WorldWideScience

Sample records for brain anatomical network

  1. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI.

    Science.gov (United States)

    He, Yong; Chen, Zhang J; Evans, Alan C

    2007-10-01

    An important issue in neuroscience is the characterization for the underlying architectures of complex brain networks. However, little is known about the network of anatomical connections in the human brain. Here, we investigated large-scale anatomical connection patterns of the human cerebral cortex using cortical thickness measurements from magnetic resonance images. Two areas were considered anatomically connected if they showed statistically significant correlations in cortical thickness and we constructed the network of such connections using 124 brains from the International Consortium for Brain Mapping database. Significant short- and long-range connections were found in both intra- and interhemispheric regions, many of which were consistent with known neuroanatomical pathways measured by human diffusion imaging. More importantly, we showed that the human brain anatomical network had robust small-world properties with cohesive neighborhoods and short mean distances between regions that were insensitive to the selection of correlation thresholds. Additionally, we also found that this network and the probability of finding a connection between 2 regions for a given anatomical distance had both exponentially truncated power-law distributions. Our results demonstrated the basic organizational principles for the anatomical network in the human brain compatible with previous functional networks studies, which provides important implications of how functional brain states originate from their structural underpinnings. To our knowledge, this study provides the first report of small-world properties and degree distribution of anatomical networks in the human brain using cortical thickness measurements.

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

    Science.gov (United States)

    Shu, Ni; Liu, Yaou; Duan, Yunyun; Li, Kuncheng

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Ni Shu

    2015-01-01

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

  4. Disruption of brain anatomical networks in schizophrenia: A longitudinal, diffusion tensor imaging based study.

    Science.gov (United States)

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

    2016-03-01

    Despite convergent neuroimaging evidence indicating a wide range of brain abnormalities in schizophrenia, our understanding of alterations in the topological architecture of brain anatomical networks and how they are modulated over time, is still rudimentary. Here, we employed graph theoretical analysis of longitudinal diffusion tensor imaging data (DTI) over a 5-year period to investigate brain network topology in schizophrenia and its relationship with clinical manifestations of the illness. Using deterministic tractography, weighted brain anatomical networks were constructed from 31 patients experiencing schizophrenia and 28 age- and gender-matched healthy control subjects. Although the overall small-world characteristics were observed at both baseline and follow-up, a scan-point independent significant deficit of global integration was found in patients compared to controls, suggesting dysfunctional integration of the brain and supporting the notion of schizophrenia as a disconnection syndrome. Specifically, several brain regions (e.g., the inferior frontal gyrus and the bilateral insula) that are crucial for cognitive and emotional integration were aberrant. Furthermore, a significant group-by-longitudinal scan interaction was revealed in the characteristic path length and global efficiency, attributing to a progressive aberration of global integration in patients compared to healthy controls. Moreover, the progressive disruptions of the brain anatomical network topology were associated with the clinical symptoms of the patients. Together, our findings provide insights into the substrates of anatomical dysconnectivity patterns for schizophrenia and highlight the potential for connectome-based metrics as neural markers of illness progression and clinical change with treatment.

  5. The anatomical distance of functional connections predicts brain network topology in health and schizophrenia.

    Science.gov (United States)

    Alexander-Bloch, Aaron F; Vértes, Petra E; Stidd, Reva; Lalonde, François; Clasen, Liv; Rapoport, Judith; Giedd, Jay; Bullmore, Edward T; Gogtay, Nitin

    2013-01-01

    The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive "pruning" of short-distance functional connections in schizophrenia.

  6. Reduced Hemispheric Asymmetry of Brain Anatomical Networks Is Linked to Schizophrenia: A Connectome Study.

    Science.gov (United States)

    Sun, Yu; Chen, Yu; Collinson, Simon L; Bezerianos, Anastasios; Sim, Kang

    2017-01-01

    Despite convergent evidence indicating a variety of regional abnormalities of hemispheric asymmetry in schizophrenia, patterns of wider neural network asymmetry remain to be determined. In this study, we investigated alterations in hemispheric white matter topology in schizophrenia and their association with clinical manifestations of the illness. Weighted hemispheric brain anatomical networks were constructed for each of 116 right-handed patients with schizophrenia and 66 matched healthy participants. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that although small-world properties were preserved in the hemispheric network, a significant hemispheric-independent deficit of global integration was found in schizophrenia. Furthermore, a significant group-by-hemisphere interaction was revealed in the characteristic path length and global efficiency, attributing to significantly reduced hemispheric asymmetry of global integration in patients compared with healthy controls. Specifically, we found reduced asymmetric nodal efficiency in several frontal regions and the hippocampus. Finally, the abnormal hemispheric asymmetry of brain anatomical network topology was associated with clinical features (duration of illness and psychotic psychopathology) in patients. Our findings provide new insights into lateralized nature of hemispheric dysconnectivity and highlight the potential for using brain network measures of hemispheric asymmetry as neural biomarkers for schizophrenia and its clinical features.

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

    Science.gov (United States)

    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.

  8. Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability.

    Science.gov (United States)

    Langs, Georg; Wang, Danhong; Golland, Polina; Mueller, Sophia; Pan, Ruiqi; Sabuncu, Mert R; Sun, Wei; Li, Kuncheng; Liu, Hesheng

    2016-10-01

    The connectivity architecture of the human brain varies across individuals. Mapping functional anatomy at the individual level is challenging, but critical for basic neuroscience research and clinical intervention. Using resting-state functional connectivity, we parcellated functional systems in an "embedding space" based on functional characteristics common across the population, while simultaneously accounting for individual variability in the cortical distribution of functional units. The functional connectivity patterns observed in resting-state data were mapped in the embedding space and the maps were aligned across individuals. A clustering algorithm was performed on the aligned embedding maps and the resulting clusters were transformed back to the unique anatomical space of each individual. This novel approach identified functional systems that were reproducible within subjects, but were distributed across different anatomical locations in different subjects. Using this approach for intersubject alignment improved the predictability of individual differences in language laterality when compared with anatomical alignment alone. Our results further revealed that the strength of association between function and macroanatomy varied across the cortex, which was strong in unimodal sensorimotor networks, but weak in association networks.

  9. Functional organization of complex brain networks modulated by acupuncture at different acupoints belonging to the same anatomic segment

    Institute of Scientific and Technical Information of China (English)

    CHEN Shang-jie; MENG Lan; YAN Hao; BAI Li-jun; WANG Fang; HUANG Yong; LI Jian-ping; PENG Xu-ming; SHI Xue-min

    2012-01-01

    Background Noninvasive functional magnetic resonance imaging (fMRI) techniques have opened a “window” into the brain.allowing us to investigate the anatomical and physiological function involving acupuncture needling.Imaging its sustained effect rather than acute effect on the brain networks may further help elucidate the mechanisms by which acupuncture achieves its therapeutic effects.In this study,we aimed to investigate the functional brain networks during the post-resting state following acupuncture at KI3 in comparison with acupuncture at GB40.Methods Needling at acupoints GB40 and KI3 was performed in twelve subjects.Six minutes of scanning at rest were adopted before and after acupuncture at different acupoints.Then we divided the whole brain into 39 regions and constructed functional brain networks during the post-acupuncture resting states (PARS).Results For direct comparisons.increased correlations during post-resting state following acupuncture at KI3compared to resting state (RS) were primarily located between the dorsolateral prefrontal cortex (DLPFC) and post temporal cortex,ventromedial prefrontal cortex (vmPFC) and post temporal cortex.These brain regions were all cognitive-related functions.In contrast.the increased connections between the anterior insula and temporal cortex mainly emerged following acupuncture at GB40 compared with the RS.Conclusions The present study demonstrates that acupuncture at different acupoints belonging to the same anatomic segment can exert different modulatory effects on the reorganizations of post-acupuncture RS networks.The heterogeneous modulation patterns between two conditions may relate to the functional specific modulatory effects of acupuncture.

  10. Increased global and local efficiency of human brain anatomical networks detected with FLAIR-DTI compared to non-FLAIR-DTI.

    Directory of Open Access Journals (Sweden)

    Shumei Li

    Full Text Available Diffusion-weighted MRI (DW-MRI, the only non-invasive technique for probing human brain white matter structures in vivo, has been widely used in both fundamental studies and clinical applications. Many studies have utilized diffusion tensor imaging (DTI and tractography approaches to explore the topological properties of human brain anatomical networks by using the single tensor model, the basic model to quantify DTI indices and tractography. However, the conventional DTI technique does not take into account contamination by the cerebrospinal fluid (CSF, which has been known to affect the estimated DTI measures and tractography in the single tensor model. Previous studies have shown that the Fluid-Attenuated Inversion Recovery (FLAIR technique can suppress the contribution of the CSF to the DW-MRI signal. We acquired DTI datasets from twenty-two subjects using both FLAIR-DTI and conventional DTI (non-FLAIR-DTI techniques, constructed brain anatomical networks using deterministic tractography, and compared the topological properties of the anatomical networks derived from the two types of DTI techniques. Although the brain anatomical networks derived from both types of DTI datasets showed small-world properties, we found that the brain anatomical networks derived from the FLAIR-DTI showed significantly increased global and local network efficiency compared with those derived from the conventional DTI. The increases in the network regional topological properties derived from the FLAIR-DTI technique were observed in CSF-filled regions, including the postcentral gyrus, periventricular regions, inferior frontal and temporal gyri, and regions in the visual cortex. Because brain anatomical networks derived from conventional DTI datasets with tractography have been widely used in many studies, our findings may have important implications for studying human brain anatomical networks derived from DW-MRI data and tractography.

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

    Science.gov (United States)

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

    2016-12-01

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

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

    Directory of Open Access Journals (Sweden)

    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.

  13. Exploring brain function from anatomical connectivity

    Directory of Open Access Journals (Sweden)

    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.

  14. Network models in anatomical systems.

    Science.gov (United States)

    Esteve-Altava, Borja; Marugán-Lobón, Jesús; Botella, Héctor; Rasskin-Gutman, Diego

    2011-01-01

    Network theory has been extensively used to model the underlying structure of biological processes. From genetics to ecology, network thinking is changing our understanding of complex systems, specifically how their internal structure determines their overall behavior. Concepts such as hubs, scale-free or small-world networks, common in the complexity literature, are now used more and more in sociology, neurosciences, as well as other anthropological fields. Even though the use of network models is nowadays so widely applied, few attempts have been carried out to enrich our understanding in the classical morphological sciences such as in comparative anatomy or physical anthropology. The purpose of this article is to introduce the usage of network tools in morphology; specifically by building anatomical networks, dealing with the most common analyses and problems, and interpreting their outcome.

  15. Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders.

    Science.gov (United States)

    Zhang, Jie; Cheng, Wei; Liu, Zhaowen; Zhang, Kai; Lei, Xu; Yao, Ye; Becker, Benjamin; Liu, Yicen; Kendrick, Keith M; Lu, Guangming; Feng, Jianfeng

    2016-08-01

    SEE MATTAR ET AL DOI101093/AWW151 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the

  16. Altered Anatomical Network in Early Blindness Revealed by Diffusion Tensor Tractography

    OpenAIRE

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

    2009-01-01

    The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. 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 e...

  17. Brain Morphometry Using Anatomical Magnetic Resonance Imaging

    Science.gov (United States)

    Bansal, Ravi; Gerber, Andrew J.; Peterson, Bradley S.

    2008-01-01

    The efficacy of anatomical magnetic resonance imaging (MRI) in studying the morphological features of various regions of the brain is described, also providing the steps used in the processing and studying of the images. The ability to correlate these features with several clinical and psychological measures can help in using anatomical MRI to…

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  20. Anatomic brain asymmetry in vervet monkeys.

    Science.gov (United States)

    Fears, Scott C; Scheibel, Kevin; Abaryan, Zvart; Lee, Chris; Service, Susan K; Jorgensen, Matthew J; Fairbanks, Lynn A; Cantor, Rita M; Freimer, Nelson B; Woods, Roger P

    2011-01-01

    Asymmetry is a prominent feature of human brains with important functional consequences. Many asymmetric traits show population bias, but little is known about the genetic and environmental sources contributing to inter-individual variance. Anatomic asymmetry has been observed in Old World monkeys, but the evidence for the direction and extent of asymmetry is equivocal and only one study has estimated the genetic contributions to inter-individual variance. In this study we characterize a range of qualitative and quantitative asymmetry measures in structural brain MRIs acquired from an extended pedigree of Old World vervet monkeys (n = 357), and implement variance component methods to estimate the proportion of trait variance attributable to genetic and environmental sources. Four of six asymmetry measures show pedigree-level bias and one of the traits has a significant heritability estimate of about 30%. We also found that environmental variables more significantly influence the width of the right compared to the left prefrontal lobe.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  3. Simple models of human brain functional networks.

    Science.gov (United States)

    Vértes, Petra E; Alexander-Bloch, Aaron F; Gogtay, Nitin; Giedd, Jay N; Rapoport, Judith L; Bullmore, Edward T

    2012-04-10

    Human brain functional networks are embedded in anatomical space and have topological properties--small-worldness, modularity, fat-tailed degree distributions--that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.

  4. Small-world brain networks in schizophrenia

    Institute of Scientific and Technical Information of China (English)

    Mingli LI; Zhuangfei CHEN; Tao LI

    2012-01-01

    Over the last decade the combination of brain neuroimaging techniques and graph theoretical analysis of the complex anatomical and functional networks in the brain have provided an exciting new platform for exploring the etiology of mental disorders such as schizophrenia. This review introduces the current status of this work, focusing on these networks in schizophrenia. The evidence supporting the findings of reduced efficiency of information exchange in schizophrenia both within local brain regions and globally throughout the brain is reviewed and the potential relationship of these changes to cognitive and clinical symptoms is discussed. Finally we propose some suggestions for future research.

  5. Anatomic Eponyms in Neuroradiology: Brain, Cerebral Vasculature, and Calvarium.

    Science.gov (United States)

    Bunch, Paul M; Zamani, Amir A

    2016-06-01

    Medical eponyms are ubiquitous, numerous, and at times controversial. They are often useful for succinctly conveying complex concepts, and familiarity with eponyms is important for proper usage and appropriate communication. In this historical review, we identify 18 anatomic eponyms used to describe structures of the brain, cerebral vasculature, and calvarium. For each structure, we first offer a biographical sketch of the individual for whom the structure is named. This is followed by a description of the anatomic structure and a brief discussion of its clinical relevance.

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

    Science.gov (United States)

    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.

  7. Consensus between pipelines in structural brain networks.

    Directory of Open Access Journals (Sweden)

    Christopher S Parker

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

  8. Anatomical and biochemical investigation of primary brain tumours

    Energy Technology Data Exchange (ETDEWEB)

    Del Sole, A. [Univ. di Milano (Italy); Falini, A. [Univ. Vita e Salute (Italy). IRCCS; Ravasi, L.; Ottobrini, L.; Lucignani, G. [Univ. di Milano (Italy). Ist. di Scienze Radiologiche; De Marchis, D. [Univ. di Milano-Bicocca (Italy); Bombardieri, E. [Istituto Nazionale dei Tumori, Milano (Italy)

    2001-12-01

    Cancerous transformation entails major biochemical changes including modifications of the energy metabolism of the cell, e.g. utilisation of glucose and other substrates, protein synthesis, and expression of receptors and antigens. Tumour growth also leads to heterogeneity in blood flow owing to focal necrosis, angiogenesis and metabolic demands, as well as disruption of transport mechanisms of substrates across cell membranes and other physiological boundaries such as the blood-brain barrier. All these biochemical, histological and anatomical changes can be assessed with emission tomography, X-ray computed tomography (CT), magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). Whereas anatomical imaging is aimed at the diagnosis of brain tumours, biochemical imaging is better suited for tissue characterisation. The identification of a tumoural mass and the assessment of its size and vascularisation are best achieved with X-ray CT and MRI, while biochemical imaging can provide additional information that is crucial for tumour classification, differential diagnosis and follow-up. As the assessment of variables such as water content, appearance of cystic lesions and location of the tumour are largely irrelevant for tissue characterisation, a number of probes have been employed for the assessment of the biochemical features of tumours. Since biochemical changes may be related to the growth rate of cancer cells, they can be thought of as markers of tumour cell proliferation. Biochemical imaging with radionuclides of processes that occur at a cellular level provides information that complements findings obtained by anatomical imaging aimed at depicting structural, vascular and histological changes. This review focusses on the clinical application of anatomical brain imaging and biochemical assessment with positron emission tomography, single-photon emission tomography and MRS in the diagnosis of primary brain tumours, as well as in follow-up. (orig.)

  9. Controllability of Brain Networks

    OpenAIRE

    Gu, Shi; Pasqualetti, Fabio; Cieslak, Matthew; Grafton, Scott T.; Bassett, Danielle S.

    2014-01-01

    Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behavior. Fundamental principles constraining these dynamic network processes have remained elusive. Here we use network control theory to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilit...

  10. 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...

  11. Multilayer motif analysis of brain networks

    CERN Document Server

    Battiston, Federico; Chavez, Mario; Latora, Vito

    2016-01-01

    In the last decade network science has shed new light on the anatomical connectivity and on correlations in the activity of different areas of the human brain. The study of brain networks has made possible in fact 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 structural and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows to perform a multiplex analysis of the human brain where the structural and functional layers are considered at the same time. In this work we describe how to classify subgraphs in multiplex networks, and we extend motif analysis to networks with many layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, respectively obtained from diffusion and functional magnetic resonance imaging. Results i...

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

    Science.gov (United States)

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

    2014-03-01

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

  13. Disrupted brain anatomical connectivity in medication-naïve patients with first-episode schizophrenia.

    Science.gov (United States)

    Zhang, Ruibin; Wei, Qinling; Kang, Zhuang; Zalesky, Andrew; Li, Meng; Xu, Yong; Li, Leijun; Wang, Junjing; Zheng, Liangrong; Wang, Bin; Zhao, Jingping; Zhang, Jinbei; Huang, Ruiwang

    2015-03-01

    Previous studies suggested that the topological properties of brain anatomical networks may be aberrant in schizophrenia (SCZ), and most of them focused on the chronic and antipsychotic-medicated SCZ patients which may introduce various confounding factors due to antipsychotic medication and duration of illness. To avoid those potential confounders, a desirable approach is to select medication-naïve, first-episode schizophrenia (FE-SCZ) patients. In this study, we acquired diffusion tensor imaging datasets from 30 FE-SCZ patients and 34 age- and gender-matched healthy controls. Taking a distinct gray matter region as a node, inter-regional connectivity as edge and the corresponding streamline counts as edge weight, we constructed whole-brain anatomical networks for both groups, calculated their topological parameters using graph theory, and compared their between-group differences using nonparametric permutation tests. In addition, network-based statistic method was utilized to identify inter-regional connections which were impaired in the FE-SCZ patients. We detected only significantly decreased inter-regional connections in the FE-SCZ patients compared to the controls. These connections were primarily located in the frontal, parietal, occipital, and subcortical regions. Although small-worldness was conserved in the FE-SCZ patients, we found that the network strength and global efficiency as well as the degree were significantly decreased, and shortest path length was significantly increased in the FE-SCZ patients compared to the controls. Most of the regions that showed significantly decreased nodal parameters belonged to the top-down control, sensorimotor, basal ganglia, and limbic-visual system systems. Correlation analysis indicated that the nodal efficiency in the sensorimotor system was negatively correlated with the severity of psychosis symptoms in the FE-SCZ patients. Our results suggest that the network organization is changed in the early stages of the

  14. Training brain networks and states.

    Science.gov (United States)

    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.

  15. Anatomical distribution of estrogen target neurons in turtle brain

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Y.S.; Stumpf, W.E.; Sar, M. (North Carolina Univ., Chapel Hill (USA))

    1981-12-28

    Autoradiographic studies with (/sup 3/H)estradiol-17..beta.. in red-eared turtle (Pseudemys scripta elegans) show concentration and retention of radioactivity in nuclei of neurons in certain regions. Accumulations of estrogen target neurons exist in the periventricular brain with relationships to ventral extensions of the forebrain ventricles, including parolfactory, amygdaloid, septal, preoptic, hypothalamic and thalamic areas, as well as the dorsal ventricular ridge, the piriform cortex, and midbrain-pontine periaqueductal structures. The general anatomical pattern of distribution of estrogen target neurons corresponds to those observed not only in another reptile (Anolis carolinensis), but also in birds and mammals, as well as in teleosts and cyclostomes. In Pseudemys, which appears to display an intermediate degree of phylogenetic differentiation, the amygdaloid-septal-preoptic groups of estrogen target neurons constitute a continuum. In phylogenetic ascendency, e.g. in mammals, these cell populations are increasingly separated and distinct, while in phylogenetic descendency, e.g. in teleosts and cyclostomes, an amygdaloid group appears to be absent or contained within the septal-preoptic target cell population.

  16. Anatomical standardization of small animal brain FDG-PET images using synthetic functional template: experimental comparison with anatomical template.

    Science.gov (United States)

    Coello, Christopher; Hjornevik, Trine; Courivaud, Frédéric; Willoch, Frode

    2011-07-15

    Anatomical standardization (also called spatial normalization) of positron emission tomography (PET) small animal brain images is required to make statistical comparisons across individuals. Frequently, PET images are co-registered to an individual MR or CT image of the same subject in order to transform the functional images to an anatomical space. In the present work, we evaluate the normalization of synthetic PET (synPET) images to a synthetic PET template. To provide absolute error in terms of pixel misregistration, we created a synthetic PET image from the individual MR image through segmentation of the brain into gray and white matter which produced functional and anatomical images in the same space. When comparing spatial normalization of synPET images to a synPET template with the gold standard (MR images to an MR template), a mean translation error of 0.24mm (±0.20) and a maximal mean rotational error of 0.85° (±0.91) were found. Significant decrease in misregistration error was measured when achieving spatial normalization of functional images to a functional template instead of an anatomical template. This accuracy strengthens the use of standardization methods where individual PET images are registered to a customized PET template in order to statistically assess physiological changes in rat brains.

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

    Directory of Open Access Journals (Sweden)

    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.

  18. 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...

  19. Hierarchical organization of brain functional network during visual task

    CERN Document Server

    Zhuo, Zhao; Fu, Zhong-Qian; Zhang, Jie

    2011-01-01

    In this paper, the brain functional networks derived from high-resolution synchronous EEG time series during visual task are generated by calculating the phase synchronization among the time series. The hierarchical modular organizations of these networks are systematically investigated by the fast Girvan-Newman algorithm. At the same time, the spatially adjacent electrodes (corresponding to EEG channels) are clustered into functional groups based on anatomical parcellation of brain cortex, and this clustering information are compared to that of the functional network. The results show that the modular architectures of brain functional network are in coincidence with that from the anatomical structures over different levels of hierarchy, which suggests that population of neurons performing the same function excite and inhibit in identical rhythms. The structure-function relationship further reveals that the correlations among EEG time series in the same functional group are much stronger than those in differe...

  20. Controllability of structural brain networks.

    Science.gov (United States)

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

    2015-10-01

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

  1. Avoiding the ventricle : a simple step to improve accuracy of anatomical targeting during deep brain stimulation

    NARCIS (Netherlands)

    Zrinzo, Ludvic; van Hulzen, Arjen L. J.; Gorgulho, Alessandra A.; Limousin, Patricia; Staal, Michiel J.; De Salles, Antonio A. F.; Hariz, Marwan I.

    2009-01-01

    Object. The authors examined the accuracy of anatomical targeting during electrode implantation for deep brain stimulation in functional neurosurgical procedures. Special attention was focused on the impact that ventricular involvement of the electrode trajectory had on targeting accuracy. Methods.

  2. Characterizing brain anatomical connections using diffusion weighted MRI and graph theory.

    Science.gov (United States)

    Iturria-Medina, Y; Canales-Rodríguez, E J; Melie-García, L; Valdés-Hernández, P A; Martínez-Montes, E; Alemán-Gómez, Y; Sánchez-Bornot, J M

    2007-07-01

    A new methodology based on Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) and Graph Theory is presented for characterizing the anatomical connections between brain gray matter areas. In a first step, brain voxels are modeled as nodes of a non-directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by nervous fibers. This probability is estimated by means of probabilistic tissue segmentation and intravoxel white matter orientational distribution function, obtained from anatomical MRI and DW-MRI, respectively. A new tractography algorithm for finding white matter routes is also introduced. This algorithm solves the most probable path problem between any two nodes, leading to the assessment of probabilistic brain anatomical connection maps. In a second step, for assessing anatomical connectivity between K gray matter structures, the previous graph is redefined as a K+1 partite graph by partitioning the initial nodes set in K non-overlapped gray matter subsets and one subset clustering the remaining nodes. Three different measures are proposed for quantifying anatomical connections between any pair of gray matter subsets: Anatomical Connection Strength (ACS), Anatomical Connection Density (ACD) and Anatomical Connection Probability (ACP). This methodology was applied to both artificial and actual human data. Results show that nervous fiber pathways between some regions of interest were reconstructed correctly. Additionally, mean connectivity maps of ACS, ACD and ACP between 71 gray matter structures for five healthy subjects are presented.

  3. Complex brain networks: From topological communities to clustered dynamics

    Indian Academy of Sciences (India)

    Lucia Zemanová; Gorka Zamora-López; Changsong Zhou; Jürgen Kurths

    2008-06-01

    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 activities. We investigate synchronisation dynamics on the corticocortical network of the cat by modelling each node of the network (cortical area) with a subnetwork of interacting excitable neurons. We find that this network of networks displays clustered synchronisation behaviour and the dynamical clusters closely coincide with the topological community structures observed in the anatomical network. The correlation between the firing rate of the areas and the areal intensity is additionally examined. Our results provide insights into the relationship between the global organisation and the functional specialisation of the brain cortex.

  4. Brain networks in aging and dementia

    NARCIS (Netherlands)

    Hafkemeijer, A.

    2016-01-01

    This thesis describes neuroimaging techniques to investigate brain networks in healthy aging and dementia. Functional and structural brain networks change with healthy and pathological aging, with differences in network degeneration between different types of dementia. These disease-specific network

  5. Resting state cortico-cerebellar functional connectivity networks: a comparison of anatomical and self-organizing map approaches.

    Science.gov (United States)

    Bernard, Jessica A; Seidler, Rachael D; Hassevoort, Kelsey M; Benson, Bryan L; Welsh, Robert C; Wiggins, Jillian Lee; Jaeggi, Susanne M; Buschkuehl, Martin; Monk, Christopher S; Jonides, John; Peltier, Scott J

    2012-01-01

    The cerebellum plays a role in a wide variety of complex behaviors. In order to better understand the role of the cerebellum in human behavior, it is important to know how this structure interacts with cortical and other subcortical regions of the brain. To date, several studies have investigated the cerebellum using resting-state functional connectivity magnetic resonance imaging (fcMRI; Krienen and Buckner, 2009; O'Reilly et al., 2010; Buckner et al., 2011). However, none of this work has taken an anatomically-driven lobular approach. Furthermore, though detailed maps of cerebral cortex and cerebellum networks have been proposed using different network solutions based on the cerebral cortex (Buckner et al., 2011), it remains unknown whether or not an anatomical lobular breakdown best encompasses the networks of the cerebellum. Here, we used fcMRI to create an anatomically-driven connectivity atlas of the cerebellar lobules. Timecourses were extracted from the lobules of the right hemisphere and vermis. We found distinct networks for the individual lobules with a clear division into "motor" and "non-motor" regions. We also used a self-organizing map (SOM) algorithm to parcellate the cerebellum. This allowed us to investigate redundancy and independence of the anatomically identified cerebellar networks. We found that while anatomical boundaries in the anterior cerebellum provide functional subdivisions of a larger motor grouping defined using our SOM algorithm, in the posterior cerebellum, the lobules were made up of sub-regions associated with distinct functional networks. Together, our results indicate that the lobular boundaries of the human cerebellum are not necessarily indicative of functional boundaries, though anatomical divisions can be useful. Additionally, driving the analyses from the cerebellum is key to determining the complete picture of functional connectivity within the structure.

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

    Science.gov (United States)

    Bohland, Jason W; Saperstein, Sara; Pereira, Francisco; Rapin, Jérémy; Grady, Leo

    2012-01-01

    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 current literature.

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

    Directory of Open Access Journals (Sweden)

    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

  8. Probabilistic MRI brain anatomical atlases based on 1,000 Chinese subjects.

    Directory of Open Access Journals (Sweden)

    Xing Wang

    Full Text Available Brain atlases are designed to provide a standard reference coordinate system of the brain for neuroscience research. Existing human brain atlases are widely used to provide anatomical references and information regarding structural characteristics of the brain. The majority of them, however, are derived from one paticipant or small samples of the Western population. This poses a limitation for scientific studies on Eastern subjects. In this study, 10 new Chinese brain atlases for different ages and genders were constructed using MR anatomical images based on HAMMER (Hierarchical Attribute Matching Mechanism for Elastic Registration. A total of 1,000 Chinese volunteers ranging from 18 to 70 years old participated in this study. These population-specific brain atlases represent the basic structural characteristics of the Chinese population. They may be utilized for basic neuroscience studies and clinical diagnosis, including evaluation of neurological and neuropsychiatric disorders, in Chinese patients and those from other Eastern countries.

