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Sample records for whole-brain structural networks

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

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    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.

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

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    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

  3. Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

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    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

  4. Whole-brain structural topology in adult attention-deficit/hyperactivity disorder: Preserved global – disturbed local network organization

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    Justina Sidlauskaite

    2015-01-01

    Full Text Available Prior studies demonstrate altered organization of functional brain networks in attention-deficit/hyperactivity disorder (ADHD. However, the structural underpinnings of these functional disturbances are poorly understood. In the current study, we applied a graph-theoretic approach to whole-brain diffusion magnetic resonance imaging data to investigate the organization of structural brain networks in adults with ADHD and unaffected controls using deterministic fiber tractography. Groups did not differ in terms of global network metrics — small-worldness, global efficiency and clustering coefficient. However, there were widespread ADHD-related effects at the nodal level in relation to local efficiency and clustering. The affected nodes included superior occipital, supramarginal, superior temporal, inferior parietal, angular and inferior frontal gyri, as well as putamen, thalamus and posterior cerebellum. Lower local efficiency of left superior temporal and supramarginal gyri was associated with higher ADHD symptom scores. Also greater local clustering of right putamen and lower local clustering of left supramarginal gyrus correlated with ADHD symptom severity. Overall, the findings indicate preserved global but altered local network organization in adult ADHD implicating regions underpinning putative ADHD-related neuropsychological deficits.

  5. Whole-brain functional connectivity predicted by indirect structural connections

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    Røge, Rasmus; Ambrosen, Karen Marie Sandø; Albers, Kristoffer Jon

    2017-01-01

    Modern functional and diffusion magnetic resonance imaging (fMRI and dMRI) provide data from which macro-scale networks of functional and structural whole brain connectivity can be estimated. Although networks derived from these two modalities describe different properties of the human brain......, they emerge from the same underlying brain organization, and functional communication is presumably mediated by structural connections. In this paper, we assess the structure-function relationship by evaluating how well functional connectivity can be predicted from structural graphs. Using high......-resolution whole brain networks generated with varying density, we contrast the performance of several non-parametric link predictors that measure structural communication flow. While functional connectivity is not well predicted directly by structural connections, we show that superior predictions can be achieved...

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

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    Jonathan Wirsich

    2016-01-01

    In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

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

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

    2016-11-01

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

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

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    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.

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

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

    2012-01-01

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

  10. Stability of whole brain and regional network topology within and between resting and cognitive states.

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    Justyna K Rzucidlo

    Full Text Available BACKGROUND: Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. METHODOLOGY/PRINCIPAL FINDINGS: fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. CONCLUSIONS/SIGNIFICANCE: These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

  11. Stability of whole brain and regional network topology within and between resting and cognitive states.

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    Rzucidlo, Justyna K; Roseman, Paige L; Laurienti, Paul J; Dagenbach, Dale

    2013-01-01

    Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

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

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    Lauren A Vanderlinden

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

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

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

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    Dipasquale, O; Cercignani, Mara

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

  15. Sleep, Plasticity and Memory from Molecules to Whole-Brain Networks

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    Abel, Ted; Havekes, Robbert; Saletin, Jared M.; Walker, Matthew P.

    2014-01-01

    Despite the ubiquity of sleep across phylogeny, its function remains elusive. In this review, we consider one compelling candidate: brain plasticity associated with memory processing. Focusing largely on hippocampus-dependent memory in rodents and humans, we describe molecular, cellular, network, whole-brain and behavioral evidence establishing a role for sleep both in preparation for initial memory encoding, and in the subsequent offline consolidation ofmemory. Sleep and sleep deprivation bidirectionally alter molecular signaling pathways that regulate synaptic strength and control plasticity-related gene transcription and protein translation. At the cellular level, sleep deprivation impairs cellular excitability necessary for inducing synaptic potentiation and accelerates the decay of long-lasting forms of synaptic plasticity. In contrast, NREM and REM sleep enhance previously induced synaptic potentiation, although synaptic de-potentiation during sleep has also been observed. Beyond single cell dynamics, large-scale cell ensembles express coordinated replay of prior learning-related firing patterns during subsequent sleep. This occurs in the hippocampus, in the cortex, and between the hippocampus and cortex, commonly in association with specific NREM sleep oscillations. At the whole-brain level, somewhat analogous learning-associated hippocampal (re)activation during NREM sleep has been reported in humans. Moreover, the same cortical NREM oscillations associated with replay in rodents also promote human hippocampal memory consolidation, and this process can be manipulated using exogenous reactivation cues during sleep. Mirroring molecular findings in rodents, specific NREM sleep oscillations before encoding refresh human hippocampal learning capacity, while deprivation of sleep conversely impairs subsequent hippocampal activity and associated encoding. Together, these cross-descriptive level findings demonstrate that the unique neurobiology of sleep exert

  16. Sleep, plasticity and memory from molecules to whole-brain networks.

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    Abel, Ted; Havekes, Robbert; Saletin, Jared M; Walker, Matthew P

    2013-09-09

    Despite the ubiquity of sleep across phylogeny, its function remains elusive. In this review, we consider one compelling candidate: brain plasticity associated with memory processing. Focusing largely on hippocampus-dependent memory in rodents and humans, we describe molecular, cellular, network, whole-brain and behavioral evidence establishing a role for sleep both in preparation for initial memory encoding, and in the subsequent offline consolidation of memory. Sleep and sleep deprivation bidirectionally alter molecular signaling pathways that regulate synaptic strength and control plasticity-related gene transcription and protein translation. At the cellular level, sleep deprivation impairs cellular excitability necessary for inducing synaptic potentiation and accelerates the decay of long-lasting forms of synaptic plasticity. In contrast, rapid eye movement (REM) and non-rapid eye movement (NREM) sleep enhance previously induced synaptic potentiation, although synaptic de-potentiation during sleep has also been observed. Beyond single cell dynamics, large-scale cell ensembles express coordinated replay of prior learning-related firing patterns during subsequent NREM sleep. At the whole-brain level, somewhat analogous learning-associated hippocampal (re)activation during NREM sleep has been reported in humans. Moreover, the same cortical NREM oscillations associated with replay in rodents also promote human hippocampal memory consolidation, and this process can be manipulated using exogenous reactivation cues during sleep. Mirroring molecular findings in rodents, specific NREM sleep oscillations before encoding refresh human hippocampal learning capacity, while deprivation of sleep conversely impairs subsequent hippocampal activity and associated encoding. Together, these cross-descriptive level findings demonstrate that the unique neurobiology of sleep exerts powerful effects on molecular, cellular and network mechanisms of plasticity that govern both initial

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

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

    2012-05-01

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

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

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

    2017-08-01

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

  19. Changes in whole-brain functional networks and memory performance in aging.

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    Sala-Llonch, Roser; Junqué, Carme; Arenaza-Urquijo, Eider M; Vidal-Piñeiro, Dídac; Valls-Pedret, Cinta; Palacios, Eva M; Domènech, Sara; Salvà, Antoni; Bargalló, Nuria; Bartrés-Faz, David

    2014-10-01

    We used resting-functional magnetic resonance imaging data from 98 healthy older adults to analyze how local and global measures of functional brain connectivity are affected by age, and whether they are related to differences in memory performance. Whole-brain networks were created individually by parcellating the brain into 90 cerebral regions and obtaining pairwise connectivity. First, we studied age-associations in interregional connectivity and their relationship with the length of the connections. Aging was associated with less connectivity in the long-range connections of fronto-parietal and fronto-occipital systems and with higher connectivity of the short-range connections within frontal, parietal, and occipital lobes. We also used the graph theory to measure functional integration and segregation. The pattern of the overall age-related correlations presented positive correlations of average minimum path length (r = 0.380, p = 0.008) and of global clustering coefficients (r = 0.454, p memory functions. In conclusion, we found that older participants showed lower connectivity of long-range connections together with higher functional segregation of these same connections, which appeared to indicate a more local clustering of information processing. Higher local clustering in older participants was negatively related to memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.

  20. Altered whole-brain white matter networks in preclinical Alzheimer's disease

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    Florian Udo Fischer

    2015-01-01

    Our results suggest an impairment of WM networks in preclinical AD that is detectable while other structural imaging markers do not yet indicate incipient neurodegeneration. Moreover, these findings are specific to WM networks and cannot be explained by other surrogates of global WM integrity.

  1. Aberrant whole-brain functional connectivity and intelligence structure in children with primary nocturnal enuresis.

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    Yu, Bing; Sun, Hongbin; Ma, Hongwei; Peng, Miao; Kong, Fanxing; Meng, Fanxing; Liu, Na; Guo, Qiyong

    2013-01-01

    To assess the potential relationship between intelligence structure abnormalities and whole-brain functional connectivity in children with primary nocturnal enuresis (PNE) with resting-state functional magnetic resonance imaging (fMRI) to provide insights into the association between these two seemingly unrelated conditions. Intelligence testing and fMRI data were obtained from 133 right-handed children, including 67 PNE children (M/F, 39:28; age, 10.5 ± 1.2 y) and 66 age-matched healthy controls (M/F, 37:29; age, 10.1 ± 1.1 y). All intelligence tests were performed using the China-Wechsler Intelligence Scale for Children (C-WISC). Each subject's full intelligence quotient (FIQ), verbal IQ (VIQ), performance IQ (PIQ), and memory/caution (M/C) factor was measured and recorded. Resting state fMRI scans were performed on a 3.0-T MR scanner and post-processed using REST software. Comparisons of z-score correlation coefficients between distinct cerebral regions were used to identify altered functional connectivity in PNE children. The PNE group had normal FIQ, VIQ, and PIQ values, indicating no significant variation from the control group. However, the M/C factor was significantly lower in the PNE group. Compared to the control group, PNE children exhibited overall lower levels of functional connectivity that were most apparent in the cerebello-thalamo-frontal pathway. The M/C factor significantly correlated with z-scores representing connectivity between Cerebellum_Crus1_L and Frontal_Mid_R. PNE children exhibit intelligence structure imbalance and attention deficits. Our findings suggest that cerebello-thalamo-frontal circuit abnormalities are likely to be involved in the onset and progression of attention impairment in PNE children.

  2. Aberrant whole-brain functional connectivity and intelligence structure in children with primary nocturnal enuresis.

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    Bing Yu

    Full Text Available AIM: To assess the potential relationship between intelligence structure abnormalities and whole-brain functional connectivity in children with primary nocturnal enuresis (PNE with resting-state functional magnetic resonance imaging (fMRI to provide insights into the association between these two seemingly unrelated conditions. METHODS: Intelligence testing and fMRI data were obtained from 133 right-handed children, including 67 PNE children (M/F, 39:28; age, 10.5 ± 1.2 y and 66 age-matched healthy controls (M/F, 37:29; age, 10.1 ± 1.1 y. All intelligence tests were performed using the China-Wechsler Intelligence Scale for Children (C-WISC. Each subject's full intelligence quotient (FIQ, verbal IQ (VIQ, performance IQ (PIQ, and memory/caution (M/C factor was measured and recorded. Resting state fMRI scans were performed on a 3.0-T MR scanner and post-processed using REST software. Comparisons of z-score correlation coefficients between distinct cerebral regions were used to identify altered functional connectivity in PNE children. RESULTS: The PNE group had normal FIQ, VIQ, and PIQ values, indicating no significant variation from the control group. However, the M/C factor was significantly lower in the PNE group. Compared to the control group, PNE children exhibited overall lower levels of functional connectivity that were most apparent in the cerebello-thalamo-frontal pathway. The M/C factor significantly correlated with z-scores representing connectivity between Cerebellum_Crus1_L and Frontal_Mid_R. CONCLUSION: PNE children exhibit intelligence structure imbalance and attention deficits. Our findings suggest that cerebello-thalamo-frontal circuit abnormalities are likely to be involved in the onset and progression of attention impairment in PNE children.

  3. Whole-brain structural connectivity in dyskinetic cerebral palsy and its association with motor and cognitive function.

