energy barriers such that they formed a single coarse-grained global minimum. Our results indicate that multistable attractor dynamics may underlie the DMN, but not the FPN, and assist memory maintenance with different memory states.

  20. Susceptibility-weighted imaging of the venous networks around the brain stem

    Energy Technology Data Exchange (ETDEWEB)

    Cai, Ming; Lin, Zhong-Xiao; Zhang, Nu [Wenzhou Medical University, Department of Neurosurgery, The 2nd Affiliated Hospital of Wenzhou Medical University, Wenzhou (China); Zhang, Xiao-Fen; Qiao, Hui-Huang; Chen, Cheng-Chun [Wenzhou Medical University, Department of Human Anatomy, Wenzhou (China); Ren, Chuan-Gen; Li, Jian-Ce [Wenzhou Medical University, Department of Radiology, The 1nd Affiliated Hospital of Wenzhou Medical University, Wenzhou (China)

    2014-10-18

    The venous network of the brainstem is complex and significant. Susceptibility-weighted imaging (SWI) is a practical technique which is sensitive to veins, especially tiny veins. Our purpose of this study was to evaluate the visualization of the venous network of brainstem by using SWI at 3.0 T. The occurrence rate of each superficial veins of brainstem was evaluated by using SWI on a 3 T MR imaging system in 60 volunteers. The diameter of the lateral mesencephalic vein and peduncular vein were measured by SWI using the reconstructed mIP images in the sagittal view. And the outflow of the veins of brainstem were studied and described according to the reconstructed images. The median anterior pontomesencephalic vein, median anterior medullary vein, peduncular vein, right vein of the pontomesencephalic sulcus, and right lateral anterior pontomesencephalic vein were detected in all the subjects (100 %). The outer diameter of peduncular vein was 1.38 ± 0.26 mm (range 0.8-1.8 mm). The lateral mesencephalic vein was found in 75 % of the subjects and the mean outer diameter was 0.81 ± 0.2 mm (range 0.5-1.2 mm). The inner veins of mesencephalon were found by using SWI. The venous networks around the brain stem can be visualized by SWI clearly. This result can not only provide data for anatomical study, but also may be available for the surgical planning in the infratentorial region. (orig.)

  1. Metabolic resting-state brain networks in health and disease.

    Science.gov (United States)

    Spetsieris, Phoebe G; Ko, Ji Hyun; Tang, Chris C; Nazem, Amir; Sako, Wataru; Peng, Shichun; Ma, Yilong; Dhawan, Vijay; Eidelberg, David

    2015-02-24

    The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.

  2. Brain extracellular matrix retains connectivity in neuronal networks

    Science.gov (United States)

    Bikbaev, Arthur; Frischknecht, Renato; Heine, Martin

    2015-01-01

    The formation and maintenance of connectivity are critically important for the processing and storage of information in neuronal networks. The brain extracellular matrix (ECM) appears during postnatal development and surrounds most neurons in the adult mammalian brain. Importantly, the removal of the ECM was shown to improve plasticity and post-traumatic recovery in the CNS, but little is known about the mechanisms. Here, we investigated the role of the ECM in the regulation of the network activity in dissociated hippocampal cultures grown on microelectrode arrays (MEAs). We found that enzymatic removal of the ECM in mature cultures led to transient enhancement of neuronal activity, but prevented disinhibition-induced hyperexcitability that was evident in age-matched control cultures with intact ECM. Furthermore, the ECM degradation followed by disinhibition strongly affected the network interaction so that it strongly resembled the juvenile pattern seen in naïve developing cultures. Taken together, our results demonstrate that the ECM plays an important role in retention of existing connectivity in mature neuronal networks that can be exerted through synaptic confinement of glutamate. On the other hand, removal of the ECM can play a permissive role in modification of connectivity and adaptive exploration of novel network architecture. PMID:26417723

  3. Modafinil enhances alerting-related brain activity in attention networks.

    Science.gov (United States)

    Ikeda, Yumiko; Funayama, Takuya; Tateno, Amane; Fukayama, Haruhisa; Okubo, Yoshiro; Suzuki, Hidenori

    2017-07-01

    Modafinil is a wake-promoting agent and has been reported to be effective in improving attention in patients with attentional disturbance. However, neural substrates underlying the modafinil effects on attention are not fully understood. We employed a functional magnetic resonance imaging (fMRI) study with the attention network test (ANT) task in healthy adults and examined which networks of attention are mainly affected by modafinil and which neural substrates are responsible for the drug effects. We used a randomized placebo-controlled within-subjects cross-over design. Twenty-three healthy adults participated in two series of an fMRI study, taking either a placebo or modafinil. The participants performed the ANT task, which is designed to measure three distinct attentional networks, alerting, orienting, and executive control, during the fMRI scanning. The effects of modafinil on behavioral performance and regional brain activity were analyzed. We found that modafinil enhanced alerting performance and showed greater alerting network activity in the left middle and inferior occipital gyri as compared with the placebo. The brain activations in the occipital regions were positively correlated with alerting performance. Modafinil enhanced alerting performance and increased activation in the occipital lobe in the alerting network possibly relevant to noradrenergic activity during the ANT task. The present study may provide a rationale for the treatment of patients with distinct symptoms of impaired attention.

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

    Science.gov (United States)

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

    2014-10-01

    Visual discrimination tasks have been widely used to evaluate many types of learning and memory processes. However, little is known about the brain regions involved at different stages of visual discrimination learning. We used cytochrome c oxidase histochemistry to evaluate changes in regional brain oxidative metabolism during visual discrimination learning in a water-T maze at different time points during training. As compared with control groups, the results of the present study reveal the gradual activation of cortical (prefrontal and temporal cortices) and subcortical brain regions (including the striatum and the hippocampus) associated to the mastery of a simple visual discrimination task. On the other hand, the brain regions involved and their functional interactions changed progressively over days of training. Regions associated with novelty, emotion, visuo-spatial orientation and motor aspects of the behavioral task seem to be relevant during the earlier phase of training, whereas a brain network comprising the prefrontal cortex was found along the whole learning process. This study highlights the relevance of functional interactions among brain regions to investigate learning and memory processes. Copyright © 2014 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    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…

  6. Mapping Language Networks Using the Structural and Dynamic Brain Connectomes.

    Science.gov (United States)

    Del Gaizo, John; Fridriksson, Julius; Yourganov, Grigori; Hillis, Argye E; Hickok, Gregory; Misic, Bratislav; Rorden, Chris; Bonilha, Leonardo

    2017-01-01

    Lesion-symptom mapping is often employed to define brain structures that are crucial for human behavior. Even though poststroke deficits result from gray matter damage as well as secondary white matter loss, the impact of structural disconnection is overlooked by conventional lesion-symptom mapping because it does not measure loss of connectivity beyond the stroke lesion. This study describes how traditional lesion mapping can be combined with structural connectome lesion symptom mapping (CLSM) and connectome dynamics lesion symptom mapping (CDLSM) to relate residual white matter networks to behavior. Using data from a large cohort of stroke survivors with aphasia, we observed improved prediction of aphasia severity when traditional lesion symptom mapping was combined with CLSM and CDLSM. Moreover, only CLSM and CDLSM disclosed the importance of temporal-parietal junction connections in aphasia severity. In summary, connectome measures can uniquely reveal brain networks that are necessary for function, improving the traditional lesion symptom mapping approach.

  7. Risk and resilience brain networks in treatment-resistant schizophrenia.

    Science.gov (United States)

    Ganella, Eleni P; Seguin, Caio; Bartholomeusz, Cali F; Whittle, Sarah; Bousman, Chad; Wannan, Cassandra M J; Di Biase, Maria A; Phassouliotis, Christina; Everall, Ian; Pantelis, Christos; Zalesky, Andrew

    2017-07-20

    Genes, molecules and neural circuits that are associated with, or confer risk to developing schizophrenia have been studied and mapped. It is hypothesized that certain neural systems may counterbalance familial risk of schizophrenia, and thus confer resilience to developing the disorder. This study sought to identify resting-state functional brain connectivity (rs-FC) representing putative risk or resilience endophenotypes in schizophrenia. Resting-state functional magnetic resonance imaging (rs-fMRI) was performed in 42 individuals with treatment resistant schizophrenia (TRS), 16 unaffected first-degree family members (UFM) and 42 healthy controls. Whole-brain rs-FC networks were mapped for each individual and analysed graph theoretically to identify network markers associated with schizophrenia risk or resilience. The ~900 functional connections showing between-group differences were operationalized as conferring: i) resilience, ii) risk, or iii) precipitating risk and/or illness effects. Approximately 95% of connections belonged to the latter two categories, with substantially fewer connections associated with resilience. Schizophrenia risk primarily involved reduced frontal and occipital rs-FC, with patients showing additional reduced frontal and temporal rs-FC. Functional brain networks were characterized by greater local efficiency in UFM, compared to TRS and controls. TRS and UFM share frontal and occipital rs-FC deficits, representing a 'risk' endophenotype. Additional reductions in frontal and temporal rs-FC appear to be associated with risk that precipitates psychosis in vulnerable individuals, or may be due to other illness-related effects, such as medication. Functional brain networks are more topologically resilient in UFM compared to TRS, which may protect UFM from psychosis onset despite familial liability. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Volumetric multimodality neural network for brain tumor segmentation

    Science.gov (United States)

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

    2017-11-01

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

  9. Insulin sensitivity predicts brain network connectivity following a meal.

    Science.gov (United States)

    Ryan, John P; Karim, Helmet T; Aizenstein, Howard J; Helbling, Nicole L; Toledo, Frederico G S

    2018-01-12

    There is converging evidence that insulin plays a role in food-reward signaling in the brain and has effects on enhancing cognition. Little is known about how these effects are altered in individuals with insulin resistance. The present study was designed to identify the relationships between insulin resistance and functional brain connectivity following a meal. Eighteen healthy adults (7 male, 11 female, age: 41-57 years-old) completed a frequently-sampled intravenous glucose tolerance test to quantify insulin resistance. On separate days at least one week apart, a resting state functional magnetic resonance imaging scan was performed: once after a mixed-meal and once after a 12-h fast. Seed-based resting state connectivity of the caudate nucleus and eigenvector centrality were used to identify relationships between insulin resistance and functional brain connectivity. Individuals with greater insulin resistance displayed stronger connectivity within reward networks following a meal suggesting insulin was less able to suppress reward. Insulin resistance was negatively associated with eigenvector centrality in the dorsal anterior cingulate cortex following a meal. These data suggest that individuals with less sensitivity to insulin may fail to shift brain networks away from reward and toward cognitive control following a meal. This altered feedback loop could promote overeating and obesity. Copyright © 2018. Published by Elsevier Inc.

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

    Science.gov (United States)

    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. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. Spatial dependencies between large-scale brain networks.

    Directory of Open Access Journals (Sweden)

    Robert Leech

    Full Text Available Functional neuroimaging reveals both increases (task-positive and decreases (task-negative in neural activation with many tasks. Many studies show a temporal relationship between task positive and task negative networks that is important for efficient cognitive functioning. Here we provide evidence for a spatial relationship between task positive and negative networks. There are strong spatial similarities between many reported task negative brain networks, termed the default mode network, which is typically assumed to be a spatially fixed network. However, this is not the case. The spatial structure of the DMN varies depending on what specific task is being performed. We test whether there is a fundamental spatial relationship between task positive and negative networks. Specifically, we hypothesize that the distance between task positive and negative voxels is consistent despite different spatial patterns of activation and deactivation evoked by different cognitive tasks. We show significantly reduced variability in the distance between within-condition task positive and task negative voxels than across-condition distances for four different sensory, motor and cognitive tasks--implying that deactivation patterns are spatially dependent on activation patterns (and vice versa, and that both are modulated by specific task demands. We also show a similar relationship between positively and negatively correlated networks from a third 'rest' dataset, in the absence of a specific task. We propose that this spatial relationship may be the macroscopic analogue of microscopic neuronal organization reported in sensory cortical systems, and that this organization may reflect homeostatic plasticity necessary for efficient brain function.