  9. Reduction of variance in measurements of average metabolite concentration in anatomically-defined brain regions

    Science.gov (United States)

    Larsen, Ryan J.; Newman, Michael; Nikolaidis, Aki

    2016-11-01

    Multiple methods have been proposed for using Magnetic Resonance Spectroscopy Imaging (MRSI) to measure representative metabolite concentrations of anatomically-defined brain regions. Generally these methods require spectral analysis, quantitation of the signal, and reconciliation with anatomical brain regions. However, to simplify processing pipelines, it is practical to only include those corrections that significantly improve data quality. Of particular importance for cross-sectional studies is knowledge about how much each correction lowers the inter-subject variance of the measurement, thereby increasing statistical power. Here we use a data set of 72 subjects to calculate the reduction in inter-subject variance produced by several corrections that are commonly used to process MRSI data. Our results demonstrate that significant reductions of variance can be achieved by performing water scaling, accounting for tissue type, and integrating MRSI data over anatomical regions rather than simply assigning MRSI voxels with anatomical region labels.

  10. An algebraic topological method for multimodal brain networks comparison

    Directory of Open Access Journals (Sweden)

    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.

  11. Evaluation and calibration of functional network modeling methods based on known anatomical connections.

    Science.gov (United States)

    Dawson, Debra Ann; Cha, Kuwook; Lewis, Lindsay B; Mendola, Janine D; Shmuel, Amir

    2013-02-15

    Recent studies have identified large scale brain networks based on the spatio-temporal structure of spontaneous fluctuations in resting-state fMRI data. It is expected that functional connectivity based on resting-state data is reflective of - but not identical to - the underlying anatomical connectivity. However, which functional connectivity analysis methods reliably predict the network structure remains unclear. Here we tested and compared network connectivity analysis methods by applying them to fMRI resting-state time-series obtained from the human visual cortex. The methods evaluated here are those previously tested against simulated data in Smith et al. (Neuroimage, 2011). To this end, we defined regions within retinotopic visual areas V1, V2, and V3 according to their eccentricity in the visual field, delineating central, intermediate, and peripheral eccentricity regions of interest (ROIs). These ROIs served as nodes in the models we study. We based our evaluation on the "ground-truth", thoroughly studied retinotopically-organized anatomical connectivity in the monkey visual cortex. For each evaluated method, we computed the fractional rate of detecting connections known to exist ("c-sensitivity"), while using a threshold of the 95th percentile of the distribution of interaction magnitudes of those connections not expected to exist. Under optimal conditions - including session duration of 68min, a relatively small network consisting of 9 nodes and artifact-free regression of the global effect - each of the top methods predicted the expected connections with 67-85% c-sensitivity. Correlation methods, including Correlation (Corr; 85%), Regularized Inverse Covariance (ICOV; 84%) and Partial Correlation (PCorr; 81%) performed best, followed by Patel's Kappa (80%), Bayesian Network method PC (BayesNet; 77%), General Synchronization measures (67-77%), and Coherence (CohB; 74%). With decreased session duration, these top methods saw decreases in c

  12. 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.

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

    Directory of Open Access Journals (Sweden)

    Florian Klimm

    2014-03-01

    Full Text Available Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful

  14. 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.

  15. Assortative mixing in functional brain networks during epileptic seizures

    CERN Document Server

    Bialonski, Stephan

    2013-01-01

    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.

  16. Individual 3D region-of-interest atlas of the human brain: knowledge-based class image analysis for extraction of anatomical objects

    Science.gov (United States)

    Wagenknecht, Gudrun; Kaiser, Hans-Juergen; Sabri, Osama; Buell, Udalrich

    2000-06-01

    After neural network-based classification of tissue types, the second step of atlas extraction is knowledge-based class image analysis to get anatomically meaningful objects. Basic algorithms are region growing, mathematical morphology operations, and template matching. A special algorithm was designed for each object. The class label of each voxel and the knowledge about the relative position of anatomical objects to each other and to the sagittal midplane of the brain can be utilized for object extraction. User interaction is only necessary to define starting, mid- and end planes for most object extractions and to determine the number of iterations for erosion and dilation operations. Extraction can be done for the following anatomical brain regions: cerebrum; cerebral hemispheres; cerebellum; brain stem; white matter (e.g., centrum semiovale); gray matter [cortex, frontal, parietal, occipital, temporal lobes, cingulum, insula, basal ganglia (nuclei caudati, putamen, thalami)]. For atlas- based quantification of functional data, anatomical objects can be convoluted with the point spread function of functional data to take into account the different resolutions of morphological and functional modalities. This method allows individual atlas extraction from MRI image data of a patient without the need of warping individual data to an anatomical or statistical MRI brain atlas.

  17. Altered small-world anatomical networks in Apolipoprotein-E4 (ApoE4) carriers using MRI.

    Science.gov (United States)

    Goryawala, Mohammed; Zhou, Qi; Duara, Ranjan; Loewenstein, David; Cabrerizo, Mercedes; Barker, Warren; Adjouadi, Malek

    2014-01-01

    Apolipoprotein E (ApoE) gene and primarily its allele e4 have been identified as a risk factor for Alzheimer's disease (AD). The prevalence of the gene in 25-30% in the population makes it essential to estimate its role in neuroregulation and its impact on distributed brain networks. In this study, we provide computational neuroanatomy based interpretation of large-scale and small-world cortical networks in cognitive normal (CN) subjects with differing Apolipoprotein-E4 (ApoE4) gene expression. We estimated large-scale anatomical networks from cortical thickness measurements derived from magnetic resonance imaging in 147 CN subjects explored in relation to ApoE4 genotype (e4+ carriers (n=41) versus e4- non-carriers (n=106)). Brain networks were constructed by thresholding cortical thickness correlation matrices of 68 bilateral regions of the brain analyzed using well-established graph theoretical approaches. Compared to ApoE4 non-carriers, carriers showed increased interregional correlation coefficients in regions like precentral, superior frontal and inferior temporal regions. Interestingly most of the altered connections were intra-hemispheric limited primarily to the right hemisphere. Furthermore, ApoE4 carriers demonstrated abnormal small-world architecture in the cortical networks with increased clustering coefficient and path lengths as compared to non-carrier, suggesting a less optimal topological organization. Additionally non-carriers demonstrated higher betweenness in regions such as middle temporal, para-hippocampal gyrus, posterior cingulate and insula of the default mode network (DMN), also seen in subjects with AD and mild cognitive impairment (MCI). The results suggest that the complex morphological cortical connectivity patterns are altered in ApoE4 carriers as compared to non-carriers, providing evidence for disruption of integrity in large-scale anatomical brain networks.

  18. 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.

  19. 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.

  20. Thermal Regulation of the Brain-An Anatomical and Physiological Review for Clinical Neuroscientists.

    Science.gov (United States)

    Wang, Huan; Kim, Miri; Normoyle, Kieran P; Llano, Daniel

    2015-01-01

    Humans, like all mammals and birds, maintain a near constant core body temperature of 36-37.5°C over a broad 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. To effectively study and examine brain temperature and its regulation, we must first understand relevant anatomical and physiological properties of thermoregulation in the head-neck regions.

  1. Aberrant anatomical brain network in first-episode schizophrenia and their healthy siblings%精神分裂症首次发病患者及其健康同胞的脑结构网络研究

    Institute of Scientific and Technical Information of China (English)

    国效峰; 郑俊杰; 齐景峰; 胡茂荣; 陈华富; 赵靖平

    2015-01-01

    Objective To determine whether the aberrant white matter network is shared by patients with schizophrenia and their healthy siblings.Methods Fourteen-three first-episode,treatment-naive patients with schizophrenia (patients),40 healthy siblings (siblings),and 55 healthy controls (controls)were scanned with 3.0T MRI scanner and diffusion tensor imaging tractography was used to construct the whiter matter brain network.The differences of white matter network were compared with analysis of variance among the 3 groups.Results The white matter network connectivity strength and the global efficiency significantly reduced in both patients (5.14 ±0.36,0.25 ±0.02) and their siblings (5.25 ±0.27,0.25 ±0.01) comparing to controls (5.41 ± 0.24,0.26 ± 0.01 ; F =16.55 P < 0.01),without significant difference between patients and siblings (P > 0.05).The degree in left precuneus,left anterior cingulate and right orbitofrontal gyrus was significantly lower in patients (7.42 ± 1.04,7.58 ± 1.25 and 3.72 ± 1.46)and siblings (7.51 ± 1.18,7.87 ± 1.10 and 4.42 ± 1.09) than controls (8.22 ± 1.07,8.31 ±0.99 and 4.80 ±0.92,P <0.05,FDR corrected); patients was also lower than siblings in right orhitofrontal gyrus (P < 0.05,FDR corrected).Additionally significantly reduced betweenness centrality in left precuneus,left anterior cingulate and right orbitofrontal gyrus in patients (0.31 ± 0.02,0.32 ± 0.03 and 0.25 ± 0.03) and siblings (0.31 ±0.02,0.33 ±0.02 and 0.27 ±0.03) compared to controls (0.32 ±0.02,0.34 ± 0.02and 0.28 ± 0.02,P < 0.05,FDR corrected) ; patients was also lower than siblings in right orbitofrontal gyrus (P < 0.05,FDR corrected).Conclusions These findings suggest that schizophrenia patients and their healthy siblings share the aberrant white matter network,which may be the susceptibility biological marker for schizophrenia.%目的 探讨精神分裂症患者及其未患病同胞是否存在相似的脑白质结构网络异常.方法 43例

  2. Anatomical Location of LPA1 Activation and LPA Phospholipid Precursors in Rodent and Human Brain

    Science.gov (United States)

    González de San Román, E; Manuel, I; Giralt, MT; Chun, J; Estivill-Torrús, G; Rodriguez de Fonseca, F; Santín, LJ; Ferrer, I; Rodriguez-Puertas, R

    2016-01-01

    Lysophosphatidic acid (LPA) is a signaling molecule that binds to six known G protein-coupled receptors (GPCRs): 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 [35S]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 [35S]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 (PA) and phosphatidylcholines (PC). Both PA and PC 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. PMID:25857358

  3. Human intelligence and brain networks.

    Science.gov (United States)

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

    2010-01-01

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

  4. Reprint of "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

    2014-02-01

    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. Future

  5. 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

    2013-11-01

    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. Future

  6. 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

  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. Quantifying agreement between anatomical and functional interhemispheric correspondences in the resting brain.

    Directory of Open Access Journals (Sweden)

    Hang Joon Jo

    Full Text Available The human brain is composed of two broadly symmetric cerebral hemispheres, with an abundance of reciprocal anatomical connections between homotopic locations. However, to date, studies of hemispheric symmetries have not identified correspondency precisely due to variable cortical folding patterns. Here we present a method to establish accurate correspondency using position on the unfolded cortical surface relative to gyral and sulcal landmarks. The landmark method is shown to outperform the method of reversing standard volume coordinates, and it is used to quantify the functional symmetry in resting fMRI data throughout the cortex. Resting brain activity was found to be maximally correlated with locations less than 1 cm away on the cortical surface from the corresponding anatomical location in nearly half of the cortex. While select locations exhibited asymmetric patterns, precise symmetric relationships were found to be the norm, with fine-grained symmetric functional maps demonstrated in motor, occipital, and inferior frontal cortex.

  9. Aging and functional brain networks

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-07-11

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

  10. Brain Network Analysis from High-Resolution EEG Signals

    Science.gov (United States)

    de Vico Fallani, Fabrizio; Babiloni, Fabio

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

  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. Post-mortem 1.5T MR quantification of regular anatomical brain structures.

    Science.gov (United States)

    Zech, Wolf-Dieter; Hottinger, Anna-Lena; Schwendener, Nicole; Schuster, Frederick; Persson, Anders; Warntjes, Marcel J; Jackowski, Christian

    2016-07-01

    Recently, post-mortem MR quantification has been introduced to the field of post-mortem magnetic resonance imaging. By usage of a particular MR quantification sequence, T1 and T2 relaxation times and proton density (PD) of tissues and organs can be quantified simultaneously. The aim of the present basic research study was to assess the quantitative T1, T2, and PD values of regular anatomical brain structures for a 1.5T application and to correlate the assessed values with corpse temperatures. In a prospective study, 30 forensic cases were MR-scanned with a quantification sequence prior to autopsy. Body temperature was assessed during MR scans. In synthetically calculated T1, T2, and PD-weighted images, quantitative T1, T2 (both in ms) and PD (in %) values of anatomical structures of cerebrum (Group 1: frontal gray matter, frontal white matter, thalamus, internal capsule, caudate nucleus, putamen, and globus pallidus) and brainstem/cerebellum (Group 2: cerebral crus, substantia nigra, red nucleus, pons, cerebellar hemisphere, and superior cerebellar peduncle) were assessed. The investigated brain structures of cerebrum and brainstem/cerebellum could be characterized and differentiated based on a combination of their quantitative T1, T2, and PD values. MANOVA testing verified significant differences between the investigated anatomical brain structures among each other in Group 1 and Group 2 based on their quantitative values. Temperature dependence was observed mainly for T1 values, which were slightly increasing with rising temperature in the investigated brain structures in both groups. The results provide a base for future computer-aided diagnosis of brain pathologies and lesions in post-mortem magnetic resonance imaging.

  13. 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).

  14. 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.

  15. 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

  16. 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.

  17. 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 BACKGROUND: 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. METHODS AND FINDINGS: 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. CONCLUSIONS: 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.

  18. 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.

  19. An automatic framework for quantitative validation of voxel based morphometry measures of anatomical brain asymmetry.

    Science.gov (United States)

    Pepe, Antonietta; Dinov, Ivo; Tohka, Jussi

    2014-10-15

    The study of anatomical brain asymmetries has been a topic of great interest in the neuroimaging community in the past decades. However, the accuracy of brain asymmetry measurements has been rarely investigated. In this study, we propose a fully automatic methodology for the quantitative validation of brain tissue asymmetries as measured by Voxel Based Morphometry (VBM) from structural magnetic resonance (MR) images. Starting from a real MR image, the methodology generates simulated 3D MR images with a known and realistic pattern of inter-hemispheric asymmetry that models the left-occipital right-frontal petalia of a normal brain and the related rightward bending of the inter-hemispheric fissure. As an example, we generated a dataset of 64 simulated MR images and applied this dataset for the quantitative validation of optimized VBM measures of asymmetries in brain tissue composition. Our results suggested that VBM analysis strongly depended on the spatial normalization of the individual brain images, the selected template space, and the amount of spatial smoothing applied. The most accurate asymmetry detections were achieved by 9-degrees of freedom registration to the symmetrical template space with 4 to 8mm spatial smoothing.

  20. Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian networks.

    Science.gov (United States)

    Guo, Xiaojuan; Wang, Yan; Chen, Kewei; Wu, Xia; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li

    2014-01-01

    Recent multivariate neuroimaging studies have revealed aging-related alterations in brain structural networks. However, the sensory/motor networks such as the auditory, visual and motor networks, have obtained much less attention in normal aging research. In this study, we used Gaussian Bayesian networks (BN), an approach investigating possible inter-regional directed relationship, to characterize aging effects on structural associations between core brain regions within each of these structural sensory/motor networks using volumetric MRI data. We then further examined the discriminability of BN models for the young (N = 109; mean age =22.73 years, range 20-28) and old (N = 82; mean age =74.37 years, range 60-90) groups. The results of the BN modeling demonstrated that structural associations exist between two homotopic brain regions from the left and right hemispheres in each of the three networks. In particular, compared with the young group, the old group had significant connection reductions in each of the three networks and lesser connection numbers in the visual network. Moreover, it was found that the aging-related BN models could distinguish the young and old individuals with 90.05, 73.82, and 88.48% accuracy for the auditory, visual, and motor networks, respectively. Our findings suggest that BN models can be used to investigate the normal aging process with reliable statistical power. Moreover, these differences in structural inter-regional interactions may help elucidate the neuronal mechanism of anatomical changes in normal aging.

  1. 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.

  2. 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.

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

    CERN Document Server

    Gallos, Lazaros K; Makse, Hernan A

    2012-01-01

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

  4. Bayesian inference of structural brain networks.

    Science.gov (United States)

    Hinne, Max; Heskes, Tom; Beckmann, Christian F; van Gerven, Marcel A J

    2013-02-01

    Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unfortunately, as of yet, none of the existing approaches provide an undisputed way of inferring brain networks from the streamline distributions which tractography produces. State-of-the-art methods rely on an arbitrary threshold or, alternatively, yield weighted results that are difficult to interpret. In this paper, we provide a generative model that explicitly describes how structural brain networks lead to observed streamline distributions. This allows us to draw principled conclusions about brain networks, which we validate using simultaneously acquired resting-state functional MRI data. Inference may be further informed by means of a prior which combines connectivity estimates from multiple subjects. Based on this prior, we obtain networks that significantly improve on the conventional approach.

  5. Molecular and anatomical signatures of sleep deprivation in the mouse brain

    Directory of Open Access Journals (Sweden)

    Carol L Thompson

    2010-10-01

    Full Text Available Sleep deprivation (SD leads to a suite of cognitive and behavioral impairments, and yet the molecular consequences of SD in the brain are poorly understood. Using a systematic immediate-early gene mapping to detect neuronal activation, the consequences of SD were mapped primarily to forebrain regions. Sleep deprivation was found to both induce and suppress immediate early gene expression (and thus neuronal activity in subregions of neocortex, striatum, and other brain regions. Laser microdissection and cDNA microarrays were used to identify the molecular consequences of SD in 7 brain regions. In situ hybridization for 222 genes selected from the microarray data and other sources confirmed that robust molecular changes were largely restricted to the forebrain. Analysis of the ISH data for 222 genes (publicly accessible at http://sleep.alleninstitute.org provided a molecular and anatomic signature of the effects of SD on the brain. The SCN and the neocortex exhibited differential regulation of the same genes, such that in the SCN genes exhibited time-of-day effects while in the neocortex, genes exhibited only SD and W effects. In the neocortex, SD activated gene expression in areal-, layer-, and cell type-specific manner. In the forebrain, SD preferentially activated excitatory neurons, as demonstrated by double-labeling, except for striatum which consists primarily of inhibitory neurons. These data provide a characterization of the anatomical and cell-type specific signatures of SD on neuronal activity and gene expression that may account for the associated cognitive and behavioral effects.

  6. Measuring structural-functional correspondence: spatial variability of specialised brain regions after macro-anatomical alignment.

    Science.gov (United States)

    Frost, Martin A; Goebel, Rainer

    2012-01-16

    The central question of the relationship between structure and function in the human brain is still not well understood. In order to investigate this fundamental relationship we create functional probabilistic maps from a large set of mapping experiments and compare the location of functionally localised regions across subjects using different whole-brain alignment schemes. To avoid the major problems associated with meta-analysis approaches, all subjects are scanned using the same paradigms, the same scanner and the same analysis pipeline. We show that an advanced, curvature driven cortex based alignment (CBA) scheme largely removes macro-anatomical variability across subjects. Remaining variability in the observed spatial location of functional regions, thus, reflects the "true" functional variability, i.e. the quantified variability is a good estimator of the underlying structural-functional correspondence. After localising 13 widely studied functional areas, we found a large variability in the degree to which functional areas respect macro-anatomical boundaries across the cortex. Some areas, such as the frontal eye fields (FEF) are strongly bound to a macro-anatomical location. Fusiform face area (FFA) on the other hand, varies in its location along the length of the fusiform gyrus even though the gyri themselves are well aligned across subjects. Language areas were found to vary greatly across subjects whilst a high degree of overlap was observed in sensory and motor areas. The observed differences in functional variability for different specialised areas suggest that a more complete estimation of the structure-function relationship across the whole cortex requires further empirical studies with an expanded test battery.

  7. Structure-function clustering in multiplex brain networks

    Science.gov (United States)

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

    2016-10-01

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

  8. Scale-Free Brain Functional Networks

    Science.gov (United States)

    Eguíluz, Victor M.; Chialvo, Dante R.; Cecchi, Guillermo A.; Baliki, Marwan; Apkarian, A. Vania

    2005-01-01

    Functional magnetic resonance imaging is used to extract functional networks connecting correlated human brain sites. Analysis of the resulting networks in different tasks shows that (a)the distribution of functional connections, and the probability of finding a link versus distance are both scale-free, (b)the characteristic path length is small and comparable with those of equivalent random networks, and (c)the clustering coefficient is orders of magnitude larger than those of equivalent random networks. All these properties, typical of scale-free small-world networks, reflect important functional information about brain states.

  9. A Review of the Functional and Anatomical Default Mode Network in Schizophrenia.

    Science.gov (United States)

    Hu, Mao-Lin; Zong, Xiao-Fen; Mann, J John; Zheng, Jun-Jie; Liao, Yan-Hui; Li, Zong-Chang; He, Ying; Chen, Xiao-Gang; Tang, Jin-Song

    2017-02-01

    Schizophrenia is a severe mental disorder characterized by impaired perception, delusions, thought disorder, abnormal emotion regulation, altered motor function, and impaired drive. The default mode network (DMN), since it was first proposed in 2001, has become a central research theme in neuropsychiatric disorders, including schizophrenia. In this review, first we define the DMN and describe its functional activity, functional and anatomical connectivity, heritability, and inverse correlation with the task positive network. Second, we review empirical studies of the anatomical and functional DMN, and anti-correlation between DMN and the task positive network in schizophrenia. Finally, we review preliminary evidence about the relationship between antipsychotic medications and regulation of the DMN, review the role of DMN as a treatment biomarker for this disease, and consider the DMN effects of individualized therapies for schizophrenia.

  10. Scaling in topological properties of brain networks

    NARCIS (Netherlands)

    Singh, S.S.; Khundrakpam, B.; 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.

  11. Functional brain network efficiency predicts intelligence.

    Science.gov (United States)

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

    2012-06-01

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

  12. Network Assemblies in the Functional Brain

    Science.gov (United States)

    Sepulcre, Jorge; Sabuncu, Mert R.; Johnson, Keith A.

    2012-01-01

    Purpose of review This review focuses on recent advances in functional connectivity MRI and renewed interest in knowing the large-scale functional network assemblies in the brain. We also consider some methodological aspects of graph theoretical analysis. Recent findings Network science applied to neuroscience is quickly growing in recent years. The characterization of the functional connectomes in normal and pathological brain conditions is now a priority for researchers in the neuropsychiatric field and current findings have provided new insights regarding the pivotal role of network epicenters and specific configurations of the functional networks in the brain. Summary Functional connectivity and its analytical tools are providing organization of the functional brain that will be key for the understanding of pathologies in neurology. PMID:22766721

  13. 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

  14. 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

  15. Bayesian inference of structural brain networks

    NARCIS (Netherlands)

    Hinne, M.; Heskes, T.; Beckmann, C.F.; Gerven, van M.A.J.

    2013-01-01

    Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unf

  16. 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.

  17. Anatomical and functional connectivity in the default mode network of post-traumatic stress disorder patients after civilian and military-related trauma.

    Science.gov (United States)

    Reuveni, Inbal; Bonne, Omer; Giesser, Ruti; Shragai, Tamir; Lazarovits, Gilad; Isserles, Moshe; Schreiber, Shaul; Bick, Atira S; Levin, Netta

    2016-02-01

    Posttraumatic stress disorder (PTSD) is characterized by unwanted intrusive thoughts and hyperarousal at rest. As these core symptoms reflect disturbance in resting-state mechanisms, we investigated the functional and anatomical involvement of the default mode network (DMN) in this disorder. The relation between symptomatology and trauma characteristics was considered. Twenty PTSD patients and 20 matched trauma-exposed controls that were exposed to a similar traumatic event were recruited for this study. In each group, 10 patients were exposed to military trauma, and 10 to civilian trauma. PTSD, anxiety, and depression symptom severity were assessed. DMN maps were identified in resting-state scans using independent component analysis. Regions of interest (medial prefrontal, precuneus, and bilateral inferior parietal) were defined and average z-scores were extracted for use in the statistical analysis. The medial prefrontal and the precuneus regions were used for cingulum tractography whose integrity was measured and compared between groups. Similar functional and anatomical connectivity patterns were identified in the DMN of PTSD patients and trauma-exposed controls. In the PTSD group, functional and anatomical connectivity parameters were strongly correlated with clinical measures, and there was evidence of coupling between the anatomical and functional properties. Type of trauma and time from trauma were found to modulate connectivity patterns. To conclude, anatomical and functional connectivity patterns are related to PTSD symptoms and trauma characteristics influence connectivity beyond clinical symptoms. Hum Brain Mapp 37:589-599, 2016. © 2015 Wiley Periodicals, Inc.

  18. Learning from redundant but inconsistent reference data: anatomical views and measurements for fetal brain screening

    Science.gov (United States)

    Waechter-Stehle, I.; Klinder, T.; Rouet, J.-M.; Roundhill, D.; Andrews, G.; Cavallaro, A.; Molloholli, M.; Norris, T.; Napolitano, R.; Papageorghiou, A.; Lorenz, C.

    2016-03-01

    In a fetal brain screening examination, a standardized set of anatomical views is inspected and certain biometric measurements are taken in these views. Acquisition of recommended planes requires a certain level of operator expertise. 3D ultrasound has the potential to reduce the manual task to only capture a volume containing the head and to subsequently determine the standard 2D views and measurements automatically. For this purpose, a segmentation model of the fetal brain was created and trained with expert annotations. It was found that the annotations show a considerable intra- and inter-observer variability. To handle the variability, we propose a method to train the model with redundant but inconsistent reference data from many expert users. If the outlier-cleaned average of all reference annotations is considered as ground truth, errors of the automatic view detection are lower than the errors of all individual users and errors of the measurements are in the same range as user error. The resulting functionality allows the completely automated estimation of views and measurements in 3D fetal ultrasound images.

  19. 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 ...

  20. 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.

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

    Science.gov (United States)

    Jubault, Thomas; Brambati, Simona M; Degroot, Clotilde; Kullmann, Benoît; Strafella, Antonio P; Lafontaine, Anne-Louise; Chouinard, Sylvain; Monchi, Oury

    2009-12-10

    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.

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Cosimo eUrgesi

    2014-05-01

    Full Text Available 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.

  4. 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

    2016-11-29

    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).

  5. Interpreting the effects of altered brain anatomical connectivity on fMRI functional connectivity: a role for computational neural modeling.

    Science.gov (United States)

    Horwitz, Barry; Hwang, Chuhern; Alstott, Jeff

    2013-01-01

    Recently, there have been a large number of studies using resting state fMRI to characterize abnormal brain connectivity in patients with a variety of neurological, psychiatric, and developmental disorders. However, interpreting what the differences in resting state fMRI functional connectivity (rsfMRI-FC) actually reflect in terms of the underlying neural pathology has proved to be elusive because of the complexity of brain anatomical connectivity. The same is the case for task-based fMRI studies. In the last few years, several groups have used large-scale neural modeling to help provide some insight into the relationship between brain anatomical connectivity and the corresponding patterns of fMRI-FC. In this paper we review several efforts at using large-scale neural modeling to investigate the relationship between structural connectivity and functional/effective connectivity to determine how alterations in structural connectivity are manifested in altered patterns of functional/effective connectivity. Because the alterations made in the anatomical connectivity between specific brain regions in the model are known in detail, one can use the results of these simulations to determine the corresponding alterations in rsfMRI-FC. Many of these simulation studies found that structural connectivity changes do not necessarily result in matching changes in functional/effective connectivity in the areas of structural modification. Often, it was observed that increases in functional/effective connectivity in the altered brain did not necessarily correspond to increases in the strength of the anatomical connection weights. Note that increases in rsfMRI-FC in patients have been interpreted in some cases as resulting from neural plasticity. These results suggest that this interpretation can be mistaken. The relevance of these simulation findings to the use of functional/effective fMRI connectivity as biomarkers for brain disorders is also discussed.

  6. 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

  7. Creative Cognition and Brain Network Dynamics

    Science.gov (United States)

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

    2015-01-01

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

  8. Functional network organization of the human brain.

    Science.gov (United States)

    Power, Jonathan D; Cohen, Alexander L; Nelson, Steven M; Wig, Gagan S; Barnes, Kelly Anne; Church, Jessica A; Vogel, Alecia C; Laumann, Timothy O; Miezin, Fran M; Schlaggar, Bradley L; Petersen, Steven E

    2011-11-17

    Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a "processing" system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex.