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    Ballester-Plané, Júlia; Schmidt, Ruben; Laporta-Hoyos, Olga; Junqué, Carme; Vázquez, Élida; Delgado, Ignacio; Zubiaurre-Elorza, Leire; Macaya, Alfons; Póo, Pilar; Toro, Esther; de Reus, Marcel A; van den Heuvel, Martijn P; Pueyo, Roser

    2017-09-01

    Dyskinetic cerebral palsy (CP) has long been associated with basal ganglia and thalamus lesions. Recent evidence further points at white matter (WM) damage. This study aims to identify altered WM pathways in dyskinetic CP from a standardized, connectome-based approach, and to assess structure-function relationship in WM pathways for clinical outcomes. Individual connectome maps of 25 subjects with dyskinetic CP and 24 healthy controls were obtained combining a structural parcellation scheme with whole-brain deterministic tractography. Graph theoretical metrics and the network-based statistic were applied to compare groups and to correlate WM state with motor and cognitive performance. Results showed a widespread reduction of WM volume in CP subjects compared to controls and a more localized decrease in degree (number of links per node) and fractional anisotropy (FA), comprising parieto-occipital regions and the hippocampus. However, supramarginal gyrus showed a significantly higher degree. At the network level, CP subjects showed a bilateral pathway with reduced FA, comprising sensorimotor, intraparietal and fronto-parietal connections. Gross and fine motor functions correlated with FA in a pathway comprising the sensorimotor system, but gross motor also correlated with prefrontal, temporal and occipital connections. Intelligence correlated with FA in a network with fronto-striatal and parieto-frontal connections, and visuoperception was related to right occipital connections. These findings demonstrate a disruption in structural brain connectivity in dyskinetic CP, revealing general involvement of posterior brain regions with relative preservation of prefrontal areas. We identified pathways in which WM integrity is related to clinical features, including but not limited to the sensorimotor system. Hum Brain Mapp 38:4594-4612, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  4. Nonparametric Bayesian Clustering of Structural Whole Brain Connectivity in Full Image Resolution

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    Ambrosen, Karen Marie Sandø; Albers, Kristoffer Jon; Dyrby, Tim B.

    2014-01-01

    Diffusion magnetic resonance imaging enables measuring the structural connectivity of the human brain at a high spatial resolution. Local noisy connectivity estimates can be derived using tractography approaches and statistical models are necessary to quantify the brain’s salient structural...... groups) that defines structural units at the resolution of statistical support. We apply the model to a network of structural brain connectivity in full image resolution with more than one hundred thousand regions (voxels in the gray-white matter boundary) and around one hundred million connections...... organization. However, statistically modeling these massive structural connectivity datasets is a computational challenging task. We develop a high-performance inference procedure for the infinite relational model (a prominent non-parametric Bayesian model for clustering networks into structurally similar...

  5. SU-E-QI-12: Morphometry Based Measurements of the Structural Response to Whole Brain Radiation

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    Fuentes, D; Castillo, R; Castillo, E; Guerrero, T [UT MD Anderson Cancer Center, Houston, TX (United States)

    2014-06-15

    Purpose: Although state of the art radiation therapy techniques for treating intracranial malignancies have eliminated acute brain injury, cognitive impairment occurs in 50–90% of patients who survive >6mo post irradiation. Quantitative characterization of therapy response is needed to facilitate therapeutic strategies to minimize radiation induced cognitive impairment [1]. Deformation based morphometry techniques [2, 3] are presented as a quantitative imaging biomarker of therapy response in patients receiving whole brain radiation for treating medulloblastoma. Methods: Post-irradiation magnetic resonance imaging (MRI) data sets were retrospectively analyzed in N=15 patients, >60 MR image datasets. As seen in Fig 1(a), volume changes at multiple time points post-irradiation were quantitatively measured in the cerebrum and ventricles with respect to pre-irradiation MRI. A high resolution image Template, was registered to the pre-irradiation MRI of each patient to create a brain atlas for the cerebrum, cerebellum, and ventricles. Skull stripped images for each patient were registered to the initial pre-treatment scan. Average volume changes in the labeled regions were measured using the determinant of the displacement field Jacobian. Results: Longitudinal measurements, Fig 1(b-c), show a negative correlation p=.06, of the cerebral volume change with the time interval from irradiation. A corresponding positive correlation, p=.01, between ventricular volume change and time interval from irradiation is seen. One sample t-test for correlations were computed using a Spearman method. An average decrease in cerebral volume, p=.08, and increase in ventricular volume, p<.001, was observed. The radiation dose was seen directly proportional to the induced volume changes in the cerebrum, r=−.44, p<.001, Fig 1(d). Conclusion: Results indicate that morphometric monitoring of brain tissue volume changes may potentially be used to quantitatively assess toxicity and response to

  6. Large Scale Computing for the Modelling of Whole Brain Connectivity

    DEFF Research Database (Denmark)

    Albers, Kristoffer Jon

    of nodes with a shared connectivity pattern. Modelling the brain in great detail on a whole-brain scale is essential to fully understand the underlying organization of the brain and reveal the relations between structure and function, that allows sophisticated cognitive behaviour to emerge from ensembles...... of neurons. Relying on Markov Chain Monte Carlo (MCMC) simulations as the workhorse in Bayesian inference however poses significant computational challenges, especially when modelling networks at the scale and complexity supported by high-resolution whole-brain MRI. In this thesis, we present how to overcome...... these computational limitations and apply Bayesian stochastic block models for un-supervised data-driven clustering of whole-brain connectivity in full image resolution. We implement high-performance software that allows us to efficiently apply stochastic blockmodelling with MCMC sampling on large complex networks...

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

    Science.gov (United States)

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

    2017-03-01

    In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca's region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners' differences. We conclude that in terms of functional connectivity, the engagement of brain's networks during grammar acquisition is coupled with one's language learning abilities. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review.

    Science.gov (United States)

    Michel, Christoph M; Koenig, Thomas

    2017-11-28

    The present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of large-scale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales. The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates. Copyright © 2017. Published by Elsevier Inc.

  9. Aging alterations in whole-brain networks during adulthood mapped with the minimum spanning tree indices: The interplay of density, connectivity cost and life-time trajectory

    NARCIS (Netherlands)

    Otte, W.M.; van Diessen, E.; Paul, S.; Ramaswamy, R.; Rallabandi, V.P.S.; Stam, C.J.; Roy, P.K.

    2015-01-01

    The organizational network changes in the human brain across the lifespan have been mapped using functional and structural connectivity data. Brain network changes provide valuable insights into the processes underlying senescence. Nonetheless, the altered network density in the elderly severely

  10. Aging alterations in whole-brain networks during adulthood mapped with the minimum spanning tree indices : The interplay of density, connectivity cost and life-time trajectory

    NARCIS (Netherlands)

    Otte, Wim; van Diessen, Eric; Paul, Subhadip; Ramaswamy, Rajiv; Subramanyam Rallabandi, V. P.; Stam, Cornelis J.; Roy, Prasun K.

    2015-01-01

    The organizational network changes in the human brain across the lifespan have been mapped using functional and structural connectivity data. Brain network changes provide valuable insights into the processes underlying senescence. Nonetheless, the altered network density in the elderly severely

  11. Altered whole-brain connectivity in albinism.

    Science.gov (United States)

    Welton, Thomas; Ather, Sarim; Proudlock, Frank A; Gottlob, Irene; Dineen, Robert A

    2017-02-01

    Albinism is a group of congenital disorders of the melanin synthesis pathway. Multiple ocular, white matter and cortical abnormalities occur in albinism, including a greater decussation of nerve fibres at the optic chiasm, foveal hypoplasia and nystagmus. Despite this, visual perception is largely preserved. It was proposed that this may be attributable to reorganisation among cerebral networks, including an increased interhemispheric connectivity of the primary visual areas. A graph-theoretic model was applied to explore brain connectivity networks derived from resting-state functional and diffusion-tensor magnetic resonance imaging data in 23 people with albinism and 20 controls. They tested for group differences in connectivity between primary visual areas and in summary network organisation descriptors. Main findings were supplemented with analyses of control regions, brain volumes and white matter microstructure. Significant functional interhemispheric hyperconnectivity of the primary visual areas in the albinism group were found (P = 0.012). Tests of interhemispheric connectivity based on the diffusion-tensor data showed no significant group difference (P = 0.713). Second, it was found that a range of functional whole-brain network metrics were abnormal in people with albinism, including the clustering coefficient (P = 0.005), although this may have been driven partly by overall differences in connectivity, rather than reorganisation. Based on the results, it was suggested that changes occur in albinism at the whole-brain level, and not just within the visual processing pathways. It was proposed that their findings may reflect compensatory adaptations to increased chiasmic decussation, foveal hypoplasia and nystagmus. Hum Brain Mapp 38:740-752, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  12. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  13. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D.; Shim, Eunsoo; Lee, Jong-Hwan

    2015-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

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

    Science.gov (United States)

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

    2017-10-01

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

  15. Prefrontal vulnerabilities and whole brain connectivity in aging and depression.

    Science.gov (United States)

    Lamar, Melissa; Charlton, Rebecca A; Ajilore, Olusola; Zhang, Aifeng; Yang, Shaolin; Barrick, Thomas R; Rhodes, Emma; Kumar, Anand

    2013-07-01

    Studies exploring the underpinnings of age-related neurodegeneration suggest fronto-limbic alterations that are increasingly vulnerable in the presence of disease including late life depression. Less work has assessed the impact of this specific vulnerability on widespread brain circuitry. Seventy-nine older adults (healthy controls=45; late life depression=34) completed translational tasks shown in non-human primates to rely on fronto-limbic networks involving dorsolateral (Self-Ordered Pointing Task) or orbitofrontal (Object Alternation Task) cortices. A sub-sample of participants also completed diffusion tensor imaging for white matter tract quantification (uncinate and cingulum bundle; n=58) and whole brain tract-based spatial statistics (n=62). Despite task associations to specific white matter tracts across both groups, only healthy controls demonstrated significant correlations between widespread tract integrity and cognition. Thus, increasing Object Alternation Task errors were associated with decreasing fractional anisotropy in the uncinate in late life depression; however, only in healthy controls was the uncinate incorporated into a larger network of white matter vulnerability associating fractional anisotropy with Object Alternation Task errors using whole brain tract-based spatial statistics. It appears that the whole brain impact of specific fronto-limbic vulnerabilities in aging may be eclipsed in the presence of disease-specific neuropathology like that seen in late life depression. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. Whole Brain Networks for Treatment of Epilepsy

    Science.gov (United States)

    2013-07-01

    Sakaie, Ph.D. CONTRACTING ORGANIZATION: The Cleveland Clinic Foundation...2. REPORT TYPE Final 3. DATES COVERED 1 July 2011 – 30 June 2013 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-11-1-0362...f DF(O , ) , mlm l l m c Y (14) where is the zenith angle, is the azimuth angle, ( , )mlY are the modified spherical

  17. Vascular damage after fractionated whole-brain irradiation in rats.

    Science.gov (United States)

    Brown, William R; Thore, Clara R; Moody, Dixon M; Robbins, Michael E; Wheeler, Kenneth T

    2005-11-01

    Whole-brain irradiation of animals and humans has been reported to lead to late delayed structural (vascular damage, demyelination, white matter necrosis) and functional (cognitive impairment) alterations. However, most of the experimental data on late delayed radiation-induced brain injury have been generated with large single doses or short fractionation schemes that may provide a less accurate indication of the events that occur after clinical whole-brain radiotherapy. The pilot study reported here investigates cerebral vascular pathology in male Fischer 344 rats after whole-brain irradiation with a fractionated total dose of 137Cs gamma rays that is expected to be biologically similar to that given to brain tumor patients. The brains of young adult rats (4 months old) were irradiated with a total dose of 40 Gy, given as eight 5-Gy fractions twice per week for 4 weeks. Brain capillary and arteriole pathology was studied using an alkaline phosphatase enzyme histochemistry method; vessel density and length were quantified using a stereology method with computerized image processing and analysis. Vessel density and length were unchanged 24 h after the last dose, but at 10 weeks postirradiation, both were substantially decreased. After 20 weeks, the rate of decline in the vessel density and length in irradiated rats was similar to that in unirradiated age-matched controls. No gross gliosis or demyelination was observed 12 months postirradiation using conventional histopathology techniques. We suggest that the early (10-week) and persistent vascular damage that occurs after a prolonged whole-brain irradiation fractionation scheme may play an important role in the development of late delayed radiation-induced brain injury.