  12. Transdiagnostic Associations Between Functional Brain Network Integrity and Cognition.

    Science.gov (United States)

    Sheffield, Julia M; Kandala, Sridhar; Tamminga, Carol A; Pearlson, Godfrey D; Keshavan, Matcheri S; Sweeney, John A; Clementz, Brett A; Lerman-Sinkoff, Dov B; Hill, S Kristian; Barch, Deanna M

    2017-06-01

    Cognitive impairment occurs across the psychosis spectrum and is associated with functional outcome. However, it is unknown whether these shared manifestations of cognitive dysfunction across diagnostic categories also reflect shared neurobiological mechanisms or whether the source of impairment differs. To examine whether the general cognitive deficit observed across psychotic disorders is similarly associated with functional integrity of 2 brain networks widely implicated in supporting many cognitive domains. A total of 201 healthy control participants and 375 patients with psychotic disorders from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium were studied from September 29, 2007, to May 31, 2011. The B-SNIP recruited healthy controls and stable outpatients from 6 sites: Baltimore, Maryland; Boston, Massachusetts; Chicago, Illinois; Dallas, Texas; Detroit, Michigan; and Hartford, Connecticut. All participants underwent cognitive testing and resting-state functional magnetic resonance imaging. Data analysis was performed from April 28, 2015, to February 21, 2017. The Brief Assessment of Cognition in Schizophrenia was used to measure cognitive ability. A principal axis factor analysis on the Brief Assessment of Cognition in Schizophrenia battery yielded a single factor (54% variance explained) that served as the measure of general cognitive ability. Functional network integrity measures included global and local efficiency of the whole brain, cingulo-opercular network (CON), frontoparietal network, and auditory network and exploratory analyses of all networks from the Power atlas. Group differences in network measures, associations between cognition and network measures, and mediation models were tested. The final sample for the current study included 201 healthy controls, 143 patients with schizophrenia, 103 patients with schizoaffective disorder, and 129 patients with psychotic bipolar disorder (mean [SD] age, 35.1 [12.0] years

  13. Hyperthermia-induced disruption of functional connectivity in the human brain network.

    Directory of Open Access Journals (Sweden)

    Gang Sun

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

  14. Morphological brain network assessed using graph theory and network filtration in deaf adults.

    Science.gov (United States)

    Kim, Eunkyung; Kang, Hyejin; Lee, Hyekyoung; Lee, Hyo-Jeong; Suh, Myung-Whan; Song, Jae-Jin; Oh, Seung-Ha; Lee, Dong Soo

    2014-09-01

    Prolonged deprivation of auditory input can change brain networks in pre- and postlingual deaf adults by brain-wide reorganization. To investigate morphological changes in these brains voxel-based morphometry, voxel-wise correlation with the primary auditory cortex, and whole brain network analyses using morphological covariance were performed in eight prelingual deaf, eleven postlingual deaf, and eleven hearing adults. Network characteristics based on graph theory and network filtration based on persistent homology were examined. Gray matter density in the primary auditor cortex was preserved in prelingual deafness, while it tended to decrease in postlingual deafness. Unlike postlingual, prelingual deafness showed increased bilateral temporal connectivity of the primary auditory cortex compared to the hearing adults. Of the graph theory-based characteristics, clustering coefficient, betweenness centrality, and nodal efficiency all increased in prelingual deafness, while all the parameters of postlingual deafness were similar to the hearing adults. Patterns of connected components changing during network filtration were different between prelingual deafness and hearing adults according to the barcode, dendrogram, and single linkage matrix representations, while these were the same in postlingual deafness. Nodes in fronto-limbic and left temporal components were closely coupled, and nodes in the temporo-parietal component were loosely coupled, in prelingual deafness. Patterns of connected components changing in postlingual deafness were the same as hearing adults. We propose that the preserved density of auditory cortex associated with increased connectivity in prelingual deafness, and closer coupling between certain brain areas, represent distinctive reorganization of auditory and related cortices compared with hearing or postlingual deaf adults. The differential network reorganization in the prelingual deaf adults could be related to the absence of auditory speech

  15. Social Rewards and Social Networks in the Human Brain.

    Science.gov (United States)

    Fareri, Dominic S; Delgado, Mauricio R

    2014-08-01

    The rapid development of social media and social networking sites in human society within the past decade has brought about an increased focus on the value of social relationships and being connected with others. Research suggests that we pursue socially valued or rewarding outcomes-approval, acceptance, reciprocity-as a means toward learning about others and fulfilling social needs of forming meaningful relationships. Focusing largely on recent advances in the human neuroimaging literature, we review findings highlighting the neural circuitry and processes that underlie pursuit of valued rewarding outcomes across non-social and social domains. We additionally discuss emerging human neuroimaging evidence supporting the idea that social rewards provide a gateway to establishing relationships and forming social networks. Characterizing the link between social network, brain, and behavior can potentially identify contributing factors to maladaptive influences on decision making within social situations. © The Author(s) 2014.

  16. Hierarchical Topological Network Analysis of Anatomical Human Brain Connectivity and Differences Related to Sex and Kinship

    Science.gov (United States)

    2011-10-01

    the1014 contribution of those walks to the communicability matrix are significantly1015 reduced by the factorial of the walk length on Equation (15...classification using support vector machines. Mach. Learn. 46 (1-1436 3), 389–422.1437 Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey , C. J., Wedeen,1438

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

    Science.gov (United States)

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

    2017-12-01

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

  18. Computed tomography of the dog's brain: normal aspects and anatomical correlation; Tomografia computadorizada do encefalo do cao: aspectos da normalidade e correlacao anatomica

    Energy Technology Data Exchange (ETDEWEB)

    Lorigados, C.A.B., E-mail: clorigados@usp.br [Faculdades Metropolitanas Unidas (UniFMU), Sao Paulo, SP (Brazil); Pinto, A.C.B.F. [Universidade de Sao Paulo (USP), SP (Brazil). Faculdade de Medicina Veterinaria e Zootecnia

    2013-06-15

    Normal tomographic images of dog's heads were obtained, aimed to familiarize them with the normal aspects of the brain and correlate these findings with the relevant anatomy of the region studied. Several anatomical structures, such as the parenchyma of the frontal, parietal, temporal and occipital lobes, the longitudinal fissure, the ventricular system, the cerebellum, the olfactory bulb, the corpus callosum, diencephalon, the pons, the medulla oblongata and the chiasmatic sulcus were directly identified or were related to neighboring structures which helped in their identification. (author)

  19. Mentor's brain functional connectivity network during robotic assisted surgery mentorship.

    Science.gov (United States)

    Shafiei, Somayeh B; Doyle, Scott T; Guru, Khurshid A

    2016-08-01

    In many complicated cognitive-motor tasks mentoring is inevitable during the learning process. Although mentors are expert in doing the task, trainee's operation might be new for a mentor. This makes mentoring a very difficult task which demands not only the knowledge and experience of a mentor, but also his/her ability to follow trainee's movements and patiently advise him/her during the operation. We hypothesize that information binding throughout the mentor's brain areas, contributed to the task, changes as the expertise level of the trainee improves from novice to intermediate and expert. This can result in the change of mentor's level of satisfaction. The brain functional connectivity network is extracted by using brain activity of a mentor during mentoring novice and intermediate surgeons, watching expert surgeon operation, and doing Urethrovesical Anasthomosis (UVA) procedure by himself. By using the extracted network, we investigate the role of modularity and neural activity efficiency in mentoring. Brain activity is measured by using a 24-channel ABM Neuro-headset with the frequency of 256 Hz. One mentor operates 26 UVA procedures and three trainees with the expertise level of novice, intermediate, and expert perform 26 UVA procedures under the supervision of mentor. Our results indicate that the modularity of functional connectivity network is higher when mentor performs the task or watches the expert operation comparing mentoring the novice and intermediate surgeons. At the end of each operation, mentor subjectively assesses the quality of operation by giving scores to NASA-TLX indexes. Performance score is used to discuss our results. The extracted significant positive correlation between performance level and modularity (r = 0.38, p - value <; 0.005) shows the increase of automaticity and decrease in neural activity cost by improving the performance.

  20. Cross-Frequency Coupling in Real and Virtual Brain Networks

    Directory of Open Access Journals (Sweden)

    Viktor eJirsa

    2013-07-01

    Full Text Available Information processing in the brain is thought to rely on the convergence and divergence of oscillatory behaviors of widely distributed brain areas. This information flow is captured in its simplest form via the concepts of synchronization and desynchronization and related metrics. More complex forms of information flow are transient synchronizations and multi-frequency behaviors with metrics related to cross-frequency coupling (CFC. It is supposed that CFC plays a crucial role in the organization of large-scale networks and functional integration across large distances. In this study we describe different CFC measures and test their applicability in simulated and real electroencephalographic (EEG data obtained during resting state. For these purposes, we derive generic oscillator equations from full brain network models. We systematically model and simulate the various scenarios of cross-frequency coupling under the influence of noise to obtain biologically realistic oscillator dynamics. We find that (i specific CFC-measures detect correctly in most cases the nature of CFC under noise conditions, (ii bispectrum and bicoherence correctly detect the CFCs in simulated data, (iii empirical resting state EEG show a prominent delta-alpha CFC as identified by specific CFC measures and the more classic bispectrum and bicoherence. This coupling was mostly asymmetric (directed and generally higher in the eyes-closed than in the eyes-open condition. In conjunction, these two sets of measures provide a powerful toolbox to reveal the nature of couplings from experimental data and as such allow inference on the brain state dependent information processing. Methodological advantages of using CFC measures and theoretical significance of delta and alpha interactions during resting and other brain states are discussed.

  1. Cross-frequency coupling in real and virtual brain networks

    Science.gov (United States)

    Jirsa, Viktor; Müller, Viktor

    2013-01-01

    Information processing in the brain is thought to rely on the convergence and divergence of oscillatory behaviors of widely distributed brain areas. This information flow is captured in its simplest form via the concepts of synchronization and desynchronization and related metrics. More complex forms of information flow are transient synchronizations and multi-frequency behaviors with metrics related to cross-frequency coupling (CFC). It is supposed that CFC plays a crucial role in the organization of large-scale networks and functional integration across large distances. In this study, we describe different CFC measures and test their applicability in simulated and real electroencephalographic (EEG) data obtained during resting state. For these purposes, we derive generic oscillator equations from full brain network models. We systematically model and simulate the various scenarios of CFC under the influence of noise to obtain biologically realistic oscillator dynamics. We find that (i) specific CFC-measures detect correctly in most cases the nature of CFC under noise conditions, (ii) bispectrum (BIS) and bicoherence (BIC) correctly detect the CFCs in simulated data, (iii) empirical resting state EEG show a prominent delta-alpha CFC as identified by specific CFC measures and the more classic BIS and BIC. This coupling was mostly asymmetric (directed) and generally higher in the eyes closed (EC) than in the eyes open (EO) condition. In conjunction, these two sets of measures provide a powerful toolbox to reveal the nature of couplings from experimental data and as such allow inference on the brain state dependent information processing. Methodological advantages of using CFC measures and theoretical significance of delta and alpha interactions during resting and other brain states are discussed. PMID:23840188

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

    Science.gov (United States)

    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. Association between structural brain network efficiency and intelligence increases during adolescence

    NARCIS (Netherlands)

    Koenis, Marinka M G; Brouwer, Rachel M; Swagerman, Suzanne C; van Soelen, Inge L C; Boomsma, Dorret I; Hulshoff Pol, Hilleke E

    2017-01-01

    Adolescence represents an important period during which considerable changes in the brain take place, including increases in integrity of white matter bundles, and increasing efficiency of the structural brain network. A more efficient structural brain network has been associated with higher

  4. Dynamic range in the C. elegans brain network

    Science.gov (United States)

    Antonopoulos, Chris G.

    2016-01-01

    We study external electrical perturbations and their responses in the brain dynamic network of the Caenorhabditis elegans soil worm, given by the connectome of its large somatic nervous system. Our analysis is inspired by a realistic experiment where one stimulates externally specific parts of the brain and studies the persistent neural activity triggered in other cortical regions. In this work, we perturb groups of neurons that form communities, identified by the walktrap community detection method, by trains of stereotypical electrical Poissonian impulses and study the propagation of neural activity to other communities by measuring the corresponding dynamic ranges and Steven law exponents. We show that when one perturbs specific communities, keeping the rest unperturbed, the external stimulations are able to propagate to some of them but not to all. There are also perturbations that do not trigger any response. We found that this depends on the initially perturbed community. Finally, we relate our findings for the former cases with low neural synchronization, self-criticality, and large information flow capacity, and interpret them as the ability of the brain network to respond to external perturbations when it works at criticality and its information flow capacity becomes maximal.