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

    Science.gov (United States)

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

    2014-01-01

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

  10. 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.

  11. 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.

  12. 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.

  13. Brain network adaptability across task states.

    Directory of Open Access Journals (Sweden)

    Elizabeth N Davison

    2015-01-01

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

  14. Brain network analysis reveals affected connectome structure in bipolar I disorder.

    Science.gov (United States)

    Collin, Guusje; van den Heuvel, Martijn P; Abramovic, Lucija; Vreeker, Annabel; de Reus, Marcel A; van Haren, Neeltje E M; Boks, Marco P M; Ophoff, Roel A; Kahn, René S

    2016-01-01

    The notion that healthy brain function emerges from coordinated neural activity constrained by the brain's network of anatomical connections--i.e., the connectome--suggests that alterations in the connectome's wiring pattern may underlie brain disorders. Corroborating this hypothesis, studies in schizophrenia are indicative of altered connectome architecture including reduced communication efficiency, disruptions of central brain hubs, and affected "rich club" organization. Whether similar deficits are present in bipolar disorder is currently unknown. This study examines structural connectome topology in 216 bipolar I disorder patients as compared to 144 healthy controls, focusing in particular on central regions (i.e., brain hubs) and connections (i.e., rich club connections, interhemispheric connections) of the brain's network. We find that bipolar I disorder patients exhibit reduced global efficiency (-4.4%, P =0.002) and that this deficit relates (r = 0.56, P brain hub connections in general, or of connections spanning brain hubs (i.e., "rich club" connections) in particular (all P > 0.1). These findings highlight a role for aberrant brain network architecture in bipolar I disorder with reduced global efficiency in association with disruptions in interhemispheric connectivity, while the central "rich club" system appears not to be particularly affected.

  15. Normal cerebral perfusion of {sup 99m}Tc-ECD brain SPECT. Evaluation by an anatomical standardization technique

    Energy Technology Data Exchange (ETDEWEB)

    Kawashima, Ryuta; Koyama, Masamichi; Ito, Hiroshi; Yoshioka, Seiro; Sato, Kazunori; Ono, Shuichi; Goto, Ryoi; Sato, Tachio; Fukuda, Hiroshi [Tohoku Univ., Sendai (Japan). Inst. of Development, Aging and Cancer

    1996-01-01

    A single photon emitter labeled tracer, {sup 99m}Tc-ethyl cysteinate dimer ({sup 99m}Tc-ECD), has now been used for rCBF studies with SPECT. However, normal distribution pattern of this agent in the brain still remains unclear. Therefore, the specific purpose of this study was to investigate the normal distribution pattern of {sup 99m}Tc-ECD SPECT image. Regional cerebral distribution was measured with SPECT and 984{+-}17 MBq of {sup 99m}Tc-ECD in ten normal subjects. During the SPECT measurement, subjects were placed comfortably in a supine position with their eyes closed. Each SPECT image was anatomically standardized using a computerized brain atlas system of Roland et al. (HBA: Human Brain Atlas) and X-CT image. Anatomically standardized SPECT images were globally normalized to 100 count/voxel. Then, the mean and SD images of brain SPECT were calculated voxel-by-voxel basis. The highest radioactivity was found in the medial aspect of the occipital lobe. The results indicate that the normal distribution pattern of {sup 99m}Tc-ECD in the human brain may be not simply reflect the regional cerebral blood flow. (author).

  16. Adaptive algorithms to map how brain trauma affects anatomical connectivity in children

    Science.gov (United States)

    Dennis, Emily L.; Prasad, Gautam; Babikian, Talin; Kernan, Claudia; Mink, Richard; Babbitt, Christopher; Johnson, Jeffrey; Giza, Christopher C.; Asarnow, Robert F.; Thompson, Paul M.

    2015-12-01

    Deficits in white matter (WM) integrity occur following traumatic brain injury (TBI), and often persist long after the visible scars have healed. Heterogeneity in injury types and locations can complicate analyses, making it harder to discover common biomarkers for tracking recovery. Here we apply a newly developed adaptive connectivity method, EPIC (evolving partitions to improve connectomics) to identify differences in structural connectivity that persist longitudinally. This data comes from a longitudinal study, in which we scanned participants (aged 8-19 years) with anatomical and diffusion MRI in both the post-acute and chronic phases (1-6 months and 13-19 months post-injury). To identify patterns of abnormal connectivity, we trained a model on data from 32 TBI patients in the post-acute phase and 45 well-matched healthy controls, reducing an initial 68x68 connectivity matrix to a 14x14 matrix. We then applied this reduced parcellation to the chronic data in participants who had returned for their chronic assessment (21 TBI and 26 healthy controls) and tested for group differences. We found significant differences in two connections, comprising callosal fibers and long anterior-posterior fibers, with the TBI group showing increased fiber density relative to controls. Longitudinal analysis revealed that these were connections that were decreasing over time in the healthy controls, as is a common developmental phenomenon, but they were increasing in the TBI group. While we cannot definitively tell why this may occur with our current data, this study provides targets for longitudinal tracking, and poses questions for future investigation.

  17. 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.

  18. The hierarchical brain network for face recognition.

    Science.gov (United States)

    Zhen, Zonglei; Fang, Huizhen; Liu, Jia

    2013-01-01

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

  19. Functional brain networks in schizophrenia: a review

    Directory of Open Access Journals (Sweden)

    Vince D Calhoun

    2009-08-01

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

  20. Brain networks for integrative rhythm formation.

    Directory of Open Access Journals (Sweden)

    Michael H Thaut

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

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

    Science.gov (United States)

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

    2016-08-01

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

  2. 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

    2017-01-18

    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.

  3. Brain mapping in stereotactic surgery: a brief overview from the probabilistic targeting to the patient-based anatomic mapping.

    Science.gov (United States)

    Lemaire, Jean-Jacques; Coste, Jérôme; Ouchchane, Lemlih; Caire, François; Nuti, Christophe; Derost, Philippe; Cristini, Vittorio; Gabrillargues, Jean; Hemm, Simone; Durif, Franck; Chazal, Jean

    2007-01-01

    In this article, we briefly review the concept of brain mapping in stereotactic surgery taking into account recent advances in stereotactic imaging. The gold standard continues to rely on probabilistic and indirect targeting, relative to a stereotactic reference, i.e., mostly the anterior (AC) and the posterior (PC) commissures. The theoretical position of a target defined on an atlas is transposed into the stereotactic space of a patient's brain; final positioning depends on electrophysiological analysis. The method is also used to analyze final electrode or lesion position for a patient or group of patients, by projection on an atlas. Limitations are precision of definition of the AC-PC line, probabilistic location and reliability of the electrophysiological guidance. Advances in MR imaging, as from 1.5-T machines, make stereotactic references no longer mandatory and allow an anatomic mapping based on an individual patient's brain. Direct targeting is enabled by high-quality images, an advanced anatomic knowledge and dedicated surgical software. Labeling associated with manual segmentation can help for the position analysis along non-conventional, interpolated planes. Analysis of final electrode or lesion position, for a patient or group of patients, could benefit from the concept of membership, the attribution of a weighted membership degree to a contact or a structure according to its level of involvement. In the future, more powerful MRI machines, diffusion tensor imaging, tractography and computational modeling will further the understanding of anatomy and deep brain stimulation effects.

  4. Normal cerebral perfusion of {sup 99m}Tc-HMPAO brain SPECT. Evaluation by an anatomical standardization technique

    Energy Technology Data Exchange (ETDEWEB)

    Koyama, Masamichi; Kawashima, Ryuta; Ito, Hiroshi [Tohoku Univ., Sendai (Japan). Inst. of Development, Aging and Cancer] [and others

    1995-09-01

    Single photon labeled tracer {sup 99m}Tc-hexamethyl-propylene amine oxime (HMPAO) has been used for rCBF studies by SPECT. However, normal perfusion pattern of this agent still remains unclear. The purpose of this study was to investigate normal {sup 99m}Tc-HMPAO SPECT image voxel by voxel. Eighteen male subjects without any prior or present history of medical illness participated in this study. All SPECT images were globally normalized to 100 count/voxel. Each subject had an X-ray CT scan at the same day of SPECT measurement. All subjects had normal X-ray CT scans. The standard anatomical structures of the computerized brain atlas of Roland et al. were fitted to X-ray CT images of a subject by linear and non-linear parameters. These parameters were subsequently used to transform SPECT images of the subject. After the anatomical standardization, mean and SD images of eight standardized images were calculated voxel-by-voxel basis. In the mean image, following structures showed relatively higher radioactivity; the putamen, the cerebellum, and the frontal lobe. In addition, the occipital lobe, parietal lobe, frontal lobe, and the putamen showed large degree of SD. Anatomical standardization of SPECT images may be useful as a reference to diagnose and evaluate various brain disorders. (author).

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

    Institute of Scientific and Technical Information of China (English)

    SUN Wei-Gang; CAO Jian-Ting; WANG Ru-Bin

    2011-01-01

    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 Sndings might provide valuable insights on the determination of brain death.%@@ 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 axe derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated.Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state.Our findings might provide valuable insights on the determination of brain death.

  6. Identifying modular relations in complex brain networks

    DEFF Research Database (Denmark)

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

    2012-01-01

    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...... 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...

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

    Science.gov (United States)

    Jalili, Mahdi

    2016-07-01

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

  8. Three-dimensional network of Drosophila brain hemisphere

    OpenAIRE

    Mizutani, Ryuta; Saiga, Rino; 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-d...

  9. Automated Brain Image classification using Neural Network Approach and Abnormality Analysis

    Directory of Open Access Journals (Sweden)

    P.Muthu Krishnammal

    2015-06-01

    Full Text Available Image segmentation of surgical images plays an important role in diagnosis and analysis the anatomical structure of human body. Magnetic Resonance Imaging (MRI helps in obtaining a structural image of internal parts of the body. This paper aims at developing an automatic support system for stage classification using learning machine and to detect brain Tumor by fuzzy clustering methods to detect the brain Tumor in its early stages and to analyze anatomical structures. The three stages involved are: feature extraction using GLCM and the tumor classification using PNN-RBF network and segmentation using SFCM. Here fast discrete curvelet transformation is used to analyze texture of an image which be used as a base for a Computer Aided Diagnosis (CAD system .The Probabilistic Neural Network with radial basis function is employed to implement an automated Brain Tumor classification. It classifies the stage of Brain Tumor that is benign, malignant or normal automatically. Then the segmentation of the brain abnormality using Spatial FCM and the severity of the tumor is analysed using the number of tumor cells in the detected abnormal region.The proposed method reports promising results in terms of training performance and classification accuracies.

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

    Science.gov (United States)

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

    2012-06-01

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

  11. 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.

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

    Science.gov (United States)

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

    2016-10-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

  14. 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

  15. Broadband criticality of human brain network synchronization.

    Directory of Open Access Journals (Sweden)

    Manfred G Kitzbichler

    2009-03-01

    Full Text Available Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological processes, and the lability of global synchronization of a (brain functional network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal "avalanches" previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05-0.11 to 62.5-125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth.

  16. 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.

  17. Including anatomical and functional information in MC simulation of PET and SPECT brain studies. Brain-VISET: a voxel-based iterative method.

    Science.gov (United States)

    Marti-Fuster, Berta; Esteban, Oscar; Thielemans, Kris; Setoain, Xavier; Santos, Andres; Ros, Domenec; Pavia, Javier

    2014-10-01

    Monte Carlo (MC) simulation provides a flexible and robust framework to efficiently evaluate and optimize image processing methods in emission tomography. In this work we present Brain-VISET (Voxel-based Iterative Simulation for Emission Tomography), a method that aims to simulate realistic [ (99m) Tc]-SPECT and [ (18) F]-PET brain databases by including anatomical and functional information. To this end, activity and attenuation maps generated using high-resolution anatomical images from patients were used as input maps in a MC projector to simulate SPECT or PET sinograms. The reconstructed images were compared with the corresponding real SPECT or PET studies in an iterative process where the activity inputs maps were being modified at each iteration. Datasets of 30 refractory epileptic patients were used to assess the new method. Each set consisted of structural images (MRI and CT) and functional studies (SPECT and PET), thereby allowing the inclusion of anatomical and functional variability in the simulation input models. SPECT and PET sinograms were obtained using the SimSET package and were reconstructed with the same protocols as those employed for the clinical studies. The convergence of Brain-VISET was evaluated by studying the behavior throughout iterations of the correlation coefficient, the quotient image histogram and a ROI analysis comparing simulated with real studies. The realism of generated maps was also evaluated. Our findings show that Brain-VISET is able to generate realistic SPECT and PET studies and that four iterations is a suitable number of iterations to guarantee a good agreement between simulated and real studies.

  18. 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.

  19. 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 (

  20. Effect of anatomical variability, reconstruction algorithms and scattered photons on the SPM output of brain PET studies.

    Science.gov (United States)

    Aguiar, P; Pareto, D; Gispert, J D; Crespo, C; Falcón, C; Cot, A; Lomeña, F; Pavía, J; Ros, D

    2008-02-01

    Statistical parametric mapping (SPM) has become the standard technique to statistically evaluate differences between functional images. The aim of this paper was to assess the effect of anatomical variability of skull, the reconstruction algorithm and the scattering of photons in the brain on the output of an SPM analysis of brain PET studies. To this end, Monte Carlo simulation was used to generate suitable PET sinograms and bootstrap techniques were employed to increase the reliability of the conclusions. Activity distribution maps were obtained by segmenting thirty nine T1-weighted magnetic resonance images. Foci were placed on the posterior cingulate cortex (PCC) and the superior temporal cortex (STC) and activation factors ranging between -25% and +25% were simulated. Preprocessing of the reconstructed images and statistical analysis were performed using SPM2. Our findings show that intersubject anatomical differences can cause the minimum sample size to increase between 10 and 42% for posterior cingulate Cortex and between 40 and 80% for superior temporal cortex. Ideal scatter correction (ISC) allowed us to diminish the sample size up to 18% and fully 3D reconstruction reduced the minimum sample size between 8 and 33%. Detection sensitivity was higher for hypo-activation than for hyper-activation situations and higher for superior temporal cortex than for posterior cingulate cortex.

  1. 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

  2. Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents

    OpenAIRE

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

    2016-01-01

    Previous neuroimaging studies suggested structural or functional brain reorganizations occurred in prelingually deaf subjects. However, little is known about the reorganizations of brain network architectures in prelingually deaf adolescents. The present study aims to investigate alterations of whole-brain functional network using resting-state fMRI and graph theory analysis. We recruited 16 prelingually deaf adolescents (10~18 years) and 16 normal controls matched in age and gender. Brain ne...

  3. Brain and Cognitive Reserve: Translation via Network Control Theory

    OpenAIRE

    2016-01-01

    Traditional approaches to understanding the brain's resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive 'reserve,' associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approa...

  4. On Shapley ratings in brain networks

    Directory of Open Access Journals (Sweden)

    Marieke Musegaas

    2016-11-01

    Full Text Available We consider the problem of computing the influence of a neuronal structure in abrain network. Abraham, Kotter, Krumnack, and Wanke (2006 computed this influence by using the Shapley value of a coalitional game corresponding to a directednetwork as a rating. Kotter, Reid, Krumnack, Wanke, and Sporns (2007 appliedthis rating to large-scale brain networks, in particular to the macaque visual cortexand the macaque prefrontal cortex. Our aim is to improve upon the above techniqueby measuring the importance of subgroups of neuronal structures in a different way.This new modelling technique not only leads to a more intuitive coalitional game,but also allows for specifying the relative influence of neuronal structures and adirect extension to a setting with missing information on the existence of certainconnections.

  5. Attention Performance Measured by Attention Network Test is Correlated with Global and Regional Efficiency of Structural Human Brain Networks

    Directory of Open Access Journals (Sweden)

    Min Xiao

    2016-10-01

    Full Text Available Functional neuroimaging studies have indicated the involvement of separate brain areas in three distinct attention systems: alerting, orienting and executive control (EC. However, the structural correlates underlying attention remains unexplored. Here, we utilized graph theory to examine the neuroanatomical substrates of the three attention systems measured by attention network test (ANT in 65 healthy subjects. White matter connectivity, assessed with DTI deterministic tractography was modeled as a structural network comprising 90 nodes defined by the Automated Anatomical Labeling (AAL template. Linear regression analyses were conducted to explore the relationship between topological parameters and the three attentional effects. We found a significant positive correlation between EC function and global efficiency of the whole brain network. At the regional level, node-specific correlations were discovered between regional efficiency and all three ANT components, including dorsolateral superior frontal gyrus, thalamus and parahippocampal gyrus for EC, thalamus and inferior parietal gyrus for alerting, and paracentral lobule and inferior occipital gyrus for orienting. Our findings highlight the fundamental architecture of interregional structural connectivity involved in attention and could provide new insights into the anatomical basis underlying human behavior.

  6. 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.

  7. Emotion-Induced Topological Changes in Functional Brain Networks.

    Science.gov (United States)

    Park, Chang-Hyun; Lee, Hae-Kook; Kweon, Yong-Sil; Lee, Chung Tai; Kim, Ki-Tae; Kim, Young-Joo; Lee, Kyoung-Uk

    2016-01-01

    In facial expression perception, a distributed network is activated according to stimulus context. We proposed that an interaction between brain activation and stimulus context in response to facial expressions could signify a pattern of interactivity across the whole brain network beyond the face processing network. Functional magnetic resonance imaging data were acquired for 19 young healthy subjects who were exposed to either emotionally neutral or negative facial expressions. We constructed group-wise functional brain networks for 12 face processing areas [bilateral inferior occipital gyri (IOG), fusiform gyri (FG), superior temporal sulci (STS), amygdalae (AMG), inferior frontal gyri (IFG), and orbitofrontal cortices (OFC)] and for 73 whole brain areas, based on partial correlation of mean activation across subjects. We compared the topological properties of the networks with respect to functional distance-based measures, global and local efficiency, between the two types of face stimulus. In both face processing and whole brain networks, global efficiency was lower and local efficiency was higher for negative faces relative to neutral faces, indicating that network topology differed according to stimulus context. Particularly in the face processing network, emotion-induced changes in network topology were attributable to interactions between core (bilateral IOG, FG, and STS) and extended (bilateral AMG, IFG, and OFC) systems. These results suggest that changes in brain activation patterns in response to emotional face stimuli could be revealed as changes in the topological properties of functional brain networks for the whole brain as well as for face processing areas.

  8. Modular organization as a basis for the functional integration/segregation in large-scale brain networks

    CERN Document Server

    Valencia, M; Fernandez-Seara, MA; Artieda, J; Martinerie, J; Chavez, M

    2009-01-01

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

  9. Mutated Genes in Schizophrenia Map to Brain Networks

    Science.gov (United States)

    ... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the ... in People with Serious Mental Illness Clues for Schizophrenia in Rare Gene Glitch Recognizing Schizophrenia: Seeking Clues to a Difficult ...

  10. Cortical networks for visual reaching: physiological and anatomical organization of frontal and parietal lobe arm regions.

    Science.gov (United States)

    Johnson, P B; Ferraina, S; Bianchi, L; Caminiti, R

    1996-01-01

    The functional and structural properties of the dorsolateral frontal lobe and posterior parietal proximal arm representations were studied in macaque monkeys. Physiological mapping of primary motor (MI), dorsal premotor (PMd), and posterior parietal (area 5) cortices was performed in behaving monkeys trained in an instructed-delay reaching task. The parietofrontal corticocortical connectivities of these same areas were subsequently examined anatomically by means of retrograde tracing techniques. Signal-, set-, movement-, and position-related directional neuronal activities were distributed nonuniformly within the task-related areas in both frontal and parietal cortices. Within the frontal lobe, moving caudally from PMd to the MI, the activity that signals for the visuo-spatial events leading to target localization decreased, while the activity more directly linked to movement generation increased. Physiological recordings in the superior parietal lobule revealed a gradient-like distribution of functional properties similar to that observed in the frontal lobe. Signal- and set-related activities were encountered more frequently in the intermediate and ventral part of the medial bank of the intraparietal sulcus (IPS), in area MIP. Movement-and position-related activities were distributed more uniformly within the superior parietal lobule (SPL), in both dorsal area 5 and in MIP. Frontal and parietal regions sharing similar functional properties were preferentially connected through their association pathways. As a result of this study, area MIP, and possibly areas MDP and 7m as well, emerge as the parietal nodes by which visual information may be relayed to the frontal lobe arm region. These parietal and frontal areas, along with their association connections, represent a potential cortical network for visual reaching. The architecture of this network is ideal for coding reaching as the result of a combination between visual and somatic information.

  11. Manifold learning on brain functional networks in aging.

    Science.gov (United States)

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

    2015-02-01

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

  12. 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.

  13. A network analysis of developing brain cultures

    Science.gov (United States)

    Christopoulos, V. N.; Boeff, D. V.; Evans, C. D.; Crowe, D. A.; Amirikian, B.; Georgopoulos, A.; Georgopoulos, A. P.

    2012-08-01

    We recorded electrical activity from four developing embryonic brain cultures (4-40 days in vitro) using multielectrode arrays (MEAs) with 60 embedded electrodes. Data were filtered for local field potentials (LFPs) and downsampled to 1 ms to yield a matrix of time series consisting of 60 electrode × 60 000 time samples per electrode per day per MEA. Each electrode time series was rendered stationary and nonautocorrelated by applying an ARIMA (25, 1, 1) model and taking the residuals (i.e. innovations). Two kinds of analyses were then performed. First, a pairwise crosscorrelation (CC) analysis (±25 1 ms lags) revealed systematic changes in CC with lag, day in vitro (DIV), and inter-electrode distance. Specifically, (i) positive CCs were 1.76× more prevalent and 1.44× stronger (absolute value) than negative ones, and (ii) the strength of CC increased with DIV and decreased with lag and inter-electrode distance. Second, a network equilibrium analysis was based on the instantaneous (1 ms resolution) logratio of the number of electrodes that were above or below their mean, called simultaneous departure from equilibrium, SDE. This measure possesses a major computational advantage over the pairwise crosscorrelation approach because it is very simple and fast to calculate, an important factor for the analysis of large networks. The results obtained with SDE covaried highly with CC over DIV, which further validates the usefulness of this measure as a computationally effective tool for large scale network analysis.

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

    Science.gov (United States)

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

    2013-12-01

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

  15. A stereotaxic MRI template set for the rat brain with tissue class distribution maps and co-registered anatomical atlas: application to pharmacological MRI.

    Science.gov (United States)

    Schwarz, Adam J; Danckaert, Anne; Reese, Torsten; Gozzi, Alessandro; Paxinos, George; Watson, Charles; Merlo-Pich, Emilio V; Bifone, Angelo

    2006-08-15

    We describe a stereotaxic rat brain MRI template set with a co-registered digital anatomical atlas and illustrate its application to the analysis of a pharmacological MRI (phMRI) study of apomorphine. The template set includes anatomical images and tissue class probability maps for brain parenchyma and cerebrospinal fluid (CSF). These facilitate the use of standard fMRI software for spatial normalisation and tissue segmentation of rat brain data. A volumetric reconstruction of the Paxinos and Watson rat brain atlas is also co-localised with the template, enabling the atlas structure and stereotaxic coordinates corresponding to a feature within a statistical map to be interactively reported, facilitating the localisation of functional effects. Moreover, voxels falling within selected brain structures can be combined to define anatomically based 3D volumes of interest (VOIs), free of operator bias. As many atlas structures are small relative to the typical resolution of phMRI studies, a mechanism for defining composite structures as agglomerations of individual atlas structures is also described. This provides a simple and robust means of interrogating structures that are otherwise difficult to delineate and an objective framework for comparing and classifying compounds based on an anatomical profile of their activity. These developments allow a closer alignment of pre-clinical and clinical analysis techniques.

  16. 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.

  17. 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.

  18. 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 g

  19. 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

    2017-01-16

    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.

  20. 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.

  1. Complex networks: new trends for the analysis of brain connectivity

    CERN Document Server

    Chavez, Mario; Latora, Vito; Martinerie, Jacques

    2010-01-01

    Today, the human brain can be studied as a whole. Electroencephalography, magnetoencephalography, or functional magnetic resonance imaging techniques provide functional connectivity patterns between different brain areas, and during different pathological and cognitive neuro-dynamical states. In this Tutorial we review novel complex networks approaches to unveil how brain networks can efficiently manage local processing and global integration for the transfer of information, while being at the same time capable of adapting to satisfy changing neural demands.

  2. 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.

  3. Insight and psychosis: Functional and anatomical brain connectivity and self-reflection in Schizophrenia.

    Science.gov (United States)

    Ćurčić-Blake, Branislava; van der Meer, Lisette; Pijnenborg, Gerdina H M; David, Anthony S; Aleman, André

    2015-12-01

    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 (vmPFC) would differ in patients according to their degree of impaired insight. Forty-five schizophrenia patients and 19 healthy subjects performed a self-reflection task during fMRI, and underwent diffusion tensor imaging. Using dynamic causal modelling we observed increased effective connectivity from the posterior cingulate cortex (PCC), inferior parietal lobule (IPL), and dorsal mPFC (dmPFC) towards the vmPFC with poorer insight and decrease from vmPFC to the IPL. Stronger connectivity from the PCC to vmPFC during judgment of traits related to self was associated with poorer insight. We found small-scale significant changes in white matter integrity associated with clinical insight. Self-reflection may be influenced by synaptic changes that lead to the observed alterations in functional connectivity accompanied by the small-scale but measurable alterations in anatomical connections. Our findings may point to a neural compensatory response to an impairment of connectivity between self-processing regions. Similarly, the observed hyper-connectivity might be a primary deficit linked to inefficiency in the component cognitive processes that lead to impaired insight. We suggest that the stronger cognitive demands placed on patients with poor insight is reflected in increased effective connectivity during the task in this study.

  4. Implantable Microsystems for Anatomical Rewiring of Cortical Circuitry: A New Approach for Brain Repair

    Science.gov (United States)

    2009-03-01

    reaching, retrieval of small food items, and locomotion demonstrate that deficits persist during the 5-week recovery period following injury. This will...Implantable microsystem; Neuroplasticity ; Rehabilitation; Traumatic brain injury 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER...we have successfully induced TBI in the CFA, sparing the RFA. Behavioral assessments of reaching, retrieval of small food items, and locomotion

  5. 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 hav

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

    OpenAIRE

    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 syna...

  7. Hierarchical statistical shape models of multiobject anatomical structures: application to brain MRI.

    Science.gov (United States)

    Cerrolaza, Juan J; Villanueva, Arantxa; Cabeza, Rafael

    2012-03-01

    The accurate segmentation of subcortical brain structures in magnetic resonance (MR) images is of crucial importance in the interdisciplinary field of medical imaging. Although statistical approaches such as active shape models (ASMs) have proven to be particularly useful in the modeling of multiobject shapes, they are inefficient when facing challenging problems. Based on the wavelet transform, the fully generic multiresolution framework presented in this paper allows us to decompose the interobject relationships into different levels of detail. The aim of this hierarchical decomposition is twofold: to efficiently characterize the relationships between objects and their particular localities. Experiments performed on an eight-object structure defined in axial cross sectional MR brain images show that the new hierarchical segmentation significantly improves the accuracy of the segmentation, and while it exhibits a remarkable robustness with respect to the size of the training set.

  8. [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.

  9. 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.

  10. Anatomical evidence for transsynaptic influences of estrogen on brain-derived neurotrophic factor expression.

    Science.gov (United States)

    Blurton-Jones, M; Kuan, P N; Tuszynski, M H

    2004-01-12

    Several studies have demonstrated that estrogen modulates brain-derived neurotrophic factor (BDNF) mRNA and protein within the adult hippocampus and cortex. However, mechanisms underlying this regulation are unknown. Although an estrogen response element (ERE)-like sequence has been identified within the BDNF gene, such a classical mechanism of estrogen-induced transcriptional activation requires the colocalized expression of estrogen receptors within cells that produce BDNF. Developmental studies have demonstrated such a relationship, but to date no studies have examined colocalization of estrogen receptors and BDNF within the adult brain. By utilizing double-label immunohistochemistry for BDNF, estrogen receptor-alpha (ER-alpha), and estrogen receptor-beta (ER-beta), we found only sparse colocalization between ER-alpha and BDNF in the hypothalamus, amygdala, prelimbic cortex, and ventral hippocampus. Furthermore, ER-beta and BDNF do not colocalize in any brain region. Given the recent finding that cortical ER-beta is almost exclusively localized to parvalbumin-immunoreactive GABAergic neurons, we performed BDNF/parvalbumin double labeling and discovered that axons from cortical ER-beta-expressing inhibitory neurons terminate on BDNF-immunoreactive pyramidal cells. Collectively, these findings support a potential transsynaptic relationship between estrogen state and cortical BDNF: By directly modulating GABAergic interneurons, estrogen may indirectly influence the activity and expression of BDNF-producing cortical neurons.