  18. A hierarchical method for whole-brain connectivity-based parcellation.

    Science.gov (United States)

    Moreno-Dominguez, David; Anwander, Alfred; Knösche, Thomas R

    2014-10-01

    In modern neuroscience there is general agreement that brain function relies on networks and that connectivity is therefore of paramount importance for brain function. Accordingly, the delineation of functional brain areas on the basis of diffusion magnetic resonance imaging (dMRI) and tractography may lead to highly relevant brain maps. Existing methods typically aim to find a predefined number of areas and/or are limited to small regions of grey matter. However, it is in general not likely that a single parcellation dividing the brain into a finite number of areas is an adequate representation of the function-anatomical organization of the brain. In this work, we propose hierarchical clustering as a solution to overcome these limitations and achieve whole-brain parcellation. We demonstrate that this method encodes the information of the underlying structure at all granularity levels in a hierarchical tree or dendrogram. We develop an optimal tree building and processing pipeline that reduces the complexity of the tree with minimal information loss. We show how these trees can be used to compare the similarity structure of different subjects or recordings and how to extract parcellations from them. Our novel approach yields a more exhaustive representation of the real underlying structure and successfully tackles the challenge of whole-brain parcellation. Copyright © 2014 Wiley Periodicals, Inc.

  19. Fast whole-brain optical tomography capable of automated slice-collection (Conference Presentation)

    Science.gov (United States)

    Yuan, Jing; Jiang, Tao; Deng, Lei; Long, Beng; Peng, Jie; Luo, Qingming; Gong, Hui

    2016-03-01

    Acquiring brain-wide composite information of neuroanatomical and molecular phenotyping is crucial to understand brain functions. However, current whole-brain imaging methods based on mechnical sectioning haven't achieved brain-wide acquisition of both neuroanatomical and molecular phenotyping due to the lack of appropriate whole-brain immunostaining of embedded samples. Here, we present a novel strategy of acquiring brain-wide structural and molecular maps in the same brain, combining whole-brain imaging and subsequent immunostaining of automated-collected slices. We developed a whole-brain imaging system capable of automatically imaging and then collecting imaged tissue slices in order. The system contains three parts: structured illumination microscopy for high-throughput optical sectioning, vibratome for high-precision sectioning and slice-collection device for automated collecting of tissue slices. Through our system, we could acquire a whole-brain dataset of agarose-embedded mouse brain at lateral resolution of 0.33 µm with z-interval sampling of 100 µm in 9 h, and automatically collect the imaged slices in sequence. Subsequently, we performed immunohistochemistry of the collected slices in the routine way. We acquired mouse whole-brain imaging datasets of multiple specific types of neurons, proteins and gene expression profiles. We believe our method could accelerate systematic analysis of brain anatomical structure with specific proteins or genes expression information and understanding how the brain processes information and generates behavior.

  20. Whole-brain functional connectivity identification of functional dyspepsia.

    Science.gov (United States)

    Nan, Jiaofen; Liu, Jixin; Li, Guoying; Xiong, Shiwei; Yan, Xuemei; Yin, Qing; Zeng, Fang; von Deneen, Karen M; Liang, Fanrong; Gong, Qiyong; Qin, Wei; Tian, Jie

    2013-01-01

    Recent neuroimaging studies have shown local brain aberrations in functional dyspepsia (FD) patients, yet little attention has been paid to the whole-brain resting-state functional network abnormalities. The purpose of this study was to investigate whether FD disrupts the patterns of whole-brain networks and the abnormal functional connectivity could reflect the severity of the disease. The dysfunctional interactions between brain regions at rest were investigated in FD patients as compared with 40 age- and gender- matched healthy controls. Multivariate pattern analysis was used to evaluate the discriminative power of our results for classifying patients from controls. In our findings, the abnormal brain functional connections were mainly situated within or across the limbic/paralimbic system, the prefrontal cortex, the tempo-parietal areas and the visual cortex. About 96% of the subjects among the original dataset were correctly classified by a leave one-out cross-validation approach, and 88% accuracy was also validated in a replication dataset. The classification features were significantly associated with the patients' dyspepsia symptoms, the self-rating depression scale and self-rating anxiety scale, but it was not correlated with duration of FD patients (p>0.05). Our results may indicate the effectiveness of the altered brain functional connections reflecting the disease pathophysiology underling FD. These dysfunctional connections may be the epiphenomena or causative agents of FD, which may be affected by clinical severity and its related emotional dimension of the disease rather than the clinical course.

  1. Global efficiency of structural networks mediates cognitive control in mild cognitive impairment

    NARCIS (Netherlands)

    Berlot, R. (Rok); Metzler-Baddeley, C. (Claudia); M.A. Ikram (Arfan); Jones, D.K. (Derek K.); O'Sullivan, M.J. (Michael J.)

    2016-01-01

    markdownabstract__Background:__ Cognitive control has been linked to both the microstructure of individual tracts and the structure of whole-brain networks, but their relative contributions in health and disease remain unclear. __Objective:__ To determine the contribution of both localized white

  2. Bayesian Modelling of Functional Whole Brain Connectivity

    DEFF Research Database (Denmark)

    Røge, Rasmus

    the prevalent strategy of standardizing of fMRI time series and model data using directional statistics or we model the variability in the signal across the brain and across multiple subjects. In either case, we use Bayesian nonparametric modeling to automatically learn from the fMRI data the number......This thesis deals with parcellation of whole-brain functional magnetic resonance imaging (fMRI) using Bayesian inference with mixture models tailored to the fMRI data. In the three included papers and manuscripts, we analyze two different approaches to modeling fMRI signal; either we accept...... of funcional units, i.e. parcels. We benchmark the proposed mixture models against state of the art methods of brain parcellation, both probabilistic and non-probabilistic. The time series of each voxel are most often standardized using z-scoring which projects the time series data onto a hypersphere...

  3. Whole-brain dynamic CT angiography and perfusion imaging

    Energy Technology Data Exchange (ETDEWEB)

    Orrison, W.W. [CHW Nevada Imaging Company, Nevada Imaging Centers, Spring Valley, Las Vegas, NV (United States); College of Osteopathic Medicine, Touro University Nevada, Henderson, NV (United States); Department of Health Physics and Diagnostic Sciences, University of Nevada Las Vegas, Las Vegas, NV (United States); Department of Medical Education, University of Nevada School of Medicine, Reno, NV (United States); Snyder, K.V.; Hopkins, L.N. [Department of Neurosurgery, Millard Fillmore Gates Circle Hospital, Buffalo, NY (United States); Roach, C.J. [School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV (United States); Advanced Medical Imaging and Genetics (Amigenics), Las Vegas, NV (United States); Ringdahl, E.N. [Department of Psychology, University of Nevada Las Vegas, Las Vegas, NV (United States); Nazir, R. [Shifa International Hospital, Islamabad (Pakistan); Hanson, E.H., E-mail: eric.hanson@amigenics.co [College of Osteopathic Medicine, Touro University Nevada, Henderson, NV (United States); Department of Health Physics and Diagnostic Sciences, University of Nevada Las Vegas, Las Vegas, NV (United States); Advanced Medical Imaging and Genetics (Amigenics), Las Vegas, NV (United States)

    2011-06-15

    The availability of whole brain computed tomography (CT) perfusion has expanded the opportunities for analysing the haemodynamic parameters associated with varied neurological conditions. Examples demonstrating the clinical utility of whole-brain CT perfusion imaging in selected acute and chronic ischaemic arterial neurovascular conditions are presented. Whole-brain CT perfusion enables the detection and focused haemodynamic analyses of acute and chronic arterial conditions in the central nervous system without the limitation of partial anatomical coverage of the brain.

  4. Brain Gym. Simple Activities for Whole Brain Learning.

    Science.gov (United States)

    Dennison, Paul E.; Dennison, Gail E.

    This booklet contains simple movements and activities that are used with students in Educational Kinesiology to enhance their experience of whole brain learning. Whole brain learning through movement repatterning and Brain Gym activities enable students to access those parts of the brain previously unavailable to them. These movements of body and…

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

    Science.gov (United States)

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

    2010-10-19

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

  6. Brain-computer interfaces increase whole-brain signal to noise.

    Science.gov (United States)

    Papageorgiou, T Dorina; Lisinski, Jonathan M; McHenry, Monica A; White, Jason P; LaConte, Stephen M

    2013-08-13

    Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.

  7. Whole Brain Functional Connectivity Using Phase Locking Measures Of Resting State Magnetoencephalography

    Directory of Open Access Journals (Sweden)

    Benjamin T Schmidt

    2014-06-01

    Full Text Available The analysis of spontaneous functional connectivity reveals the statistical connections between regions of the brain consistent with underlying functional communication networks within the brain. In this work, we describe the implementation of a complete all-to-all network analysis of resting state neuronal activity from magnetoencephalography (MEG. Using graph theory to define networks at the dipole level, we established functionally defined regions by k-means clustering cortical surface locations using Eigenvector centrality scores from the all-to-all adjacency model. Permutation testing was used to estimate regions with statistically significant connections compared to empty room data, which adjusts for spatial dependencies introduced by the MEG inverse problem. In order to test this model, we preformed a series of numerical simulations investigating the effects of the MEG reconstruction on connectivity estimates. We subsequently applied the approach to subject data to investigate the effectiveness of our method in obtaining whole brain networks. Our findings indicated that our model provides statistically robust estimates of functional region networks. Application of our phase locking network methodology to real data produced networks with similar connectivity to previously published findings, specifically, we found connections between contralateral areas of the arcuate fasciculus that have been previously investigated. The use of data-driven methods for neuroscientific investigations provides a new tool for researchers in identifying and characterizing whole brain functional connectivity networks.

  8. Whole brain functional connectivity using phase locking measures of resting state magnetoencephalography.

    Science.gov (United States)

    Schmidt, Benjamin T; Ghuman, Avniel S; Huppert, Theodore J

    2014-01-01

    The analysis of spontaneous functional connectivity (sFC) reveals the statistical connections between regions of the brain consistent with underlying functional communication networks within the brain. In this work, we describe the implementation of a complete all-to-all network analysis of resting state neuronal activity from magnetoencephalography (MEG). Using graph theory to define networks at the dipole level, we established functionally defined regions by k-means clustering cortical surface locations using Eigenvector centrality (EVC) scores from the all-to-all adjacency model. Permutation testing was used to estimate regions with statistically significant connections compared to empty room data, which adjusts for spatial dependencies introduced by the MEG inverse problem. In order to test this model, we performed a series of numerical simulations investigating the effects of the MEG reconstruction on connectivity estimates. We subsequently applied the approach to subject data to investigate the effectiveness of our method in obtaining whole brain networks. Our findings indicated that our model provides statistically robust estimates of functional region networks. Application of our phase locking network methodology to real data produced networks with similar connectivity to previously published findings, specifically, we found connections between contralateral areas of the arcuate fasciculus that have been previously investigated. The use of data-driven methods for neuroscientific investigations provides a new tool for researchers in identifying and characterizing whole brain functional connectivity networks.

  9. Development of a Model for Whole Brain Learning of Physiology

    Science.gov (United States)

    Eagleton, Saramarie; Muller, Anton

    2011-01-01

    In this report, a model was developed for whole brain learning based on Curry's onion model. Curry described the effect of personality traits as the inner layer of learning, information-processing styles as the middle layer of learning, and environmental and instructional preferences as the outer layer of learning. The model that was developed…

  10. Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models

    DEFF Research Database (Denmark)

    Puonti, Oula; Van Leemput, Koen

    2016-01-01

    In this paper we propose a new generative model for simultaneous brain parcellation and white matter lesion segmentation from multi-contrast magnetic resonance images. The method combines an existing whole-brain segmentation technique with a novel spatial lesion model based on a convolutional...... in multiple sclerosis indicate that the method’s lesion segmentation accuracy compares well to that of the current state-of-the-art in the field, while additionally providing robust whole-brain segmentations....... restricted Boltzmann machine. Unlike current state-of-the-art lesion detection techniques based on discriminative modeling, the proposed method is not tuned to one specific scanner or imaging protocol, and simultaneously segments dozens of neuroanatomical structures. Experiments on a public benchmark dataset...