  5. Altered brain network centrality in depressed Parkinson's disease patients.

    Science.gov (United States)

    Lou, Yuting; Huang, Peiyu; Li, Dan; Cen, Zhidong; Wang, Bo; Gao, Jixiang; Xuan, Min; Yu, Hualiang; Zhang, Minming; Luo, Wei

    2015-11-01

    Depression is a relatively common and serious nonmotor symptom of Parkinson's disease (PD), which reduces the quality of patients' life. Although disturbances in some related brain networks have been reported, the pathophysiology of depression in PD is still unclear. Here, we aim to investigate whole-brain functional connectivity patterns in depressed PD patients. We recruited 17 PD patients diagnosed with major depressive disorder, 17 PD patients without depression, and 17 healthy control subjects. Resting-state functional MRI and eigenvector centrality mapping were used to identify functional connectivity alterations among these groups. Results showed that depressed PD patients had decreased functional connectivity in the left dorsolateral prefrontal cortex and right superior temporal gyrus and increased functional connectivity in the right posterior cingulate cortex, compared to nondepressed patients. In addition, there was a significant negative correlation between functional connectivity and depression scores in the posterior cingulate cortex. This study suggests that functional connectivity changes in certain nodes of brain networks might contribute to depression in patients with PD. © 2015 International Parkinson and Movement Disorder Society.

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

    Science.gov (United States)

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

    2017-05-15

    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. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. A brain network processing the age of faces.

    Directory of Open Access Journals (Sweden)

    György A Homola

    Full Text Available Age is one of the most salient aspects in faces and of fundamental cognitive and social relevance. Although face processing has been studied extensively, brain regions responsive to age have yet to be localized. Using evocative face morphs and fMRI, we segregate two areas extending beyond the previously established face-sensitive core network, centered on the inferior temporal sulci and angular gyri bilaterally, both of which process changes of facial age. By means of probabilistic tractography, we compare their patterns of functional activation and structural connectivity. The ventral portion of Wernicke's understudied perpendicular association fasciculus is shown to interconnect the two areas, and activation within these clusters is related to the probability of fiber connectivity between them. In addition, post-hoc age-rating competence is found to be associated with high response magnitudes in the left angular gyrus. Our results provide the first evidence that facial age has a distinct representation pattern in the posterior human brain. We propose that particular face-sensitive nodes interact with additional object-unselective quantification modules to obtain individual estimates of facial age. This brain network processing the age of faces differs from the cortical areas that have previously been linked to less developmental but instantly changeable face aspects. Our probabilistic method of associating activations with connectivity patterns reveals an exemplary link that can be used to further study, assess and quantify structure-function relationships.

  8. A Multiscale Model for the Brain Vascular Network

    Science.gov (United States)

    Grinberg, Leopold; Karniadakis, George

    2007-11-01

    Simulations of blood flow in arterial networks requires physiologicaly correct boundary condition at inlets and outlets. Outflow boundary conditions for the Macrovascular Network (MaN) can be imposed by solving a closure problem based on modeling the rest of the flow in ten millions arterioles (Mesovascular Network, MeN) and one billion capillaries (Microvascular Network, MiN). Numerical solution of the three-level MaN-MeN-MiN integration can be performed on the future generation of petaflop supercomputers. An alternative approach for the MaN simulation is to impose the clinically measured flow rates at outlets. We have developed a new method to incorporate such measurements at multiple outlets, it is based on imposing Neumann boundary condition for the velocity and time-dependent resistance boundary conditions for the pressure. The convergence of numerical solution for the outlet flow-rates is achieved immediately. The computational complexity of the method is comparable to the widely used constant pressure boundary condition. Our approach is verified on a model of Brain Vascular Network with tens of arterial segments and outlets.

  9. Review of Evidence Suggesting That the Fascia Network Could Be the Anatomical Basis for Acupoints and Meridians in the Human Body

    Directory of Open Access Journals (Sweden)

    Yu Bai

    2011-01-01

    Full Text Available The anatomical basis for the concept of meridians in traditional Chinese medicine (TCM has not been resolved. This paper reviews the evidence supporting a relationship between acupuncture points/meridians and fascia. The reviewed evidence supports the view that the human body's fascia network may be the physical substrate represented by the meridians of TCM. Specifically, this hypothesis is supported by anatomical observations of body scan data demonstrating that the fascia network resembles the theoretical meridian system in salient ways, as well as physiological, histological, and clinical observations. This view represents a theoretical basis and means for applying modern biomedical research to examining TCM principles and therapies, and it favors a holistic approach to diagnosis and treatment.

  10. The minimum spanning tree : An unbiased method for brain network analysis

    NARCIS (Netherlands)

    Tewarie, P.; van Dellen, E.; Hillebrand, A.; Stam, C. J.

    2015-01-01

    The brain is increasingly studied with graph theoretical approaches, which can be used to characterize network topology. However, studies on brain networks have reported contradictory findings, and do not easily converge to a clear concept of the structural and functional network organization of the

  11. The minimum spanning tree: An unbiased method for brain network analysis

    NARCIS (Netherlands)

    Tewarie, P.; van Dellen, E.; Hillebrand, A.; Stam, C.J.

    2015-01-01

    The brain is increasingly studied with graph theoretical approaches, which can be used to characterize network topology. However, studies on brain networks have reported contradictory findings, and do not easily converge to a clear concept of the structural and functional network organization of the

  12. Quantifying Uncertainty in Brain Network Measures using Bayesian Connectomics

    Directory of Open Access Journals (Sweden)

    Ronald Johannes Janssen

    2014-10-01

    Full Text Available The wiring diagram of the human brain can be described in terms of graph measures that characterize structural regularities. These measures require an estimate of whole-brain structural connectivity for which one may resort to deterministic or thresholded probabilistic streamlining procedures. While these procedures have provided important insights about the characteristics of human brain networks, they ultimately rely on unwarranted assumptions such as those of noise-free data or the use of an arbitrary threshold. Therefore, resulting structural connectivity estimates as well as derived graph measures fail to fully take into account the inherent uncertainty in the structural estimate.In this paper, we illustrate an easy way of obtaining posterior distributions over graph metrics using Bayesian inference. It is shown that this posterior distribution can be used to quantify uncertainty about graph-theoretical measures at the single subject level, thereby providing a more nuanced view of the graph-theoretical properties of human brain connectivity. We refer to this model-based approach to connectivity analysis as Bayesian connectomics.

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

    Science.gov (United States)

    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. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. A Novel Human Body Area Network for Brain Diseases Analysis.

    Science.gov (United States)

    Lin, Kai; Xu, Tianlang

    2016-10-01

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

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

    Science.gov (United States)

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

    2017-10-01

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

  16. The human functional brain network demonstrates structural and dynamical resilience to targeted attack.

    Science.gov (United States)

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

    2013-01-01

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

  17. The human functional brain network demonstrates structural and dynamical resilience to targeted attack.

    Directory of Open Access Journals (Sweden)

    Karen E Joyce

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

  18. Impaired topological architecture of brain structural networks in idiopathic Parkinson's disease: a DTI study.

    Science.gov (United States)

    Li, Changhong; Huang, Biao; Zhang, Ruibin; Ma, Qing; Yang, Wanqun; Wang, Lijuan; Wang, Limin; Xu, Qin; Feng, Jieying; Liu, Liqing; Zhang, Yuhu; Huang, Ruiwang

    2017-02-01

    Parkinson's disease (PD) is considered as a neurodegenerative disorder of the brain central nervous system. But, to date, few studies adopted the network model to reveal topological changes in brain structural networks in PD patients. Additionally, although the concept of rich club organization has been widely used to study brain networks in various brain disorders, there is no study to report the changed rich club organization of brain networks in PD patients. Thus, we collected diffusion tensor imaging (DTI) data from 35 PD patients and 26 healthy controls and adopted deterministic tractography to construct brain structural networks. During the network analysis, we calculated their topological properties, and built the rich club organization of brain structural networks for both subject groups. By comparing the between-group differences in topological properties and rich club organizations, we found that the connectivity strength of the feeder and local connections are lower in PD patients compared to those of the healthy controls. Furthermore, using a network-based statistic (NBS) approach, we identified uniformly significantly decreased connections in two modules, the limbic/paralimbic/subcortical module and the cognitive control/attention module, in patients compared to controls. In addition, for the topological properties of brain network topology in the PD patients, we found statistically increased shortest path length and decreased global efficiency. Statistical comparisons of nodal properties were also widespread in the frontal and parietal regions for the PD patients. These findings may provide useful information to better understand the abnormalities of brain structural networks in PD patients.

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

    Directory of Open Access Journals (Sweden)

    Caterina Gratton

    2016-10-01

    Full Text Available 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.

  20. Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling.

    Science.gov (United States)

    Struzyna, Laura A; Adewole, Dayo O; Gordián-Vélez, Wisberty J; Grovola, Michael R; Burrell, Justin C; Katiyar, Kritika S; Petrov, Dmitriy; Harris, James P; Cullen, D Kacy

    2017-05-31

    Functional recovery rarely occurs following injury or disease-induced degeneration within the central nervous system (CNS) due to the inhibitory environment and the limited capacity for neurogenesis. We are developing a strategy to simultaneously address neuronal and axonal pathway loss within the damaged CNS. This manuscript presents the fabrication protocol for micro-tissue engineered neural networks (micro-TENNs), implantable constructs consisting of neurons and aligned axonal tracts spanning the extracellular matrix (ECM) lumen of a preformed hydrogel cylinder hundreds of microns in diameter that may extend centimeters in length. Neuronal aggregates are delimited to the extremes of the three-dimensional encasement and are spanned by axonal projections. Micro-TENNs are uniquely poised as a strategy for CNS reconstruction, emulating aspects of brain connectome cytoarchitecture and potentially providing means for network replacement. The neuronal aggregates may synapse with host tissue to form new functional relays to restore and/or modulate missing or damaged circuitry. These constructs may also act as pro-regenerative "living scaffolds" capable of exploiting developmental mechanisms for cell migration and axonal pathfinding, providing synergistic structural and soluble cues based on the state of regeneration. Micro-TENNs are fabricated by pouring liquid hydrogel into a cylindrical mold containing a longitudinally centered needle. Once the hydrogel has gelled, the needle is removed, leaving a hollow micro-column. An ECM solution is added to the lumen to provide an environment suitable for neuronal adhesion and axonal outgrowth. Dissociated neurons are mechanically aggregated for precise seeding within one or both ends of the micro-column. This methodology reliably produces self-contained miniature constructs with long-projecting axonal tracts that may recapitulate features of brain neuroanatomy. Synaptic immunolabeling and genetically encoded calcium

  1. 5-HTTLPR differentially predicts brain network responses to emotional faces

    DEFF Research Database (Denmark)

    Fisher, Patrick M; Grady, Cheryl L; Madsen, Martin K

    2015-01-01

    The effects of the 5-HTTLPR polymorphism on neural responses to emotionally salient faces have been studied extensively, focusing on amygdala reactivity and amygdala-prefrontal interactions. Despite compelling evidence that emotional face paradigms engage a distributed network of brain regions...... resonance imaging in 76 healthy adults. We observed robust increased response to emotional faces in the amygdala, hippocampus, caudate, fusiform gyrus, superior temporal sulcus and lateral prefrontal and occipito-parietal cortices. We observed dissociation between 5-HTTLPR groups such that LA LA individuals...

  2. Disrupted brain network topology in Parkinson's disease: a longitudinal magnetoencephalography study.

    NARCIS (Netherlands)

    Olde Dubbelink, K.T.E.; Hillebrand, A.; Stoffers, D.; Deijen, J.B.; Twisk, J.W.R.; Stam, C.J.; Berendse, H.W.

    2014-01-01

    Although alterations in resting-state functional connectivity between brain regions have previously been reported in Parkinson's disease, the spatial organization of these changes remains largely unknown. Here, we longitudinally studied brain network topology in Parkinson's disease in relation to

  3. The evolution of distributed association networks in the human brain.

    Science.gov (United States)

    Buckner, Randy L; Krienen, Fenna M

    2013-12-01

    The human cerebral cortex is vastly expanded relative to other primates and disproportionately occupied by distributed association regions. Here we offer a hypothesis about how association networks evolved their prominence and came to possess circuit properties vital to human cognition. The rapid expansion of the cortical mantle may have untethered large portions of the cortex from strong constraints of molecular gradients and early activity cascades that lead to sensory hierarchies. What fill the gaps between these hierarchies are densely interconnected networks that widely span the cortex and mature late into development. Limitations of the tethering hypothesis are discussed as well as its broad implications for understanding critical features of the human brain as a byproduct of size scaling. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    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. © 2015 John Wiley & Sons Ltd.

  5. Anatomical dysconnectivity in bipolar disorder compared with schizophrenia: A selective review of structural network analyses using diffusion MRI.