  11. Investigating changes in brain network properties in HIV-associated neurocognitive disease (HAND) using mutual connectivity analysis (MCA)

    Science.gov (United States)

    Abidin, Anas Zainul; D'Souza, Adora M.; Nagarajan, Mahesh B.; Wismüller, Axel

    2016-03-01

    About 50% of subjects infected with HIV present deficits in cognitive domains, which are known collectively as HIV associated neurocognitive disorder (HAND). The underlying synaptodendritic damage can be captured using resting state functional MRI, as has been demonstrated by a few earlier studies. Such damage may induce topological changes of brain connectivity networks. We test this hypothesis by capturing the functional interdependence of 90 brain network nodes using a Mutual Connectivity Analysis (MCA) framework with non-linear time series modeling based on Generalized Radial Basis function (GRBF) neural networks. The network nodes are selected based on the regions defined in the Automated Anatomic Labeling (AAL) atlas. Each node is represented by the average time series of the voxels of that region. 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 tested for differences in these properties in network graphs obtained for 10 subjects (6 male and 4 female, 5 HIV+ and 5 HIV-). Global network properties captured some differences between these subject cohorts, though significant differences were seen only with the clustering coefficient measure. Local network properties, such as local efficiency and the degree of connections, captured significant differences in regions of the frontal lobe, precentral and cingulate cortex amongst a few others. These results suggest that our method can be used to effectively capture differences occurring in brain network connectivity properties revealed by resting-state functional MRI in neurological disease states, such as HAND.

  12. 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.

  13. 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

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

    Science.gov (United States)

    Jalili, Mahdi

    2014-11-12

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

  15. Developmental Changes in Brain Network Hub Connectivity in Late Adolescence.

    Science.gov (United States)

    Baker, Simon T E; Lubman, Dan I; Yücel, Murat; Allen, Nicholas B; Whittle, Sarah; Fulcher, Ben D; Zalesky, Andrew; Fornito, Alex

    2015-06-17

    The human brain undergoes substantial development throughout adolescence and into early adulthood. This maturational process is thought to include the refinement of connectivity between putative connectivity hub regions of the brain, which collectively form a dense core that enhances the functional integration of anatomically distributed, and functionally specialized, neural systems. Here, we used longitudinal diffusion magnetic resonance imaging to characterize changes in connectivity between 80 cortical and subcortical anatomical regions over a 2 year period in 31 adolescents between the ages of 15 and 19 years. Connectome-wide analysis indicated that only a small subset of connections showed evidence of statistically significant developmental change over the study period, with 8% and 6% of connections demonstrating decreased and increased structural connectivity, respectively. Nonetheless, these connections linked 93% and 90% of the 80 regions, respectively, pointing to a selective, yet anatomically distributed pattern of developmental changes that involves most of the brain. Hub regions showed a distinct tendency to be highly connected to each other, indicating robust "rich-club" organization. Moreover, connectivity between hubs was disproportionately influenced by development, such that connectivity between subcortical hubs decreased over time, whereas frontal-subcortical and frontal-parietal hub-hub connectivity increased over time. These findings suggest that late adolescence is characterized by selective, yet significant remodeling of hub-hub connectivity, with the topological organization of hubs shifting emphasis from subcortical hubs in favor of an increasingly prominent role for frontal hub regions.

  16. Functional and anatomical basis for brain plasticity in facial palsy rehabilitation using the masseteric nerve.

    Science.gov (United States)

    Buendia, Javier; Loayza, Francis R; Luis, Elkin O; Celorrio, Marta; Pastor, Maria A; Hontanilla, Bernardo

    2016-03-01

    Several techniques have been described for smile restoration after facial nerve paralysis. When a nerve other than the contralateral facial nerve is used to restore the smile, some controversy appears because of the nonphysiological mechanism of smile recovering. Different authors have reported natural results with the masseter nerve. The physiological pathways which determine whether this is achieved continue to remain unclear. Using functional magnetic resonance imaging, brain activation pattern measuring blood-oxygen-level-dependent (BOLD) signal during smiling and jaw clenching was recorded in a group of 24 healthy subjects (11 females). Effective connectivity of premotor regions was also compared in both tasks. The brain activation pattern was similar for smile and jaw-clenching tasks. Smile activations showed topographic overlap though more extended for smile than clenching. Gender comparisons during facial movements, according to kinematics and BOLD signal, did not reveal significant differences. Effective connectivity results of psychophysiological interaction (PPI) from the same seeds located in bilateral facial premotor regions showed significant task and gender differences (p facial nerve and masseter nerve areas is supported by the broad cortical overlap in the representation of facial and masseter muscles.

  17. 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.

  18. 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.

  19. Large-Scale Functional Brain Network Abnormalities in Alzheimer’s Disease: Insights from Functional Neuroimaging

    Directory of Open Access Journals (Sweden)

    Bradford C. Dickerson

    2009-01-01

    Full Text Available Functional MRI (fMRI studies of mild cognitive impairment (MCI and Alzheimer’s disease (AD have begun to reveal abnormalities in large-scale memory and cognitive brain networks. Since the medial temporal lobe (MTL memory system is a site of very early pathology in AD, a number of studies have focused on this region of the brain. Yet it is clear that other regions of the large-scale episodic memory network are affected early in the disease as well, and fMRI has begun to illuminate functional abnormalities in frontal, temporal, and parietal cortices as well in MCI and AD. Besides predictable hypoactivation of brain regions as they accrue pathology and undergo atrophy, there are also areas of hyperactivation in brain memory and cognitive circuits, possibly representing attempted compensatory activity. Recent fMRI data in MCI and AD are beginning to reveal relationships between abnormalities of functional activity in the MTL memory system and in functionally connected brain regions, such as the precuneus. Additional work with “resting state” fMRI data is illuminating functional-anatomic brain circuits and their disruption by disease. As this work continues to mature, it will likely contribute to our understanding of fundamental memory processes in the human brain and how these are perturbed in memory disorders. We hope these insights will translate into the incorporation of measures of task-related brain function into diagnostic assessment or therapeutic monitoring, which will hopefully one day be useful for demonstrating beneficial effects of treatments being tested in clinical trials.

  20. 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.

  1. Dynamics of brain networks in the aesthetic appreciation.

    Science.gov (United States)

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

    2013-06-18

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

  2. Concepts and principles in the analysis of brain networks.

    Science.gov (United States)

    Wig, Gagan S; Schlaggar, Bradley L; Petersen, Steven E

    2011-04-01

    The brain is a large-scale network, operating at multiple levels of information processing ranging from neurons, to local circuits, to systems of brain areas. Recent advances in the mathematics of graph theory have provided tools with which to study networks. These tools can be employed to understand how the brain's behavioral repertoire is mediated by the interactions of objects of information processing. Within the graph-theoretic framework, networks are defined by independent objects (nodes) and the relationships shared between them (edges). Importantly, the accurate incorporation of graph theory into the study of brain networks mandates careful consideration of the assumptions, constraints, and principles of both the mathematics and the underlying neurobiology. This review focuses on understanding these principles and how they guide what constitutes a brain network and its elements, specifically focusing on resting-state correlations in humans. We argue that approaches that fail to take the principles of graph theory into consideration and do not reflect the underlying neurobiological properties of the brain will likely mischaracterize brain network structure and function.

  3. 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

  4. Small-World Propensity and Weighted Brain Networks.

    Science.gov (United States)

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

    2016-02-25

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

  5. Small-World Propensity and Weighted Brain Networks

    Science.gov (United States)

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

    2016-02-01

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

  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. 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.

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

    NARCIS (Netherlands)

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

    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, constit

  9. Structural Brain Network Disturbances in the Psychosis Spectrum

    NARCIS (Netherlands)

    van Dellen, Edwin; Bohlken, Marc M; Draaisma, Laurijn; Tewarie, Prejaas K; van Lutterveld, Remko; Mandl, René; Stam, Cornelis J; Sommer, Iris E

    2016-01-01

    BACKGROUND: Individuals with subclinical psychotic symptoms provide a unique window on the pathophysiology of psychotic experiences as these individuals are free of confounders such as hospitalization, negative and cognitive symptoms and medication use. Brain network disturbances of white matter con

  10. 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.

  11. Regulation of the nascent brain vascular network by neural progenitors.

    Science.gov (United States)

    Santhosh, Devi; Huang, Zhen

    2015-11-01

    Neural progenitors are central players in the development of the brain neural circuitry. They not only produce the diverse neuronal and glial cell types in the brain, but also guide their migration in this process. Recent evidence indicates that neural progenitors also play a critical role in the development of the brain vascular network. At an early stage, neural progenitors have been found to facilitate the ingression of blood vessels from outside the neural tube, through VEGF and canonical Wnt signaling. Subsequently, neural progenitors directly communicate with endothelial cells to stabilize nascent brain vessels, in part through down-regulating Wnt pathway activity. Furthermore, neural progenitors promote nascent brain vessel integrity, through integrin αvβ8-dependent TGFβ signaling. In this review, we will discuss the evidence for, as well as questions that remain, regarding these novel roles of neural progenitors and the underlying mechanisms in their regulation of the nascent brain vascular network.

  12. The Efficiency of a Small-World Functional Brain Network

    Institute of Scientific and Technical Information of China (English)

    ZHAO Qing-Bai; ZHANG Xiao-Fei; SUI Dan-Ni; ZHOU Zhi-Jin; CHEN Qi-Cai; TANG Yi-Yuan

    2012-01-01

    We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task.Functional brain networks are constructed by multichannel eventrelated potential data,in which the electrodes are the nodes and the functional connectivities between them are the edges.The results show that the correlation between small-world measures and reaction time is task-specific,such that in global imagery,there is a positive correlation between the clustering coefficient and reaction time,while in local imagery the average path length is positively correlated with the reaction time.This suggests that the efficiency of a functional brain network is task-dependent.%We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task. Functional brain networks are constructed by multichannel event-related potential data, in which the electrodes are the nodes and the functional connectivities between them are the edges. The results show that the correlation between small-world measures and reaction time is task-specific, such that in global imagery, there is a positive correlation between the clustering coefficient and reaction time, while in local imagery the average path length is positively correlated with the reaction time. This suggests that the efficiency of a functional brain network is task-dependent.

  13. Large-Scale Identification of Coregulated Enhancer Networks in the Adult Human Brain

    Directory of Open Access Journals (Sweden)

    Marit W. Vermunt

    2014-10-01

    Full Text Available Understanding the complexity of the human brain and its functional diversity remain a major challenge. Distinct anatomical regions are involved in an array of processes, including organismal homeostasis, cognitive functions, and susceptibility to neurological pathologies, many of which define our species. Distal enhancers have emerged as key regulatory elements that acquire histone modifications in a cell- and species-specific manner, thus enforcing specific gene expression programs. Here, we survey the epigenomic landscape of promoters and cis-regulatory elements in 136 regions of the adult human brain. We identify a total of 83,553 promoter-distal H3K27ac-enriched regions showing global characteristics of brain enhancers. We use coregulation of enhancer elements across many distinct regions of the brain to uncover functionally distinct networks at high resolution and link these networks to specific neuroglial functions. Furthermore, we use these data to understand the relevance of noncoding genomic variations previously linked to Parkinson’s disease incidence.

  14. Individuality manifests in the dynamic reconfiguration of large-scale brain networks during movie viewing

    Science.gov (United States)

    Jang, Changwon; Knight, Elizabeth Quattrocki; Pae, Chongwon; Park, Bumhee; Yoon, Shin-Ae; Park, Hae-Jeong

    2017-01-01

    Individuality, the uniqueness that distinguishes one person from another, may manifest as diverse rearrangements of functional connectivity during heterogeneous cognitive demands; yet, the neurobiological substrates of individuality, reflected in inter-individual variations of large-scale functional connectivity, have not been fully evidenced. Accordingly, we explored inter-individual variations of functional connectivity dynamics, subnetwork patterns and modular architecture while subjects watched identical video clips designed to induce different arousal levels. How inter-individual variations are manifested in the functional brain networks was examined with respect to four contrasting divisions: edges within the anterior versus posterior part of the brain, edges with versus without corresponding anatomically-defined structural pathways, inter- versus intra-module connections, and rich club edge types. Inter-subject variation in dynamic functional connectivity occurred to a greater degree within edges localized to anterior rather than posterior brain regions, without adhering to structural connectivity, between modules as opposed to within modules, and in weak-tie local edges rather than strong-tie rich-club edges. Arousal level significantly modulates inter-subject variability in functional connectivity, edge patterns, and modularity, and particularly enhances the synchrony of rich-club edges. These results imply that individuality resides in the dynamic reconfiguration of large-scale brain networks in response to a stream of cognitive demands. PMID:28112247

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

    NARCIS (Netherlands)

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

    2016-01-01

    Brain networks, localized or brain-wide, exist only at the cellular level, i.e., between specific pre- and post-synaptic neurons, which are connected through functionally diverse synapses located at specific points of their cell membranes. "Connectomics" is the emerging subfield of neuroanatomy expl

  16. Midbrain raphe stimulation improves behavioral and anatomical recovery from fluid-percussion brain injury.

    Science.gov (United States)

    Carballosa Gonzalez, Melissa M; Blaya, Meghan O; Alonso, Ofelia F; Bramlett, Helen M; Hentall, Ian D

    2013-01-15

    The midbrain median raphe (MR) and dorsal raphe (DR) nuclei were tested for their capacity to regulate recovery from traumatic brain injury (TBI). An implanted, wireless self-powered stimulator delivered intermittent 8-Hz pulse trains for 7 days to the rat's MR or DR, beginning 4-6 h after a moderate parasagittal (right) fluid-percussion injury. MR stimulation was also examined with a higher frequency (24 Hz) or a delayed start (7 days after injury). Controls had sham injuries, inactive stimulators, or both. The stimulation caused no apparent acute responses or adverse long-term changes. In water-maze trials conducted 5 weeks post-injury, early 8-Hz MR and DR stimulation restored the rate of acquisition of reference memory for a hidden platform of fixed location. Short-term spatial working memory, for a variably located hidden platform, was restored only by early 8-Hz MR stimulation. All stimulation protocols reversed injury-induced asymmetry of spontaneous forelimb reaching movements tested 6 weeks post-injury. Post-mortem histological measurement at 8 weeks post-injury revealed volume losses in parietal-occipital cortex and decussating white matter (corpus callosum plus external capsule), but not hippocampus. The cortical losses were significantly reversed by early 8-Hz MR and DR stimulation, the white matter losses by all forms of MR stimulation. The generally most effective protocol, 8-Hz MR stimulation, was tested 3 days post-injury for its acute effect on forebrain cyclic adenosine monophosphate (cAMP), a key trophic signaling molecule. This procedure reversed injury-induced declines of cAMP levels in both cortex and hippocampus. In conclusion, midbrain raphe nuclei can enduringly enhance recovery from early disseminated TBI, possibly in part through increased signaling by cAMP in efferent targets. A neurosurgical treatment for TBI using interim electrical stimulation in raphe repair centers is suggested.

  17. 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.

  18. A Note on Shapley Ratings in Brain Networks

    NARCIS (Netherlands)

    Musegaas, Marieke; Dietzenbacher, Bas; Borm, Peter

    2016-01-01

    We consider the problem of computing the in uence of a neuronal structure in a brain network. Abraham, Kotter, Krumnack, and Wanke (2006) computed this influence by using the Shapley value of a coalitional game corresponding to a directed network as a rating. Kotter, Reid, Krumnack, Wanke, and Spo

  19. 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...

  20. Brain Network Activity in Monolingual and Bilingual Older Adults

    Science.gov (United States)

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

    2016-01-01

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

  1. 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

  2. Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks.

    Science.gov (United States)

    Lin, Lan; Jin, Cong; Fu, Zhenrong; Zhang, Baiwen; Bin, Guangyu; Wu, Shuicai

    2016-03-01

    Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50-79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future.

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

    Science.gov (United States)

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

    2015-05-01

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

  4. 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.

  5. 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.

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

    NARCIS (Netherlands)

    Shumskaya, E.; Andriessen, T.; Norris, D.G.; 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, mat

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

    NARCIS (Netherlands)

    Shumskaya, A.N.; Andriessen, T.M.J.C.; Norris, D.G.; 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, match

  8. 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

    2016-01-01

    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 (group-matc

  9. 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.

  10. 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.

  11. 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...... 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.......-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...

  12. 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.

  13. 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

  14. Adaptation of brain functional and structural networks in aging.

    Science.gov (United States)

    Lee, Annie; Ratnarajah, Nagulan; Tuan, Ta Anh; Chen, Shen-Hsing Annabel; Qiu, Anqi

    2015-01-01

    The human brain, especially the prefrontal cortex (PFC), is functionally and anatomically reorganized in order to adapt to neuronal challenges in aging. This study employed structural MRI, resting-state fMRI (rs-fMRI), and high angular resolution diffusion imaging (HARDI), and examined the functional and structural reorganization of the PFC in aging using a Chinese sample of 173 subjects aged from 21 years and above. We found age-related increases in the structural connectivity between the PFC and posterior brain regions. Such findings were partially mediated by age-related increases in the structural connectivity of the occipital lobe within the posterior brain. Based on our findings, it is thought that the PFC reorganization in aging could be partly due to the adaptation to age-related changes in the structural reorganization of the posterior brain. This thus supports the idea derived from task-based fMRI that the PFC reorganization in aging may be adapted to the need of compensation for resolving less distinctive stimulus information from the posterior brain regions. In addition, we found that the structural connectivity of the PFC with the temporal lobe was fully mediated by the temporal cortical thickness, suggesting that the brain morphology plays an important role in the functional and structural reorganization with aging.

  15. Adaptation of brain functional and structural networks in aging.

    Directory of Open Access Journals (Sweden)

    Annie Lee

    Full Text Available The human brain, especially the prefrontal cortex (PFC, is functionally and anatomically reorganized in order to adapt to neuronal challenges in aging. This study employed structural MRI, resting-state fMRI (rs-fMRI, and high angular resolution diffusion imaging (HARDI, and examined the functional and structural reorganization of the PFC in aging using a Chinese sample of 173 subjects aged from 21 years and above. We found age-related increases in the structural connectivity between the PFC and posterior brain regions. Such findings were partially mediated by age-related increases in the structural connectivity of the occipital lobe within the posterior brain. Based on our findings, it is thought that the PFC reorganization in aging could be partly due to the adaptation to age-related changes in the structural reorganization of the posterior brain. This thus supports the idea derived from task-based fMRI that the PFC reorganization in aging may be adapted to the need of compensation for resolving less distinctive stimulus information from the posterior brain regions. In addition, we found that the structural connectivity of the PFC with the temporal lobe was fully mediated by the temporal cortical thickness, suggesting that the brain morphology plays an important role in the functional and structural reorganization with aging.

  16. Mapping Multiplex Hubs in Human Functional Brain Networks

    Science.gov (United States)

    De Domenico, Manlio; Sasai, Shuntaro; Arenas, Alex

    2016-01-01

    Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches. PMID:27471443

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

    Science.gov (United States)

    Bressler, Steven L.

    2014-09-01

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

  18. Exploration of whole brain networks modulated by acupuncture at analgesia acupoint ST36 using scale-specific wavelet correlation analysis

    Institute of Scientific and Technical Information of China (English)

    CHENG Hao; YAN Hao; BAI Li-jun; WANG Bao-guo

    2013-01-01

    Background Previous studies have demonstrated that acupuncture could modulate various brain systems in the resting brain networks.Graph theoretical analysis offers a novel way to investigate the functional organization of the large-scale cortical networks modulated by acupuncture at whole brain level.In this study,we used wavelets correlation analysis to estimate the pairwise correlations between 90 cortical and subcortical human brain regions in normal human volunteers scanned during the post-stimulus resting state.Methods Thirty-two college students,all right-handed and acupuncture na(i)ve,participated in this study.Every participant received only one acupoint stimulation,resulting in 16 subjects in one group.Both structural functional magnetic resonance imaging (fMRI) data (3D sequence with a voxel size of 1 mm3 for anatomical localization) and functional fMRI data (TR=1500 ms,TE=30 ms,flip angle=90°) were collected for each subject.After thresholding the resulting scale-specific wavelet correlation matrices to generate undirected binary graphs,we compared graph metrics of brain organization following verum manual acupuncture (ACU) and sham acupuncture (SHAM) groups.Results The topological parameters of the large-scale brain networks in ACU group were different from those of the SHAM group at multiple scales.There existed distinct modularity functional brain networks during the post-stimulus resting state following ACU and SHAM at multiple scales.Conclusions The distinct modulation patterns of the resting brain attributed to the specific effects evoked by acupuncture.In addition,we also identified that there existed frequency-specific modulation in the post-stimulus resting brain following ACU and SHAM.The modulation may be related to the effects of verum acupuncture on modulating special disorder treatment.This preliminary finding may provide a new clue to understand the relatively functionoriented specificity of acupuncture effects.

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

    Directory of Open Access Journals (Sweden)

    Rene MH Besseling

    2014-09-01

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

  20. Effects of brain-stem and thalamic lesions on the corneal reflex: an electrophysiological and anatomical study.

    Science.gov (United States)

    Ongerboer de Visser, B W; Moffie, D

    1979-09-01

    In 9 patients with Wallenberg's lateral medullary syndrome, one patient with a midbrain lesion involving the right side of the tegmentum, and 2 patients with a thalamic lesion, corneal reflexes were investigated by a new electromyographic technique. The electrophysical results were compared with the results obtained by clinical observation. In the lateral medullary lesions the electrophysiologically obtained reflex responses showed four types of abnormality. Type A consisted of a bilateral delay and type B a bilateral absence of the corneal reflex response to stimulation on the affected side in combination with a normal reflex response on both sides when the cornea on the normal side was stimulated. Type C, which was present in one case, and type D which was seen in 3 cases, consisted of a bilateral absence of the corneal reflex upon stimulation on the affected side; stimulation on the unaffected side produced a normal reflex response on the intact side in combination with, respectively, a delay or absence of the corneal reflex response on the affected side. Comparison of the clinical observations with the electrophysiological findings revealed minor discrepancies in type A and B abnormalities. However, the electrophysiological type C and D abnormalities were not detected by clinical observation. These findings demonstrate that electrophysiological recording of the corneal reflex may reveal clinically undetectable abnormalities. From the electrophysiological findings it is concluded that the corneal reflex is conducted along medullary pathways running both ipsilaterally and contralaterally from the stimulated side before connecting, respectively, with the ipsilateral and contralateral facial nucleus. From the anatomical findings it is suggested that the ascending pathways from the spinal fifth nerve complex to the facial nuclei are located in the lateral reticular formation of the lower brain-stem. The normal corneal reflex responses in the presence of thalamic and

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

    Directory of Open Access Journals (Sweden)

    Annabelle Marie Belcher

    2016-03-01

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

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

    Science.gov (United States)

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

    2015-08-01

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

  3. 复杂脑网络研究:现状与挑战%Complex Brain Networks:Progresses and Challenges

    Institute of Scientific and Technical Information of China (English)

    张方风; 郑志刚

    2012-01-01

    Progresses in studies of complex networks and its applications in brain network were retrospected, including the research on topology structure features of anatomical and functional brain networks, as well as on the relationship between brain structures and functions. Based on complex networks theory, some important topology features of anatomical and functional brain networks were reported, such as small world,scale free,modular and hub regions; then some new findings were presented about the relationship between cognitive function and neuropsychiatry disorder with abnormalities in functional connectivity and changes in topological structure changes. Several challenges and key issues that should be addressed in the future were further raised.%以大脑网络研究为例,详细介绍了大脑网络的构建、结构网络、功能网络以及结构与功能的联系等方面的研究进展.基于复杂网络理论,对大脑结构网络和功能网络的分析得到很多重要的拓扑性质,如“小世界”、“无标度”、模块化以及核心脑区的分布等;另外发现认知功能、神经精神疾病与大脑结构和功能网络的拓扑结构变化或异常有关;总结了国内外在此领域的研究成果,提出了大脑网络研究工作面临的挑战,并展望了将来发展方向.

  4. 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.)

  5. Modelling Human Cortical Network in Real Brain Space

    Institute of Scientific and Technical Information of China (English)

    ZHAO Qing-Bai; FENG Hong-Bo; TANG Yi-Yuan

    2007-01-01

    Highly specific structural organization is of great significance in the topology of cortical networks.We introduce a human cortical network model.taking the specific cortical structure into account,in which nodes are brain sites placed in the actual positions of cerebral cortex and the establishment of edges depends on the spatial path length rather than the linear distance.The resulting network exhibits the essential features of cortical connectivity,properties of small-world networks and multiple clusters structure.Additionally.assortative mixing is also found in this roodel.All of these findings may be attributed to the spedtic cortical architecture.

  6. Sleeping of a Complex Brain Networks with Hierarchical Organization

    Institute of Scientific and Technical Information of China (English)

    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.

  7. T1-weighted gradient-echo imaging, with and without inversion recovery, in the identification of anatomical structures on the lateral surface of the brain*

    Science.gov (United States)

    Georgeto, Sergio Murilo; Zicarelli, Carlos Alexandre Martins; Gariba, Munir Antônio; Aguiar, Luiz Roberto

    2016-01-01

    Objective To compare brain structures using volumetric magnetic resonance imaging with isotropic resolution, in T1-weighted gradient-echo (GRE) acquisition, with and without inversion recovery (IR). Materials and methods From 30 individuals, we evaluated 120 blocks of images of the left and right cerebral hemispheres being acquired by T1 GRE and by T1 IR GRE. On the basis of the Naidich et al. method for localization of anatomical landmarks, 27 anatomical structures were divided into two categories: identifiable and inconclusive. Those two categories were used in the analyses of repeatability (intraobserver agreement) and reproducibility (interobserver agreement). McNemar's test was used in order to compare the T1 GRE and T1 IR GRE techniques. Results There was good agreement in the intraobserver and interobserver analyses (mean kappa > 0.60). McNemar's test showed that the frequency of identifiable anatomical landmarks was slightly higher when the T1 IR GRE technique was employed than when the T1 GRE technique was employed. The difference between the two techniques was statistically significant. Conclusion In the identification of anatomical landmarks, the T1 IR GRE technique appears to perform slightly better than does the T1 GRE technique. PMID:28057964

  8. 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.

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

    Science.gov (United States)

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

    2015-08-01

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

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

    Science.gov (United States)

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

    2017-02-01

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

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

    Science.gov (United States)

    Latora, Vito; Chavez, Mario

    2017-01-01

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

  12. Self-organized Criticality in Hierarchical Brain Network

    Institute of Scientific and Technical Information of China (English)

    YANG Qiu-Ying; ZHANG Ying-Yue; CHEN Tian-Lun

    2008-01-01

    It is shown that the cortical brain network of the macaque displays a hierarchically clustered organization and the neuron network shows small-world properties. Now the two factors will be considered in our model and the dynamical behavior of the model will be studied. We study the characters of the model and find that the distribution of avalanche size of the model follows power-law behavior.

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

    CERN Document Server

    de Santos-Sierra, Daniel; Leyva, Inmaculada; Almendral, Juan A; Anava, Sarit; Ayali, Amir; Papo, David; Boccaletti, Stefano

    2014-01-01

    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 transformati...

  14. Topological isomorphisms of human brain and financial market networks

    Directory of Open Access Journals (Sweden)

    Petra E Vértes

    2011-09-01

    Full Text Available 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 timeseries of 90 stocks from the New York Stock Exchange over a three-year period, and the fMRI-derived timeseries 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 optimised 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. 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.

  16. Connectomic analysis of brain networks: novel techniques and future directions

    Directory of Open Access Journals (Sweden)

    Leonie Cazemier

    2016-11-01

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

  17. Mapping multiplex hubs in human functional brain networks

    Directory of Open Access Journals (Sweden)

    Alex Arenas

    2016-07-01

    Full Text Available Typical brain networks consist of many peripheral regions and a few highly centralones, i.e. hubs, playing key functional roles in cerebral inter-regional interactions. Studieshave shown that networks, obtained from the analysis of specific frequency components ofbrain activity, present peculiar architectures with unique profiles of region centrality. However,the identification of hubs in networks built from different frequency bands simultaneouslyis still a challenging problem, remaining largely unexplored. Here we identify eachfrequency component with one layer of a multiplex network and face this challenge by exploitingthe recent advances in the analysis of multiplex topologies. First, we show that eachfrequency band carries unique topological information, fundamental to accurately modelbrain functional networks. We then demonstrate that hubs in the multiplex network, in generaldifferent from those ones obtained after discarding or aggregating the measured signalsas usual, provide a more accurate map of brain’s most important functional regions, allowingto distinguish between healthy and schizophrenic populations better than conventionalnetwork approaches.