  11. From hippocampus to whole-brain: The role of integrative processing in episodic memory retrieval.

    Science.gov (United States)

    Geib, Benjamin R; Stanley, Matthew L; Dennis, Nancy A; Woldorff, Marty G; Cabeza, Roberto

    2017-04-01

    Multivariate functional connectivity analyses of neuroimaging data have revealed the importance of complex, distributed interactions between disparate yet interdependent brain regions. Recent work has shown that topological properties of functional brain networks are associated with individual and group differences in cognitive performance, including in episodic memory. After constructing functional whole-brain networks derived from an event-related fMRI study of memory retrieval, we examined differences in functional brain network architecture between forgotten and remembered words. This study yielded three main findings. First, graph theory analyses showed that successfully remembering compared to forgetting was associated with significant changes in the connectivity profile of the left hippocampus and a corresponding increase in efficient communication with the rest of the brain. Second, bivariate functional connectivity analyses indicated stronger interactions between the left hippocampus and a retrieval assembly for remembered versus forgotten items. This assembly included the left precuneus, left caudate, bilateral supramarginal gyrus, and the bilateral dorsolateral superior frontal gyrus. Integrative properties of the retrieval assembly were greater for remembered than forgotten items. Third, whole-brain modularity analyses revealed that successful memory retrieval was marginally significantly associated with a less segregated modular architecture in the network. The magnitude of the decreases in modularity between remembered and forgotten conditions was related to memory performance. These findings indicate that increases in integrative properties at the nodal, retrieval assembly, and whole-brain topological levels facilitate memory retrieval, while also underscoring the potential of multivariate brain connectivity approaches for providing valuable new insights into the neural bases of memory processes. Hum Brain Mapp 38:2242-2259, 2017. © 2017 Wiley

  12. Whole-brain atrophy differences between progressive supranuclear palsy and idiopathic Parkinson's disease

    Directory of Open Access Journals (Sweden)

    Carlos Guevara

    2016-09-01

    Full Text Available Background: The absence of markers for ante-mortem diagnosis of progressive supranuclear palsy (PSP results in this disorder’s being commonly mistaken for other conditions, such as idiopathic Parkinson's disease (IPD. Such mistakes occur particularly in the initial stages, when ‘plus syndrome’ has not yet clinically emerged.Objective. To investigate global brain volume and tissue loss in patients with PSP relative to patients with IPD and healthy controls and correlations between clinical parameters and magnetic resonance imaging (MRI-derived brain volume estimates.Methods: T1-weighted images were obtained from three groups of Chilean Latin American adults: 21 patients with IPD, 18 patients with PSP and 14 healthy controls. We used Structural Imaging Evaluation with Normalization of Atrophy (SIENAX to assess white matter, gray matter and whole-brain volumes (normalized to cranial volume. Imaging data were used to analyze putative correlations with the clinical status of PSP and IPD patients using the Unified Parkinson’s Disease Rating Scale Part III, Hoehn and Yahr, the Clinical Global Impression for Disease Severity Scale and the Frontal Assessment Battery.Results: PSP patients had significantly lower whole brain volume than both IPD patients and controls. Whole brain volume reduction in PSP patients was primarily attributable to gray matter volume reduction. We found a significant correlation between brain volume reduction and clinical status in the PSP group.Conclusions: At the group level, whole brain and gray matter volumes differentiated patients with PSP from patients with IPD. There was also significant clinical-imaging correlations with motor disturbances in PSP.

  13. Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data.

    Science.gov (United States)

    Deco, Gustavo; Cabral, Joana; Woolrich, Mark W; Stevner, Angus B A; van Hartevelt, Tim J; Kringelbach, Morten L

    2017-05-15

    During rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies. Considering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Late effects of whole brain irradiation within the therapeutic range

    Energy Technology Data Exchange (ETDEWEB)

    Caveness, W.F.; Carsten, A.L.

    1978-01-01

    Whole brain exposure with supervoltage x irradiation was carried out in three sets of Macaca mulatta. Two sets of 12 monkeys each, at puberty, received single and fractionated exposures, respectively. One set of 21 monkeys in adulthood received a fractionated exposure. Exposure to 1000 rads in a single dose, at puberty, caused no late effects. Exposure to 1500 rads caused small areas of necrosis in the forebrain white matter at 26 weeks, but a much more extensive involvement at and beyond 52 weeks that included confluent areas of necrosis in gray and white matter. Brain loss resulted in ventricular dilatation. Gliomas appeared in two out of three monkeys at or beyond 52 weeks. Exposure to 2000 rads caused such a wide scatter of focal areas of necrosis, including those in the brain stem, that survival beyond 20 to 26 weeks was not possible. All showed enlarged ventricular systems. Whole brain exposure, 200 rads a day, five days a week, for a course of 4000 rads, at puberty, resulted in no delayed effects. Whole brain exposure to 6000 rads in a six weeks course, in the adult, produced less effects than the same dose at puberty. The onset of the scattered necrotic lesions was later than expected, appearing in one out of three animals at 33 weeks, two out of three animals at 52 weeks, and two out of three at 104 weeks. The lesions at 104 weeks were predominantly mineralized, but were accompanied by a greater extent of telangiectasia than seen in the pubescent monkeys.

  15. Abnormal whole-brain functional connectivity in patients with primary insomnia

    Directory of Open Access Journals (Sweden)

    Li C

    2017-02-01

    Full Text Available Chao Li, Mengshi Dong, Yi Yin, Kelei Hua, Shishun Fu, Guihua Jiang Department of Medical Imaging, The Affiliated Guangdong No 2 Provincial People’s Hospital of Southern Medical University, The Third Clinical Medical College of Southern Medical University, Guangzhou, People’s Republic of China Abstract: The investigation of the mechanism of insomnia could provide the basis for improved understanding and treatment of insomnia. The aim of this study is to investigate the abnormal functional connectivity throughout the entire brain of insomnia patients, and analyze the global distribution of these abnormalities. Whole brains of 50 patients with insomnia and 40 healthy controls were divided into 116 regions and abnormal connectivities were identified by comparing the Pearson’s correlation coefficients of each pair using general linear model analyses with covariates of age, sex, and duration of education. In patients with insomnia, regions that relate to wakefulness, emotion, worry/rumination, saliency/attention, and sensory-motor showed increased positive connectivity with each other; however, regions that often restrain each other, such as regions in salience network with regions in default mode network, showed decreased positive connectivity. Correlation analysis indicated that some increased positive functional connectivity was associated with the Self-Rating Depression Scale, Insomnia Severity Index, and Pittsburgh Sleep Quality Index scores. According to our findings, increased and decreased positive connectivities suggest function strengthening and function disinhibition, respectively, which offers a parsimonious explanation for the hyperarousal hypothesis in the level of the whole-brain functional connectivity in patients with insomnia. Keywords: primary insomnia, hyperarousal hypothesis, resting-state functional magnetic resonance imaging, functional connectivity, whole brain

  16. Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

    Science.gov (United States)

    Deligianni, Fani; Centeno, Maria; Carmichael, David W.; Clayden, Jonathan D.

    2014-01-01

    Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity. PMID:25221467

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

    Science.gov (United States)

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

    2017-05-16

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

  18. Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

    Science.gov (United States)

    Luo, Yuan; Tang, Xiaoying

    2017-03-01

    Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.

  19. Evolving production network structures

    DEFF Research Database (Denmark)

    Grunow, Martin; Gunther, H.O.; Burdenik, H.

    2007-01-01

    When deciding about future production network configurations, the current structures have to be taken into account. Further, core issues such as the maturity of the products and the capacity requirements for test runs and ramp-ups must be incorporated. Our approach is based on optimization...... modelling and assigns products and capacity expansions to production sites under the above constraints. It also considers the production complexity at the individual sites and the flexibility of the network. Our implementation results for a large manufacturing network reveal substantial possible cost...... reductions compared to the traditional manual planning results of our industrial partner....

  20. Development of a model for whole brain learning of physiology.

    Science.gov (United States)

    Eagleton, Saramarie; Muller, Anton

    2011-12-01

    In this report, a model was developed for whole brain learning based on Curry's onion model. Curry described the effect of personality traits as the inner layer of learning, information-processing styles as the middle layer of learning, and environmental and instructional preferences as the outer layer of learning. The model that was developed elaborates on these layers by relating the personality traits central to learning to the different quadrants of brain preference, as described by Neethling's brain profile, as the inner layer of the onion. This layer is encircled by the learning styles that describe different information-processing preferences for each brain quadrant. For the middle layer, the different stages of Kolb's learning cycle are classified into the four brain quadrants associated with the different brain processing strategies within the information processing circle. Each of the stages of Kolb's learning cycle is also associated with a specific cognitive learning strategy. These two inner circles are enclosed by the circle representing the role of the environment and instruction on learning. It relates environmental factors that affect learning and distinguishes between face-to-face and technology-assisted learning. This model informs on the design of instructional interventions for physiology to encourage whole brain learning.

  1. Global efficiency of structural networks mediates cognitive control in Mild Cognitive Impairment

    Directory of Open Access Journals (Sweden)

    Rok Berlot

    2016-12-01

    Full Text Available Background: Cognitive control has been linked to both the microstructure of individual tracts and the structure of whole-brain networks, but their relative contributions in health and disease remain unclear. Objective: To determine the contribution of both localised white matter tract damage and disruption of global network architecture to cognitive control, in older age and Mild Cognitive Impairment (MCI.Methods: 25 patients with MCI and 20 age, sex and intelligence-matched healthy volunteers were investigated with 3 Tesla structural magnetic resonance imaging (MRI. Cognitive control and episodic memory were evaluated with established tests. Structural network graphs were constructed from diffusion MRI-based whole-brain tractography. Their global measures were calculated using graph theory. Regression models utilized both global network metrics and microstructure of specific connections, known to be critical for each domain, to predict cognitive scores. Results: Global efficiency and the mean clustering coefficient of networks were reduced in MCI. Cognitive control was associated with global network topology. Episodic memory, in contrast, correlated with individual temporal tracts only. Relationships between cognitive control and network topology were attenuated by addition of single tract measures to regression models, consistent with a partial mediation effect. The mediation effect was stronger in MCI than healthy volunteers, explaining 23-36% of the effect of cingulum microstructure on cognitive control performance. Network clustering was a significant mediator in the relationship between tract microstructure and cognitive control in both groups. Conclusions: The status of critical connections and large-scale network topology are both important for maintenance of cognitive control in MCI. Mediation via large-scale networks is more important in patients with MCI than healthy volunteers. This effect is domain-specific, and true for cognitive

  2. Whole brain imaging with Serial Two-Photon Tomography

    Directory of Open Access Journals (Sweden)

    Stephen P Amato

    2016-03-01

    Full Text Available Imaging entire mouse brains at submicron resolution has historically been a challenging undertaking and largely confined to the province of dedicated atlasing initiatives. The has limited systematic investigations into important areas of neuroscience, such as neural circuits, brain mapping and neurodegeneration. In this paper, we describe in detail Serial Two-Photon (STP tomography, a robust, reliable method for imaging entire brains with histological detail. We provide examples of how the basic methodology can be extended to other imaging modalities, such as optical coherence tomography, in order to provide unique contrast mechanisms. Furthermore we provide a survey of the research that STP tomography has enabled in the field of neuroscience, provide examples of how this technology enables quantitative whole brain studies, and discuss the current limitations of STP tomography-based approaches

  3. Hippocampal-Sparing Whole-Brain Radiotherapy for Lung Cancer.

    Science.gov (United States)

    Zhao, Ren; Kong, Wei; Shang, Jun; Zhe, Hong; Wang, Yan-Yang

    2017-03-01

    Brain metastases occur in 20% to 40% of lung cancer patients. Whole-brain radiotherapy (WBRT) has long been considered the treatment of choice for many patients with lung cancer, because of its wide availability, ease of delivery, and effectiveness in prolonging survival. However, WBRT is also associated with several side effects, such as decline in memory and other cognitive functions. There exists significant preclinical and clinical evidence that radiation-induced injury to the hippocampus correlates with neurocognitive decline of patients who receive WBRT. Technological advances in treatment planning and delivery facilitate the use of hippocampal-sparing (HS) WBRT as prophylactic cranial irradiation or the primary treatment modality for lung cancer patients with brain metastases. In this review, we provide a detailed and comprehensive discussion of the safety profile, techniques for hippocampus-sparing, and the clinical evidence of HS-WBRT for lung cancer patients. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Retrieving binary answers using whole-brain activity pattern classification

    Directory of Open Access Journals (Sweden)

    Norberto Eiji Nawa

    2015-12-01

    Full Text Available Multivariate pattern analysis (MVPA has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healthy volunteers performed two types of mental task while in the MRI scanner: counting down numbers and recalling positive autobiographical events. Data from these runs were used to train individual machine learning based classifiers that predicted which mental task was being performed based on the voxel-based brain activity patterns. On a different day, the same volunteers reentered the scanner and listened to six statements (e.g., the month you were born is an odd number, and were told to countdown numbers if the statement was true (yes or recall positive events otherwise (no. The previously trained classifiers were then used to assign labels (yes/no to the scans collected during the 24-second response periods following each one of the statements. Mean classification accuracies at the single scan level were in the range of 73.6% to 80.8%, significantly above chance for all participants. When applying a majority vote on the scans within each response period, i.e., the most frequent label (yes/no in the response period becomes the answer to the previous statement, 5.0 to 5.8 sentences, out of 6, were correctly classified in each one of the runs, on average. These results indicate that binary answers can be retrieved from whole-brain activity patterns, suggesting that MVPA provides an alternative way to establish basic communication with unresponsive patients when other techniques are not successful.