    Science.gov (United States)

    O'Donoghue, Stefani; Holleran, Laurena; Cannon, Dara M; McDonald, Colm

    2017-02-01

    The dysconnectivity hypothesis suggests that psychotic illnesses arise not from regionally specific focal pathophysiology, but rather from impaired neuroanatomical integration across networks of brain regions. Decreased white matter organization has been hypothesized to be a feature of psychotic illnesses in general, which is supported by meta-analyses of DTI studies in bipolar disorder and schizophrenia. Although many diffusion MRI studies investigate bipolar disorder and schizophrenia alone, relatively few studies directly compare structural features in these psychotic illnesses. Recently, the application of graph theory analyses to DTI data has supported the dysconnectivity hypothesis in bipolar disorder and schizophrenia, employing topological properties to assess neuroanatomical dysconnectivity. This selective review evaluates white matter alterations using Diffusion Tensor Imaging (DTI) in bipolar disorder and schizophrenia, with a focus upon direct comparison DTI studies in both psychotic illnesses. We then expand in more detail on the development of network analyses and the application of these techniques in bipolar disorder and schizophrenia. Converging evidence indicates that frontal connectivity alterations are common to both disorders, with prominent fronto-temporal deficits identified in schizophrenia and inter-hemispheric and limbic alterations reported in bipolar disorder. In bipolar disorder, most connectome reports use cortical maps alone, which given the importance of the limbic system in emotional regulation may limit the scope of network approaches in mood disorders. Further direct connectivity comparisons between these psychotic illnesses may assist in unravelling the neuroanatomical deviations underpinning the overlapping features of psychosis and cognitive impairment, and the more diagnostically distinctive features of affective disturbance in bipolar disorder and deficit syndrome in schizophrenia. Copyright © 2016 Elsevier B.V. All rights

  6. Quetiapine modulates functional connectivity in brain aggression networks.

    Science.gov (United States)

    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. Copyright © 2013 Elsevier Inc. All rights reserved.

  7. Intrinsic and Task-Evoked Network Architectures of the Human Brain

    OpenAIRE

    Cole, Michael W.; Bassett, Danielle S.; Power, Jonathan D.; Braver, Todd S.; Petersen, Steven E.

    2014-01-01

    Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an “intrinsic”, standard architecture of functional brain organiz...

  8. Functional brain networks and cognitive deficits in Parkinson's disease.

    Science.gov (United States)

    Baggio, Hugo-Cesar; Sala-Llonch, Roser; Segura, Bàrbara; Marti, Maria-José; Valldeoriola, Francesc; Compta, Yaroslau; Tolosa, Eduardo; Junqué, Carme

    2014-09-01

    Graph-theoretical analyses of functional networks obtained with resting-state functional magnetic resonance imaging (fMRI) have recently proven to be a useful approach for the study of the substrates underlying cognitive deficits in different diseases. We used this technique to investigate whether cognitive deficits in Parkinson's disease (PD) are associated with changes in global and local network measures. Thirty-six healthy controls (HC) and 66 PD patients matched for age, sex, and education were classified as having mild cognitive impairment (MCI) or not based on performance in the three mainly affected cognitive domains in PD: attention/executive, visuospatial/visuoperceptual (VS/VP), and declarative memory. Resting-state fMRI and graph theory analyses were used to evaluate network measures. We have found that patients with MCI had connectivity reductions predominantly affecting long-range connections as well as increased local interconnectedness manifested as higher measures of clustering, small-worldness, and modularity. The latter measures also tended to correlate negatively with cognitive performance in VS/VP and memory functions. Hub structure was also reorganized: normal hubs displayed reduced centrality and degree in MCI PD patients. Our study indicates that the topological properties of brain networks are changed in PD patients with cognitive deficits. Our findings provide novel data regarding the functional substrate of cognitive impairment in PD, which may prove to have value as a prognostic marker. Copyright © 2014 Wiley Periodicals, Inc.

  9. Morphological covariance in anatomical MRI scans can identify discrete neural pathways in the brain and their disturbances in persons with neuropsychiatric disorders.

    Science.gov (United States)

    Bansal, Ravi; Hao, Xuejun; Peterson, Bradley S

    2015-05-01

    We hypothesize that coordinated functional activity within discrete neural circuits induces morphological organization and plasticity within those circuits. Identifying regions of morphological covariation that are independent of morphological covariation in other regions therefore may therefore allow us to identify discrete neural systems within the brain. Comparing the magnitude of these variations in individuals who have psychiatric disorders with the magnitude of variations in healthy controls may allow us to identify aberrant neural pathways in psychiatric illnesses. We measured surface morphological features by applying nonlinear, high-dimensional warping algorithms to manually defined brain regions. We transferred those measures onto the surface of a unit sphere via conformal mapping and then used spherical wavelets and their scaling coefficients to simplify the data structure representing these surface morphological features of each brain region. We used principal component analysis (PCA) to calculate covariation in these morphological measures, as represented by their scaling coefficients, across several brain regions. We then assessed whether brain subregions that covaried in morphology, as identified by large eigenvalues in the PCA, identified specific neural pathways of the brain. To do so, we spatially registered the subnuclei for each eigenvector into the coordinate space of a Diffusion Tensor Imaging dataset; we used these subnuclei as seed regions to track and compare fiber pathways with known fiber pathways identified in neuroanatomical atlases. We applied these procedures to anatomical MRI data in a cohort of 82 healthy participants (42 children, 18 males, age 10.5 ± 2.43 years; 40 adults, 22 males, age 32.42 ± 10.7 years) and 107 participants with Tourette's Syndrome (TS) (71 children, 59 males, age 11.19 ± 2.2 years; 36 adults, 21 males, age 37.34 ± 10.9 years). We evaluated the construct validity of the identified covariation in morphology

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

    Science.gov (United States)

    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. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  11. Classification of CT brain images based on deep learning networks.

    Science.gov (United States)

    Gao, Xiaohong W; Hui, Rui; Tian, Zengmin

    2017-01-01

    While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3-5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information

  12. Dismissing attachment characteristics dynamically modulate brain networks subserving social aversion.

    Directory of Open Access Journals (Sweden)

    Anna Linda eKrause

    2016-03-01

    Full Text Available Attachment patterns influence actions, thoughts and feeling through a person’s ‘Inner Working Model’. Speech charged with attachment-dependent content was proposed to modulate the activation of cognitive-emotional schemata in listeners. We performed a 7 Tesla rest-task-rest fMRI-experiment, presenting auditory narratives prototypical of dismissing attachment representations to investigate their effect on 23 healthy males. We then examined effects of participants’ attachment style and childhood trauma on brain state changes using seed-based functional connectivity (FC analyses, and finally tested whether subjective differences in responsivity to narratives could be predicted by baseline network states. In comparison to a baseline state, we observed increased FC in a previously described ‘social aversion network’ including dorsal anterior cingulated cortex (dACC and left anterior middle temporal gyrus (aMTG specifically after exposure to insecure-dismissing attachment narratives. Increased dACC-seeded FC within the social aversion network was positively related to the participants’ avoidant attachment style and presence of a history of childhood trauma. Anxious attachment style on the other hand was positively correlated with FC between the dACC and a region outside of the ‘social aversion network’, namely the dorsolateral prefrontal cortex, which suggests decreased network segregation as a function of anxious attachment. Finally, the extent of subjective experience of friendliness towards the dismissing narrative was predicted by low baseline FC-values between hippocampus and inferior parietal lobule. Taken together, our study demonstrates an activation of networks related to social aversion in terms of increased connectivity after listening to insecure-dismissing attachment narratives. A causal interrelation of brain state changes and subsequent changes in social reactivity was further supported by our observation of direct

  13. Investigating a Novel Measure of Brain Networking Following Sports Concussion.

    Science.gov (United States)

    Broglio, S P; Rettmann, A; Greer, J; Brimacombe, S; Moore, B; Narisetty, N; He, X; Eckner, J

    2016-08-01

    Clinicians managing sports-related concussions are left to their clinical judgment in making diagnoses and return-to-play decisions. This study was designed to evaluate the utility of a novel measure of functional brain networking for concussion management. 24 athletes with acutely diagnosed concussion and 21 control participants were evaluated in a research laboratory. At each of the 4 post-injury time points, participants completed the Axon assessment of neurocognitive function, a self-report symptom inventory, and the auditory oddball and go/no-go tasks while electroencephalogram (EEG) readings were recorded. Brain Network Activation (BNA) scores were calculated from EEG data related to the auditory oddball and go/no-go tasks. BNA scores were unable to differentiate between the concussed and control groups or by self-report symptom severity. These findings conflict with previous work implementing electrophysiological assessments in concussed athletes, suggesting that BNA requires additional investigation and refinement before clinical implementation. © Georg Thieme Verlag KG Stuttgart · New York.

  14. Human-specific transcriptional networks in the brain

    Science.gov (United States)

    Konopka, Genevieve; Friedrich, Tara; Davis-Turak, Jeremy; Winden, Kellen; Oldham, Michael C.; Gao, Fuying; Chen, Leslie; Wang, Guang-Zhong; Luo, Rui; Preuss, Todd M.; Geschwind, Daniel H.

    2013-01-01

    Summary Understanding human-specific patterns of brain gene expression and regulation can provide key insights into human brain evolution and speciation. Here, we use next generation sequencing, and Illumina and Affymetrix microarray platforms, to compare the transcriptome of human, chimpanzee, and macaque telencephalon. Our analysis reveals a predominance of genes differentially expressed within human frontal lobe and a striking increase in transcriptional complexity specific to the human lineage in the frontal lobe. In contrast, caudate nucleus gene expression is highly conserved. We also identify gene co-expression signatures related to either neuronal processes or neuropsychiatric diseases, including a human-specific module with CLOCK as its hub gene and another module enriched for neuronal morphological processes and genes co-expressed with FOXP2, a gene important for language evolution. These data demonstrate that transcriptional networks have undergone evolutionary remodeling even within a given brain region, providing a new window through which to view the foundation of uniquely human cognitive capacities. PMID:22920253

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    Harm J van der Horn

    Full Text Available 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

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

    Science.gov (United States)

    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. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders

    Directory of Open Access Journals (Sweden)

    Qingying Meng

    2016-05-01

    Full Text Available Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient–host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control and hippocampus (cognitive processing from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine.

  19. Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

    Science.gov (United States)

    Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

    2016-05-01

    Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  20. Personality and complex brain networks: The role of openness to experience in default network efficiency.

    Science.gov (United States)

    Beaty, Roger E; Kaufman, Scott Barry; Benedek, Mathias; Jung, Rex E; Kenett, Yoed N; Jauk, Emanuel; Neubauer, Aljoscha C; Silvia, Paul J

    2016-02-01

    The brain's default network (DN) has been a topic of considerable empirical interest. In fMRI research, DN activity is associated with spontaneous and self-generated cognition, such as mind-wandering, episodic memory retrieval, future thinking, mental simulation, theory of mind reasoning, and creative cognition. Despite large literatures on developmental and disease-related influences on the DN, surprisingly little is known about the factors that impact normal variation in DN functioning. Using structural equation modeling and graph theoretical analysis of resting-state fMRI data, we provide evidence that Openness to Experience-a normally distributed personality trait reflecting a tendency to engage in imaginative, creative, and abstract cognitive processes-underlies efficiency of information processing within the DN. Across two studies, Openness predicted the global efficiency of a functional network comprised of DN nodes and corresponding edges. In Study 2, Openness remained a robust predictor-even after controlling for intelligence, age, gender, and other personality variables-explaining 18% of the variance in DN functioning. These findings point to a biological basis of Openness to Experience, and suggest that normally distributed personality traits affect the intrinsic architecture of large-scale brain systems. Hum Brain Mapp 37:773-779, 2016. © 2015 Wiley Periodicals, Inc. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  1. A simple rat model of mild traumatic brain injury: a device to reproduce anatomical and neurological changes of mild traumatic brain injury

    Directory of Open Access Journals (Sweden)

    Ho Jeong Kim

    2017-01-01

    Full Text Available Mild traumatic brain injury typically involves temporary impairment of neurological function. Previous studies used water pressure or rotational injury for designing the device to make a rat a mild traumatic brain injury model. The objective of this study was to make a simple model of causing mild traumatic brain injury in rats. The device consisted of a free-fall impactor that was targeted onto the rat skull. The weight (175 g was freely dropped 30 cm to rat’s skull bregma. We installed a safety device made of acrylic panel. To confirm a mild traumatic brain injury in 36 Sprague-Dawley rats, we performed magnetic resonance imaging (MRI of the brain within 24 h after injury. We evaluated behavior and chemical changes in rats before and after mild traumatic brain injury. The brain MRI did not show high or low signal intensity in 34 rats. The mobility on grid floor was decreased after mild traumatic brain injury. The absolute number of foot-fault and foot-fault ratio were decreased after mild traumatic brain injury. However, the difference of the ratio was a less than absolute number of foot-fault. These results show that the device is capable of reproducing mild traumatic brain injury in rats. Our device can reduce the potential to cause brain hemorrhage and reflect the mechanism of real mild traumatic brain injury compared with existing methods and behaviors. This model can be useful in exploring physiology and management of mild traumatic brain injury.