  18. 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.

  19. Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive level.

    Science.gov (United States)

    Hartwigsen, Gesa; Bergmann, Til Ole; Herz, Damian Marc; Angstmann, Steffen; Karabanov, Anke; Raffin, Estelle; Thielscher, Axel; Siebner, Hartwig Roman

    2015-01-01

    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 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 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 be used to construct causal network models. These models can identify spatiotemporal changes in effective connectivity during distinct cognitive states and allow for examining how effective connectivity is shaped by NTBS. (iii) Modeling the NTBS effects based on neuroimaging can be complemented by behavior-based cognitive models that exploit variations in task performance. For instance, NTBS-induced changes in response speed and accuracy can be explicitly modeled in a cognitive framework accounting for the speed-accuracy trade-off. This enables to dissociate between behavioral NTBS effects that emerge in the context of rapid automatic responses or in the context of slow deliberate responses. We argue that these complementary modeling approaches facilitate the use of NTBS as a means of dissecting the causal architecture of cognitive systems of the human brain.

  20. A Statistical Method to Distinguish Functional Brain Networks

    Science.gov (United States)

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

    2017-01-01

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

  1. 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

  2. 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

  3. Brain networks disconnection in early multiple sclerosis cognitive deficits: an anatomofunctional study.

    Science.gov (United States)

    Louapre, Céline; Perlbarg, Vincent; García-Lorenzo, Daniel; Urbanski, Marika; Benali, Habib; Assouad, Rana; Galanaud, Damien; Freeman, Léorah; Bodini, Benedetta; Papeix, Caroline; Tourbah, Ayman; Lubetzki, Catherine; Lehéricy, Stéphane; Stankoff, Bruno

    2014-09-01

    Severe cognitive impairment involving multiple cognitive domains can occur early during the course of multiple sclerosis (MS). We investigated resting state functional connectivity changes in large-scale brain networks and related structural damage underlying cognitive dysfunction in patients with early MS. Patients with relapsing MS (3-5 years disease duration) were prospectively assigned to two groups based on a standardized neuropsychological evaluation: (1) cognitively impaired group (CI group, n = 15), with abnormal performances in at least 3 tests; (2) cognitively preserved group (CP group, n = 20) with normal performances in all tests. Patients and age-matched healthy controls underwent a multimodal 3T magnetic resonance imaging (MRI) including anatomical T1 and T2 images, diffusion imaging and resting state functional MRI. Structural MRI analysis revealed that CI patients had a higher white matter lesion load compared to CP and a more severe atrophy in gray matter regions highly connected to networks involved in cognition. Functional connectivity measured by integration was increased in CP patients versus controls in attentional networks (ATT), while integration was decreased in CI patients compared to CP both in the default mode network (DMN) and ATT. An anatomofunctional study within the DMN revealed that functional connectivity was mostly altered between the medial prefrontal cortex (MPFC) and the posterior cingulate cortex (PCC) in CI patients compared to CP and controls. In a multilinear regression model, functional correlation between MPFC and PCC was best predicted by PCC atrophy. Disconnection in the DMN and ATT networks may deprive the brain of compensatory mechanisms required to face widespread structural damage.

  4. 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.

  5. 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

  6. Estimation of brain network ictogenicity predicts outcome from epilepsy surgery

    Science.gov (United States)

    Goodfellow, M.; Rummel, C.; Abela, E.; Richardson, M. P.; Schindler, K.; Terry, J. R.

    2016-07-01

    Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal brain lesions or the presence of epileptiform rhythms, do not necessarily predict the best resection strategy. We validate our framework by analysing electrocorticogram (ECoG) recordings from patients who have undergone epilepsy surgery. We find that when post-operative outcome is good, model predictions for optimal strategies align better with the actual surgery undertaken than when post-operative outcome is poor. Crucially, this allows the prediction of optimal surgical strategies and the provision of quantitative prognoses for patients undergoing epilepsy surgery.

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

    Directory of Open Access Journals (Sweden)

    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.

  8. 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.

  9. Pro-cognitive drug effects modulate functional brain network organization

    Directory of Open Access Journals (Sweden)

    Carsten eGiessing

    2012-08-01

    Full Text Available Previous studies document that cholinergic and noradrenergic drugs improve attention, memory and cognitive control in healthy subjects and patients with neuropsychiatric disorders. In humans neural mechanisms of cholinergic and noradrenergic modulation have mainly been analyzed by investigating drug-induced changes of task-related neural activity measured with fMRI. Endogenous neural activity has often been neglected. Further, although drugs affect the coupling between neurons, only a few human studies have explicitly addressed how drugs modulate the functional connectome, i.e. the functional neural interactions within the brain. These studies have mainly focused on synchronization or correlation of brain activations. Recently, there are some drug studies using graph theory and other new mathematical approaches to model the brain as a complex network of interconnected processing nodes. Using such measures it is possible to detect not only focal, but also subtle, widely distributed drug effects on functional network topology. Most important, graph theoretical measures also quantify whether drug-induced changes in topology or network organization facilitate or hinder information processing. Several studies could show that functional brain integration is highly correlated with behavioral performance suggesting that cholinergic and noradrenergic drugs which improve measures of cognitive performance should increase functional network integration. The purpose of this paper is to show that graph theory provides a mathematical tool to develop theory-driven biomarkers of pro-cognitive drug effects, and also to discuss how these approaches can contribute to the understanding of the role of cholinergic and noradrenergic modulation in the human brain. Finally we discuss the global workspace theory as a theoretical framework of pro-cognitive drug effects and argue that pro-cognitive effects of cholinergic and noradrenergic drugs might be related to higher

  10. 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)

  11. 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...

  12. 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.

  13. Frequency dependent topological patterns of resting-state brain networks.

    Directory of Open Access Journals (Sweden)

    Long Qian

    Full Text Available The topological organization underlying brain networks has been extensively investigated using resting-state fMRI, focusing on the low frequency band from 0.01 to 0.1 Hz. However, the frequency specificities regarding the corresponding brain networks remain largely unclear. In the current study, a data-driven method named complementary ensemble empirical mode decomposition (CEEMD was introduced to separate the time series of each voxel into several intrinsic oscillation rhythms with distinct frequency bands. Our data indicated that the whole brain BOLD signals could be automatically divided into five specific frequency bands. After applying the CEEMD method, the topological patterns of these five temporally correlated networks were analyzed. The results showed that global topological properties, including the network weighted degree, network efficiency, mean characteristic path length and clustering coefficient, were observed to be most prominent in the ultra-low frequency bands from 0 to 0.015 Hz. Moreover, the saliency of small-world architecture demonstrated frequency-density dependency. Compared to the empirical mode decomposition method (EMD, CEEMD could effectively eliminate the mode-mixing effects. Additionally, the robustness of CEEMD was validated by the similar results derived from a split-half analysis and a conventional frequency division method using the rectangular window band-pass filter. Our findings suggest that CEEMD is a more effective method for extracting the intrinsic oscillation rhythms embedded in the BOLD signals than EMD. The application of CEEMD in fMRI data analysis will provide in-depth insight in investigations of frequency specific topological patterns of the dynamic brain networks.

  14. Altered functional brain networks in Prader-Willi syndrome.

    Science.gov (United States)

    Zhang, Yi; Zhao, Heng; Qiu, Siyou; Tian, Jie; Wen, Xiaotong; Miller, Jennifer L; von Deneen, Karen M; Zhou, Zhenyu; Gold, Mark S; Liu, Yijun

    2013-06-01

    Prader-Willi syndrome (PWS) is a genetic imprinting disorder characterized mainly by hyperphagia and early childhood obesity. Previous functional neuroimaging studies used visual stimuli to examine abnormal activities in the eating-related neural circuitry of patients with PWS. It was found that patients with PWS exhibited both excessive hunger and hyperphagia consistently, even in situations without any food stimulation. In the present study, we employed resting-state functional MRI techniques to investigate abnormal brain networks related to eating disorders in children with PWS. First, we applied amplitude of low-frequency fluctuation analysis to define the regions of interest that showed significant alterations in resting-state brain activity levels in patients compared with their sibling control group. We then applied a functional connectivity (FC) analysis to these regions of interest in order to characterize interactions among the brain regions. Our results demonstrated that patients with PWS showed decreased FC strength in the medial prefrontal cortex (MPFC)/inferior parietal lobe (IPL), MPFC/precuneus, IPL/precuneus and IPL/hippocampus in the default mode network; decreased FC strength in the pre-/postcentral gyri and dorsolateral prefrontal cortex (DLPFC)/orbitofrontal cortex (OFC) in the motor sensory network and prefrontal cortex network, respectively; and increased FC strength in the anterior cingulate cortex/insula, ventrolateral prefrontal cortex (VLPFC)/OFC and DLPFC/VLPFC in the core network and prefrontal cortex network, respectively. These findings indicate that there are FC alterations among the brain regions implicated in eating as well as rewarding, even during the resting state, which may provide further evidence supporting the use of PWS as a model to study obesity and to provide information on potential neural targets for the medical treatment of overeating.

  15. A straight nasal septum and right unilateral hypertrophied inferior nasal turbinate, a very rare anatomical phenomenon, in skilled language translators: relevance to anomalous dominance, brain hemisphericity and second language acquisition.

    Science.gov (United States)

    Backon, J; Negeris, B; Kurzon, D; Amit-Chochavi, H

    1991-06-01

    Research on second language acquisition has recently focused on the concept of brain hemisphericity. Since the nasal cycle indexes brain hemisphericity and a deviated nasal septum itself affected by structural brain dominance may prevent nasal cycling, we investigated the presence of septal deviation and inferior turbinate hypertrophy in 11 expert translators. We found that 10 of the 11 subjects demonstrated both a straight nasal septum and right unilateral inferior turbinate hypertrophy, an extremely rare anatomical phenomenon. The one-tailed binomial test was extremely significant (p < .0000001). This anatomical phenomenon, which can be noninvasively checked in less than 30 seconds may predict excellence in second language acquisition.

  16. 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…

  17. Developmental changes in organization of structural brain networks.

    Science.gov (United States)

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

    2013-09-01

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

  18. Developmental Changes in Organization of Structural Brain Networks

    Science.gov (United States)

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

    2013-01-01

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

  19. 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.

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

    Science.gov (United States)

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

    2016-10-01

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

  2. Brain tumour classification using Gaussian decomposition and neural networks.

    Science.gov (United States)

    Arizmendi, Carlos; Sierra, Daniel A; Vellido, Alfredo; Romero, Enrique

    2011-01-01

    The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN). This combination of techniques is shown to yield high diagnostic classification accuracy in problems concerning diverse brain tumour pathologies, some of which have received little attention in the literature.

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

    Science.gov (United States)

    2011-10-01

    gain? Neuroimage 34 (1), 144–55.1361 Benjamini, Y., Heller , R., Yekutieli, D., 2009. Selective inference in complex1362 research. Philos. Trans. R. Soc...Hayashi, K. M., de Zubiricaray, G., Janke, A. L., Rose,1531 S. E., Semple, J., Herman , D., Hong, M. S., Dittmer, S., Doddrell, D. M.,1532 Toga, A

  4. 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.

  5. 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.

  6. Brain extracellular matrix retains connectivity in neuronal networks.

    Science.gov (United States)

    Bikbaev, Arthur; Frischknecht, Renato; Heine, Martin

    2015-09-29

    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.

  7. 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

  8. Stereoscopic depth increases intersubject correlations of brain networks.

    Science.gov (United States)

    Gaebler, Michael; Biessmann, Felix; Lamke, Jan-Peter; Müller, Klaus-Robert; Walter, Henrik; Hetzer, Stefan

    2014-10-15

    Three-dimensional movies presented via stereoscopic displays have become more popular in recent years aiming at a more engaging viewing experience. However, neurocognitive processes associated with the perception of stereoscopic depth in complex and dynamic visual stimuli remain understudied. Here, we investigate the influence of stereoscopic depth on both neurophysiology and subjective experience. Using multivariate statistical learning methods, we compare the brain activity of subjects when freely watching the same movies in 2D and in 3D. Subjective reports indicate that 3D movies are more strongly experienced than 2D movies. On the neural level, we observe significantly higher intersubject correlations of cortical networks when subjects are watching 3D movies relative to the same movies in 2D. We demonstrate that increases in intersubject correlations of brain networks can serve as neurophysiological marker for stereoscopic depth and for the strength of the viewing experience.

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

    Institute of Scientific and Technical Information of China (English)

    LI Ling; JIN Zhen-Lan

    2011-01-01

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

  10. 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

    2012-08-23

    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 coexpression 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 coexpressed 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 window through which to view the foundation of uniquely human cognitive capacities.

  11. Functional networks underlying latent inhibition learning in the mouse brain

    OpenAIRE

    Puga, Frank; Barrett, Douglas W.; Bastida, Christel C.; Gonzalez-Lima, F.

    2007-01-01

    The present study reports the first comprehensive map of brain networks underlying latent inhibition learning and the first application of structural equation modeling to cytochrome oxidase data. In latent inhibition, repeated exposure to a stimulus results in a latent form of learning that inhibits subsequent associations with that stimulus. As neuronal energy demand to form learned associations changes, so does the induction of the respiratory enzyme cytochrome oxidase. Therefore, cytochrom...

  12. Changes in brain functional network connectivity after stroke

    Institute of Scientific and Technical Information of China (English)

    Wei Li; Yapeng Li; Wenzhen Zhu; Xi Chen

    2014-01-01

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

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

    Science.gov (United States)

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

    2011-01-01

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

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

    Science.gov (United States)

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

    2009-10-01

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

  15. 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.

  16. The relationship of anatomical and functional connectivity to resting-state connectivity in primate somatosensory cortex.

    Science.gov (United States)

    Wang, Zheng; Chen, Li Min; Négyessy, László; Friedman, Robert M; Mishra, Arabinda; Gore, John C; Roe, Anna W

    2013-06-19

    Studies of resting-state activity in the brain have provoked critical questions about the brain's functional organization, but the biological basis of this activity is not clear. Specifically, the relationships between interregional correlations in resting-state measures of activity, neuronal functional connectivity and anatomical connectivity are much debated. To investigate these relationships, we have examined both anatomical and steady-state functional connectivity within the hand representation of primary somatosensory cortex (areas 3b and 1) in anesthetized squirrel monkeys. The comparison of three data sets (fMRI, electrophysiological, and anatomical) indicate two primary axes of information flow within the SI: prominent interdigit interactions within area 3b and predominantly homotopic interactions between area 3b and area 1. These data support a strikingly close relationship between baseline functional connectivity and anatomical connections. This study extends findings derived from large-scale cortical networks to the realm of local millimeter-scale networks.

  17. 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.

  18. Computational neuropsychiatry – schizophrenia as a cognitive brain network disorder

    Directory of Open Access Journals (Sweden)

    Maria R Dauvermann

    2014-03-01

    Full Text Available Computational modelling of functional brain networks has advanced the understanding of higher cognitive function. It is hypothesised that functional networks mediating higher cognitive processes are disrupted in people with schizophrenia. In this article, we review studies that applied measures of functional and effective connectivity to fMRI data during cognitive tasks, in particular working memory fMRI studies. We provide a conceptual summary of the main findings in fMRI data and their relationship with neurotransmitter systems, which are known to be altered in individuals with schizophrenia. We consider possible developments in computational neuropsychiatry, which are likely to further our understanding of how functional networks are altered in schizophrenia.

  19. Brain oscillations and electroencephalography scalp networks during tempo perception.

    Science.gov (United States)

    Tian, Yin; Ma, Weiyi; Tian, Chunyang; Xu, Peng; Yao, Dezhong

    2013-12-01

    In the current study we used electroencephalography (EEG) to investigate the relation between musical tempo perception and the oscillatory activity in specific brain regions, and the scalp EEG networks in the theta, alpha, and beta bands. The results showed that the theta power at the frontal midline decreased with increased arousal level related to tempo. The alpha power induced by original music at the bilateral occipital-parietal regions was stronger than that by tempo-transformed music. The beta power did not change with tempo. At the network level, the original music-related alpha network had high global efficiency and the optimal path length. This study was the first to use EEG to investigate multi-oscillatory activities and the data support the tempo-specific timing hypothesis.

  20. A stereotaxic method of anatomical localization by means of H{sub 2}{sup 15}O positron emission tomography applicable to the brain activation study in cats. Registration of images of cerebral blood flow to brain atlas

    Energy Technology Data Exchange (ETDEWEB)

    Sakiyama, Yojiro; Toyama, Hinako; Oda, Keiichi; Ishii, Shin-ichi; Ishiwata, Kiichi; Ishii, Kenji; Suzuki, Atsuko; Nakayama, Hitomi; Senda, Michio [Tokyo Metropolitan Inst. of Gerontology (Japan)

    1997-11-01

    In the neuronal activation study of normal animals, precise anatomical correlation, preferentially to a detailed brain atlas, is required for the activation foci co-registration. To obtain precise regional correlation between H{sub 2}{sup 15}O-PET images and the brain atlas, a method of stereotaxic image reorientation was applied to an activation study with vibrotactile stimulation. Cats anesthetized with halothane underwent repeated measurements of regional cerebral blood flow (rCBF) in the resting condition and during vibration of the right forepaw. The image set was adjusted three-dimensionally to the atlas. The postmortem brain was sectioned according to the atlas planes. The activated areas were determined by the stimulus-minus-resting subtraction images, and the areas were projected to the atlas. The PET images of the cat brain were compatible both to the postmortem brain slices and to the brain atlas. The activation foci obtained from the subtraction images corresponded to the area around the coronal sulcus, which is electrophysiologically known as the primary sensory area as described in the atlas. There were precise regional correlations between the PET image and anatomy in a PET activation study of the cat by means of stereotaxic image reorientation. (author)

  1. Anatomic dissection of the inferior fronto-occipital fasciculus revisited in the lights of brain stimulation data.

    Science.gov (United States)

    Martino, Juan; Brogna, Christian; Robles, Santiago G; Vergani, Francesco; Duffau, Hugues

    2010-05-01

    Despite electrostimulation studies of the white matter pathways, supporting the role of the inferior fronto-occipital fasciculus (IFOF) in semantic processing, little is known about the precise anatomical course of this fascicle, especially regarding its exact cortical terminations. Here, in the lights of these new functional data, we dissected 14 post-mortem human hemispheres using the Klingler fiber dissection technique, to study the IFOF fibers and to identify their actual cortical terminations in the parietal, occipital and temporal lobes. We identified two different components of the IFOF: (i) a superficial and dorsal subcomponent, which connects the frontal lobe with the superior parietal lobe and the posterior portion of the superior and middle occipital gyri, (ii) a deep and ventral subcomponent, which connects the frontal lobe with the posterior portion of the inferior occipital gyrus and the posterior temporo-basal area. Thus, our results are in line with the hypothesis of the functional role of the IFOF in the semantic system, by showing that it is mainly connected with two areas involved in semantics: the occipital associative extrastriate cortex and the temporo-basal region. Further combined anatomical (dissection and Diffusion Tensor Imaging) and functional (intraoperative subcortical stimulation) studies are needed, to clarify the exact participation of each IFOF subcomponent in semantic processing.

  2. 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.

  3. 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

    2017-03-14

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

  4. Dyspnea and pain share emotion-related brain network.

    Science.gov (United States)

    von Leupoldt, Andreas; Sommer, Tobias; Kegat, Sarah; Baumann, Hans Jörg; Klose, Hans; Dahme, Bernhard; Büchel, Christian

    2009-10-15

    The early detection of stimuli signalling threat to an organism is a crucial evolutionary advantage. For example, the perception of aversive bodily sensations such as dyspnea and pain strongly motivates fast adaptive behaviour to ensure survival. Their similarly threatening and motivating characters led to the speculation that both sensations are mediated by common brain areas, which has also been suggested by neuroimaging studies on either dyspnea or pain. By using functional magnetic resonance imaging (fMRI), we formally tested this hypothesis and compared the cortical processing of perceived heat pain and resistive load induced dyspnea in the same group of participants. Here we show that the perception of both aversive sensations is processed in similar brain areas including the insula, dorsal anterior cingulate cortex, amygdala and medial thalamus. These areas have a documented role in the processing of emotions such as fear and anxiety. Thus, the current study highlights the role of a common emotion-related human brain network which underlies the perception of aversive bodily sensations such as dyspnea and pain. This network seems crucial for translating the threatening character of different bodily signals into behavioural consequences that promote survival.

  5. 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.

  6. 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.

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

    Science.gov (United States)

    Li, Ling; Jin, Zhen-Lan

    2011-04-01

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

  8. 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.

  9. Stability constraints on large-scale structural brain networks

    Directory of Open Access Journals (Sweden)

    Richard T. Gray

    2013-04-01

    Full Text Available Stability is an important dynamical property of complex systems and underpins a broad range of coherent self-organized behaviour. Based on evidence that some neurological disorders correspond to linear instabilities,we hypothesize that stability constrains the brain’s electrical activity and influences its structure and physiology. Using a physiologically based model of brain electrical activity, we investigated the stability and dispersion solutions of networks of neuronal populations with propagation time delays and dendritic time constants. We find that stability is determined by the spectrum of the network’s matrix of connection strengths and is independent of the temporal damping rate of axonal propagation with stability restricting the spectrum to a region in the complex plane. Time delays and dendritic time constants modify the shape of this region but it always contains the unit disk. Instabilities resulting from changes in connection strength initially have frequencies less than a critical frequency. For physiologically plausible parameter values based on the corticothalamic system, this critical frequency is approximately $10$ Hz. For excitatory networks and networks with randomlydistributed excitatory and inhibitory connections, time delays and nonzero dendritic time constants have no impact on network stability but do effect dispersion frequencies. Random networks with both excitatory and inhibitory connections can have multiple marginally stable modes at low delta frequencies.

  10. 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.

  11. Understanding entangled cerebral networks: A prerequisite for restoring brain function with brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Emmanuel eMandonnet

    2014-05-01

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

  12. 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.

  13. 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.

  14. 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

  15. Structural brain network characteristics can differentiate CIS from early RRMS

    Directory of Open Access Journals (Sweden)

    Muthuraman eMuthuraman

    2016-02-01

    Full Text Available Focal demyelinated lesions, diffuse white matter (WM damage and grey matter (GM atrophy influence directly the disease progression in patients with multiple sclerosis. The aim of this study was to identify specific characteristics of GM and WM structural networks in subjects with clinically isolated syndrome (CIS in comparison to patients with early relapsing-remitting multiple sclerosis (RRMS.Twenty patients with CIS, thirty three with RRMS and forty healthy subjects were investigated using 3 T-MRI. Diffusion tensor imaging was applied, together with probabilistic tractography and fractional anisotropy (FA maps for WM and cortical thickness correlation analysis for GM, to determine the structural connectivity patterns. A network topology analysis with the aid of graph theoretical approaches was used to characterize the network at different community levels (modularity, clustering coefficient, global and local efficiencies. Finally, we applied support vector machines (SVM to automatically discriminate the two groups. .In comparison to CIS subjects, patients with RRMS were found to have increased modular connectivity and higher local clustering, highlighting increased local processing in both GM and WM. Both groups presented increased modularity and clustering coefficients in comparison to healthy controls. SVM algorithms achieved 97% accuracy using the clustering coefficient as classifier derived from GM and 65% using WM from probabilistic tractography and 67 % from modularity of FA maps to differentiate between CIS and RRMS patients. We demonstrate a clear increase of modular and local connectivity in patients with early RRMS in comparison to CIS and healthy subjects. Based only on a single anatomic scan and without a priori information, we developed an automated and investigator-independent paradigm that can accurately discriminate between patients with these clinically similar disease entities, and could thus complement the current

  16. Anatomical and functional neuroimaging in awake, behaving marmosets.

    Science.gov (United States)

    Silva, Afonso C

    2017-03-01

    The common marmoset (Callithrix jacchus) is a small New World monkey that has gained significant recent interest in neuroscience research, not only because of its compatibility with gene editing techniques, but also due to its tremendous versatility as an experimental animal model. Neuroimaging modalities, including anatomical (MRI) and functional magnetic resonance imaging (fMRI), complemented by two-photon laser scanning microscopy and electrophysiology, have been at the forefront of unraveling the anatomical and functional organization of the marmoset brain. High-resolution anatomical MRI of the marmoset brain can be obtained with remarkable cytoarchitectonic detail. Functional MRI of the marmoset brain has been used to study various sensory systems, including somatosensory, auditory, and visual pathways, while resting-state fMRI studies have unraveled functional brain networks that bear great correspondence to those previously described in humans. Two-photon laser scanning microscopy of the marmoset brain has enabled the simultaneous recording of neuronal activity from thousands of neurons with single cell spatial resolution. In this article, we aim to review the main results obtained by our group and by our colleagues in applying neuroimaging techniques to study the marmoset brain. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 373-389, 2017.

  17. 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.

  18. 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.

  19. Griffiths phases and the stretching of criticality in brain networks

    Science.gov (United States)

    Moretti, Paolo; Muñoz, Miguel A.

    2013-10-01

    Hallmarks of criticality, such as power-laws and scale invariance, have been empirically found in cortical-network dynamics and it has been conjectured that operating at criticality entails functional advantages, such as optimal computational capabilities, memory and large dynamical ranges. As critical behaviour requires a high degree of fine tuning to emerge, some type of self-tuning mechanism needs to be invoked. Here we show that, taking into account the complex hierarchical-modular architecture of cortical networks, the singular critical point is replaced by an extended critical-like region that corresponds—in the jargon of statistical mechanics—to a Griffiths phase. Using computational and analytical approaches, we find Griffiths phases in synthetic hierarchical networks and also in empirical brain networks such as the human connectome and that of Caenorhabditis elegans. Stretched critical regions, stemming from structural disorder, yield enhanced functionality in a generic way, facilitating the task of self-organizing, adaptive and evolutionary mechanisms selecting for criticality.

  20. Brain tumor grading based on Neural Networks and Convolutional Neural Networks.

    Science.gov (United States)

    Yuehao Pan; Weimin Huang; Zhiping Lin; Wanzheng Zhu; Jiayin Zhou; Wong, Jocelyn; Zhongxiang Ding

    2015-08-01

    This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, with some combination operations between multiphase MRIs. Compared to other researches, which involve additional effort to design and choose feature sets, the approach used in this paper leverages the learning capability of deep learning machine. We present the grading performance on the testing data measured by the sensitivity and specificity. The results show a maximum improvement of 18% on grading performance of Convolutional Neural Networks based on sensitivity and specificity compared to Neural Networks. We also visualize the kernels trained in different layers and display some self-learned features obtained from Convolutional Neural Networks.

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. Dismissing Attachment Characteristics Dynamically Modulate Brain Networks Subserving Social Aversion

    Science.gov (United States)

    Krause, Anna Linda; Borchardt, Viola; Li, Meng; van Tol, Marie-José; Demenescu, Liliana Ramona; Strauss, Bernhard; Kirchmann, Helmut; Buchheim, Anna; Metzger, Coraline D.; Nolte, Tobias; Walter, Martin

    2016-01-01

    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 functional magnetic resonance imaging (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 (IPL). 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

  6. How networks communicate: propagation patterns in spontaneous brain activity.

    Science.gov (United States)

    Mitra, Anish; Raichle, Marcus E

    2016-10-05

    Initially regarded as 'noise', spontaneous (intrinsic) activity accounts for a large portion of the brain's metabolic cost. Moreover, it is now widely known that infra-slow (less than 0.1 Hz) spontaneous activity, measured using resting state functional magnetic resonance imaging of the blood oxygen level-dependent (BOLD) signal, is correlated within functionally defined resting state networks (RSNs). However, despite these advances, the temporal organization of spontaneous BOLD fluctuations has remained elusive. By studying temporal lags in the resting state BOLD signal, we have recently shown that spontaneous BOLD fluctuations consist of remarkably reproducible patterns of whole brain propagation. Embedded in these propagation patterns are unidirectional 'motifs' which, in turn, give rise to RSNs. Additionally, propagation patterns are markedly altered as a function of state, whether physiological or pathological. Understanding such propagation patterns will likely yield deeper insights into the role of spontaneous activity in brain function in health and disease.This article is part of the themed issue 'Interpreting blood oxygen level-dependent: a dialogue between cognitive and cellular neuroscience'.

  7. 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.

  8. Evidence for two independent factors that modify brain networks to meet task goals

    OpenAIRE

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

    2016-01-01

    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...

  9. Self-regulation of circumscribed brain activity modulates spatially selective and frequency specific connectivity of distributed resting state networks

    Directory of Open Access Journals (Sweden)

    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

  10. Community detection in weighted brain connectivity networks beyond the resolution limit

    CERN Document Server

    Nicolini, Carlo; Bifone, Angelo

    2016-01-01

    Graph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or communities, that are smaller than a specific scale. Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules, in contrast with many previous reports. However, the use of Surprise is limited to binary networks, while brain networks are intrinsically weighted, reflecting a continuous distribution of connectivity strengths between different brain regions. Here, we propose Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground-truth modular structure. We compare Asymp...