  5. Hippocampus sparing in whole-brain radiotherapy. A review

    Energy Technology Data Exchange (ETDEWEB)

    Oskan, F. [University of Munich, Department of Radiation Oncology and CCC Neuro-Oncology, Munich (Germany); Saarland University Medical Center, Department of Radiation Oncology, Homburg/Saar (Germany); Ganswindt, U.; Schwarz, S.B.; Manapov, F.; Belka, C.; Niyazi, M. [University of Munich, Department of Radiation Oncology and CCC Neuro-Oncology, Munich (Germany)

    2014-04-15

    Radiation treatment techniques for whole-brain radiation therapy (WBRT) have not changed significantly since development of the procedure. However, the recent development of novel techniques such as intensity-modulated radiation therapy (IMRT), volumetric-modulated arc therapy (VMAT) and helical tomotherapy, as well as an increasing body of evidence concerning neural stem cells (NSCs) have altered the conventional WBRT treatment paradigm. In this regard, hippocampus-sparing WBRT is a novel technique that aims to spare critical hippocampus regions without compromising tumour control. Published data on this new technique are limited to planning and feasibility studies; data on patient outcome are still lacking. However, several prospective trials to analyse the feasibility of this technique and to document clinical outcome in terms of reduced neurotoxicity are ongoing. (orig.) [German] Die Technik der Ganzhirnbestrahlung (''whole-brain radiation therapy'', WBRT) hat sich seit der Entwicklung nicht wesentlich veraendert. Allerdings stellten die Neuentwicklung von Techniken wie die intensitaetsmodulierte Strahlentherapie (IMRT), die volumenmodulierte Arc-Therapie (VMAT) oder die helikale Tomotherapie sowie immer groesseres Wissen ueber das neurale Stammzellkompartiment (NSCs) das herkoemmliche Ganzhirn-Paradigma in Frage. Die hippocampusschonende Ganzhirnbestrahlung ist eine neuartige Technik, welche die kritische Region des Hippocampus schont, ohne die Tumorkontrolle zu gefaehrden. Ueber diese Technik gibt es bisher nur eine begrenzte Datenlage im Sinne von Planungs- und Machbarkeitsstudien. Klinische Daten bzgl. der Behandlungsergebnisse fehlen nach wie vor, aber einige prospektive Studien sind im Gange, um nicht nur die Machbarkeit zu belegen, sondern auch das klinische Outcome im Sinne einer verringerten Neurotoxizitaet nachzuweisen. (orig.)

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

    Science.gov (United States)

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

    2015-01-01

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

  7. 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.

  8. Whole-brain MRI phenotyping in dysplasia-related frontal lobe epilepsy.

    Science.gov (United States)

    Hong, Seok-Jun; Bernhardt, Boris C; Schrader, Dewi S; Bernasconi, Neda; Bernasconi, Andrea

    2016-02-16

    To perform whole-brain morphometry in patients with frontal lobe epilepsy and evaluate the utility of group-level patterns for individualized diagnosis and prognosis. We compared MRI-based cortical thickness and folding complexity between 2 frontal lobe epilepsy cohorts with histologically verified focal cortical dysplasia (FCD) (13 type I; 28 type II) and 41 closely matched controls. Pattern learning algorithms evaluated the utility of group-level findings to predict histologic FCD subtype, the side of the seizure focus, and postsurgical seizure outcome in single individuals. Relative to controls, FCD type I displayed multilobar cortical thinning that was most marked in ipsilateral frontal cortices. Conversely, type II showed thickening in temporal and postcentral cortices. Cortical folding also diverged, with increased complexity in prefrontal cortices in type I and decreases in type II. Group-level findings successfully guided automated FCD subtype classification (type I: 100%; type II: 96%), seizure focus lateralization (type I: 92%; type II: 86%), and outcome prediction (type I: 92%; type II: 82%). FCD subtypes relate to diverse whole-brain structural phenotypes. While cortical thickening in type II may indicate delayed pruning, a thin cortex in type I likely results from combined effects of seizure excitotoxicity and the primary malformation. Group-level patterns have a high translational value in guiding individualized diagnostics. © 2016 American Academy of Neurology.

  9. Improving Functional MRI Registration Using Whole-Brain Functional Correlation Tensors.

    Science.gov (United States)

    Zhou, Yujia; Yap, Pew-Thian; Zhang, Han; Zhang, Lichi; Feng, Qianjin; Shen, Dinggang

    2017-09-01

    Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) largely rely on the accurate inter-subject registration of functional areas. This is typically achieved through registration of the corresponding T1-weighted MR images with more structural details. However, accumulating evidence has suggested that such strategy cannot well-align functional regions which are not necessarily confined by the anatomical boundaries defined by the T1-weighted MR images. To mitigate this problem, various registration algorithms based directly on rs-fMRI data have been developed, most of which have utilized functional connectivity (FC) as features for registration. However, most of the FC-based registration methods usually extract the functional features only from the thin and highly curved cortical grey matter (GM), posing a great challenge in accurately estimating the whole-brain deformation field. In this paper, we demonstrate that the additional useful functional features can be extracted from brain regions beyond the GM, particularly, white-matter (WM) based on rs-fMRI, for improving the overall functional registration. Specifically, we quantify the local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals, modeled by functional correlation tensors (FCTs), in both GM and WM. Functional registration is then performed based on multiple components of the whole-brain FCTs using a multichannel Large Deformation Diffeomorphic Metric Mapping (mLDDMM) algorithm. Experimental results show that our proposed method achieves superior functional registration performance, compared with other conventional registration methods.

  10. High resolution whole brain diffusion imaging at 7 T for the Human Connectome Project

    Science.gov (United States)

    Vu, AT; Auerbach, E; Lenglet, C; Moeller, S; Sotiropoulos, SN; Jbabdi, S; Andersson, J; Yacoub, E; Ugurbil, K

    2015-01-01

    Mapping structural connectivity in healthy adults for the Human Connectome Project (HCP) benefits from high quality, high resolution, multiband (MB)-accelerated whole brain diffusion MRI (dMRI). Acquiring such data at ultrahigh fields (7 T and above) can improve intrinsic signal-to-noise ratio (SNR), but suffers from shorter T2 and T2* relaxation times, increased B1+ inhomogeneity (resulting in signal loss in cerebellar and temporal lobe regions), and increased power deposition (i.e. Specific Absorption Rate (SAR)), thereby limiting our ability to reduce the repetition time (TR). Here, we present recent developments and optimizations in 7 T image acquisitions for the HCP that allow us to efficiently obtain high-quality, high-resolution whole brain in-vivo dMRI data at 7 T. These data show spatial details typically seen only in ex-vivo studies and complement already very high quality 3 T HCP data in the same subjects. The advances are the result of intensive pilot studies aimed at mitigating the limitations of dMRI at 7 T. The data quality and methods described here are representative of the datasets that will be made freely available to the community in 2015. PMID:26260428

  11. Social Networks and Network Structures

    Science.gov (United States)

    2006-11-01

    Research in Command & Control • Latent Semantic Analysis – Team communication – Emergent team dynamics – Shared situation awareness • Dynamic Network...requirements – Information technology requirements 28 LSA Essentials of Latent Semantic Analysis 29 Communication Analysis • Goal: Automatically monitor and

  12. Aberrant Functional Connectivity Architecture in Alzheimer's Disease and Mild Cognitive Impairment: A Whole-Brain, Data-Driven Analysis.

    Science.gov (United States)

    Zhou, Bo; Yao, Hongxiang; Wang, Pan; Zhang, Zengqiang; Zhan, Yafeng; Ma, Jianhua; Xu, Kaibin; Wang, Luning; An, Ningyu; Liu, Yong; Zhang, Xi

    2015-01-01

    The purpose of our study was to investigate whether the whole-brain functional connectivity pattern exhibits disease severity-related alterations in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Resting-state functional magnetic resonance imaging data were acquired in 27 MCI subjects, 35 AD patients, and 27 age- and gender-matched subjects with normal cognition (NC). Interregional functional connectivity was assessed based on a predefined template which parcellated the brain into 90 regions. Altered whole-brain functional connectivity patterns were identified via connectivity comparisons between the AD and NC subjects. Finally, the relationship between functional connectivity strength and cognitive ability according to the mini-mental state examination (MMSE) was evaluated in the MCI and AD groups. Compared with the NC group, the AD group exhibited decreased functional connectivities throughout the brain. The most significantly affected regions included several important nodes of the default mode network and the temporal lobe. Moreover, changes in functional connectivity strength exhibited significant associations with disease severity-related alterations in the AD and MCI groups. The present study provides novel evidence and will facilitate meta-analysis of whole-brain analyses in AD and MCI, which will be critical to better understand the neural basis of AD.

  13. [Network structures in biological systems].

    Science.gov (United States)

    Oleskin, A V

    2013-01-01

    Network structures (networks) that have been extensively studied in the humanities are characterized by cohesion, a lack of a central control unit, and predominantly fractal properties. They are contrasted with structures that contain a single centre (hierarchies) as well as with those whose elements predominantly compete with one another (market-type structures). As far as biological systems are concerned, their network structures can be subdivided into a number of types involving different organizational mechanisms. Network organization is characteristic of various structural levels of biological systems ranging from single cells to integrated societies. These networks can be classified into two main subgroups: (i) flat (leaderless) network structures typical of systems that are composed of uniform elements and represent modular organisms or at least possess manifest integral properties and (ii) three-dimensional, partly hierarchical structures characterized by significant individual and/or intergroup (intercaste) differences between their elements. All network structures include an element that performs structural, protective, and communication-promoting functions. By analogy to cell structures, this element is denoted as the matrix of a network structure. The matrix includes a material and an immaterial component. The material component comprises various structures that belong to the whole structure and not to any of its elements per se. The immaterial (ideal) component of the matrix includes social norms and rules regulating network elements' behavior. These behavioral rules can be described in terms of algorithms. Algorithmization enables modeling the behavior of various network structures, particularly of neuron networks and their artificial analogs.

  14. Whole-brain atrophy rate and cognitive decline: longitudinal MR study of memory clinic patients

    NARCIS (Netherlands)

    Sluimer, J.D.; van der Flier, W.M.; Karas, G.B.; Fox, N.C.; Scheltens, P.; Barkhof, F.; Vrenken, H.