  2. A Bayesian network meta-analysis of whole brain radiotherapy and stereotactic radiotherapy for brain metastasis.

    Science.gov (United States)

    Yuan, Xi; Liu, Wen-Jie; Li, Bing; Shen, Ze-Tian; Shen, Jun-Shu; Zhu, Xi-Xu

    2017-08-01

    This study was conducted to compare the effects of whole brain radiotherapy (WBRT) and stereotactic radiotherapy (SRS) in treatment of brain metastasis.A systematical retrieval in PubMed and Embase databases was performed for relative literatures on the effects of WBRT and SRS in treatment of brain metastasis. A Bayesian network meta-analysis was performed by using the ADDIS software. The effect sizes included odds ratio (OR) and 95% confidence interval (CI). A random effects model was used for the pooled analysis for all the outcome measures, including 1-year distant control rate, 1-year local control rate, 1-year survival rate, and complication. The consistency was tested by using node-splitting analysis and inconsistency standard deviation. The convergence was estimated according to the Brooks-Gelman-Rubin method.A total of 12 literatures were included in this meta-analysis. WBRT + SRS showed higher 1-year distant control rate than SRS. WBRT + SRS was better for the 1-year local control rate than WBRT. SRS and WBRT + SRS had higher 1-year survival rate than the WBRT. In addition, there was no difference in complication among the three therapies.Comprehensively, WBRT + SRS might be the choice of treatment for brain metastasis.

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

    Science.gov (United States)

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

    2018-02-09

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

  4. Anatomical Network Comparison of Human Upper and Lower, Newborn and Adult, and Normal and Abnormal Limbs, with Notes on Development, Pathology and Limb Serial Homology vs. Homoplasy.

    Science.gov (United States)

    Diogo, Rui; Esteve-Altava, Borja; Smith, Christopher; Boughner, Julia C; Rasskin-Gutman, Diego

    2015-01-01

    How do the various anatomical parts (modules) of the animal body evolve into very different integrated forms (integration) yet still function properly without decreasing the individual's survival? This long-standing question remains unanswered for multiple reasons, including lack of consensus about conceptual definitions and approaches, as well as a reasonable bias toward the study of hard tissues over soft tissues. A major difficulty concerns the non-trivial technical hurdles of addressing this problem, specifically the lack of quantitative tools to quantify and compare variation across multiple disparate anatomical parts and tissue types. In this paper we apply for the first time a powerful new quantitative tool, Anatomical Network Analysis (AnNA), to examine and compare in detail the musculoskeletal modularity and integration of normal and abnormal human upper and lower limbs. In contrast to other morphological methods, the strength of AnNA is that it allows efficient and direct empirical comparisons among body parts with even vastly different architectures (e.g. upper and lower limbs) and diverse or complex tissue composition (e.g. bones, cartilages and muscles), by quantifying the spatial organization of these parts-their topological patterns relative to each other-using tools borrowed from network theory. Our results reveal similarities between the skeletal networks of the normal newborn/adult upper limb vs. lower limb, with exception to the shoulder vs. pelvis. However, when muscles are included, the overall musculoskeletal network organization of the upper limb is strikingly different from that of the lower limb, particularly that of the more proximal structures of each limb. Importantly, the obtained data provide further evidence to be added to the vast amount of paleontological, gross anatomical, developmental, molecular and embryological data recently obtained that contradicts the long-standing dogma that the upper and lower limbs are serial homologues

  5. Anatomical Network Comparison of Human Upper and Lower, Newborn and Adult, and Normal and Abnormal Limbs, with Notes on Development, Pathology and Limb Serial Homology vs. Homoplasy.

    Directory of Open Access Journals (Sweden)

    Rui Diogo

    Full Text Available How do the various anatomical parts (modules of the animal body evolve into very different integrated forms (integration yet still function properly without decreasing the individual's survival? This long-standing question remains unanswered for multiple reasons, including lack of consensus about conceptual definitions and approaches, as well as a reasonable bias toward the study of hard tissues over soft tissues. A major difficulty concerns the non-trivial technical hurdles of addressing this problem, specifically the lack of quantitative tools to quantify and compare variation across multiple disparate anatomical parts and tissue types. In this paper we apply for the first time a powerful new quantitative tool, Anatomical Network Analysis (AnNA, to examine and compare in detail the musculoskeletal modularity and integration of normal and abnormal human upper and lower limbs. In contrast to other morphological methods, the strength of AnNA is that it allows efficient and direct empirical comparisons among body parts with even vastly different architectures (e.g. upper and lower limbs and diverse or complex tissue composition (e.g. bones, cartilages and muscles, by quantifying the spatial organization of these parts-their topological patterns relative to each other-using tools borrowed from network theory. Our results reveal similarities between the skeletal networks of the normal newborn/adult upper limb vs. lower limb, with exception to the shoulder vs. pelvis. However, when muscles are included, the overall musculoskeletal network organization of the upper limb is strikingly different from that of the lower limb, particularly that of the more proximal structures of each limb. Importantly, the obtained data provide further evidence to be added to the vast amount of paleontological, gross anatomical, developmental, molecular and embryological data recently obtained that contradicts the long-standing dogma that the upper and lower limbs are

  6. Plasticity of brain wave network interactions and evolution across physiologic states

    Science.gov (United States)

    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

  7. Plasticity of brain wave network interactions and evolution across physiologic states.

    Science.gov (United States)

    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

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

    NARCIS (Netherlands)

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

    2015-01-01

    The brain is a network and our intelligence depends in part on the efficiency of this network. The network of adolescents differs from that of adults suggesting developmental changes. However, whether the network changes over time at the individual level and, if so, how this relates to intelligence,

  9. Driving and driven architectures of directed small-world human brain functional networks.

    Directory of Open Access Journals (Sweden)

    Chaogan Yan

    Full Text Available Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86 to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule. Further split-half analyses indicated that our results were highly reproducible between two

  10. Hierarchical brain networks active in approach and avoidance goal pursuit

    Directory of Open Access Journals (Sweden)

    Jeffrey Martin Spielberg

    2013-06-01

    Full Text Available Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal pursuit processes (e.g., motivation has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity vital to goal pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.

  11. Deep brain stimulation modulates synchrony within spatially and spectrally distinct resting state networks in Parkinson's disease

    NARCIS (Netherlands)

    Oswal, Ashwini; Beudel, Martijn; Zrinzo, Ludvic; Limousin, Patricia; Hariz, Marwan; Foltynie, Tom; Litvak, Vladimir; Brown, Peter

    2016-01-01

    Oswal et al. characterise the effect of deep brain stimulation (DBS) on STN-cortical synchronisation in Parkinson-s disease. They propose that cortical driving of the STN in beta frequencies is subdivided anatomically and spectrally, corresponding to the hyperdirect and indirect pathways. DBS

  12. Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes.

    Science.gov (United States)

    Jacob, Yael; Winetraub, Yonatan; Raz, Gal; Ben-Simon, Eti; Okon-Singer, Hadas; Rosenberg-Katz, Keren; Hendler, Talma; Ben-Jacob, Eshel

    2016-06-07

    Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (DEPNA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the DEPNA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the DEPNA correctly captures the network's hierarchy of influence. Applying DEPNA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that DEPNA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions.

  13. Efficacy Assessment of Endovascular Stenting in Patients with Unilateral Middle Cerebral Artery Stenosis Using Statistical Probabilistic Anatomical Mapping Analysis of Basal/Acetazolamide Brain Perfusion SPECT

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Hae Won; Won, Kyoung Sook; Zeon, Seok Kil; Lee, Chang Young [Keimyung University, School of Medicine, Daegu (Korea, Republic of)

    2009-08-15

    The aim of this study was to evaluate the hemodynamic changes after endovascular stenting in patients with unilateral middle cerebral artery (MCA) stenosis using statistical probabilistic anatomical mapping (SPAM) analysis of basal/acetazolamide (ACZ) Tc-99m ECD brain perfusion SPECT. Eight patients (3 men and 5 women, 64.8{+-}10.5 years) who underwent endovascular stenting for unilateral MCA stenosis were enrolled. Basal/ACZ Tc-99m ECD brain perfusion SPECT studies were performed by one-day protocol before and after stenting. Using SPAM analysis, we compared basal cerebral perfusion (BCP) counts and cerebrovascular reserve (CVR) index of the MCA territory before stenting with those after stenting. After stenting, no patient had any complication nor additional stroke. In SPAM analysis, 7 out of the 8 patients had improved BCP counts of the MCA territory and 7 out of the 8 patients had improved CVR index of the MCA territory after stenting. Before stenting, the mean BCP counts and CVR index in the affected MCA territory were 47.1{+-}2.2 ml/min/100 g and -2.1{+-}2.9%, respectively. After stenting, the mean BCP counts and CVR index in the affected MCA territory were improved significantly (48.3{+-}2.9 ml/min/100 g, p=0.025 and 0.1{+-}1.3%, p=0.036). This study revealed that SPAM analysis of basal/ACZ brain perfusion SPECT would be helpful to evaluate hemodynamic efficacy of endovascular stenting in unilateral MCA stenosis.

  14. Exploring brain functional plasticity in world class gymnasts: a network analysis.

    Science.gov (United States)

    Wang, Junjing; Lu, Min; Fan, Yuanyuan; Wen, Xue; Zhang, Ruibin; Wang, Bin; Ma, Qing; Song, Zheng; He, Yong; Wang, Jun; Huang, Ruiwang

    2016-09-01

    Long-term motor skill learning can induce plastic structural and functional reorganization of the brain. Our previous studies detected brain structural plasticity related to long-term intensive gymnastic training in world class gymnasts (WCGs). The goal of this study was to investigate brain functional plasticity in WCGs by using network measures of brain functional networks. Specifically, we acquired resting-state fMRI data from 13 WCGs and 14 controls, constructed their brain functional networks, and compared the differences in their network parameters. At the whole brain level, we detected significantly decreased overall functional connectivity (FC) and decreased local and global efficiency in the WCGs compared to the controls. At the modular level, we found intra- and inter-modular reorganization in three modules, the cerebellum, the cingulo-opercular and fronto-parietal networks, in the WCGs. On the nodal level, we revealed significantly decreased nodal strength and efficiency in several non-rich club regions of these three modules in the WCGs. These results suggested that functional plasticity can be detected in the brain functional networks of WCGs, especially in the cerebellum, fronto-parietal network, and cingulo-opercular network. In addition, we found that the FC between the fronto-parietal network and the sensorimotor network was significantly negatively correlated with the number of years of training in the WCGs. These findings may help us to understand the outstanding gymnastic performance of the gymnasts and to reveal the neural mechanisms that distinguish WCGs from controls.

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

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

    2017-03-01

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

  16. Self-regulation of circumscribed brain activity modulates spatially selective and frequency specific connectivity of distributed resting state networks

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    Mathias eVukelić

    2015-07-01

    Full Text Available The mechanisms of learning involved in brain self-regulation have still to be unveiled to exploit the full potential of this methodology for therapeutic interventions. This skill of volitionally changing brain activity presumably resembles motor skill learning which in turn is accompanied by plastic changes modulating resting state networks. Along these lines, we hypothesized that brain regulation and neurofeedback would similarly modify intrinsic networks at rest while presenting a distinct spatio-temporal pattern. High-resolution EEG preceded and followed a single neurofeedback training intervention of modulating circumscribed sensorimotor low β -activity by motor imagery in eleven healthy participants. They were kept in the deliberative phase of skill acquisition with high demands for learning self-regulation through stepwise increases of task difficulty. By applying the corrected imaginary part of the coherency function, we observed increased functional connectivity of both the primary motor and the primary somatosensory cortex with their respective contralateral homologous cortices in the low β-frequency band which was self-regulated during feedback. At the same time, the primary motor cortex - but none of the surrounding cortical areas - showed connectivity to contralateral supplementary motor and dorsal premotor areas in the high β-band. Simultaneously, the neurofeedback target displayed a specific increase of functional connectivity with an ipsilateral fronto-parietal network in the α-band while presenting a de-coupling with contralateral primary and secondary sensorimotor areas in the very same frequency band.Brain self-regulating modifies resting state connections spatially selective to the neurofeedback target of the dominant hemisphere. These are anatomically distinct with regard to the cortico-cortical connectivity pattern and are functionally specific with regard to the time domain of coherent activity consistent with a Hebbian

  17. Differential synchronization in default and task-specific networks of the human brain

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    Aaron eKirschner

    2012-05-01

    Full Text Available On a regional scale the brain is organized into dynamic functional networks. The activity within one of these, the default network, can be dissociated from that in other task-specific networks. All brain networks are connected structurally, but evidently are only transiently connected functionally. One hypothesis as to how such transient functional coupling occurs is that network formation and dissolution is mediated, or at least accompanied, by increases and decreases in oscillatory synchronization between constituent brain regions. If so, then we should be able to find transient differences in intra-network synchronization between the default network and a task-specific network. In order to investigate this hypothesis we conducted two experiments in which subjects engaged in a Sustained Attention to Response Task (SART while having brain activity recorded via high-density electroencephalography (EEG. We found that during periods when attention was focused internally (mind-wandering there was significantly more neural phase synchronization between brain regions associated with the default network, whereas during periods when subjects were focused on performing the visual task there was significantly more neural phase synchrony within a task-specific brain network that shared some of the same brain regions. These differences in network synchrony occurred in each of theta, alpha, and gamma frequency bands. A similar pattern of differential oscillatory power changes, indicating modulation of local synchronization by attention state, was also found. These results provide further evidence that the human brain is intrinsically organized into default and task-specific brain networks, and confirm that oscillatory synchronization is a potential mechanism for functional coupling within these networks.