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

    OpenAIRE

    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 functio...

  12. 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

  13. Deep sleep divides the cortex into opposite modes of anatomical-functional coupling.

    Science.gov (United States)

    Tagliazucchi, Enzo; Crossley, Nicolas; Bullmore, Edward T; Laufs, Helmut

    2016-11-01

    The coupling of anatomical and functional connectivity at rest suggests that anatomy is essential for wake-typical activity patterns. Here, we study the development of this coupling from wakefulness to deep sleep. Globally, similarity between whole-brain anatomical and functional connectivity networks increased during deep sleep. Regionally, we found differential coupling: during sleep, functional connectivity of primary cortices resembled more the underlying anatomical connectivity, while we observed the opposite in associative cortices. Increased anatomical-functional similarity in sensory areas is consistent with their stereotypical, cross-modal response to the environment during sleep. In distinction, looser coupling-relative to wakeful rest-in higher order integrative cortices suggests that sleep actively disrupts default patterns of functional connectivity in regions essential for the conscious access of information and that anatomical connectivity acts as an anchor for the restoration of their functionality upon awakening.

  14. 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,

  15. 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).

  16. 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 rehabilitation and warrants further study.

  17. 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.

  18. Differential synchronization in default and task-specific networks of the human brain

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

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

    2010-04-29

    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 processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100-500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a "router" network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates.

  20. Network Dynamics with BrainX3: A Large-Scale Simulation of the Human Brain Network with Real-Time Interaction

    Directory of Open Access Journals (Sweden)

    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.

  1. Selective vulnerability related to aging in large-scale resting brain networks.

    Science.gov (United States)

    Zhang, Hong-Ying; Chen, Wen-Xin; Jiao, Yun; Xu, Yao; Zhang, Xiang-Rong; Wu, Jing-Tao

    2014-01-01

    Normal aging is associated with cognitive decline. Evidence indicates that large-scale brain networks are affected by aging; however, it has not been established whether aging has equivalent effects on specific large-scale networks. In the present study, 40 healthy subjects including 22 older (aged 60-80 years) and 18 younger (aged 22-33 years) adults underwent resting-state functional MRI scanning. Four canonical resting-state networks, including the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN) and salience network, were extracted, and the functional connectivities in these canonical networks were compared between the younger and older groups. We found distinct, disruptive alterations present in the large-scale aging-related resting brain networks: the ECN was affected the most, followed by the DAN. However, the DMN and salience networks showed limited functional connectivity disruption. The visual network served as a control and was similarly preserved in both groups. Our findings suggest that the aged brain is characterized by selective vulnerability in large-scale brain networks. These results could help improve our understanding of the mechanism of degeneration in the aging brain. Additional work is warranted to determine whether selective alterations in the intrinsic networks are related to impairments in behavioral performance.

  2. Gene Regulatory Network Analysis Reveals Differences in Site-specific Cell Fate Determination in Mammalian Brain

    Directory of Open Access Journals (Sweden)

    Gokhan eErtaylan

    2014-12-01

    Full Text Available Neurogenesis - the generation of new neurons - is an ongoing process that persists in the adult mammalian brain of several species, including humans. In this work we analyze two discrete brain regions: the subventricular zone (SVZ lining the walls of the lateral ventricles; and the subgranular zone (SGZ of the dentate gyrus of the hippocampus in mice and shed light on the SVZ and SGZ specific neurogenesis. We propose a computational model that relies on the construction and analysis of region specific gene regulatory networks from the publicly available data on these two regions. Using this model a number of putative factors involved in neuronal stem cell (NSC identity and maintenance were identified. We also demonstrate potential gender and niche-derived differences based on cell surface and nuclear receptors via Ar, Hif1a and Nr3c1.We have also conducted cell fate determinant analysis for SVZ NSC populations to Olfactory Bulb interneurons and SGZ NSC populations to the granule cells of the Granular Cell Layer. We report thirty-one candidate cell fate determinant gene pairs, ready to be validated. We focus on Ar - Pax6 in SVZ and Sox2 - Ncor1 in SGZ. Both pairs are expressed and localized in the suggested anatomical structures as shown by in situ hybridization and found to physically interact.Finally, we conclude that there are fundamental differences between SGZ and SVZ neurogenesis. We argue that these regulatory mechanisms are linked to the observed differential neurogenic potential of these regions. The presence of nuclear and cell surface receptors in the region specific regulatory circuits indicate the significance of niche derived extracellular factors, hormones and region specific factors such as the oxygen sensitivity, dictating SGZ and SVZ specific neurogenesis.

  3. Real-time fMRI brain computer interfaces: self-regulation of single brain regions to networks.

    Science.gov (United States)

    Ruiz, Sergio; Buyukturkoglu, Korhan; Rana, Mohit; Birbaumer, Niels; Sitaram, Ranganatha

    2014-01-01

    With the advent of brain computer interfaces based on real-time fMRI (rtfMRI-BCI), the possibility of performing neurofeedback based on brain hemodynamics has become a reality. In the early stage of the development of this field, studies have focused on the volitional control of activity in circumscribed brain regions. However, based on the understanding that the brain functions by coordinated activity of spatially distributed regions, there have recently been further developments to incorporate real-time feedback of functional connectivity and spatio-temporal patterns of brain activity. The present article reviews the principles of rtfMRI neurofeedback, its applications, benefits and limitations. A special emphasis is given to the discussion of novel developments that have enabled the use of this methodology to achieve self-regulation of the functional connectivity between different brain areas and of distributed brain networks, anticipating new and exciting applications for cognitive neuroscience and for the potential alleviation of neuropsychiatric disorders.

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

    Science.gov (United States)

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

    2014-10-05

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

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

    Science.gov (United States)

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

    2015-11-15

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

  6. 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.

  7. An abnormal resting-state functional brain network indicates progression towards Alzheimer’s disease*****

    Institute of Scientific and Technical Information of China (English)

    Jie Xiang; Hao Guo; Rui Cao; Hong Liang; Junjie Chen

    2013-01-01

    Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer’s disease, and brain network-connection strength, network efficiency, and nodal attributes are abnormal. However, existing research has only analyzed the differences between these patients and normal controls. In this study, we constructed brain networks using resting-state functional MRI data that was extracted from four populations mal controls, patients with early mild cognitive impairment, patients with late mild cognitive impairment, and patients with Alzheimer’s disease) using the Alzheimer’s Disease Neuroimaging Initiative data set. The aim was to analyze the characteristics of resting-state functional neural networks, and to observe mild cognitive impairment at different stages before the transformation to Alzheimer’s disease. Results showed that as cognitive deficits increased across the four groups, the shortest path in the rest-ing-state functional network gradual y increased, while clustering coefficients gradual y decreased. This evidence indicates that dementia is associated with a decline of brain network efficiency. In tion, the changes in functional networks revealed the progressive deterioration of network function across brain regions from healthy elderly adults to those with mild cognitive impairment and Alzhei-mer’s disease. The alterations of node attributes in brain regions may reflect the cognitive functions in brain regions, and we speculate that early impairments in memory, hearing, and language function can eventual y lead to diffuse brain injury and other cognitive impairments.

  8. Networked neuroscience : brain scans and visual knowing at the intersection of atlases and databases

    NARCIS (Netherlands)

    Beaulieu, Anne; de Rijcke, Sarah; Coopmans, Catelijne; Woolgar, Steve

    2014-01-01

    This chapter discusses the development of authoritative collections of brain scans known as “brain atlases”, focusing in particular on how such scans are constituted as authoritative visual objects. Three dimensions are identified: first, brain scans are parts of suites of networked technologies rat

  9. Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections.

    Science.gov (United States)

    Pyka, Martin; Klatt, Sebastian; Cheng, Sen

    2014-01-01

    Computational models of neural networks can be based on a variety of different parameters. These parameters include, for example, the 3d shape of neuron layers, the neurons' spatial projection patterns, spiking dynamics and neurotransmitter systems. While many well-developed approaches are available to model, for example, the spiking dynamics, there is a lack of approaches for modeling the anatomical layout of neurons and their projections. We present a new method, called Parametric Anatomical Modeling (PAM), to fill this gap. PAM can be used to derive network connectivities and conduction delays from anatomical data, such as the position and shape of the neuronal layers and the dendritic and axonal projection patterns. Within the PAM framework, several mapping techniques between layers can account for a large variety of connection properties between pre- and post-synaptic neuron layers. PAM is implemented as a Python tool and integrated in the 3d modeling software Blender. We demonstrate on a 3d model of the hippocampal formation how PAM can help reveal complex properties of the synaptic connectivity and conduction delays, properties that might be relevant to uncover the function of the hippocampus. Based on these analyses, two experimentally testable predictions arose: (i) the number of neurons and the spread of connections is heterogeneously distributed across the main anatomical axes, (ii) the distribution of connection lengths in CA3-CA1 differ qualitatively from those between DG-CA3 and CA3-CA3. Models created by PAM can also serve as an educational tool to visualize the 3d connectivity of brain regions. The low-dimensional, but yet biologically plausible, parameter space renders PAM suitable to analyse allometric and evolutionary factors in networks and to model the complexity of real networks with comparatively little effort.

  10. Brain network alterations and vulnerability to simulated neurodegeneration in breast cancer.

    Science.gov (United States)

    Kesler, Shelli R; Watson, Christa L; Blayney, Douglas W

    2015-08-01

    Breast cancer and its treatments are associated with mild cognitive impairment and brain changes that could indicate an altered or accelerated brain aging process. We applied diffusion tensor imaging and graph theory to measure white matter organization and connectivity in 34 breast cancer survivors compared with 36 matched healthy female controls. We also investigated how brain networks (connectomes) in each group responded to simulated neurodegeneration based on network attack analysis. Compared with controls, the breast cancer group demonstrated significantly lower fractional anisotropy, altered small-world connectome properties, lower brain network tolerance to systematic region (node), and connection (edge) attacks and significant cognitive impairment. Lower tolerance to network attack was associated with cognitive impairment in the breast cancer group. These findings provide further evidence of diffuse white matter pathology after breast cancer and extend the literature in this area with unique data demonstrating increased vulnerability of the post-breast cancer brain network to future neurodegenerative processes.

  11. Characterization of Large Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction

    OpenAIRE

    2016-01-01

    Several experiments evidence that specialized brain regions functionally interact and reveal that the brain processes and integrates information in a specific and structured manner. Networks can be used to model brain functional activities constituting a way to characterize and quantify this structured form of organization. Reports state that different physiological states or even diseases that affect the central nervous system may be associated to alterations on those networks, that might re...

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

    Science.gov (United States)

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

    2014-08-01

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

  13. 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. Methods: Twenty tinnitus patients with normal hearing and 17 healthy controls, matched for sex, age and years of education, were subjected to Single Photon Emission Computed Tomography using the radiotracer ethylenedicysteine diethyl ester, labeled with Technetium 99 m (99 mTc-ECD SPECT). The severity of tinnitus was assessed using the “Tinnitus Handicap Inventory” (THI). The images were processed and analyzed using “Statistical Parametric Mapping” (SPM8). Results: A significant increase in cerebral perfusion in the left parahippocampal gyrus (pFWE <0.05) was observed in patients with tinnitus compared with healthy controls. The average total THI score was 50.8+18.24, classified as moderate tinnitus. Conclusion: It was possible to identify significant changes in the limbic system of the brain perfusion in tinnitus patients with normal hearing, suggesting that central mechanisms, not specific to the auditory pathway, are involved in the pathophysiology of symptoms, even in the absence of clinically diagnosed peripheral changes. PMID:24516567

  14. Pedophilia: neuropsychological evidence encouraging a brain network perspective.

    Science.gov (United States)

    Tost, Heike; Vollmert, Christian; Brassen, Stefanie; Schmitt, Andrea; Dressing, Harald; Braus, Dieter F

    2004-01-01

    Although the vast majority of current pathogenetic theories support a neurobiological understanding of psychiatric disorders, the brain functional correlates of pedophilia are largely unknown. Based on prior behavior genetics research on human sexual orientation and phenomenology as well as the phenotypical intersection of pedophilia with other psychiatric spectrum disorders, we hypothesize the involvement of striato-thalamo-cortical processing loops in the formation of pedophilic urges and behaviors. Data from a current neuropsychological pilot study in four pedophiles encourage our brain functional perspective. As deduced from the network model, all four patients exhibited pronounced and circumscribed deficits in cognitive domains mediated by striato-thalamically controlled areas of the frontal cortex. All patients were especially impaired in neuropsychological functions associated with the prefrontal and motor processing loops (e.g., response inhibition, working memory and cognitive flexibility), with a performance level located up to five standard deviations below the normative data. Contrary to this, neuropsychological performances in cognitive domains without a comparable high frontal loading were in all participants unobtrusive. In future, studying gene by environment interactions in combination with functional neuroimaging and neuropsychological assessment is promising to elucidate the pathophysiological relationship of psychiatric disorders that are characterized by inadequate urges and poor behavioral inhibition.

  15. 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.

  16. 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

  17. 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

  18. Cell cycle networks link gene expression dysregulation, mutation, and brain maldevelopment in autistic toddlers.

    Science.gov (United States)

    Pramparo, Tiziano; Lombardo, Michael V; Campbell, Kathleen; Barnes, Cynthia Carter; Marinero, Steven; Solso, Stephanie; Young, Julia; Mayo, Maisi; Dale, Anders; Ahrens-Barbeau, Clelia; Murray, Sarah S; Lopez, Linda; Lewis, Nathan; Pierce, Karen; Courchesne, Eric

    2015-12-14

    Genetic mechanisms underlying abnormal early neural development in toddlers with Autism Spectrum Disorder (ASD) remain uncertain due to the impossibility of direct brain gene expression measurement during critical periods of early development. Recent findings from a multi-tissue study demonstrated high expression of many of the same gene networks between blood and brain tissues, in particular with cell cycle functions. We explored relationships between blood gene expression and total brain volume (TBV) in 142 ASD and control male toddlers. In control toddlers, TBV variation significantly correlated with cell cycle and protein folding gene networks, potentially impacting neuron number and synapse development. In ASD toddlers, their correlations with brain size were lost as a result of considerable changes in network organization, while cell adhesion gene networks significantly correlated with TBV variation. Cell cycle networks detected in blood are highly preserved in the human brain and are upregulated during prenatal states of development. Overall, alterations were more pronounced in bigger brains. We identified 23 candidate genes for brain maldevelopment linked to 32 genes frequently mutated in ASD. The integrated network includes genes that are dysregulated in leukocyte and/or postmortem brain tissue of ASD subjects and belong to signaling pathways regulating cell cycle G1/S and G2/M phase transition. Finally, analyses of the CHD8 subnetwork and altered transcript levels from an independent study of CHD8 suppression further confirmed the central role of genes regulating neurogenesis and cell adhesion processes in ASD brain maldevelopment.

  19. 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.

  20. 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.

  1. A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain

    Directory of Open Access Journals (Sweden)

    Xiaojin Li

    2013-01-01

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

  2. 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.

  3. 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

    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...... 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...... 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...

  4. 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

    2016-01-01

    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. PMID:27271458

  5. Handbook of Brain Connectivity

    CERN Document Server

    Jirsa, Viktor K

    2007-01-01

    Our contemporary understanding of brain function is deeply rooted in the ideas of the nonlinear dynamics of distributed networks. Cognition and motor coordination seem to arise from the interactions of local neuronal networks, which themselves are connected in large scales across the entire brain. The spatial architectures between various scales inevitably influence the dynamics of the brain and thereby its function. But how can we integrate brain connectivity amongst these structural and functional domains? Our Handbook provides an account of the current knowledge on the measurement, analysis and theory of the anatomical and functional connectivity of the brain. All contributors are leading experts in various fields concerning structural and functional brain connectivity. In the first part of the Handbook, the chapters focus on an introduction and discussion of the principles underlying connected neural systems. The second part introduces the currently available non-invasive technologies for measuring struct...

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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 i

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

    Science.gov (United States)

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

    2013-01-01

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

  12. The alterations in biochemical signaling of hippocampal network activity in the autism brain The alterations in biochemical signaling of hippocampal network activity in the autism brain The alterations in biochemical signaling of hippocampal network activity in the autism brain

    Institute of Scientific and Technical Information of China (English)

    田允; 黄继云; 王锐; 陶蓉蓉; 卢应梅; 廖美华; 陆楠楠; 李静; 芦博; 韩峰

    2012-01-01

    Autism is a highly heritable neurodevelopmental condition characterized by impaired social interaction and communication. However, the role of synaptic dysfunction during development of autism remains unclear. In the present study, we address the alterations of biochemical signaling in hippocampal network following induction of the autism in experimental animals. Here, the an- imal disease model and DNA array being used to investigate the differences in transcriptome or- ganization between autistic and normal brain by gene co--expression network analysis.

  13. 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.

  14. Anatomical adaptations of aquatic mammals.

    Science.gov (United States)

    Reidenberg, Joy S

    2007-06-01

    This special issue of the Anatomical Record explores many of the anatomical adaptations exhibited by aquatic mammals that enable life in the water. Anatomical observations on a range of fossil and living marine and freshwater mammals are presented, including sirenians (manatees and dugongs), cetaceans (both baleen whales and toothed whales, including dolphins and porpoises), pinnipeds (seals, sea lions, and walruses), the sea otter, and the pygmy hippopotamus. A range of anatomical systems are covered in this issue, including the external form (integument, tail shape), nervous system (eye, ear, brain), musculoskeletal systems (cranium, mandible, hyoid, vertebral column, flipper/forelimb), digestive tract (teeth/tusks/baleen, tongue, stomach), and respiratory tract (larynx). Emphasis is placed on exploring anatomical function in the context of aquatic life. The following topics are addressed: evolution, sound production, sound reception, feeding, locomotion, buoyancy control, thermoregulation, cognition, and behavior. A variety of approaches and techniques are used to examine and characterize these adaptations, ranging from dissection, to histology, to electron microscopy, to two-dimensional (2D) and 3D computerized tomography, to experimental field tests of function. The articles in this issue are a blend of literature review and new, hypothesis-driven anatomical research, which highlight the special nature of anatomical form and function in aquatic mammals that enables their exquisite adaptation for life in such a challenging environment.

  15. The development of medical museums in the antebellum American South: slave bodies in networks of anatomical exchange.

    Science.gov (United States)

    Kenny, Stephen C

    2013-01-01

    Prior to the American Civil War, museums were enthusiastically promoted in the annual circulars of southern medical colleges as valuable aids to medical education. Using case history narratives, medical college circulars, and announcements, this article examines the social origins of the region's collections of anatomical and pathological specimens and explores the professional agents and organizations responsible for their maintenance and development. The article is also concerned with exploring the racial framework in which these bodies and specimens were sourced and displayed. The social relations embodied in natural history and medical museum collections, and the emerging specialism of "negro medicine," were all elements of a context that subordinated and objectified blackness, as well as permitting and legitimizing the exploitation of black bodies. Medical museums function as a key case study for examining power relations among physicians, slaves, and slave owners, as well as underscoring southern medicine's dependence on slavery for its development.

  16. Probing Intrinsic Resting-State Networks in the Infant Rat Brain

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

    Mears, David; Pollard, Harvey B

    2016-06-01

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

  18. 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.

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

    Science.gov (United States)

    Olde Dubbelink, Kim T E; Hillebrand, Arjan; Stoffers, Diederick; Deijen, Jan Berend; Twisk, Jos W R; Stam, Cornelis J; Berendse, Henk 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 clinical measures of disease progression, using magnetoencephalography and concepts from graph theory. We characterized whole-brain functional networks by means of a standard graph analysis approach, measuring clustering coefficient and shortest path length, as well as the construction of a minimum spanning tree, a novel approach that allows a unique and unbiased characterization of brain networks. We observed that brain networks in early stage untreated patients displayed lower local clustering with preserved path length in the delta frequency band in comparison to controls. Longitudinal analysis over a 4-year period in a larger group of patients showed a progressive decrease in local clustering in multiple frequency bands together with a decrease in path length in the alpha2 frequency band. In addition, minimum spanning tree analysis revealed a decentralized and less integrated network configuration in early stage, untreated Parkinson's disease that also progressed over time. Moreover, the longitudinal changes in network topology identified with both techniques were associated with deteriorating motor function and cognitive performance. Our results indicate that impaired local efficiency and network decentralization are very early features of Parkinson's disease that continue to progress over time, together with reductions in global efficiency. As these network changes appear to reflect clinically relevant phenomena, they hold promise as markers of disease progression.

  20. Adaptability of language-related brain network in a low-grade glioma patient

    Institute of Scientific and Technical Information of China (English)

    Olivera Sveljo; Katarina Koprivsek; Milos Lucic

    2011-01-01

    Because functional magnetic resonance imaging can be used for dynamic observation of functional cortical changes after brain injuries, we followed up functional magnetic resonance imaging manifestationsof a language-related brain network in a low -grade glioma patient. Disease progressionand therapy during a 3-year period were followed up at different time points: before and after reoperation,after radiation therapy, and 1 year after irradiation. During the whole 3-year follow -up period,the patient exhibited no neurological deficits while functional magnetic resonance imaging revealeddifferent topologies of the language-related brain network. During disease progression and after irradiation,the language-related brain network was extended or completely transferred to the nondominant(right) hemisphere. In addition, after reoperation and 1 year after irradiation, languageareas were primarily found in the language dominant (left) hemisphere. Our results suggest a highlevel of adaptability of the language-related cortical network of the bilateral hemispheres in thislow -grade glioma patient.

  1. [Improving the management of rare brain cancers with the POLA network].

    Science.gov (United States)

    Terziev, Robert; Ravin, Mylène; Carpentier, Catherine; Dehais, Caroline

    2014-04-01

    The national POLA network is dedicated to the management of certain rare brain tumours, mainly anaplastic oligodendrogliomas, anaplastic oligoastrocytomas and glioblastomas with oligodendroglioma component. The nursing team and the patient are at the heart of the organisation.

  2. Spontaneous Functional Network Dynamics and Associated Structural Substrates in the Human Brain

    Directory of Open Access Journals (Sweden)

    Xuhong eLiao

    2015-09-01

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

  3. Plasticity of brain networks in a randomized intervention trial of exercise training in older adults

    Directory of Open Access Journals (Sweden)

    Michelle W Voss

    2010-08-01

    Full Text Available Research has shown the human brain is organized into separable functional networks during rest and varied states of cognition, and that aging is associated with specific network dysfunctions. The present study used functional magnetic resonance imaging (fMRI to examine low-frequency (.008<.08 Hz coherence of cognitively relevant and sensory brain networks in older adults who participated in a one-year intervention trial, comparing the effects of aerobic and non-aerobic fitness training on brain function and cognition. Results showed that aerobic training improved the aging brain’s resting functional efficiency in higher-level cognitive networks. One year of walking increased functional connectivity between aspects of the frontal, posterior, and temporal cortices within the Default Mode Network and a Frontal Executive Network, two brain networks central to brain dysfunction in aging. Length of training was also an important factor. Effects in favor of the walking group were observed only after 12 months of training, compared to non-significant trends after six months. A non-aerobic stretching and toning group also showed increased functional connectivity in the DMN after six months and in a Frontal Parietal Network after 12 months, possibly reflecting experience-dependent plasticity. Finally, we found that changes in functional connectivity were behaviorally relevant. Increased functional connectivity was associated with greater improvement in executive function. Therefore the study provides the first evidence for exercise-induced functional plasticity in large-scale brain systems in the aging brain, using functional connectivity techniques, and offers new insight into the role of aerobic fitness in attenuating age-related brain dysfunction.

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

    Institute of Scientific and Technical Information of China (English)

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

    2016-01-01

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

  5. New approaches to the study of human brain networks underlying spatial attention and related processes

    OpenAIRE

    Driver, Jon; Blankenburg, Felix; Bestmann, Sven; Ruff, Christian C.

    2010-01-01

    Cognitive processes, such as spatial attention, are thought to rely on extended networks in the human brain. Both clinical data from lesioned patients and fMRI data acquired when healthy subjects perform particular cognitive tasks typically implicate a wide expanse of potentially contributing areas, rather than just a single brain area. Conversely, evidence from more targeted interventions, such as transcranial magnetic stimulation (TMS) or invasive microstimulation of the brain, or selective...

  6. The relation between structural and functional connectivity patterns in complex brain networks

    NARCIS (Netherlands)

    Stam, C. J.; van Straaten, E. C W; Van Dellen, E.; Tewarie, P.; Gong, G.; Hillebrand, A.; Meier, J.; Van Mieghem, P.

    2016-01-01

    Objective An important problem in systems neuroscience is the relation between complex structural and functional brain networks. Here we use simulations of a simple dynamic process based upon the susceptible–infected–susceptible (SIS) model of infection dynamics on an empirical structural brain netw

  7. On the Centrality of the Focus in Human Epileptic Brain Networks

    CERN Document Server

    Geier, Christian; Elger, Christian E; Lehnertz, Klaus

    2016-01-01

    There is increasing evidence for specific cortical and subcortical large-scale human epileptic networks to be involved in the generation, spread, and termination of not only primary generalized but also focal onset seizures. The complex dynamics of such networks has been studied with methods of analysis from graph theory. In addition to investigating network-specific characteristics, recent studies aim to determine the functional role of single nodes---such as the epileptic focus---in epileptic brain networks and their relationship to ictogenesis. Utilizing the concept of betweenness centrality to assess the importance of network nodes, previous studies reported the epileptic focus to be of highest importance prior to seizures, which would support the notion of a network hub that facilitates seizure activity. We performed a time-resolved analysis of various aspects of node importance in epileptic brain networks derived from long-term, multi-channel, intracranial electroencephalographic recordings from an epil...

  8. Astrocytes and the evolution of the human brain.

    Science.gov (United States)

    Robertson, James M

    2014-02-01

    Cells within the astroglial lineage are proposed as the origin of human brain evolution. It is now widely accepted that they direct mammalian fetal neurogenesis, gliogenesis, laminar cytoarchitectonics, synaptic connectivity and neuronal network formation. Furthermore, genetic, anatomical and functional studies have recently identified multiple astrocyte exaptations that strongly suggest a direct relation to the increased size and complexity of the human brain.

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

    Directory of Open Access Journals (Sweden)

    Qiu eXiangzhe

    2016-05-01

    Full Text Available Recent studies have demonstrated alterations in the topological organization of structural brain networks in diabetes mellitus (DM. However, the DM-related changes in the topological properties in functional brain networks are almost unexplored so far. We therefore used fluoro-D-glucose positron emission tomography (FDG-PET data to construct functional brain networks of 73 DM patients and 91 sex- and age-matched normal controls (NCs, followed by a graph theoretical analysis. We found that both DM patients and NCs had a small-world topology in functional brain network. In comparison to the NC group, the DM group was found to have significantly lower small-world index, lower normalized clustering coefficients and higher normalized shortest path length. Moreover, for diabetic patients, the nodal centrality was significantly reduced in the right rectus, the right cuneus, the left middle occipital gyrus, and the left postcentral gyrus, and it was significantly increased in the orbitofrontal region of the left middle frontal gyrus, the left olfactory region, and the right paracentral lobule. Our results demonstrated that the diabetic brain was associated with disrupted topological organization in the functional PET network, thus providing the functional evidence for the abnormalities of brain networks in DM.

  10. Altered topological properties of functional network connectivity in schizophrenia during resting state: a small-world brain network study.

    Science.gov (United States)

    Yu, Qingbao; Sui, Jing; Rachakonda, Srinivas; He, Hao; Gruner, William; Pearlson, Godfrey; Kiehl, Kent A; Calhoun, Vince D

    2011-01-01

    Aberrant topological properties of small-world human brain networks in patients with schizophrenia (SZ) have been documented in previous neuroimaging studies. Aberrant functional network connectivity (FNC, temporal relationships among independent component time courses) has also been found in SZ by a previous resting state functional magnetic resonance imaging (fMRI) study. However, no study has yet determined if topological properties of FNC are also altered in SZ. In this study, small-world network metrics of FNC during the resting state were examined in both healthy controls (HCs) and SZ subjects. FMRI data were obtained from 19 HCs and 19 SZ. Brain images were decomposed into independent components (ICs) by group independent component analysis (ICA). FNC maps were constructed via a partial correlation analysis of ICA time courses. A set of undirected graphs were built by thresholding the FNC maps and the small-world network metrics of these maps were evaluated. Our results demonstrated significantly altered topological properties of FNC in SZ relative to controls. In addition, topological measures of many ICs involving frontal, parietal, occipital and cerebellar areas were altered in SZ relative to controls. Specifically, topological measures of whole network and specific components in SZ were correlated with scores on the negative symptom scale of the Positive and Negative Symptom Scale (PANSS). These findings suggest that aberrant architecture of small-world brain topology in SZ consists of ICA temporally coherent brain networks.