    2008-01-01

    Purpose: To prospectively determine whole-brain atrophy rate in mild cognitive impairment (MCI) and Alzheimer disease (AD) and its association with cognitive decline, and investigate the risk of progression to dementia in initially non-demented patients given baseline brain volume and whole-brain

  15. Memory as the "whole brain work": a large-scale model based on "oscillations in super-synergy".

    Science.gov (United States)

    Başar, Erol

    2005-01-01

    According to recent trends, memory depends on several brain structures working in concert across many levels of neural organization; "memory is a constant work-in progress." The proposition of a brain theory based on super-synergy in neural populations is most pertinent for the understanding of this constant work in progress. This report introduces a new model on memory basing on the processes of EEG oscillations and Brain Dynamics. This model is shaped by the following conceptual and experimental steps: 1. The machineries of super-synergy in the whole brain are responsible for formation of sensory-cognitive percepts. 2. The expression "dynamic memory" is used for memory processes that evoke relevant changes in alpha, gamma, theta and delta activities. The concerted action of distributed multiple oscillatory processes provides a major key for understanding of distributed memory. It comprehends also the phyletic memory and reflexes. 3. The evolving memory, which incorporates reciprocal actions or reverberations in the APLR alliance and during working memory processes, is especially emphasized. 4. A new model related to "hierarchy of memories as a continuum" is introduced. 5. The notions of "longer activated memory" and "persistent memory" are proposed instead of long-term memory. 6. The new analysis to recognize faces emphasizes the importance of EEG oscillations in neurophysiology and Gestalt analysis. 7. The proposed basic framework called "Memory in the Whole Brain Work" emphasizes that memory and all brain functions are inseparable and are acting as a "whole" in the whole brain. 8. The role of genetic factors is fundamental in living system settings and oscillations and accordingly in memory, according to recent publications. 9. A link from the "whole brain" to "whole body," and incorporation of vegetative and neurological system, is proposed, EEG oscillations and ultraslow oscillations being a control parameter.

  16. Whole brain radiotherapy with radiosensitizer for brain metastases

    Directory of Open Access Journals (Sweden)

    Viani Gustavo

    2009-01-01

    Full Text Available Abstract Purpose To study the efficacy of whole brain radiotherapy (WBRT with radiosensitizer in comparison with WBRT alone for patients with brain metastases in terms of overall survival, disease progression, response to treatment and adverse effects of treatment. Methods A meta-analysis of randomized controlled trials (RCT was performed in order to compare WBRT with radiosensitizer for brain metastases and WBRT alone. The MEDLINE, EMBASE, LILACS, and Cochrane Library databases, in addition to Trial registers, bibliographic databases, and recent issues of relevant journals were researched. Significant reports were reviewed by two reviewers independently. Results A total of 8 RCTs, yielding 2317 patients were analyzed. Pooled results from this 8 RCTs of WBRT with radiosensitizer have not shown a meaningful improvement on overall survival compared to WBRT alone OR = 1.03 (95% CI0.84–1.25, p = 0.77. Also, there was no difference in local brain tumor response OR = 0.8(95% CI 0.5 – 1.03 and brain tumor progression (OR = 1.11, 95% CI 0.9 – 1.3 when the two arms were compared. Conclusion Our data show that WBRT with the following radiosentizers (ionidamine, metronidazole, misonodazole, motexafin gadolinium, BUdr, efaproxiral, thalidomide, have not improved significatively the overall survival, local control and tumor response compared to WBRT alone for brain metastases. However, 2 of them, motexafin- gadolinium and efaproxiral have been shown in recent publications (lung and breast to have positive action in lung and breast carcinoma brain metastases in association with WBRT.

  17. Physics strategies for sparing neural stem cells during whole-brain radiation treatments.

    Science.gov (United States)

    Kirby, Neil; Chuang, Cynthia; Pouliot, Jean; Hwang, Andrew; Barani, Igor J

    2011-10-01

    Currently, there are no successful long-term treatments or preventive strategies for radiation-induced cognitive impairments, and only a few possibilities have been suggested. One such approach involves reducing the dose to neural stem cell compartments (within and outside of the hippocampus) during whole-brain radiation treatments for brain metastases. This study investigates the fundamental physics issues associated with the sparing of neural stem cells during photon radiotherapy for brain metastases. Several factors influence the stem cell dose: intracranial scattering, collimator leakage, beam energy, and total number of beams. The relative importance of these factors is investigated through a set of radiation therapy plans, which are all variations of an initial 6 MV intensity-modulated radiation therapy (IMRT) plan designed to simultaneously deliver a whole-brain dose of 30 Gy and maximally reduce stem cell compartment dose. Additionally, an in-house leaf segmentation algorithm was developed that utilizes jaw motion to minimize the collimator leakage. The plans are all normalized such that 50% of the PTV receives 30 Gy. For the initial 6 MV IMRT plan, 50% of the stem cells receive a dose greater than 6.3 Gy. Calculations indicate that 3.6 Gy of this dose originates from intracranial scattering. The jaw-tracking segmentation algorithm, used in conjunction with direct machine parameter optimization, reduces the 50% stem cell dose to 4.3 and 3.7 Gy for 6 and 10 MV treatment beams, respectively. Intracranial scattering alone is responsible for a large dose contribution to the stem cell compartment. It is, therefore, important to minimize other contributing factors, particularly the collimator leakage, to maximally reduce dose to these critical structures. The use of collimator jaw tracking in conjunction with modern collimators can minimize this leakage.

  18. Fast high resolution whole brain T2* weighted imaging using echo planar imaging at 7T.

    Science.gov (United States)

    Zwanenburg, Jaco J M; Versluis, Maarten J; Luijten, Peter R; Petridou, Natalia

    2011-06-15

    Magnetic susceptibility based (T(2)* weighted) contrast in MRI at high magnetic field strength is of great value in research on brain structure and cortical architecture, but its use is hampered by the low signal-to-noise ratio (SNR) efficiency of the conventional spoiled gradient echo sequence (GRE) leading to long scan times even for a limited number of slices. In this work, we show that high resolution (0.5mm isotropic) T(2)* weighted images of the whole brain can be obtained in 6min by utilizing the high SNR efficiency of echo-planar imaging (EPI). A volumetric (3D) EPI protocol is presented and compared to conventional 3D GRE images acquired with the same resolution, amount of T(2)* weighting, and imaging duration. Spatial coverage in 3D EPI was increased by a factor of 4.5 compared to 3D GRE, while also the SNR was increased by a factor of 2. Image contrast for both magnitude and phase between gray and white matter was similar for both sequences, with enhanced conspicuity of anatomic details in the 3D EPI images due to the increased SNR. Even at 7T, image blurring and distortion is limited if the EPI train length remains short (not longer than the T(2)* of the imaged tissue). 3D EPI provides steps (speed, whole brain coverage, and high isotropic resolution) that are necessary to utilize the benefits of high field MRI in research that employs T(2)* weighted imaging. Copyright © 2011 Elsevier Inc. All rights reserved.

  19. Physics strategies for sparing neural stem cells during whole-brain radiation treatments

    Energy Technology Data Exchange (ETDEWEB)

    Kirby, Neil; Chuang, Cynthia; Pouliot, Jean; Hwang, Andrew; Barani, Igor J. [Department of Radiation Oncology, University of California San Francisco, San Francisco, California 94143-1708 (United States)

    2011-10-15

    Purpose: Currently, there are no successful long-term treatments or preventive strategies for radiation-induced cognitive impairments, and only a few possibilities have been suggested. One such approach involves reducing the dose to neural stem cell compartments (within and outside of the hippocampus) during whole-brain radiation treatments for brain metastases. This study investigates the fundamental physics issues associated with the sparing of neural stem cells during photon radiotherapy for brain metastases. Methods: Several factors influence the stem cell dose: intracranial scattering, collimator leakage, beam energy, and total number of beams. The relative importance of these factors is investigated through a set of radiation therapy plans, which are all variations of an initial 6 MV intensity-modulated radiation therapy (IMRT) plan designed to simultaneously deliver a whole-brain dose of 30 Gy and maximally reduce stem cell compartment dose. Additionally, an in-house leaf segmentation algorithm was developed that utilizes jaw motion to minimize the collimator leakage. Results: The plans are all normalized such that 50% of the PTV receives 30 Gy. For the initial 6 MV IMRT plan, 50% of the stem cells receive a dose greater than 6.3 Gy. Calculations indicate that 3.6 Gy of this dose originates from intracranial scattering. The jaw-tracking segmentation algorithm, used in conjunction with direct machine parameter optimization, reduces the 50% stem cell dose to 4.3 and 3.7 Gy for 6 and 10 MV treatment beams, respectively. Conclusions: Intracranial scattering alone is responsible for a large dose contribution to the stem cell compartment. It is, therefore, important to minimize other contributing factors, particularly the collimator leakage, to maximally reduce dose to these critical structures. The use of collimator jaw tracking in conjunction with modern collimators can minimize this leakage.

  20. Mapping Critical Language Sites in Children Performing Verb Generation: Whole-Brain Connectivity and Graph Theoretical Analysis in MEG.

    Science.gov (United States)

    Youssofzadeh, Vahab; Williamson, Brady J; Kadis, Darren S

    2017-01-01

    A classic left frontal-temporal brain network is known to support language processes. However, the level of participation of constituent regions, and the contribution of extra-canonical areas, is not fully understood; this is particularly true in children, and in individuals who have experienced early neurological insult. In the present work, we propose whole-brain connectivity and graph-theoretical analysis of magnetoencephalography (MEG) source estimates to provide robust maps of the pediatric expressive language network. We examined neuromagnetic data from a group of typically-developing young children (n = 15, ages 4-6 years) and adolescents (n = 14, 16-18 years) completing an auditory verb generation task in MEG. All source analyses were carried out using a linearly-constrained minimum-variance (LCMV) beamformer. Conventional differential analyses revealed significant (p eigenvector centrality (EVC). Hub analysis revealed the importance of left perisylvian sites, i.e., Broca's and Wernicke's areas, across groups. The hemispheric distribution of frontal and temporal lobe EVC values was asymmetrical in most subjects; left dominant EVC was observed in 20% of young children, and 71% of adolescents. Interestingly, the adolescent group demonstrated increased critical sites in the right cerebellum, left inferior frontal gyrus (IFG) and left putamen. Here, we show that whole brain connectivity and network analysis can be used to map critical language sites in typical development; these methods may be useful for defining the margins of eloquent tissue in neurosurgical candidates.

  1. 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.

  2. Network structure of production

    Science.gov (United States)

    Atalay, Enghin; Hortaçsu, Ali; Roberts, James; Syverson, Chad

    2011-01-01

    Complex social networks have received increasing attention from researchers. Recent work has focused on mechanisms that produce scale-free networks. We theoretically and empirically characterize the buyer–supplier network of the US economy and find that purely scale-free models have trouble matching key attributes of the network. We construct an alternative model that incorporates realistic features of firms’ buyer–supplier relationships and estimate the model’s parameters using microdata on firms’ self-reported customers. This alternative framework is better able to match the attributes of the actual economic network and aids in further understanding several important economic phenomena. PMID:21402924

  3. Advanced Polymer Network Structures

    Science.gov (United States)

    2016-02-01

    characteristic time 02 /UmaLJ =τ . Topologically bound monomers interact through the sum of the purely repulsive LJ potential ( arc 6/12= ) or so-called Weeks...3 Content of the simulated polymer double network. Self- attraction coefficient between particles within a network (first or second) is fixed at 1...technique to the study the microscopic topology and dynamics of a wide variety of polymer networks and gels.5–8 The pair interaction between

  4. Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation

    OpenAIRE

    Huo, Yuankai; Bao, Shunxing; Parvathaneni, Prasanna; Landman, Bennett A.

    2017-01-01

    Whole brain segmentation and cortical surface parcellation are essential in understanding the anatomical-functional relationships of the brain. Multi-atlas segmentation has been regarded as one of the leading segmentation methods for the whole brain segmentation. In our recent work, the multi-atlas technique has been adapted to surface reconstruction using a method called Multi-atlas CRUISE (MaCRUISE). The MaCRUISE method not only performed consistent volume-surface analyses but also showed a...