  18. Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction

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    Xerxes D. Arsiwalla

    2015-02-01

    Full Text Available BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for real-time exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably, due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

  19. Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

    Science.gov (United States)

    Arsiwalla, Xerxes D.; Zucca, Riccardo; Betella, Alberto; Martinez, Enrique; Dalmazzo, David; Omedas, Pedro; Deco, Gustavo; Verschure, Paul F. M. J.

    2015-01-01

    BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas. PMID:25759649

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

    Science.gov (United States)

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

    2017-06-01

    The analysis of the topology and organization 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 characterizing 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 multi-layer networks, in which each layer models brain connectivity at time window t + Δt. 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 characterized by dynamic graph metrics.

  1. Targeting Neuronal Networks with Combined Drug and Stimulation Paradigms Guided by Neuroimaging to Treat Brain Disorders.

    Science.gov (United States)

    Faingold, Carl L; Blumenfeld, Hal

    2015-10-01

    Improved therapy of brain disorders can be achieved by focusing on neuronal networks, utilizing combined pharmacological and stimulation paradigms guided by neuroimaging. Neuronal networks that mediate normal brain functions, such as hearing, interact with other networks, which is important but commonly neglected. Network interaction changes often underlie brain disorders, including epilepsy. "Conditional multireceptive" (CMR) brain areas (e.g., brainstem reticular formation and amygdala) are critical in mediating neuroplastic changes that facilitate network interactions. CMR neurons receive multiple inputs but exhibit extensive response variability due to milieu and behavioral state changes and are exquisitely sensitive to agents that increase or inhibit GABA-mediated inhibition. Enhanced CMR neuronal responsiveness leads to expression of emergent properties--nonlinear events--resulting from network self-organization. Determining brain disorder mechanisms requires animals that model behaviors and neuroanatomical substrates of human disorders identified by neuroimaging. However, not all sites activated during network operation are requisite for that operation. Other active sites are ancillary, because their blockade does not alter network function. Requisite network sites exhibit emergent properties that are critical targets for pharmacological and stimulation therapies. Improved treatment of brain disorders should involve combined pharmacological and stimulation therapies, guided by neuroimaging, to correct network malfunctions by targeting specific network neurons. © The Author(s) 2015.

  2. Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles.

    Science.gov (United States)

    Tang, Xiaoying; Crocetti, Deana; Kutten, Kwame; Ceritoglu, Can; Albert, Marilyn S; Mori, Susumu; Mostofsky, Stewart H; Miller, Michael I

    2015-01-01

    We propose a hierarchical pipeline for skull-stripping and segmentation of anatomical structures of interest from T1-weighted images of the human brain. The pipeline is constructed based on a two-level Bayesian parameter estimation algorithm called multi-atlas likelihood fusion (MALF). In MALF, estimation of the parameter of interest is performed via maximum a posteriori estimation using the expectation-maximization (EM) algorithm. The likelihoods of multiple atlases are fused in the E-step while the optimal estimator, a single maximizer of the fused likelihoods, is then obtained in the M-step. There are two stages in the proposed pipeline; first the input T1-weighted image is automatically skull-stripped via a fast MALF, then internal brain structures of interest are automatically extracted using a regular MALF. We assess the performance of each of the two modules in the pipeline based on two sets of images with markedly different anatomical and photometric contrasts; 3T MPRAGE scans of pediatric subjects with developmental disorders vs. 1.5T SPGR scans of elderly subjects with dementia. Evaluation is performed quantitatively using the Dice overlap as well as qualitatively via visual inspections. As a result, we demonstrate subject-level differences in the performance of the proposed pipeline, which may be accounted for by age, diagnosis, or the imaging parameters (particularly the field strength). For the subcortical and ventricular structures of the two datasets, the hierarchical pipeline is capable of producing automated segmentations with Dice overlaps ranging from 0.8 to 0.964 when compared with the gold standard. Comparisons with other representative segmentation algorithms are presented, relative to which the proposed hierarchical pipeline demonstrates comparative or superior accuracy.

  3. Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles

    Directory of Open Access Journals (Sweden)

    Xiaoying eTang

    2015-03-01

    Full Text Available We propose a hierarchical pipeline for skull-stripping and segmentation of anatomical structures of interest from T1-weighted images of the human brain. The pipeline is constructed based on a two-level Bayesian parameter estimation algorithm called multi-atlas likelihood fusion (MALF. In MALF, estimation of the parameter of interest is performed via maximum a posteriori estimation using the expectation-maximization (EM algorithm. The likelihoods of multiple atlases are fused in the E-step while the optimal estimator, a single maximizer of the fused likelihoods, is then obtained in the M-step. There are two stages in the proposed pipeline; first the input T1-weighted image is automatically skull-stripped via a fast MALF, then internal brain structures of interest are automatically extracted using a regular MALF. We assess the performance of each of the two modules in the pipeline based on two sets of images with markedly different anatomical and photometric contrasts; 3T MPRAGE scans of pediatric subjects with developmental disorders versus 1.5T SPGR scans of elderly subjects with dementia. Evaluation is performed quantitatively using the Dice overlap as well as qualitatively via visual inspections. As a result, we demonstrate subject-level differences in the performance of the proposed pipeline, which may be accounted for by age, diagnosis, or the imaging parameters (particularly the field strength. For the subcortical and ventricular structures of the two datasets, the hierarchical pipeline is capable of producing automated segmentations with Dice overlaps ranging from 0.8 to 0.964 when compared with the gold standard. Comparisons with other representative segmentation algorithms are presented, relative to which the proposed hierarchical pipeline demonstrates comparative or superior accuracy.

  4. Single-subject morphological brain networks: connectivity mapping, topological characterization and test-retest reliability.

    Science.gov (United States)

    Wang, Hao; Jin, Xiaoqing; Zhang, Ye; Wang, Jinhui

    2016-04-01

    Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual-level morphological brain networks and systematically examined their topological organization and long-term test-retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type. This study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback-Leibler divergence measure. Graph-based global and nodal network measures were then calculated, followed by the statistical comparison and intra-class correlation analysis. The morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph-based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small-worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long-term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree

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

    Science.gov (United States)

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

    2016-03-04

    Goal-directed human behaviors are driven by motives. Motives are, however, purely mental constructs that are not directly observable. Here, we show that the brain's functional network architecture captures information that predicts different motives behind the same altruistic act with high accuracy. In contrast, mere activity in these regions contains no information about motives. Empathy-based altruism is primarily characterized by a positive connectivity from the anterior cingulate cortex (ACC) to the anterior insula (AI), whereas reciprocity-based altruism additionally invokes strong positive connectivity from the AI to the ACC and even stronger positive connectivity from the AI to the ventral striatum. Moreover, predominantly selfish individuals show distinct functional architectures compared to altruists, and they only increase altruistic behavior in response to empathy inductions, but not reciprocity inductions. Copyright © 2016, American Association for the Advancement of Science.

  6. Distinct Patterns of Temporal and Directional Connectivity among Intrinsic Networks in the Human Brain.

    Science.gov (United States)

    Shine, James M; Kucyi, Aaron; Foster, Brett L; Bickel, Stephan; Wang, Danhong; Liu, Hesheng; Poldrack, Russell A; Hsieh, Liang-Tien; Hsiang, Jen Chun; Parvizi, Josef

    2017-10-04

    To determine the spatiotemporal relationships among intrinsic networks of the human brain, we recruited seven neurosurgical patients (four males and three females) who were implanted with intracranial depth electrodes. We first identified canonical resting-state networks at the individual subject level using an iterative matching procedure on each subject's resting-state fMRI data. We then introduced single electrical pulses to fMRI pre-identified nodes of the default network (DN), frontoparietal network (FPN), and salience network (SN) while recording evoked responses in other recording sites within the same networks. We found bidirectional signal flow across the three networks, albeit with distinct patterns of evoked responses within different time windows. We used a data-driven clustering approach to show that stimulation of the FPN and SN evoked a rapid (130 ms) in other nodes of the DN, as well as FPN and SN. Our results provide temporal information about the patterns of signal flow between intrinsic networks that provide insights into the spatiotemporal dynamics that are likely to constrain the architecture of the brain networks supporting human cognition and behavior. SIGNIFICANCE STATEMENT Despite great progress in the functional neuroimaging of the human brain, we still do not know the precise set of rules that define the patterns of temporal organization between large-scale networks of the brain. In this study, we stimulated and then recorded electrical evoked potentials within and between three large-scale networks of the brain, the default network (DN), frontoparietal network (FPN), and salience network (SN), in seven subjects undergoing invasive neurosurgery. Using a data-driven clustering approach, we observed distinct temporal and directional patterns between the three networks, with FPN and SN activity predominant in early windows and DN stimulation affecting the network in later windows. These results provide important temporal information about

  7. Subgraph "backbone" analysis of dynamic brain networks during consciousness and anesthesia.

    Science.gov (United States)

    Shin, Jeongkyu; Mashour, George A; Ku, Seungwoo; Kim, Seunghwan; Lee, Uncheol

    2013-01-01

    General anesthesia significantly alters brain network connectivity. Graph-theoretical analysis has been used extensively to study static brain networks but may be limited in the study of rapidly changing brain connectivity during induction of or recovery from general anesthesia. Here we introduce a novel method to study the temporal evolution of network modules in the brain. We recorded multichannel electroencephalograms (EEG) from 18 surgical patients who underwent general anesthesia with either propofol (n = 9) or sevoflurane (n = 9). Time series data were used to reconstruct networks; each electroencephalographic channel was defined as a node and correlated activity between the channels was defined as a link. We analyzed the frequency of subgraphs in the network with a defined number of links; subgraphs with a high probability of occurrence were deemed network "backbones." We analyzed the behavior of network backbones across consciousness, anesthetic induction, anesthetic maintenance, and two points of recovery. Constitutive, variable and state-specific backbones were identified across anesthetic state transitions. Brain networks derived from neurophysiologic data can be deconstructed into network backbones that change rapidly across states of consciousness. This technique enabled a granular description of network evolution over time. The concept of network backbones may facilitate graph-theoretical analysis of dynamically changing networks.

  8. Personality and complex brain networks: The role of openness to experience in default network efficiency

    Science.gov (United States)

    Kaufman, Scott Barry; Benedek, Mathias; Jung, Rex E.; Kenett, Yoed N.; Jauk, Emanuel; Neubauer, Aljoscha C.; Silvia, Paul J.

    2015-01-01

    Abstract The brain's default network (DN) has been a topic of considerable empirical interest. In fMRI research, DN activity is associated with spontaneous and self‐generated cognition, such as mind‐wandering, episodic memory retrieval, future thinking, mental simulation, theory of mind reasoning, and creative cognition. Despite large literatures on developmental and disease‐related influences on the DN, surprisingly little is known about the factors that impact normal variation in DN functioning. Using structural equation modeling and graph theoretical analysis of resting‐state fMRI data, we provide evidence that Openness to Experience—a normally distributed personality trait reflecting a tendency to engage in imaginative, creative, and abstract cognitive processes—underlies efficiency of information processing within the DN. Across two studies, Openness predicted the global efficiency of a functional network comprised of DN nodes and corresponding edges. In Study 2, Openness remained a robust predictor—even after controlling for intelligence, age, gender, and other personality variables—explaining 18% of the variance in DN functioning. These findings point to a biological basis of Openness to Experience, and suggest that normally distributed personality traits affect the intrinsic architecture of large‐scale brain systems. Hum Brain Mapp 37:773–779, 2016. © 2015 Wiley Periodicals, Inc. PMID:26610181

  9. Brain networks supporting perceptual grouping and contour selection

    Directory of Open Access Journals (Sweden)

    Gregor eVolberg

    2014-04-01

    Full Text Available The human visual system groups local elements into global objects seemingly without effort. Using a contour integration task and EEG source level analyses, we tested the hypothesis that perceptual grouping requires a top-down selection, rather than a passive pooling, of neural information that codes local elements in the visual image. The participants were presented visual displays with or without a hidden contour. Two tasks were performed: a central luminance-change detection task and a peripheral contour detection task. Only in the contour-detection task could we find differential brain activity between contour and non-contour conditions, within a distributed brain network including parietal, lateral occipital and primary visual areas. Contour processing was associated with an inflow of information from lateral occipital into primary visual regions, as revealed from the slope of phase differences between source level oscillations within these areas. The findings suggest that contour integration results from a selection of neural information from lower visual areas, and that this selection is driven by the lateral occipital cortex.