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

    Science.gov (United States)

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

    2016-05-01

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

  12. A longitudinal study of structural brain network changes with normal aging

    Directory of Open Access Journals (Sweden)

    Kai eWu

    2013-04-01

    Full Text Available The aim of this study was to investigate age-related changes in the topological organization of structural brain networks by applying a longitudinal design over 6 years. Structural brain networks were derived from measurements of regional gray matter volume and were constructed in age-specific groups from baseline and follow-up scans. The structural brain networks showed economical small-world properties, providing high global and local efficiency for parallel information processing at low connection costs. In the analysis of the global network properties, the local and global efficiency of the baseline scan were significantly lower compared to the follow-up scan. Moreover, the annual rate of changes in local and global efficiency showed a positive and negative quadratic correlation with the baseline age, respectively; both curvilinear correlations peaked at approximately the age of 50. In the analysis of the regional nodal properties, significant negative correlations between the annual rate of changes in nodal strength and the baseline age were found in the brain regions primarily involved in the visual and motor/ control systems, whereas significant positive quadratic correlations were found in the brain regions predominately associated with the default-mode, attention, and memory systems. The results of the longitudinal study are consistent with the findings of our previous cross-sectional study: the structural brain networks develop into a fast distribution from young to middle age (approximately 50 years old and eventually became a fast localization in the old age. Our findings elucidate the network topology of structural brain networks and its longitudinal changes, thus enhancing the understanding of the underlying physiology of normal aging in the human brain.

  13. Alteration and reorganization of functional networks: a new perspective in brain injury study

    Directory of Open Access Journals (Sweden)

    Nazareth P. Castellanos

    2011-09-01

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

  14. Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Eun Hyun Seo

    Full Text Available The conceptual significance of understanding functional brain alterations and cognitive deficits associated with Alzheimer's disease (AD process has been widely established. However, the whole-brain functional networks of AD and its prodromal stage, mild cognitive impairment (MCI, are not well clarified yet. In this study, we compared the characteristics of the whole-brain functional networks among cognitively normal (CN, MCI, and AD individuals by applying graph theoretical analyses to [(18F] fluorodeoxyglucose positron emission tomography (FDG-PET data. Ninety-four CN elderly, 183 with MCI, and 216 with AD underwent clinical evaluation and FDG-PET scan. The overall small-world property as seen in the CN whole-brain network was preserved in MCI and AD. In contrast, individual parameters of the network were altered with the following patterns of changes: local clustering of networks was lower in both MCI and AD compared to CN, while path length was not different among the three groups. Then, MCI had a lower level of local clustering than AD. Subgroup analyses for AD also revealed that very mild AD had lower local clustering and shorter path length compared to mild AD. Regarding the local properties of the whole-brain networks, MCI and AD had significantly decreased normalized betweenness centrality in several hubs regionally associated with the default mode network compared to CN. Our results suggest that the functional integration in whole-brain network progressively declines due to the AD process. On the other hand, functional relatedness between neighboring brain regions may not gradually decrease, but be the most severely altered in MCI stage and gradually re-increase in clinical AD stages.

  15. Brain network characterization of high-risk preterm-born school-age children.

    Science.gov (United States)

    Fischi-Gomez, Elda; Muñoz-Moreno, Emma; Vasung, Lana; Griffa, Alessandra; Borradori-Tolsa, Cristina; Monnier, Maryline; Lazeyras, François; Thiran, Jean-Philippe; Hüppi, Petra S

    2016-01-01

    Higher risk for long-term cognitive and behavioral impairments is one of the hallmarks of extreme prematurity (EP) and pregnancy-associated fetal adverse conditions such as intrauterine growth restriction (IUGR). While neurodevelopmental delay and abnormal brain function occur in the absence of overt brain lesions, these conditions have been recently associated with changes in microstructural brain development. Recent imaging studies indicate changes in brain connectivity, in particular involving the white matter fibers belonging to the cortico-basal ganglia-thalamic loop. Furthermore, EP and IUGR have been related to altered brain network architecture in childhood, with reduced network global capacity, global efficiency and average nodal strength. In this study, we used a connectome analysis to characterize the structural brain networks of these children, with a special focus on their topological organization. On one hand, we confirm the reduced averaged network node degree and strength due to EP and IUGR. On the other, the decomposition of the brain networks in an optimal set of clusters remained substantially different among groups, talking in favor of a different network community structure. However, and despite the different community structure, the brain networks of these high-risk school-age children maintained the typical small-world, rich-club and modularity characteristics in all cases. Thus, our results suggest that brain reorganizes after EP and IUGR, prioritizing a tight modular structure, to maintain the small-world, rich-club and modularity characteristics. By themselves, both extreme prematurity and IUGR bear a similar risk for neurocognitive and behavioral impairment, and the here defined modular network alterations confirm similar structural changes both by IUGR and EP at school age compared to control. Interestingly, the combination of both conditions (IUGR + EP) does not result in a worse outcome. In such cases, the alteration in network

  16. Brain network characterization of high-risk preterm-born school-age children

    Directory of Open Access Journals (Sweden)

    Elda Fischi-Gomez

    2016-01-01

    Full Text Available Higher risk for long-term cognitive and behavioral impairments is one of the hallmarks of extreme prematurity (EP and pregnancy-associated fetal adverse conditions such as intrauterine growth restriction (IUGR. While neurodevelopmental delay and abnormal brain function occur in the absence of overt brain lesions, these conditions have been recently associated with changes in microstructural brain development. Recent imaging studies indicate changes in brain connectivity, in particular involving the white matter fibers belonging to the cortico-basal ganglia-thalamic loop. Furthermore, EP and IUGR have been related to altered brain network architecture in childhood, with reduced network global capacity, global efficiency and average nodal strength. In this study, we used a connectome analysis to characterize the structural brain networks of these children, with a special focus on their topological organization. On one hand, we confirm the reduced averaged network node degree and strength due to EP and IUGR. On the other, the decomposition of the brain networks in an optimal set of clusters remained substantially different among groups, talking in favor of a different network community structure. However, and despite the different community structure, the brain networks of these high-risk school-age children maintained the typical small-world, rich-club and modularity characteristics in all cases. Thus, our results suggest that brain reorganizes after EP and IUGR, prioritizing a tight modular structure, to maintain the small-world, rich-club and modularity characteristics. By themselves, both extreme prematurity and IUGR bear a similar risk for neurocognitive and behavioral impairment, and the here defined modular network alterations confirm similar structural changes both by IUGR and EP at school age compared to control. Interestingly, the combination of both conditions (IUGR + EP does not result in a worse outcome. In such cases, the alteration

  17. Self-organized Critical Model Based on Complex Brain Networks with Hierarchical Organization

    Institute of Scientific and Technical Information of China (English)

    ZHANG Ying-Yue; ZHANG Gui-Qing; YANG Qiu-Ying; CHEN Tian-Lun

    2008-01-01

    The dynamical behavior in the cortical brain network of macaque is studied by modelling each cortical area with a subnetwork of interacting excitable neurons.We find that the avalanche of our model on different levels exhibits power-law.Furthermore the power-law exponent of the distribution and the average avalanche Size are affected by the topology of the network.

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

    Science.gov (United States)

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

    2016-03-22

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

  19. A common brain network links development, aging, and vulnerability to disease.

    Science.gov (United States)

    Douaud, Gwenaëlle; Groves, Adrian R; Tamnes, Christian K; Westlye, Lars Tjelta; Duff, Eugene P; Engvig, Andreas; Walhovd, Kristine B; James, Anthony; Gass, Achim; Monsch, Andreas U; Matthews, Paul M; Fjell, Anders M; Smith, Stephen M; Johansen-Berg, Heidi

    2014-12-09

    Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8-85 y) reveals a largely--but not only--transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer's disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer's disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer's disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.

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

    Directory of Open Access Journals (Sweden)

    Jian-Huai Chen

    2016-01-01

    Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.

  1. [Research resource network and Parkinson disease brain bank donor registration program in Japan].

    Science.gov (United States)

    Arima, Kunimasa

    2010-10-01

    In spite of the increasing need for brain tissue in biomedical research, overall brain banking activities in Japan has been lagging behind. On the initiative of the National Center of Neurology and Psychiatry, 2 projects have been carried out; the Research Resource Network (RRN) and the Parkinson's Disease Brain Bank (PDBB) donor registration program. RRN is a nation-wide network that links 15 brain repositories, and 1,463 autopsy brains have been registered in this network as of December 2009. The brain donor registration program for PDBB was established in 2006. A donor without cognitive impairment can enroll in this PDBB donor registration program. When the donor dies, the next-of-kin will contact the PDBB coordinators for subsequent autopsy services and brain retention. On obtaining the next-of-kin's consent at the time of donor's death, autopsy will be performed at PDBB collaborating hospitals of National Center of Neurology and Psychiatry, Juntendo University Hospital, and Tokyo Metropolitan Geriatric Hospital. In order to arouse public interest, lecture meetings for citizens have been held on a regular basis. Fifty individuals have registered in the PDBB donor registration program including 27 patients with PD, 4 patient with Parkinson syndrome, 1 patient with progressive supranuclear palsy, and 18 individuals without PD or related disorders as of December 2009. Autopsies have been performed for 2 of these donors. To promote brain banking activities,it is necessary to establish legal and ethical guidelines for the use of autopsied materials in biomedical research.

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

    Science.gov (United States)

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

    2016-06-01

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

  3. Language in the aging brain: the network dynamics of cognitive decline and preservation.

    Science.gov (United States)

    Shafto, Meredith A; Tyler, Lorraine K

    2014-10-31

    Language is a crucial and complex lifelong faculty, underpinned by dynamic interactions within and between specialized brain networks. Whereas normal aging impairs specific aspects of language production, most core language processes are robust to brain aging. We review recent behavioral and neuroimaging evidence showing that language systems remain largely stable across the life span and that both younger and older adults depend on dynamic neural responses to linguistic demands. Although some aspects of network dynamics change with age, there is no consistent evidence that core language processes are underpinned by different neural networks in younger and older adults.

  4. BrainCrafter: An investigation into human-based neural network engineering

    DEFF Research Database (Denmark)

    Piskur, J.; Greve, P.; Togelius, J.

    2015-01-01

    This paper presents the online application Brain-Crafter, in which users can manually build artificial neural networks (ANNs) to control a robot in a maze environment. Users can either start to construct networks from scratch or elaborate on networks created by other users. In particular, Brain......Crafter was designed to study how good we as humans are at building ANNs for control problems and if collaborating with other users can facilitate this process. The results in this paper show that (1) some users were in fact able to successfully construct ANNs that solve the navigation tasks, (2) collaboration between...

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

    Science.gov (United States)

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

    2016-11-01

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

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

    Directory of Open Access Journals (Sweden)

    Tetsuo Kida

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

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

    Science.gov (United States)

    Kida, Tetsuo; Kakigi, Ryusuke

    2013-01-01

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

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

    Science.gov (United States)

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

    2016-11-01

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

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

    Institute of Scientific and Technical Information of China (English)

    Li Ling; Jin Zhen-Lan; Li Bin

    2011-01-01

    Rhythm of brain activities represents oscillations of postsynaptic potentials in neocortex, therefore it can serve as an indicator of the brain activity state. In order to check the connectivity of brain rhythm, this paper develops a new method of constructing functional network based on phase synchronization. Electroencephalogram (EEG) data were collected while subjects looking at a green cross in two states, performing an attention task and relaxing with eyes-open. The EEG from these two states was filtered by three band-pass filters to obtain signals of theta (4-7 Hz),alpha (8-13 Hz) and beta (14-30 Hz) bands. Mean resultant length was used to estimate strength of phase synchronization in three bands to construct networks of both states, and mean degree K and cluster coefficient C of networks were calculated as a function of threshold. The result shows higher cluster coefficient in the attention state than in the eyes-open state in all three bands, suggesting that cluster coefficient reflects brain state. In addition, an obvious fronto-parietal network is found in the attention state, which is a well-known attention network. These results indicate that attention modulates the fronto-parietal connectivity in different modes as compared with the eyes-open state. Taken together this method is an objective and important tool to study the properties of neural networks of brain rhythm.

  10. Subspace-based Identification Algorithm for characterizing causal networks in resting brain.

    Science.gov (United States)

    Kadkhodaeian Bakhtiari, Shahab; Hossein-Zadeh, Gholam-Ali

    2012-04-02

    The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity (FC). However the other main stream of brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity (EC), has been little explored. Inherent complexity of brain activities in resting-state, as observed in BOLD (Blood Oxygenation-Level Dependant) fluctuations, calls for exploratory methods for characterizing these causal networks. On the other hand, the inevitable effects that hemodynamic system imposes on causal inferences in fMRI data, lead us toward the methods in which causal inferences can take place in latent neuronal level, rather than observed BOLD time-series. To simultaneously satisfy these two concerns, in this paper, we introduce a novel state-space system identification approach for studying causal interactions among brain regions in the absence of explicit cognitive task. This algorithm is a geometrically inspired method for identification of stochastic systems, purely based on output observations. Using extensive simulations, three aspects of our proposed method are investigated: ability in discriminating existent interactions from non-existent ones, the effect of observation noise, and downsampling on algorithm performance. Our simulations demonstrate that Subspace-based Identification Algorithm (SIA) is sufficiently robust against above-mentioned factors, and can reliably uncover the underlying causal interactions of resting-state fMRI. Furthermore, in contrast to previously established state-space approaches in Effective Connectivity studies, this method is able to characterize causal networks with large number of brain regions. In addition, we utilized the proposed algorithm for identification of causal relationships underlying anti-correlation of default-mode and Dorsal Attention Networks during the rest, using fMRI. We observed that Default-Mode Network places in a

  11. Traumatic Brain Injury Induces Genome-Wide Transcriptomic, Methylomic, and Network Perturbations in Brain and Blood Predicting Neurological Disorders.

    Science.gov (United States)

    Meng, Qingying; Zhuang, Yumei; Ying, Zhe; Agrawal, Rahul; Yang, Xia; Gomez-Pinilla, Fernando

    2017-02-01

    The complexity of the traumatic brain injury (TBI) pathology, particularly concussive injury, is a serious obstacle for diagnosis, treatment, and long-term prognosis. Here we utilize modern systems biology in a rodent model of concussive injury to gain a thorough view of the impact of TBI on fundamental aspects of gene regulation, which have the potential to drive or alter the course of the TBI pathology. TBI perturbed epigenomic programming, transcriptional activities (expression level and alternative splicing), and the organization of genes in networks centered around genes such as Anax2, Ogn, and Fmod. Transcriptomic signatures in the hippocampus are involved in neuronal signaling, metabolism, inflammation, and blood function, and they overlap with those in leukocytes from peripheral blood. The homology between genomic signatures from blood and brain elicited by TBI provides proof of concept information for development of biomarkers of TBI based on composite genomic patterns. By intersecting with human genome-wide association studies, many TBI signature genes and network regulators identified in our rodent model were causally associated with brain disorders with relevant link to TBI. The overall results show that concussive brain injury reprograms genes which could lead to predisposition to neurological and psychiatric disorders, and that genomic information from peripheral leukocytes has the potential to predict TBI pathogenesis in the brain.

  12. Traumatic Brain Injury Induces Genome-Wide Transcriptomic, Methylomic, and Network Perturbations in Brain and Blood Predicting Neurological Disorders

    Directory of Open Access Journals (Sweden)

    Qingying Meng

    2017-02-01

    Full Text Available The complexity of the traumatic brain injury (TBI pathology, particularly concussive injury, is a serious obstacle for diagnosis, treatment, and long-term prognosis. Here we utilize modern systems biology in a rodent model of concussive injury to gain a thorough view of the impact of TBI on fundamental aspects of gene regulation, which have the potential to drive or alter the course of the TBI pathology. TBI perturbed epigenomic programming, transcriptional activities (expression level and alternative splicing, and the organization of genes in networks centered around genes such as Anax2, Ogn, and Fmod. Transcriptomic signatures in the hippocampus are involved in neuronal signaling, metabolism, inflammation, and blood function, and they overlap with those in leukocytes from peripheral blood. The homology between genomic signatures from blood and brain elicited by TBI provides proof of concept information for development of biomarkers of TBI based on composite genomic patterns. By intersecting with human genome-wide association studies, many TBI signature genes and network regulators identified in our rodent model were causally associated with brain disorders with relevant link to TBI. The overall results show that concussive brain injury reprograms genes which could lead to predisposition to neurological and psychiatric disorders, and that genomic information from peripheral leukocytes has the potential to predict TBI pathogenesis in the brain.

  13. Network theory: key issues for the analysis of the "brain drain"

    Directory of Open Access Journals (Sweden)

    Diana Carolina Henao

    2012-12-01

    Full Text Available This paper offers an analysis of the brain drain from the perspective of the network theory. Some definitions and key concepts of the network theory have been discussed in relation to criteria and reasons that are taken into account by people with broad educational capital from developing countries who are involved in the research in different areas of knowledge and who seek to adapt to other scientific collaboration networks in the developed countries.

  14. Long-term effects of attentional performance on functional brain network topology.

    Directory of Open Access Journals (Sweden)

    Thomas P K Breckel

    Full Text Available Individuals differ in their cognitive resilience. Less resilient people demonstrate a greater tendency to vigilance decrements within sustained attention tasks. We hypothesized that a period of sustained attention is followed by prolonged changes in the organization of "resting state" brain networks and that individual differences in cognitive resilience are related to differences in post-task network reorganization. We compared the topological and spatial properties of brain networks as derived from functional MRI data (N = 20 recorded for 6 mins before and 12 mins after the performance of an attentional task. Furthermore we analysed changes in brain topology during task performance and during the switches between rest and task conditions. The cognitive resilience of each individual was quantified as the rate of increase in response latencies over the 32-minute time course of the attentional paradigm. On average, functional networks measured immediately post-task demonstrated significant and prolonged changes in network organization compared to pre-task networks with higher connectivity strength, more clustering, less efficiency, and shorter distance connections. Individual differences in cognitive resilience were significantly correlated with differences in the degree of recovery of some network parameters. Changes in network measures were still present in less resilient individuals in the second half of the post-task period (i.e. 6-12 mins after task completion, while resilient individuals already demonstrated significant reductions of functional connectivity and clustering towards pre-task levels. During task performance brain topology became more integrated with less clustering and higher global efficiency, but linearly decreased with ongoing time-on-task. We conclude that sustained attentional task performance has prolonged, "hang-over" effects on the organization of post-task resting-state brain networks; and that more cognitively

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

    Science.gov (United States)

    Demuru, Matteo; Fara, Francesca; Fraschini, Matteo

    2013-12-01

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

  16. Using network science to evaluate exercise-associated brain changes in older adults

    Directory of Open Access Journals (Sweden)

    Jonathan H Burdette

    2010-06-01

    Full Text Available Literature has shown that exercise is beneficial for cognitive function in older adults and that aerobic fitness is associated with increased hippocampal tissue and blood volumes. The current study used novel network science methods to shed light on the neurophysiological implications of exercise-induced changes in the hippocampus of older adults. Participants represented a volunteer subgroup of older adults that were part of either the exercise training (ET or healthy aging educational control (HAC treatment arms from the Seniors Health and Activity Research Program Pilot (SHARP-P trial. Following the four-month interventions, MRI measures of resting brain blood flow and connectivity were performed. The ET group’s hippocampal CBF exhibited statistically significant increases compared to the HAC group. Novel whole-brain network connectivity analyses showed greater connectivity in the hippocampi of the ET participants compared to HAC. Furthermore, the hippocampus was consistently shown to be within the same network neighborhood (module as the anterior cingulate cortex only within the ET group. Thus, within the ET group, the hippocampus and anterior cingulate were highly interconnected and localized to the same network neighborhood. This project shows the power of network science to investigate potential mechanisms for exercise-induced benefits to the brain in older adults. We show a link between neurological network features and cerebral blood flow, and it is possible that this alteration of functional brain networks may lead to the known improvement in cognitive function among older adults following exercise.

  17. Using network science to evaluate exercise-associated brain changes in older adults.

    Science.gov (United States)

    Burdette, Jonathan H; Laurienti, Paul J; Espeland, Mark A; Morgan, Ashley; Telesford, Qawi; Vechlekar, Crystal D; Hayasaka, Satoru; Jennings, Janine M; Katula, Jeffrey A; Kraft, Robert A; Rejeski, W Jack

    2010-01-01

    Literature has shown that exercise is beneficial for cognitive function in older adults and that aerobic fitness is associated with increased hippocampal tissue and blood volumes. The current study used novel network science methods to shed light on the neurophysiological implications of exercise-induced changes in the hippocampus of older adults. Participants represented a volunteer subgroup of older adults that were part of either the exercise training (ET) or healthy aging educational control (HAC) treatment arms from the Seniors Health and Activity Research Program Pilot (SHARP-P) trial. Following the 4-month interventions, MRI measures of resting brain blood flow and connectivity were performed. The ET group's hippocampal cerebral blood flow (CBF) exhibited statistically significant increases compared to the HAC group. Novel whole-brain network connectivity analyses showed greater connectivity in the hippocampi of the ET participants compared to HAC. Furthermore, the hippocampus was consistently shown to be within the same network neighborhood (module) as the anterior cingulate cortex only within the ET group. Thus, within the ET group, the hippocampus and anterior cingulate were highly interconnected and localized to the same network neighborhood. This project shows the power of network science to investigate potential mechanisms for exercise-induced benefits to the brain in older adults. We show a link between neurological network features and CBF, and it is possible that this alteration of functional brain networks may lead to the known improvement in cognitive function among older adults following exercise.

  18. Aging brain from a network science perspective: something to be positive about?

    Directory of Open Access Journals (Sweden)

    Michelle W Voss

    Full Text Available To better understand age differences in brain function and behavior, the current study applied network science to model functional interactions between brain regions. We observed a shift in network topology whereby for older adults subcortical and cerebellar structures overlapping with the Salience network had more connectivity to the rest of the brain, coupled with fragmentation of large-scale cortical networks such as the Default and Fronto-Parietal networks. Additionally, greater integration of the dorsal medial thalamus and red nucleus in the Salience network was associated with greater satisfaction with life for older adults, which is consistent with theoretical predictions of age-related increases in emotion regulation that are thought to help maintain well-being and life satisfaction in late adulthood. In regard to cognitive abilities, greater ventral medial prefrontal cortex coherence with its topological neighbors in the Default Network was associated with faster processing speed. Results suggest that large-scale organizing properties of the brain differ with normal aging, and this perspective may offer novel insight into understanding age-related differences in cognitive function and well-being.

  19. Intrinsic brain network abnormalities in migraines without aura revealed in resting-state fMRI.

    Directory of Open Access Journals (Sweden)

    Ting Xue

    Full Text Available BACKGROUND: Previous studies have defined low-frequency, spatially consistent intrinsic connectivity networks (ICN in resting functional magnetic resonance imaging (fMRI data which reflect functional interactions among distinct brain areas. We sought to explore whether and how repeated migraine attacks influence intrinsic brain connectivity, as well as how activity in these networks correlates with clinical indicators of migraine. METHODS/PRINCIPAL FINDINGS: Resting-state fMRI data in twenty-three patients with migraines without aura (MwoA and 23 age- and gender-matched healthy controls (HC were analyzed using independent component analysis (ICA, in combination with a "dual-regression" technique to identify the group differences of three important pain-related networks [default mode network (DMN, bilateral central executive network (CEN, salience network (SN] between the MwoA patients and HC. Compared with the HC, MwoA patients showed aberrant intrinsic connectivity within the bilateral CEN and SN, and greater connectivity between both the DMN and right CEN (rCEN and the insula cortex - a critical region involving in pain processing. Furthermore, greater connectivity between both the DMN and rCEN and the insula correlated with duration of migraine. CONCLUSIONS: Our findings may provide new insights into the characterization of migraine as a condition affecting brain activity in intrinsic connectivity networks. Moreover, the abnormalities may be the consequence of a persistent central neural system dysfunction, reflecting cumulative brain insults due to frequent ongoing migraine attacks.

  20. Natural and artificial intelligence misconceptions about brains and neural networks

    CERN Document Server

    de Callataÿ, A

    1992-01-01

    How does the mind work? How is data stored in the brain? How does the mental world connect with the physical world? The hybrid system developed in this book shows a radically new view on the brain. Briefly, in this model memory remains permanent by changing the homeostasis rebuilding the neuronal organelles. These transformations are approximately abstracted as all-or-none operations. Thus the computer-like neural systems become plausible biological models. This illustrated book shows how artificial animals with such brains learn invariant methods of behavior control from their repeated action

  1. Quantifying the link between anatomical connectivity, gray matter volume and regional cerebral blood flow: an integrative MRI study.

    Directory of Open Access Journals (Sweden)

    Bálint Várkuti

    Full Text Available BACKGROUND: In the graph theoretical analysis of anatomical brain connectivity, the white matter connections between regions of the brain are identified and serve as basis for the assessment of regional connectivity profiles, for example, to locate the hubs of the brain. But regions of the brain can be characterised further with respect to their gray matter volume or resting state perfusion. Local anatomical connectivity, gray matter volume and perfusion are traits of each brain region that are likely to be interdependent, however, particular patterns of systematic covariation have not yet been identified. METHODOLOGY/PRINCIPAL FINDINGS: We quantified the covariation of these traits by conducting an integrative MRI study on 23 subjects, utilising a combination of Diffusion Tensor Imaging, Arterial Spin Labeling and anatomical imaging. Based on our hypothesis that local connectivity, gray matter volume and perfusion are linked, we correlated these measures and particularly isolated the covariation of connectivity and perfusion by statistically controlling for gray matter volume. We found significant levels of covariation on the group- and regionwise level, particularly in regions of the Default Brain Mode Network. CONCLUSIONS/SIGNIFICANCE: Connectivity and perfusion are systematically linked throughout a number of brain regions, thus we discuss these results as a starting point for further research on the role of homology in the formation of functional connectivity networks and on how structure/function relationships can manifest in the form of such trait interdependency.

  2. Hierarchical alteration of brain structural and functional networks in female migraine sufferers.

    Directory of Open Access Journals (Sweden)

    Jixin Liu

    Full Text Available BACKGROUND: Little is known about the changes of brain structural and functional connectivity networks underlying the pathophysiology in migraine. We aimed to investigate how the cortical network reorganization is altered by frequent cortical overstimulation associated with migraine. METHODOLOGY/PRINCIPAL FINDINGS: Gray matter volumes and resting-state functional magnetic resonance imaging signal correlations were employed to construct structural and functional networks between brain regions in 43 female patients with migraine (PM and 43 gender-matched healthy controls (HC by using graph theory-based approaches. Compared with the HC group, the patients showed abnormal global topology in both structural and functional networks, characterized by higher mean clustering coefficients without significant change in the shortest absolute path length, which indicated that the PM lost optimal topological organization in their cortical networks. Brain hubs related to pain-processing revealed abnormal nodal centrality in both structural and functional networks, including the precentral gyrus, orbital part of the inferior frontal gyrus, parahippocampal gyrus, anterior cingulate gyrus, thalamus, temporal pole of the middle temporal gyrus and the inferior parietal gyrus. Negative correlations were found between migraine duration and regions with abnormal centrality. Furthermore, the dysfunctional connections in patients' cortical networks formed into a connected component and three dysregulated modules were identified involving pain-related information processing and motion-processing visual networks. CONCLUSIONS: Our results may reflect brain alteration dynamics resulting from migraine and suggest that long-term and high-frequency headache attacks may cause both structural and functional connectivity network reorganization. The disrupted information exchange between brain areas in migraine may be reshaped into a hierarchical modular structure progressively.

  3. Disrupted small-world brain networks in moderate Alzheimer's disease: a resting-state FMRI study.

    Directory of Open Access Journals (Sweden)

    Xiaohu Zhao

    Full Text Available The small-world organization has been hypothesized to reflect a balance between local processing and global integration in the human brain. Previous multimodal imaging studies have consistently demonstrated that the topological architecture of the brain network is disrupted in Alzheimer's disease (AD. However, these studies have reported inconsistent results regarding the topological properties of brain alterations in AD. One potential explanation for these inconsistent results lies with the diverse homogeneity and distinct progressive stages of the AD involved in these studies, which are thought to be critical factors that might affect the results. We investigated the topological properties of brain functional networks derived from resting functional magnetic resonance imaging (fMRI of carefully selected moderate AD patients and normal controls (NCs. Our results showed that the topological properties were found to be disrupted in AD patients, which showing increased local efficiency but decreased global efficiency. We found that the altered brain regions are mainly located in the default mode network, the temporal lobe and certain subcortical regions that are closely associated with the neuropathological changes in AD. Of note, our exploratory study revealed that the ApoE genotype modulates brain network properties, especially in AD patients.