  5. Sensorimotor Functional and Structural Networks after Intracerebral Stem Cell Grafts in the Ischemic Mouse Brain.

    Science.gov (United States)

    Green, Claudia; Minassian, Anuka; Vogel, Stefanie; Diedenhofen, Michael; Beyrau, Andreas; Wiedermann, Dirk; Hoehn, Mathias

    2018-02-14

    Past investigations on stem cell-mediated recovery after stroke have limited their focus on the extent and morphological development of the ischemic lesion itself over time or on the integration capacity of the stem cell graft ex vivo However, an assessment of the long-term functional and structural improvement in vivo is essential to reliably quantify the regenerative capacity of cell implantation after stroke. We induced ischemic stroke in nude mice and implanted human neural stem cells (H9 derived) into the ipsilateral cortex in the acute phase. Functional and structural connectivity changes of the sensorimotor network were noninvasively monitored using magnetic resonance imaging for 3 months after stem cell implantation. A sharp decrease of the functional sensorimotor network extended even to the contralateral hemisphere, persisting for the whole 12 weeks of observation. In mice with stem cell implantation, functional networks were stabilized early on, pointing to a paracrine effect as an early supportive mechanism of the graft. This stabilization required the persistent vitality of the stem cells, monitored by bioluminescence imaging. Thus, we also observed deterioration of the early network stabilization upon vitality loss of the graft after a few weeks. Structural connectivity analysis showed fiber-density increases between the cortex and white matter regions occurring predominantly on the ischemic hemisphere. These fiber-density changes were nearly the same for both study groups. This motivated us to hypothesize that the stem cells can influence, via early paracrine effect, the functional networks, while observed structural changes are mainly stimulated by the ischemic event. SIGNIFICANCE STATEMENT In recent years, research on strokes has made a shift away from a focus on immediate ischemic effects and towards an emphasis on the long-range effects of the lesion on the whole brain. Outcome improvements in stem cell therapies also require the understanding of

  6. Repeated intravenous administration of gadobutrol does not lead to increased signal intensity on unenhanced T1-weighted images - a voxel-based whole brain analysis

    Energy Technology Data Exchange (ETDEWEB)

    Langner, Soenke; Kromrey, Marie-Luise [University Medicine Greifswald, Institute of Diagnostic Radiology and Neuroradiology, Greifswald (Germany); Kuehn, Jens-Peter [University Medicine Greifswald, Institute of Diagnostic Radiology and Neuroradiology, Greifswald (Germany); University Hospital, Carl Gustav Carus University Dresden, Institute for Radiology, Dresden (Germany); Grothe, Matthias [University Medicine Greifswald, Department of Neurology, Greifswald (Germany); Domin, Martin [University Medicine Greifswald, Functional Imaging Unit, Institute of Diagnostic Radiology and Neuroradiology, Greifswald (Germany)

    2017-09-15

    To identify a possible association between repeated intravenous administration of gadobutrol and increased signal intensity in the grey and white matter using voxel-based whole-brain analysis. In this retrospective single-centre study, 217 patients with a clinically isolated syndrome underwent baseline brain magnetic resonance imaging and at least one annual follow-up examination with intravenous administration of 0.1 mmol/kg body weight of gadobutrol. Using the ''Diffeomorphic Anatomical Registration using Exponentiated Lie algebra'' (DARTEL) normalisation process, tissue templates for grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were calculated, as were GM-CSF and WM-CSF ratios. Voxel-based whole-brain analysis was used to calculate the signal intensity for each voxel in each data set. Paired t-test was applied to test differences to baseline MRI for significance. Voxel-based whole-brain analysis demonstrated no significant changes in signal intensity of grey and white matter after up to five gadobutrol administrations. There was no significant change in GM-CSF and grey WM-CSF ratios. Voxel-based whole-brain analysis did not demonstrate increased signal intensity of GM and WM on unenhanced T1-weighted images after repeated gadobutrol administration. The molecular structure of gadolinium-based contrast agent preparations may be an essential factor causing SI increase on unenhanced T1-weighted images. (orig.)

  7. Detecting Hierarchical Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

    2012-01-01

    a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure......Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....

  8. Discovering network structure beyond communities.

    Science.gov (United States)

    Nishikawa, Takashi; Motter, Adilson E

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

  9. Discovering Network Structure Beyond Communities

    OpenAIRE

    Nishikawa, Takashi; Motter, Adilson E.

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes chara...

  10. Collective network for computer structures

    Energy Technology Data Exchange (ETDEWEB)

    Blumrich, Matthias A [Ridgefield, CT; Coteus, Paul W [Yorktown Heights, NY; Chen, Dong [Croton On Hudson, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk [Ossining, NY; Takken, Todd E [Brewster, NY; Steinmacher-Burow, Burkhard D [Wernau, DE; Vranas, Pavlos M [Bedford Hills, NY

    2011-08-16

    A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices ate included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network and class structures. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to needs of a processing algorithm.

  11. Altered topological organization of white matter structural networks in patients with neuromyelitis optica.

    Directory of Open Access Journals (Sweden)

    Yaou Liu

    Full Text Available OBJECTIVE: To investigate the topological alterations of the whole-brain white-matter (WM structural networks in patients with neuromyelitis optica (NMO. METHODS: The present study involved 26 NMO patients and 26 age- and sex-matched healthy controls. WM structural connectivity in each participant was imaged with diffusion-weighted MRI and represented in terms of a connectivity matrix using deterministic tractography method. Graph theory-based analyses were then performed for the characterization of brain network properties. A multiple linear regression analysis was performed on each network metric between the NMO and control groups. RESULTS: The NMO patients exhibited abnormal small-world network properties, as indicated by increased normalized characteristic path length, increased normalized clustering and increased small-worldness. Furthermore, largely similar hub distributions of the WM structural networks were observed between NMO patients and healthy controls. However, regional efficiency in several brain areas of NMO patients was significantly reduced, which were mainly distributed in the default-mode, sensorimotor and visual systems. Furthermore, we have observed increased regional efficiency in a few brain regions such as the orbital parts of the superior and middle frontal and fusiform gyri. CONCLUSION: Although the NMO patients in this study had no discernible white matter T2 lesions in the brain, we hypothesize that the disrupted topological organization of WM networks provides additional evidence for subtle, widespread cerebral WM pathology in NMO.

  12. Inferring network structure from cascades

    Science.gov (United States)

    Ghonge, Sushrut; Vural, Dervis Can

    2017-07-01

    Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

  13. The effects of methylphenidate on whole brain intrinsic functional connectivity.

    Science.gov (United States)

    Mueller, Sophia; Costa, Anna; Keeser, Daniel; Pogarell, Oliver; Berman, Albert; Coates, Ute; Reiser, Maximilian F; Riedel, Michael; Möller, Hans-Jürgen; Ettinger, Ulrich; Meindl, Thomas

    2014-11-01

    Methylphenidate (MPH) is an indirect dopaminergic and noradrenergic agonist that is used to treat attention deficit hyperactivity disorder and that has shown therapeutic potential in neuropsychiatric diseases such as depression, dementia, and Parkinson's disease. While effects of MPH on task-induced brain activation have been investigated, little is known about how MPH influences the resting brain. To investigate the effects of 40 mg of oral MPH on intrinsic functional connectivity, we used resting state fMRI in 54 healthy male subjects in a double-blind, randomized, placebo-controlled study. Functional connectivity analysis employing ICA revealed seven resting state networks (RSN) of interest. Connectivity strength between the dorsal attention network and the thalamus was increased after MPH intake. Other RSN located in association cortex areas, such as the left and right frontoparietal networks and the executive control network, showed MPH-induced connectivity increase to sensory-motor and visual cortex regions and connectivity decrease to cortical and subcortical components of cortico-striato-thalamo-cortical circuits (CST). RSN located in sensory-motor cortex areas showed the opposite pattern with MPH-induced connectivity increase to CST components and connectivity decrease to sensory-motor and visual cortex regions. Our results provide evidence that MPH does not only alter intrinsic connectivity between brain areas involved in sustained attention, but that it also induces significant changes in the cortico-cortical and cortico-subcortical connectivity of many other cognitive and sensory-motor RSN. Copyright © 2014 Wiley Periodicals, Inc.

  14. Optimization of CLARITY for Clearing Whole-Brain and Other Intact Organs1,2,3

    Science.gov (United States)

    Niibori, Yosuke; (Liz) Hsiang, Hwa-Lin; Mercaldo, Valentina; Deisseroth, Karl

    2015-01-01

    Abstract The development, refinement, and use of techniques that allow high-throughput imaging of whole brains with cellular resolution will help us understand the complex functions of the brain. Such techniques are crucial for the analysis of complete neuronal morphology—anatomical and functional—connectivity, and repeated molecular phenotyping. CLARITY is a recently introduced technique that produces structurally intact, yet optically transparent tissue, which may be labeled and imaged without sectioning. However, the utility of this technique depends on several procedural variables during the process in which the light-scattering lipids in a tissue are replaced by a transparent hydrogel matrix. Here, we systematically varied a number of factors (including temperature, hydrogel composition, and polymerization conditions) to provide an optimized, highly replicable CLARITY procedure for clearing mouse brains. We found that for these preparations optimal tissue clearing requires electrophoresis (and cannot be achieved with passive clearing alone) for 5 d with a combination of 37 and 55°C temperature. Although this protocol is optimized for brains, we also show that it can be used to clear and analyze a variety of organs. Brain or other tissue prepared using this protocol is suitable for high-throughput imaging with confocal or single-plane illumination microscopy. PMID:26464982

  15. Optimization of CLARITY for Clearing Whole-Brain and Other Intact Organs

    Science.gov (United States)

    Epp, Jonathan R; Niibori, Yosuke; Liz Hsiang, Hwa-Lin; Mercaldo, Valentina; Deisseroth, Karl; Josselyn, Sheena A; Frankland, Paul W

    2015-01-01

    The development, refinement, and use of techniques that allow high-throughput imaging of whole brains with cellular resolution will help us understand the complex functions of the brain. Such techniques are crucial for the analysis of complete neuronal morphology-anatomical and functional-connectivity, and repeated molecular phenotyping. CLARITY is a recently introduced technique that produces structurally intact, yet optically transparent tissue, which may be labeled and imaged without sectioning. However, the utility of this technique depends on several procedural variables during the process in which the light-scattering lipids in a tissue are replaced by a transparent hydrogel matrix. Here, we systematically varied a number of factors (including temperature, hydrogel composition, and polymerization conditions) to provide an optimized, highly replicable CLARITY procedure for clearing mouse brains. We found that for these preparations optimal tissue clearing requires electrophoresis (and cannot be achieved with passive clearing alone) for 5 d with a combination of 37 and 55°C temperature. Although this protocol is optimized for brains, we also show that it can be used to clear and analyze a variety of organs. Brain or other tissue prepared using this protocol is suitable for high-throughput imaging with confocal or single-plane illumination microscopy.

  16. Confidence sets for network structure

    CERN Document Server

    Airoldi, Edoardo M; Wolfe, Patrick J

    2011-01-01

    Latent variable models are frequently used to identify structure in dichotomous network data, in part because they give rise to a Bernoulli product likelihood that is both well understood and consistent with the notion of exchangeable random graphs. In this article we propose conservative confidence sets that hold with respect to these underlying Bernoulli parameters as a function of any given partition of network nodes, enabling us to assess estimates of 'residual' network structure, that is, structure that cannot be explained by known covariates and thus cannot be easily verified by manual inspection. We demonstrate the proposed methodology by analyzing student friendship networks from the National Longitudinal Survey of Adolescent Health that include race, gender, and school year as covariates. We employ a stochastic expectation-maximization algorithm to fit a logistic regression model that includes these explanatory variables as well as a latent stochastic blockmodel component and additional node-specific...

  17. Prefrontal vulnerabilities and whole brain connectivity in aging and depression

    OpenAIRE

    Lamar, Melissa; Charlton, Rebecca A.; Ajilore, Olusola; Zhang, Aifeng; Yang, Shaolin; Barrick, Thomas R.; Rhodes, Emma; Kumar, Anand

    2013-01-01

    Studies exploring the underpinnings of age-related neurodegeneration suggest fronto-limbic alterations that are increasingly vulnerable in the presence of disease including late life depression. Less work has assessed the impact of this specific vulnerability on widespread brain circuitry. Seventy-nine older adults (healthy controls=45; late life depression=34) completed translational tasks shown in non-human primates to rely on fronto-limbic networks involving dorsolateral (Self-Ordered Poin...