  10. Memory networks in tinnitus: a functional brain image study.

    Science.gov (United States)

    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. 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). 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. 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.

  11. Memory networks in tinnitus: a functional brain image study.

    Directory of Open Access Journals (Sweden)

    Maura Regina Laureano

    Full Text Available 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.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.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.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.

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

    Science.gov (United States)

    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. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  15. A healthy brain in a healthy body: brain network correlates of physical and mental fitness.

    Directory of Open Access Journals (Sweden)

    Linda Douw

    Full Text Available A healthy lifestyle is an important focus in today's society. The physical benefits of regular exercise are abundantly clear, but physical fitness is also associated with better cognitive performance. How these two factors together relate to characteristics of the brain is still incompletely understood. By applying mathematical concepts from 'network theory', insights in the organization and dynamics of brain functioning can be obtained. We test the hypothesis that neural network organization mediates the association between cardio respiratory fitness (i.e. VO₂ max and cognitive functioning. A healthy cohort was studied (n = 219, 113 women, age range 41-44 years. Subjects underwent resting-state eyes-closed magneto-encephalography (MEG. Five artifact-free epochs were analyzed and averaged in six frequency bands (delta-gamma. The phase lag index (PLI was used as a measure of functional connectivity between all sensors. Modularity analysis was performed, and both within and between-module connectivity of each sensor was calculated. Subjects underwent a maximum oxygen uptake (VO₂ max measurement as an indicator of cardio respiratory fitness. All subjects were tested with a commonly used Dutch intelligence test. Intelligence quotient (IQ was related to VO₂ max. In addition, VO₂ max was negatively associated with upper alpha and beta band modularity. Particularly increased intermodular connectivity in the beta band was associated with higher VO₂ max and IQ, further indicating a benefit of more global network integration as opposed to local connections. Within-module connectivity showed a spatially varied pattern of correlation, while average connectivity did not show significant results. Mediation analysis was not significant. The occurrence of less modularity in the resting-state is associated with better cardio respiratory fitness, while having increased intermodular connectivity, as opposed to within-module connections, is related to

  16. A healthy brain in a healthy body: brain network correlates of physical and mental fitness.

    Science.gov (United States)

    Douw, Linda; Nieboer, Dagmar; van Dijk, Bob W; Stam, Cornelis J; Twisk, Jos W R

    2014-01-01

    A healthy lifestyle is an important focus in today's society. The physical benefits of regular exercise are abundantly clear, but physical fitness is also associated with better cognitive performance. How these two factors together relate to characteristics of the brain is still incompletely understood. By applying mathematical concepts from 'network theory', insights in the organization and dynamics of brain functioning can be obtained. We test the hypothesis that neural network organization mediates the association between cardio respiratory fitness (i.e. VO₂ max) and cognitive functioning. A healthy cohort was studied (n = 219, 113 women, age range 41-44 years). Subjects underwent resting-state eyes-closed magneto-encephalography (MEG). Five artifact-free epochs were analyzed and averaged in six frequency bands (delta-gamma). The phase lag index (PLI) was used as a measure of functional connectivity between all sensors. Modularity analysis was performed, and both within and between-module connectivity of each sensor was calculated. Subjects underwent a maximum oxygen uptake (VO₂ max) measurement as an indicator of cardio respiratory fitness. All subjects were tested with a commonly used Dutch intelligence test. Intelligence quotient (IQ) was related to VO₂ max. In addition, VO₂ max was negatively associated with upper alpha and beta band modularity. Particularly increased intermodular connectivity in the beta band was associated with higher VO₂ max and IQ, further indicating a benefit of more global network integration as opposed to local connections. Within-module connectivity showed a spatially varied pattern of correlation, while average connectivity did not show significant results. Mediation analysis was not significant. The occurrence of less modularity in the resting-state is associated with better cardio respiratory fitness, while having increased intermodular connectivity, as opposed to within-module connections, is related to better

  17. A study of brain networks associated with swallowing using graph-theoretical approaches.

    Directory of Open Access Journals (Sweden)

    Bo Luan

    Full Text Available Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, [Formula: see text] years of age. To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia.

  18. Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.

    Science.gov (United States)

    Stephen, Emily P; Lepage, Kyle Q; Eden, Uri T; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S; Guenther, Frank H; Kramer, Mark A

    2014-01-01

    The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty-both in the functional network edges and the corresponding aggregate measures of network topology-are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here-appropriate for static and dynamic network inference and different statistical measures of coupling-permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.

  19. Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs.

    Science.gov (United States)

    Yuan, Jing; Li, Xiang; Zhang, Jinhe; Luo, Liao; Dong, Qinglin; Lv, Jinglei; Zhao, Yu; Jiang, Xi; Zhang, Shu; Zhang, Wei; Liu, Tianming

    2017-11-09

    Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions. Second, the temporal dynamics of spatial network interactions is modeled by a weighted time-evolving graph, and then a data-driven unsupervised learning algorithm based on the dynamic behavioral mixed-membership model (DBMM) is adopted to identify behavioral patterns of brain networks during the temporal evolution process of spatial overlaps/interactions. Experimental results on the Human Connectome Project (HCP) task fMRI data showed that our methods can reveal meaningful, diverse behavior patterns of connectome-scale network interactions. In particular, those networks' behavior patterns are distinct across HCP tasks such as motor, working memory, language and social tasks, and their dynamics well correspond to the temporal changes of specific task designs. In general, our framework offers a new approach to characterizing human brain function by quantitative description for the temporal evolution of spatial overlaps/interactions of connectome-scale brain networks in a standard reference space. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Genetic targeting and anatomical registration of neuronal populations in the zebrafish brain with a new set of BAC transgenic tools.

    Science.gov (United States)

    Förster, Dominique; Arnold-Ammer, Irene; Laurell, Eva; Barker, Alison J; Fernandes, António M; Finger-Baier, Karin; Filosa, Alessandro; Helmbrecht, Thomas O; Kölsch, Yvonne; Kühn, Enrico; Robles, Estuardo; Slanchev, Krasimir; Thiele, Tod R; Baier, Herwig; Kubo, Fumi

    2017-07-12

    Genetic access to small, reproducible sets of neurons is key to an understanding of the functional wiring of the brain. Here we report the generation of a new Gal4- and Cre-driver resource for zebrafish neurobiology. Candidate genes, including cell type-specific transcription factors, neurotransmitter-synthesizing enzymes and neuropeptides, were selected according to their expression patterns in small and unique subsets of neurons from diverse brain regions. BAC recombineering, followed by Tol2 transgenesis, was used to generate driver lines that label neuronal populations in patterns that, to a large but variable extent, recapitulate the endogenous gene expression. We used image registration to characterize, compare, and digitally superimpose the labeling patterns from our newly generated transgenic lines. This analysis revealed highly restricted and mutually exclusive tissue distributions, with striking resolution of layered brain regions such as the tectum or the rhombencephalon. We further show that a combination of Gal4 and Cre transgenes allows intersectional expression of a fluorescent reporter in regions where the expression of the two drivers overlaps. Taken together, our study offers new tools for functional studies of specific neural circuits in zebrafish.

  1. Graph analysis of structural brain networks in Alzheimer's disease: beyond small world properties.

    Science.gov (United States)

    John, Majnu; Ikuta, Toshikazu; Ferbinteanu, Janina

    2017-03-01

    Changes in brain connectivity in patients with early Alzheimer's disease (AD) have been investigated using graph analysis. However, these studies were based on small data sets, explored a limited range of network parameters, and did not focus on more restricted sub-networks, where neurodegenerative processes may introduce more prominent alterations. In this study, we constructed structural brain networks out of 87 regions using data from 135 healthy elders and 100 early AD patients selected from the Open Access Series of Imaging Studies (OASIS) database. We evaluated the graph properties of these networks by investigating metrics of network efficiency, small world properties, segregation, product measures of complexity, and entropy. Because degenerative processes take place at different rates in different brain areas, analysis restricted to sub-networks may reveal changes otherwise undetected. Therefore, we first analyzed the graph properties of a network encompassing all brain areas considered together, and then repeated the analysis after dividing the brain areas into two sub-networks constructed by applying a clustering algorithm. At the level of large scale network, the analysis did not reveal differences between AD patients and controls. In contrast, the same analysis performed on the two sub-networks revealed that small worldness diminished with AD only in the sub-network containing the areas of medial temporal lobe known to be heaviest and earliest affected. The second sub-network, which did not present significant AD-induced modifications of 'classical' small world parameters, nonetheless showed a trend towards an increase in small world propensity, a novel metric that unbiasedly quantifies small world structure. Beyond small world properties, complexity and entropy measures indicated that the intricacy of connection patterns and structural diversity decreased in both sub-networks. These results show that neurodegenerative processes impact volumetric

  2. Sparse and shrunken estimates of MRI networks in the brain and their influence on network properties

    DEFF Research Database (Denmark)

    Romero-Garcia, Rafael; Clemmensen, Line Katrine Harder

    2014-01-01

    Estimation of morphometric relationships between cortical regions is a widely used approach to identify and characterize structural connectivity. The elevated number of regions that can be considered in a whole-brain correlation analysis might lead to overfitted models. However, the overfitting can...... approaches showed more stable results with a relative low variance at the expense of a little bias. Interestingly, topological properties as local and global efficiency estimated in networks constructed from traditional non-regularized correlations also showed higher variability when compared to those from...

  3. Shannon entropy of brain functional complex networks under the influence of the psychedelic Ayahuasca

    OpenAIRE

    Viol, A.; Palhano-Fontes, Fernanda; Onias, Heloisa; de Araujo, Draulio B.; Viswanathan, G. M.

    2016-01-01

    The entropic brain hypothesis holds that the key facts concerning psychedelics are partially explained in terms of increased entropy of the brain?s functional connectivity. Ayahuasca is a psychedelic beverage of Amazonian indigenous origin with legal status in Brazil in religious and scientific settings. In this context, we use tools and concepts from the theory of complex networks to analyze resting state fMRI data of the brains of human subjects under two distinct conditions: (i) under ordi...

  4. EEG classification of emotions using emotion-specific brain functional network.

    Science.gov (United States)

    Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

    2015-08-01

    The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

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

    OpenAIRE

    Yu Sun; Junhua Li; John Suckling; Lei Feng

    2017-01-01

    Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between struct...

  6. Association of structural global brain network properties with intelligence in normal aging.

    Science.gov (United States)

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

    2014-01-01

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

  7. Association of structural global brain network properties with intelligence in normal aging.

    Directory of Open Access Journals (Sweden)

    Florian U Fischer

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

  8. Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

    NARCIS (Netherlands)

    Sinke, Michel R T; Dijkhuizen, Rick M; Caimo, Alberto; Stam, Cornelis J; Otte, Wim

    2016-01-01

    Descriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have limitations for inter-subject or between-group comparisons in which network sizes and edge densities may differ, such as

  9. Cluster imaging of multi-brain networks (CIMBN: a general framework for hyperscanning and modeling a group of interacting brains

    Directory of Open Access Journals (Sweden)

    Lian eDuan

    2015-07-01

    Full Text Available Studying the neural basis of human social interactions is a key topic in the field of social neuroscience. Brain imaging studies in this field usually focus on the neural correlates of the social interactions between two participants. However, as the participant number further increases, even by a small amount, great difficulties raise. One challenge is how to concurrently scan all the interacting brains with high ecological validity, especially for a large number of participants. The other challenge is how to effectively model the complex group interaction behaviors emerging from the intricate neural information exchange among a group of socially organized people. Confronting these challenges, we propose a new approach called Cluster Imaging of Multi-brain Networks (CIMBN. CIMBN consists of two parts. The first part is a cluster imaging technique with high ecological validity based on multiple functional near-infrared spectroscopy (fNIRS systems. Using this technique, we can easily extend the simultaneous imaging capacity of social neuroscience studies up to dozens of participants. The second part of CIMBN is a multi-brain network (MBN modeling method based on graph theory. By taking each brain as a network node and the relationship between any two brains as a network edge, one can construct a network model for a group of interacting brains. The emergent group social behaviors can then be studied using the network’s properties, such as its topological structure and information exchange efficiency. Although there is still much work to do, as a general framework for hyperscanning and modeling a group of interacting brains, CIMBN can provide new insights into the neural correlates of group social interactions, and advance social neuroscience and social psychology.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Bosiljka Tadić

    Full Text Available 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.