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

    Science.gov (United States)

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

    2010-11-01

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

  5. Attention Performance Measured by Attention Network Test Is Correlated with Global and Regional Efficiency of Structural Brain Networks

    OpenAIRE

    Min Xiao; Haitao Ge; Budhachandra Singh Khundrakpam; Junhai Xu; Gleb Bezgin; Yuan Leng; Lu Zhao; Yuchun Tang; Xinting Ge; Seun Jeon; Wenjian Xu; Alan Charles Evans; Shuwei Liu

    2016-01-01

    Functional neuroimaging studies have indicated the involvement of separate brain areas in three distinct attention systems: alerting, orienting, and executive control (EC). However, the structural correlates underlying attention remains unexplored. Here, we utilized graph theory to examine the neuroanatomical substrates of the three attention systems measured by attention network test (ANT) in 65 healthy subjects. White matter connectivity, assessed with diffusion tensor imaging deterministic...

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

    Science.gov (United States)

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

    2016-04-24

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

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

    Science.gov (United States)

    2016-01-01

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

  8. Insights into brain architectures from the homological scaffolds of functional connectivity networks

    Directory of Open Access Journals (Sweden)

    Louis-David Lord

    2016-11-01

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

  9. Chimera in a neuronal network model of the cat brain

    OpenAIRE

    Santos, M. S.; Szezech Jr., J. D.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.; Viana, R. L.; Kurths, J.

    2016-01-01

    Neuronal systems have been modeled by complex networks in different description levels. Recently, it has been verified that networks can simultaneously exhibit one coherent and other incoherent domain, known as chimera states. In this work, we study the existence of chimera states in a network considering the connectivity matrix based on the cat cerebral cortex. The cerebral cortex of the cat can be separated in 65 cortical areas organised into the four cognitive regions: visual, auditory, so...

  10. Additional brain functional network in adults with attention-deficit/hyperactivity disorder: a phase synchrony analysis.

    Directory of Open Access Journals (Sweden)

    Dongchuan Yu

    Full Text Available We develop a method to construct a new type of functional networks by the usage of phase synchrony degree that is different from the widely used Pearson's correlation approach. By a series of very strict statistical tests, we found that there is an additional network in attention-deficit/hyperactivity disorder (ADHD subjects, superimposing the original (normal brain functional network corresponding to healthy controls. The additional network leads to the increase in clustering coefficient, cost, local efficiency, and global efficiency. Our findings are inconsistent with many previous researches (using the Pearson's correlation approach revealing both increased and decreased functional connections between brain regions and many reports revealing that the brain functional networks of ADHD patients have slow information flow and low global efficiency. We also confirm that the additional network in ADHD subjects contains 6 communities, and three of them are associated with emotional control, sensory information integration, and motor control, respectively. Furthermore, we find that there is a pathway connecting the left insula and left anterior cingular gyrus via the frontal gyrus and putamen in the additional network in ADHD subjects. This implies that due to the pathway connecting brain regions in the salience network, the ADHD patients are more sensitive to external stimuli or internal thoughts and are easier to switch to the executive network and hence harder to inhibit. For clinical diagnostic purposes, we apply the k-means clustering method to distinguish ADHD patients with healthy controls at the individual subject level, and obtain a meaningful diagnostic result. More interestingly, we find that the suggested technique using phase synchrony degree to construct functional networks may obtain higher classification accuracy than the method using the Pearson's correlation coefficient.

  11. Yoga lessons for consciousness research: a paralimbic network balancing brain resource allocation

    Directory of Open Access Journals (Sweden)

    Hans C Lou

    2011-12-01

    Full Text Available Consciousness has been proposed to play a key role in shaping flexible learning and as such is thought to confer an evolutionary advantage. Attention and awareness are the perhaps most important underlying processes, yet their precise relationship is presently unclear. Both of these processes must, however, serve the evolutionary imperatives of survival and procreation. They are thus intimately bound by reward and emotion to help to prioritize efficient brain resource allocation in order to predict and optimize behaviour. Here we show how this process is served by a paralimbic network consisting primarily of regions located on the midline of the human brain. Using many different techniques, experiments have demonstrated that this network is effective and specific for self-awareness and contributes to the sense of unity of consciousness by acting as a common neural path for a wide variety of conscious experiences. Interestingly, haemodynamic activity in the network decreases with focusing on external stimuli, which has led to the idea of a default mode network. This network is one of many networks that wax and vane as resources are allocated to accommodate the different cyclical needs of the organism primarily related the fundamental pleasures afforded by evolution: food, sex and conspecifics. Here we hypothesize, however, that the paralimbic network serves a crucial role in balancing and regulating brain resource allocation, and discuss how it can be thought of as a link between current theories of so-called default mode, resting state networks and global workspace. We show how major developmental disorders of self-awareness and self-control can arise from problems in the paralimbic network as demonstrated here by the example of Asperger syndrome. We conclude that attention, awareness and emotion are integrated by a paralimbic network that helps to efficiently allocate brain resources to optimize behaviour and help survival.

  12. Cerebral blood flow in posterior cortical nodes of the default mode network decreases with task engagement but remains higher than in most brain regions.

    Science.gov (United States)

    Pfefferbaum, Adolf; Chanraud, Sandra; Pitel, Anne-Lise; Müller-Oehring, Eva; Shankaranarayanan, Ajit; Alsop, David C; Rohlfing, Torsten; Sullivan, Edith V

    2011-01-01

    Functional neuroimaging studies provide converging evidence for existence of intrinsic brain networks activated during resting states and deactivated with selective cognitive demands. Whether task-related deactivation of the default mode network signifies depressed activity relative to the remaining brain or simply lower activity relative to its resting state remains controversial. We employed 3D arterial spin labeling imaging to examine regional cerebral blood flow (CBF) during rest, a spatial working memory task, and a second rest. Change in regional CBF from rest to task showed significant normalized and absolute CBF reductions in posterior cingulate, posterior-inferior precuneus, and medial frontal lobes . A Statistical Parametric Mapping connectivity analysis, with an a priori seed in the posterior cingulate cortex, produced deactivation connectivity patterns consistent with the classic "default mode network" and activation connectivity anatomically consistent with engagement in visuospatial tasks. The large task-related CBF decrease in posterior-inferior precuneus relative to its anterior and middle portions adds evidence for the precuneus' heterogeneity. The posterior cingulate and posterior-inferior precuneus were also regions of the highest CBF at rest and during task performance. The difference in regional CBF between intrinsic (resting) and evoked (task) activity levels may represent functional readiness or reserve vulnerable to diminution by conditions affecting perfusion.

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

    Science.gov (United States)

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

    2014-10-01

    The effects of interictal epileptiform discharges on neurocognitive development in children with medically-intractable epilepsy are poorly understood. Such discharges may have a deleterious effect on the brain's intrinsic connectivity networks, which reflect the organization of functional networks at rest, and in turn on neurocognitive development. Using a combined functional magnetic resonance imaging-magnetoencephalography approach, we examine the effects of interictal epileptiform discharges on intrinsic connectivity networks and neurocognitive outcome. Functional magnetic resonance imaging was used to determine the location of regions comprising various intrinsic connectivity networks in 26 children (7-17 years), and magnetoencephalography data were reconstructed from these locations. Inter-regional phase synchronization was then calculated across interictal epileptiform discharges and graph theoretical analysis was applied to measure event-related changes in network topology in the peri-discharge period. The magnitude of change in network topology (network resilience/vulnerability) to interictal epileptiform discharges was associated with neurocognitive outcomes and functional magnetic resonance imaging networks using dual regression. Three main findings are reported: (i) large-scale network changes precede and follow interictal epileptiform discharges; (ii) the resilience of network topologies to interictal discharges is associated with stronger resting-state network connectivity; and (iii) vulnerability to interictal discharges is associated with worse neurocognitive outcomes. By combining the spatial resolution of functional magnetic resonance imaging with the temporal resolution of magnetoencephalography, we describe the effects of interictal epileptiform discharges on neurophysiological synchrony in intrinsic connectivity networks and establish the impact of interictal disruption of functional networks on cognitive outcome in children with epilepsy. The

  14. Hyperglycemia Reduces Efficiency of Brain Networks in Subjects with Type 2 Diabetes.

    Directory of Open Access Journals (Sweden)

    Dae-Jin Kim

    Full Text Available Previous research has shown that the brain is an important target of diabetic complications. Since brain regions are interconnected to form a large-scale neural network, we investigated whether severe hyperglycemia affects the topology of the brain network in people with type 2 diabetes. Twenty middle-aged (average age: 54 years individuals with poorly controlled diabetes (HbA1c: 8.9-14.6%, 74-136 mmol/mol and 20 age-, sex-, and education-matched healthy volunteers were recruited. Graph theoretic network analysis was performed with axonal fiber tractography and tract-based spatial statistics (TBSS using diffusion tensor imaging. Associations between the blood glucose level and white matter network characteristics were investigated. Individuals with diabetes had lower white matter network efficiency (P<0.001 and longer white matter path length (P<0.05 compared to healthy individuals. Higher HbA1c was associated with lower network efficiency (r = -0.53, P = 0.001 and longer network path length (r = 0.40, P<0.05. A disruption in local microstructural integrity was found in the multiple white matter regions and associated with higher HbA1c and fasting plasma glucose levels (corrected P<0.05. Poorer glycemic control is associated with lower efficiency and longer connection paths of the global brain network in individuals with diabetes. Chronic hyperglycemia in people with diabetes may disrupt the brain's topological integration, and lead to mental slowing and cognitive impairment.

  15. Brain Networks Subserving Emotion Regulation and Adaptation after Mild Traumatic Brain Injury

    NARCIS (Netherlands)

    van der Horn, Harm J.; Liemburg, Edith J.; Aleman, Andre; Spikman, Jacoba M.; van der Naalt, Joukje

    2016-01-01

    The majority of patients with traumatic brain injury (TBI) sustain a mild injury (mTBI). One out of 4 patients experiences persistent complaints, despite their often normal neuropsychological test results and the absence of structural brain damage on conventional neuroimaging. Susceptibility to deve

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

    Science.gov (United States)

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

    2014-07-01

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

  17. Brain network dynamics underlying visuospatial judgment: an FMRI connectivity study.

    Science.gov (United States)

    de Graaf, Tom A; Roebroeck, Alard; Goebel, Rainer; Sack, Alexander T

    2010-09-01

    Previous functional imaging research has consistently indicated involvement of bilateral fronto-parietal networks during the execution of visuospatial tasks. Studies with TMS have suggested that the right hemispheric network, but not the left, is functionally relevant for visuospatial judgments. However, very little is still known about the interactions within these fronto-parietal networks underlying visuospatial processing. In the current study, we investigated task modulation of functional connectivity (instantaneous correlations of regional time courses), and task-specific effective connectivity (direction of influences), within the right fronto-parietal network activated during visuospatial judgments. Ten healthy volunteers performed a behaviorally controlled visuospatial judgment task (ANGLE) or a control task (COLOR) in an fMRI experiment. Visuospatial task-specific activations were found in posterior parietal cortex (PPC) and middle/inferior frontal gyrus (MFG). Functional connectivity within this network was task-modulated, with significantly higher connectivity between PPC and MFG during ANGLE than during COLOR. Effective connectivity analysis for directed influence revealed that visuospatial task-specific projections within this network were predominantly in a frontal-to-parietal direction. Moreover, ANGLE-specific influences from thalamic nuclei to PPC were identified. Exploratory effective connectivity analysis revealed that closely neighboring clusters, within visuospatial regions, were differentially involved in the network. These neighboring clusters had opposite effective connectivity patterns to other nodes of the fronto-parietal network. Our data thus reveal that visuospatial judgments are supported by massive fronto-parietal backprojections, thalamo-parietal influence, and multiple stages, or loops, of information flow within the visuospatial network. We speculate on possible functional contributions of the various network nodes and

  18. Identification and analysis of signaling networks potentially involved in breast carcinoma metastasis to the brain.

    Directory of Open Access Journals (Sweden)

    Feng Li

    Full Text Available Brain is a common site of breast cancer metastasis associated with significant neurologic morbidity, decreased quality of life, and greatly shortened survival. However, the molecular and cellular mechanisms underpinning brain colonization by breast carcinoma cells are poorly understood. Here, we used 2D-DIGE (Difference in Gel Electrophoresis proteomic analysis followed by LC-tandem mass spectrometry to identify the proteins differentially expressed in brain-targeting breast carcinoma cells (MB231-Br compared with parental MDA-MB-231 cell line. Between the two cell lines, we identified 12 proteins consistently exhibiting greater than 2-fold (p<0.05 difference in expression, which were associated by the Ingenuity Pathway Analysis (IPA with two major signaling networks involving TNFα/TGFβ-, NFκB-, HSP-70-, TP53-, and IFNγ-associated pathways. Remarkably, highly related networks were revealed by the IPA analysis of a list of 19 brain-metastasis-associated proteins identified recently by the group of Dr. A. Sierra using MDA-MB-435-based experimental system (Martin et al., J Proteome Res 2008 7:908-20, or a 17-gene classifier associated with breast cancer brain relapse reported by the group of Dr. J. Massague based on a microarray analysis of clinically annotated breast tumors from 368 patients (Bos et al., Nature 2009 459: 1005-9. These findings, showing that different experimental systems and approaches (2D-DIGE proteomics used on brain targeting cell lines or gene expression analysis of patient samples with documented brain relapse yield highly related signaling networks, suggest strongly that these signaling networks could be essential for a successful colonization of the brain by metastatic breast carcinoma cells.

  19. Using chaotic artificial neural networks to model memory in the brain

    Science.gov (United States)

    Aram, Zainab; Jafari, Sajad; Ma, Jun; Sprott, Julien C.; Zendehrouh, Sareh; Pham, Viet-Thanh

    2017-03-01

    In the current study, a novel model for human memory is proposed based on the chaotic dynamics of artificial neural networks. This new model explains a biological fact about memory which is not yet explained by any other model: There are theories that the brain normally works in a chaotic mode, while during attention it shows ordered behavior. This model uses the periodic windows observed in a previously proposed model for the brain to store and then recollect the information.

  20. 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......: 10 healthy people from the Copenhagen area who routinely celebrate Christmas and 10 healthy people living in the same area who have no Christmas traditions. MAIN OUTCOME MEASURES: Brain activation unique to the group with Christmas traditions during visual stimulation with images with a Christmas...... theme. METHODS: Functional brain scans optimised for detection of the blood oxygen level dependent (BOLD) response were performed while participants viewed a series of images with Christmas themes interleaved with neutral images having similar characteristics but containing nothing that symbolises...

  1. Intrinsic and task-evoked network architectures of the human brain

    Science.gov (United States)

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

    2014-01-01

    Summary 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 organization. Further, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain’s functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity – areas of neuroscientific inquiry typically considered separately. PMID:24991964

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

    Science.gov (United States)

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

    2016-04-01

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

  3. Detection of dynamic brain networks modulated by acupuncture using a graph theory model

    Institute of Scientific and Technical Information of China (English)

    Lijun Bai; Wei Qin; Jie Tian; Jianping Dai; Wanhai Yang

    2009-01-01

    Neuroimaging studies involving acute acupuncture manipulation have already demonstrated significant modulatory effects on wide limbic/paralimbic nuclei, subcortical gray structures and the neocortical system of the brain. Due to the sustained effect of acupuncture, however, knowledge on the organization of such large-scale cortical networks behind the active needle stimulation phase is lacking. In this study, we originally adopted a network model analysis from graph theory to evaluate the functional connectivity among multiple brain regions during the post-stimulus phase. Evidence from our findings clearly supported the existence of a large organized functional connectivity network related to acupuncture function in the resting brain. More importantly, acupuncture can change such a network into a functional state underlying both pain perception and modulation, which is exhibited by significant changes in the functional con-nectivity of some brain regions. This analysis may help us to better understand the long-lasting effects of acupuncture on brain function, as well as the potential benefits of clinical treatments.

  4. A geometric network model of intrinsic grey-matter connectivity of the human brain

    Science.gov (United States)

    Lo, Yi-Ping; O'Dea, Reuben; Crofts, Jonathan J.; Han, Cheol E.; Kaiser, Marcus

    2015-10-01

    Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuroscience is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (i.e. white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct ‘shortcuts’ through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections.

  5. Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain.

    Science.gov (United States)

    Xia, Mingrui; Lin, Qixiang; Bi, Yanchao; He, Yong

    2016-01-01

    White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

  6. Functional Brain Network Changes Associated with Maintenance of Cognitive Function in Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Santosh A Helekar

    2010-11-01

    Full Text Available In multiple sclerosis (MS functional changes in connectivity due to cortical reorganization could lead to cognitive impairment (CI, or reflect a re-adjustment to reduce the clinical effects of widespread tissue damage. Such alterations in connectivity could result in changes in neural activation as assayed by executive function tasks. We examined cognitive function in MS patients with mild to moderate cognitive impairment and age-matched controls. We evaluated brain activity using functional magnetic resonance imaging (fMRI during the successful performance of the Wisconsin-card sorting (WCS task by MS patients, showing compensatory maintenance of normal function, as measured by response latency and error rate. To assess changes in functional connectivity throughout the brain, we performed a global functional brain network analysis by computing voxel by voxel correlations on the fMRI time series data and carrying out a hierarchical cluster analysis. We found that during the WCS task there is a significant reduction in the number of smaller size brain functional networks, and a change in the brain areas representing the nodes of these networks in MS patients compared to age-matched controls. There is also a concomitant increase in the strength of functional connections between brain loci separated at intermediate scale distances in these patients. These functional alterations might reflect compensatory neuroplastic reorganization underlying maintenance of relatively normal cognitive function in the face of white matter lesions and cortical atrophy produced by MS.

  7. Genetic compendium of 1511 human brains available through the UK Medical Research Council Brain Banks Network Resource

    Science.gov (United States)

    Keogh, Michael J.; Wei, Wei; Wilson, Ian; Coxhead, Jon; Ryan, Sarah; Rollinson, Sara; Griffin, Helen; Kurzawa-Akanbi, Marzena; Santibanez-Koref, Mauro; Talbot, Kevin; Turner, Martin R.; McKenzie, Chris-Anne; Troakes, Claire; Attems, Johannes; Smith, Colin; Al Sarraj, Safa; Morris, Chris M.; Ansorge, Olaf; Pickering-Brown, Stuart; Ironside, James W.; Chinnery, Patrick F.

    2017-01-01

    Given the central role of genetic factors in the pathogenesis of common neurodegenerative disorders, it is critical that mechanistic studies in human tissue are interpreted in a genetically enlightened context. To address this, we performed exome sequencing and copy number variant analysis on 1511 frozen human brains with a diagnosis of Alzheimer's disease (AD, n = 289), frontotemporal dementia/amyotrophic lateral sclerosis (FTD/ALS, n = 252), Creutzfeldt-Jakob disease (CJD, n = 239), Parkinson's disease (PD, n = 39), dementia with Lewy bodies (DLB, n = 58), other neurodegenerative, vascular, or neurogenetic disorders (n = 266), and controls with no significant neuropathology (n = 368). Genomic DNA was extracted from brain tissue in all cases before exome sequencing (Illumina Nextera 62 Mb capture) with variants called by FreeBayes; copy number variant (CNV) analysis (Illumina HumanOmniExpress-12 BeadChip); C9orf72 repeat expansion detection; and APOE genotyping. Established or likely pathogenic heterozygous, compound heterozygous, or homozygous variants, together with the C9orf72 hexanucleotide repeat expansions and a copy number gain of APP, were found in 61 brains. In addition to known risk alleles in 349 brains (23.9% of 1461 undergoing exome sequencing), we saw an association between rare variants in GRN and DLB. Rare CNVs were found in genetic data are available, enabling the retrieval of specific frozen brains through the UK Medical Research Council Brain Banks Network. This allows direct access to pathological and control human brain tissue based on an individual's genetic architecture, thus enabling the functional validation of known genetic risk factors and potentially pathogenic alleles identified in future studies. PMID:28003435

  8. Asymmetric development of dorsal and ventral attention networks in the human brain

    Directory of Open Access Journals (Sweden)

    Kristafor Farrant

    2015-04-01

    Full Text Available Two neural systems for goal-directed and stimulus-driven attention have been described in the adult human brain; the dorsal attention network (DAN centered in the frontal eye fields (FEF and intraparietal sulcus (IPS, and the ventral attention network (VAN anchored in the temporoparietal junction (TPJ and ventral frontal cortex (VFC. Little is known regarding the processes governing typical development of these attention networks in the brain. Here we use resting state functional MRI data collected from thirty 7 to 12 year-old children and thirty 18 to 31 year-old adults to examine two key regions of interest from the dorsal and ventral attention networks. We found that for the DAN nodes (IPS and FEF, children showed greater functional connectivity with regions within the network compared with adults, whereas adults showed greater functional connectivity between the FEF and extra-network regions including the posterior cingulate cortex. For the VAN nodes (TPJ and VFC, adults showed greater functional connectivity with regions within the network compared with children. Children showed greater functional connectivity between VFC and nodes of the salience network. This asymmetric pattern of development of attention networks may be a neural signature of the shift from over-representation of bottom-up attention mechanisms to greater top-down attentional capacities with development.

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

    Science.gov (United States)

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

    2014-12-01

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

  10. Emerging subspecialties in neurology: deep brain stimulation and electrical neuro-network modulation.

    Science.gov (United States)

    Hassan, Anhar; Okun, Michael S

    2013-01-29

    Deep brain stimulation (DBS) is a surgical therapy that involves the delivery of an electrical current to one or more brain targets. This technology has been rapidly expanding to address movement, neuropsychiatric, and other disorders. The evolution of DBS has created a niche for neurologists, both in the operating room and in the clinic. Since DBS is not always deep, not always brain, and not always simply stimulation, a more accurate term for this field may be electrical neuro-network modulation (ENM). Fellowships will likely in future years evolve their scope to include other technologies, and other nervous system regions beyond typical DBS therapy.

  11. The impact of normalization and segmentation on resting-state brain networks.

    Science.gov (United States)

    Magalhães, Ricardo; Marques, Paulo; Soares, José; Alves, Victor; Sousa, Nuno

    2015-04-01

    Graph theory has recently received a lot of attention from the neuroscience community as a method to represent and characterize brain networks. Still, there is a lack of a gold standard for the methods that should be employed for the preprocessing of the data and the construction of the networks, as well as a lack of knowledge on how different methodologies can affect the metrics reported. The authors used graph theory analysis applied to resting-state functional magnetic resonance imaging to investigate the influence of different node-defining strategies and the effect of normalizing the functional acquisition on several commonly reported metrics used to characterize brain networks. The nodes of the network were defined using either the individual FreeSurfer segmentation of each subject or the FreeSurfer segmented Montreal National Institute (MNI) 152 template, using the Destrieux and subcortical atlas. The functional acquisition was either kept on the functional native space or normalized into MNI standard space. The comparisons were done at three levels: on the connections, on the edge properties, and on the network properties levels. The results reveal that different registration and brain parcellation strategies have a strong impact on all the levels of analysis, possibly favoring the use of individual segmentation strategies and conservative registration approaches. In conclusion, several technical aspects must be considered so that graph theoretical analysis of connectivity MRI data can provide a framework to understand brain pathologies.

  12. Comparisons of topological properties in autism for the brain network construction methods

    Science.gov (United States)

    Lee, Min-Hee; Kim, Dong Youn; Lee, Sang Hyeon; Kim, Jin Uk; Chung, Moo K.

    2015-03-01

    Structural brain networks can be constructed from the white matter fiber tractography of diffusion tensor imaging (DTI), and the structural characteristics of the brain can be analyzed from its networks. When brain networks are constructed by the parcellation method, their network structures change according to the parcellation scale selection and arbitrary thresholding. To overcome these issues, we modified the Ɛ -neighbor construction method proposed by Chung et al. (2011). The purpose of this study was to construct brain networks for 14 control subjects and 16 subjects with autism using both the parcellation and the Ɛ-neighbor construction method and to compare their topological properties between two methods. As the number of nodes increased, connectedness decreased in the parcellation method. However in the Ɛ-neighbor construction method, connectedness remained at a high level even with the rising number of nodes. In addition, statistical analysis for the parcellation method showed significant difference only in the path length. However, statistical analysis for the Ɛ-neighbor construction method showed significant difference with the path length, the degree and the density.

  13. Brain networks engaged in audiovisual integration during speech perception revealed by persistent homology-based network filtration.

    Science.gov (United States)

    Kim, Heejung; Hahm, Jarang; Lee, Hyekyoung; Kang, Eunjoo; Kang, Hyejin; Lee, Dong Soo

    2015-05-01

    The human brain naturally integrates audiovisual information to improve speech perception. However, in noisy environments, understanding speech is difficult and may require much effort. Although the brain network is supposed to be engaged in speech perception, it is unclear how speech-related brain regions are connected during natural bimodal audiovisual or unimodal speech perception with counterpart irrelevant noise. To investigate the topological changes of speech-related brain networks at all possible thresholds, we used a persistent homological framework through hierarchical clustering, such as single linkage distance, to analyze the connected component of the functional network during speech perception using functional magnetic resonance imaging. For speech perception, bimodal (audio-visual speech cue) or unimodal speech cues with counterpart irrelevant noise (auditory white-noise or visual gum-chewing) were delivered to 15 subjects. In terms of positive relationship, similar connected components were observed in bimodal and unimodal speech conditions during filtration. However, during speech perception by congruent audiovisual stimuli, the tighter couplings of left anterior temporal gyrus-anterior insula component and right premotor-visual components were observed than auditory or visual speech cue conditions, respectively. Interestingly, visual speech is perceived under white noise by tight negative coupling in the left inferior frontal region-right anterior cingulate, left anterior insula, and bilateral visual regions, including right middle temporal gyrus, right fusiform components. In conclusion, the speech brain network is tightly positively or negatively connected, and can reflect efficient or effortful processes during natural audiovisual integration or lip-reading, respectively, in speech perception.

  14. A rat brain MRI template with digital stereotaxic atlas of fine anatomical delineations in paxinos space and its automated application in voxel-wise analysis.

    Science.gov (United States)

    Nie, Binbin; Chen, Kewei; Zhao, Shujun; Liu, Junhua; Gu, Xiaochun; Yao, Qunli; Hui, Jiaojie; Zhang, Zhijun; Teng, Gaojun; Zhao, Chunjie; Shan, Baoci

    2013-06-01

    This study constructs a rat brain T2 -weighted magnetic resonance imaging template including olfactory bulb and a compatible digital atlas. The atlas contains 624 carefully delineated brain structures based on the newest (2005) edition of rat brain atlas by Paxinos and Watson. An automated procedure, as an SPM toolbox, was introduced for spatially normalizing individual rat brains, conducting statistical analysis and visually localizing the results in the Atlas coordinate space. The brain template/atlas and the procedure were evaluated using functional images between rats with the right side middle cerebral artery occlusion (MCAO) and normal controls. The result shows that the brain region with significant signal decline in the MCAO rats was consistent with the occlusion position.

  15. A Brain Network Processing the Age of Faces

    NARCIS (Netherlands)

    Homola, G.A.; Jbabdi, S.; Beckmann, C.F.; Bartsch, A.J.

    2012-01-01

    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 previo

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

    Science.gov (United States)

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

    2014-12-01

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

  17. Attentional Performance is Correlated with the Local Regional Efficiency of Intrinsic Brain Networks

    Directory of Open Access Journals (Sweden)

    Junhai eXu

    2015-07-01

    Full Text Available Attention is a crucial brain function for human beings. Using neuropsychological paradigms and task-based functional brain imaging, previous studies have indicated that widely distributed brain regions are engaged in three distinct attention subsystems: alerting, orienting and executive control (EC. Here, we explored the potential contribution of spontaneous brain activity to attention by examining whether resting-state activity could account for individual differences of the attentional performance in normal individuals. The resting-state functional images and behavioral data from attention network test (ANT task were collected in 59 healthy subjects. Graph analysis was conducted to obtain the characteristics of functional brain networks and linear regression analyses were used to explore their relationships with behavioral performances of the three attentional components. We found that there was no significant relationship between the attentional performance and the global measures, while the attentional performance was associated with specific local regional efficiency. These regions related to the scores of alerting, orienting and EC largely overlapped with the regions activated in previous task-related functional imaging studies, and were consistent with the intrinsic dorsal and ventral attention networks (DAN/VAN. In addition, the strong associations between the attentional performance and specific regional efficiency suggested that there was a possible relationship between the DAN/VAN and task performances in the ANT. We concluded that the intrinsic activity of the human brain could reflect the processing