  12. Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive Level

    DEFF Research Database (Denmark)

    Hartwigsen, Gesa; Bergmann, Til Ole; Herz, Damian Marc

    2015-01-01

    these approaches advance the scientific potential of NTBS as an interventional tool in cognitive neuroscience. (i) Leveraging the anatomical information provided by structural imaging, the electric field distribution in the brain can be modeled and simulated. Biophysical modeling approaches generate testable......Noninvasive transcranial brain stimulation (NTBS) is widely used to elucidate the contribution of different brain regions to various cognitive functions. Here we present three modeling approaches that are informed by functional or structural brain mapping or behavior profiling and discuss how...... predictions regarding the impact of interindividual variations in cortical anatomy on the injected electric fields or the influence of the orientation of current flow on the physiological stimulation effects. (ii) Functional brain mapping of the spatiotemporal neural dynamics during cognitive tasks can...

  13. Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity

    Science.gov (United States)

    Murugesan, Sugeerth; Bouchard, Kristofer; Brown, Jesse A.; Hamann, Bernd; Seeley, William W.; Trujillo, Andrew; Weber, Gunther H.

    2017-01-01

    We present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views—such as heat maps, node link diagrams and anatomical views—using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parameters gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. To demonstrate the utility of our tool, we present two case studies—exploring progressive supranuclear palsy, as well as memory encoding and retrieval. PMID:28113724

  14. Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity.

    Science.gov (United States)

    Murugesan, Sugeerth; Bouchard, Kristofer; Brown, Jesse A; Hamann, Bernd; Seeley, William W; Trujillo, Andrew; Weber, Gunther H

    2016-05-09

    We present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views-such as heat maps, node link diagrams and anatomical views-using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parameters gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. To demonstrate the utility of our tool, we present two case studies-exploring progressive supranuclear palsy, as well as memory encoding and retrieval.

  15. Pilot study: Computer-based virtual anatomical interactivity for rehabilitation of individuals with chronic acquired brain injury.

    Science.gov (United States)

    Simmons, C Douglas; Arthanat, Sajay; Macri, Vincent J

    2014-01-01

    Deficiencies in upper-limb motor function and executive functioning can compromise an affected individual's ability to complete everyday activities. Impaired motor and executive functioning therefore pose a risk to increasing numbers of veterans who have been diagnosed with acquired brain injury. This article reports on changes in upper-limb motor function and executive functioning of 12 adult participants with chronic acquired brain injury using a novel, computer-based, motor and cognitive rehabilitation program called PreMotor Exercise Games (PEGs). Manual muscle, goniometric range of motion, and dynamometer assessments were used to determine motor functioning while the Executive Function Performance Test measured cognitive functioning. A three-level repeated measures design was conducted to determine changes pre- and postintervention. Participants demonstrated significant improvement in shoulder (p = 0.01) and wrist (p = 0.01) range of motion and clinically relevant improvement for elbow range of motion. Participants demonstrated clinically relevant improvement in shoulder, elbow, and wrist strength. Finally, participants demonstrated significant improvement in executive functioning (p < 0.05). Using PEGs as a modality for both motor and cognitive intervention is a potentially beneficial adjunct to rehabilitation and warrants further study.

  16. Study on CT changes in autistic children; Anatomical correlation of the damaged brain and delay of psychomotor development

    Energy Technology Data Exchange (ETDEWEB)

    Yaguchi, Katsumi (Juntendo Univ., Tokyo (Japan). School of Medicine)

    1993-05-01

    Since 1979 we have performed CT examinations on 132 autistic children. Neurological diagnosis of the lesion was established by Dr. Segawa's group. On the CT of many autistic children, we found a small low density change located in the anterior wall of the temporal horn, or localized dilatation of the inferior horn near the damaged brain. We reviewed 96 of these patients who all had the obvious low density changes, or localized irregular dilatations in the anterior wall of the temporal horn. By measuring the distance of damage from the midline, we divided the 96 cases into two groups. Group 1 consisted of those with damage located laterally more than 30 mm line from the midline. Group 2 consisted of those with damage medially to the 30 mm line from the midline. Those cases with a large lesion both laterally and medially of the 30 mm line were categorized into group 1. In the adult brain the lateral border of the amygdaloid nucleus was never located laterally more than 30 mm from the midline. Laterally over the 30 mm line there were two marked fiber systems running near the anterior wall of the temporal horn: the fiber of the anterior commissure and the uncinate fascicle. Group 1 consisted of 62 patients and group 2 of 34 patients. The majority of the two group patients were pure autism children. This suggested that the main lesion in autism was in the amygdala. (author).

  17. Which Brain Regions are Important for Seizure Dynamics in Epileptic Networks? Influence of Link Identification and EEG Recording Montage on Node Centralities.

    Science.gov (United States)

    Geier, Christian; Lehnertz, Klaus

    2017-02-01

    Nodes in large-scale epileptic networks that are crucial for seizure facilitation and termination can be regarded as potential targets for individualized focal therapies. Graph-theoretical approaches based on centrality concepts can help to identify such important nodes, however, they may be influenced by the way networks are derived from empirical data. Here we investigate evolving functional epileptic brain networks during 82 focal seizures with different anatomical onset locations that we derive from multichannel intracranial electroencephalographic recordings from 51 patients. We demonstrate how the various methodological steps (from the recording montage via node and link inference to the assessment of node centralities) affect importance estimation and discuss their impact on the interpretability of findings in the context of pathophysiological aspects of seizure dynamics.

  18. Altered network topology in pediatric traumatic brain injury

    Science.gov (United States)

    Dennis, Emily L.; Rashid, Faisal; Babikian, Talin; Mink, Richard; Babbitt, Christopher; Johnson, Jeffrey; Giza, Christopher C.; Asarnow, Robert F.; Thompson, Paul M.

    2017-11-01

    Outcome after a traumatic brain injury (TBI) is quite variable, and this variability is not solely accounted for by severity or demographics. Identifying sub-groups of patients who recover faster or more fully will help researchers and clinicians understand sources of this variability, and hopefully lead to new therapies for patients with a more prolonged recovery profile. We have previously identified two subgroups within the pediatric TBI patient population with different recovery profiles based on an ERP-derived (event-related potential) measure of interhemispheric transfer time (IHTT). Here we examine structural network topology across both patient groups and healthy controls, focusing on the `rich-club' - the core of the network, marked by high degree nodes. These analyses were done at two points post-injury - 2-5 months (post-acute), and 13-19 months (chronic). In the post-acute time-point, we found that the TBI-slow group, those showing longitudinal degeneration, showed hyperconnectivity within the rich-club nodes relative to the healthy controls, at the expense of local connectivity. There were minimal differences between the healthy controls and the TBI-normal group (those patients who show signs of recovery). At the chronic phase, these disruptions were no longer significant, but closer analysis showed that this was likely due to the loss of power from a smaller sample size at the chronic time-point, rather than a sign of recovery. We have previously shown disruptions to white matter (WM) integrity that persist and progress over time in the TBI-slow group, and here we again find differences in the TBI-slow group that fail to resolve over the first year post-injury.

  19. Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome

    Science.gov (United States)

    Haimovici, Ariel; Tagliazucchi, Enzo; Balenzuela, Pablo; Chialvo, Dante R.

    2013-04-01

    The relation between large-scale brain structure and function is an outstanding open problem in neuroscience. We approach this problem by studying the dynamical regime under which realistic spatiotemporal patterns of brain activity emerge from the empirically derived network of human brain neuroanatomical connections. The results show that critical dynamics unfolding on the structural connectivity of the human brain allow the recovery of many key experimental findings obtained from functional magnetic resonance imaging, such as divergence of the correlation length, the anomalous scaling of correlation fluctuations, and the emergence of large-scale resting state networks.

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

    Directory of Open Access Journals (Sweden)

    Zhiliang Liu

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  2. Graph theoretical analysis and application of fMRI-based brain network in Alzheimer's disease

    Directory of Open Access Journals (Sweden)

    LIU Xue-na

    2012-08-01

    Full Text Available Alzheimer's disease (AD, a progressive neurodegenerative disease, is clinically characterized by impaired memory and many other cognitive functions. However, the pathophysiological mechanisms underlying the disease are not thoroughly understood. In recent years, using functional magnetic resonance imaging (fMRI as well as advanced graph theory based network analysis approach, several studies of patients with AD suggested abnormal topological organization in both global and regional properties of functional brain networks, specifically, as demonstrated by a loss of small-world network characteristics. These studies provide novel insights into the pathophysiological mechanisms of AD and could be helpful in developing imaging biomarkers for disease diagnosis. In this paper we introduce the essential concepts of complex brain networks theory, and review recent advances of the study on human functional brain networks in AD, especially focusing on the graph theoretical analysis of small-world network based on fMRI. We also propound the existent problems and research orientation.

  3. The research of constructing dynamic cognition model based on brain network

    Directory of Open Access Journals (Sweden)

    Fang Chunying

    2017-03-01

    Full Text Available Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function

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

    Science.gov (United States)

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

    2017-01-01

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

  5. Sparse and shrunken estimates of MRI networks in the brain and their influence on network properties

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    Romero-Garcia, Rafael; Clemmensen, Line H.

    2014-03-01

    Estimation of morphometric relationships between cortical regions is a widely used approach to identify and characterize structural connectivity. The elevated number of regions that can be considered in a whole-brain correlation analysis might lead to overfitted models. However, the overfitting can be avoided by using regularization methods. We found that, as expected, non-regularized correlations had low variability when a scarce number of variables were considered. However, a slight increase of variables led to an increase of variance of several magnitude orders. On the other hand, the regularized approaches showed more stable results with a relative low variance at the expense of a little bias. Interestingly, topological properties as local and global efficiency estimated in networks constructed from traditional non-regularized correlations also showed higher variability when compared to those from regularized networks. Our findings suggest that a population-based connectivity study can achieve a more robust description of cortical topology through regularization of the correlation estimates. Four regularization methods were examined: Two with shrinkage (Ridge and Schäfer's shrinkage), one with sparsity (Lasso) and one with both shrinkage and sparsity (Elastic net). Furthermore, the different regularizations resulted in different correlation estimates as well as network properties. The shrunken estimates resulted in lower variance of the estimates than the sparse estimates.

  6. Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding.

    Science.gov (United States)

    Pedersen, Mangor; Omidvarnia, Amir H; Walz, Jennifer M; Jackson, Graeme D

    2015-01-01

    Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications.

  7. The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue.

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    Chunlin Zhao; Min Zhao; Yong Yang; Junfeng Gao; Nini Rao; Pan Lin

    2017-05-01

    The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroen-cephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient (Cp) for beta, alpha, and delta bands and the character path length (Lp) for all EEG bands. The normalized measures γ showed significant increases in beta, alpha, and delta bands, and λ showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.

  8. The many levels of causal brain network discovery. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino and S.L. Bressler

    Science.gov (United States)

    Valdes-Sosa, Pedro A.

    2015-12-01

    Unraveling the dynamically changing networks of the brain is probably the single most important current task for the neurosciences. I wish to commend the authors on this refreshing and provocative paper [1], which not only recapitulates some of the longstanding philosophical difficulties involved in the analysis of causality in the sciences, but also summarizes current work on statistical methods for determining causal networks in the brain. I fully concur with several of the opinions defended by the authors: The most fruitful level of analysis for systems neuroscience is that of neural masses, each comprising thousands of neurons. This is what is known as the mesoscopic scale.

  9. A disease-specific metabolic brain network associated with corticobasal degeneration

    NARCIS (Netherlands)

    Niethammer, Martin; Tang, Chris C.; Feigin, Andrew; Allen, Patricia J.; Heinen, Lisette; Hellwig, Sabine; Amtage, Florian; Hanspal, Era; Vonsattel, Jean Paul; Poston, Kathleen L.; Meyer, Philipp T.; Leenders, Klaus L.; Eidelberg, David

    2014-01-01

    Corticobasal degeneration is an uncommon parkinsonian variant condition that is diagnosed mainly on clinical examination. To facilitate the differential diagnosis of this disorder, we used metabolic brain imaging to characterize a specific network that can be used to discriminate corticobasal

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