  18. Structurally Dynamic Spin Market Networks

    Science.gov (United States)

    Horváth, Denis; Kuscsik, Zoltán

    The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.

  19. T1-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250 μm

    Science.gov (United States)

    Lüsebrink, Falk; Sciarra, Alessandro; Mattern, Hendrik; Yakupov, Renat; Speck, Oliver

    2017-03-01

    We present an ultrahigh resolution in vivo human brain magnetic resonance imaging (MRI) dataset. It consists of T1-weighted whole brain anatomical data acquired at 7 Tesla with a nominal isotropic resolution of 250 μm of a single young healthy Caucasian subject and was recorded using prospective motion correction. The raw data amounts to approximately 1.2 TB and was acquired in eight hours total scan time. The resolution of this dataset is far beyond any previously published in vivo structural whole brain dataset. Its potential use is to build an in vivo MR brain atlas. Methods for image reconstruction and image restoration can be improved as the raw data is made available. Pre-processing and segmentation procedures can possibly be enhanced for high magnetic field strength and ultrahigh resolution data. Furthermore, potential resolution induced changes in quantitative data analysis can be assessed, e.g., cortical thickness or volumetric measures, as high quality images with an isotropic resolution of 1 and 0.5 mm of the same subject are included in the repository as well.

  20. Detection of whole-brain abnormalities in temporal lobe epilepsy using tensor-based morphometry with DARTEL

    Science.gov (United States)

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

    2009-10-01

    Tensor-based morphometry (TBM) is an automated technique for detecting the anatomical differences between populations by examining the gradients of the deformation fields used to nonlinearly warp MR images. The purpose of this study was to investigate the whole-brain volume changes between the patients with unilateral temporal lobe epilepsy (TLE) and the controls using TBM with DARTEL, which could achieve more accurate inter-subject registration of brain images. T1-weighted images were acquired from 21 left-TLE patients, 21 right-TLE patients and 21 healthy controls, which were matched in age and gender. The determinants of the gradient of deformation fields at voxel level were obtained to quantify the expansion or contraction for individual images relative to the template, and then logarithmical transformation was applied on it. A whole brain analysis was performed using general lineal model (GLM), and the multiple comparison was corrected by false discovery rate (FDR) with p<0.05. For left-TLE patients, significant volume reductions were found in hippocampus, cingulate gyrus, precentral gyrus, right temporal lobe and cerebellum. These results potentially support the utility of TBM with DARTEL to study the structural changes between groups.

  1. Spatial patterns of whole brain grey and white matter injury in patients with occult spastic diplegic cerebral palsy.

    Science.gov (United States)

    Mu, Xuetao; Nie, Binbin; Wang, Hong; Duan, Shaofeng; Zhang, Zan; Dai, Guanghui; Ma, Qiaozhi; Shan, Baoci; Ma, Lin

    2014-01-01

    Spastic diplegic cerebral palsy (SDCP) is a common type of cerebral palsy (CP), which presents as a group of motor-impairment syndromes. Previous conventional MRI studies have reported abnormal structural changes in SDCP, such as periventricular leucomalacia. However, there are roughly 27.8% SDCP patients presenting normal appearance in conventional MRI, which were considered as occult SDCP. In this study, sixteen patients with occult SDCP and 16 age- and sex-matched healthy control subjects were collected and the data were acquired on a 3T MR system. We applied voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) analysis to investigate whole brain grey and white matter injury in occult SDCP. By using VBM method, the grey matter volume reduction was revealed in the bilateral basal ganglia regions, thalamus, insula, and left cerebral peduncle, whereas the white matter atrophy was found to be located in the posterior part of corpus callosum and right posterior corona radiata in the occult SDCP patients. By using TBSS, reduced fractional anisotropy (FA) values were detected in multiple white matter regions, including bilateral white matter tracts in prefrontal lobe, temporal lobe, internal and external capsule, corpus callosum, cingulum, thalamus, brainstem and cerebellum. Additionally, several regions of white matter tracts injury were found to be significantly correlated with motor dysfunction. These results collectively revealed the spatial patterns of whole brain grey and white matter injury in occult SDCP.

  2. Moderate-Heavy Alcohol Consumption Lifestyle in Older Adults Is Associated with Altered Central Executive Network Community Structure during Cognitive Task.

    Science.gov (United States)

    Mayhugh, Rhiannon E; Moussa, Malaak N; Simpson, Sean L; Lyday, Robert G; Burdette, Jonathan H; Porrino, Linda J; Laurienti, Paul J

    2016-01-01

    Older adults today consume more alcohol than previous generations, the majority being social drinkers. The effects of heavy alcohol use on brain functioning closely resemble age-related changes, but it is not known if moderate-heavy alcohol consumption intensifies brain aging. Whether a lifestyle of moderate-heavy alcohol use in older adults increased age-related brain changes was examined. Forty-one older adults (65-80 years) that consumed light (moderate-heavy (7-21 drinks/week, non-bingers, n = 21) amounts of alcohol were enrolled. Twenty-two young adults (24-35 years) were also enrolled (light, n = 11 and moderate-heavy, n = 11). Functional brain networks based on magnetic resonance imaging data were generated for resting state and during a working memory task. Whole-brain, Central Executive Network (CEN), and Default Mode Network (DMN) connectivity were assessed in light and moderate-heavy alcohol consuming older adults with comparisons to young adults. The older adults had significantly lower whole brain connectivity (global efficiency) and lower regional connectivity (community structure) in the CEN during task and in the DMN at rest. Moderate-heavy older drinkers did not exhibit whole brain connectivity differences compared to the low drinkers. However, decreased CEN connectivity was observed during the task. There were no differences in the DMN connectivity between drinking groups. Taken together, a lifestyle including moderate-heavy alcohol consumption may be associated with further decreases in brain network connectivity within task-related networks in older adults. Further research is required to determine if this decrease is compensatory or an early sign of decline.

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

    Science.gov (United States)

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

    2017-07-04

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

  4. Predictive structural dynamic network analysis.

    Science.gov (United States)

    Chen, Rong; Herskovits, Edward H

    2015-04-30

    Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data. Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models. We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity. For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002). We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. Aberrant Functional Connectivity Architecture in Alzheimer’s Disease and Mild Cognitive Impairment: A Whole-Brain, Data-Driven Analysis

    Directory of Open Access Journals (Sweden)

    Bo Zhou

    2015-01-01

    Full Text Available The purpose of our study was to investigate whether the whole-brain functional connectivity pattern exhibits disease severity-related alterations in patients with Alzheimer’s disease (AD and mild cognitive impairment (MCI. Resting-state functional magnetic resonance imaging data were acquired in 27 MCI subjects, 35 AD patients, and 27 age- and gender-matched subjects with normal cognition (NC. Interregional functional connectivity was assessed based on a predefined template which parcellated the brain into 90 regions. Altered whole-brain functional connectivity patterns were identified via connectivity comparisons between the AD and NC subjects. Finally, the relationship between functional connectivity strength and cognitive ability according to the mini-mental state examination (MMSE was evaluated in the MCI and AD groups. Compared with the NC group, the AD group exhibited decreased functional connectivities throughout the brain. The most significantly affected regions included several important nodes of the default mode network and the temporal lobe. Moreover, changes in functional connectivity strength exhibited significant associations with disease severity-related alterations in the AD and MCI groups. The present study provides novel evidence and will facilitate meta-analysis of whole-brain analyses in AD and MCI, which will be critical to better understand the neural basis of AD.

  6. Stability from Structure : Metabolic Networks Are Unlike Other Biological Networks

    NARCIS (Netherlands)

    Van Nes, P.; Bellomo, D.; Reinders, M.J.T.; De Ridder, D.

    2009-01-01

    In recent work, attempts have been made to link the structure of biochemical networks to their complex dynamics. It was shown that structurally stable network motifs are enriched in such networks. In this work, we investigate to what extent these findings apply to metabolic networks. To this end, we

  7. A whole brain approach to teaching and learning in higher education

    African Journals Online (AJOL)

    The knowledge pertaining to the educators' preferred thinking styles was used as a point of departure to foster an awareness for the whole brain concept and the existence of diversity in thinking style preferences. This diversity poses challenges for all classroom practices. South African Journal of Higher Education Vol.15(3) ...

  8. Improving the Students' Spiritual Intelligence in English Writing through Whole Brain Learning

    Science.gov (United States)

    Santoso, Didik

    2016-01-01

    The objective of this research was to improve the students' spiritual intelligence in English writing through Whole Brain Learning strategy. Therefore, this study was conducted as a classroom action research. The research pocedure followed the cyclonic process of planning, action, observation, and reflection. This process was preceeded by…

  9. Parameterization of the Age-Dependent Whole Brain Apparent Diffusion Coefficient Histogram

    Directory of Open Access Journals (Sweden)

    Uwe Klose

    2015-01-01

    Full Text Available Purpose. The distribution of apparent diffusion coefficient (ADC values in the brain can be used to characterize age effects and pathological changes of the brain tissue. The aim of this study was the parameterization of the whole brain ADC histogram by an advanced model with influence of age considered. Methods. Whole brain ADC histograms were calculated for all data and for seven age groups between 10 and 80 years. Modeling of the histograms was performed for two parts of the histogram separately: the brain tissue part was modeled by two Gaussian curves, while the remaining part was fitted by the sum of a Gaussian curve, a biexponential decay, and a straight line. Results. A consistent fitting of the histograms of all age groups was possible with the proposed model. Conclusions. This study confirms the strong dependence of the whole brain ADC histograms on the age of the examined subjects. The proposed model can be used to characterize changes of the whole brain ADC histogram in certain diseases under consideration of age effects.

  10. Whole brain white matter connectivity analysis using machine learning: An application to autism.

    Science.gov (United States)

    Zhang, Fan; Savadjiev, Peter; Cai, Weidong; Song, Yang; Rathi, Yogesh; Tunç, Birkan; Parker, Drew; Kapur, Tina; Schultz, Robert T; Makris, Nikos; Verma, Ragini; O'Donnell, Lauren J

    2017-10-25

    In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. From network structure to network reorganization: implications for adult neurogenesis

    Science.gov (United States)

    Schneider-Mizell, Casey M.; Parent, Jack M.; Ben-Jacob, Eshel; Zochowski, Michal R.; Sander, Leonard M.

    2010-12-01

    Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells.

  12. A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease.

    Science.gov (United States)

    Demirtaş, Murat; Falcon, Carles; Tucholka, Alan; Gispert, Juan Domingo; Molinuevo, José Luis; Deco, Gustavo

    2017-01-01

    Alzheimer's disease (AD) is the most common dementia with dramatic consequences. The research in structural and functional neuroimaging showed altered brain connectivity in AD. In this study, we investigated the whole-brain resting state functional connectivity (FC) of the subjects with preclinical Alzheimer's disease (PAD), mild cognitive impairment due to AD (MCI) and mild dementia due to Alzheimer's disease (AD), the impact of APOE4 carriership, as well as in relation to variations in core AD CSF biomarkers. The synchronization in the whole-brain was monotonously decreasing during the course of the disease progression. Furthermore, in AD patients we found widespread significant decreases in functional connectivity (FC) strengths particularly in the brain regions with high global connectivity. We employed a whole-brain computational modeling approach to study the mechanisms underlying these alterations. To characterize the causal interactions between brain regions, we estimated the effective connectivity (EC) in the model. We found that the significant EC differences in AD were primarily located in left temporal lobe. Then, we systematically manipulated the underlying dynamics of the model to investigate simulated changes in FC based on the healthy control subjects. Furthermore, we found distinct patterns involving CSF biomarkers of amyloid-beta (Aβ1 - 42) total tau (t-tau) and phosphorylated tau (p-tau). CSF Aβ1 - 42 was associated to the contrast between healthy control subjects and clinical groups. Nevertheless, tau CSF biomarkers were associated to the variability in whole-brain synchronization and sensory integration regions. These associations were robust across clinical groups, unlike the associations that were found for CSF Aβ1 - 42. APOE4 carriership showed no significant correlations with the connectivity measures.

  13. Community structure of complex networks based on continuous neural network

    Science.gov (United States)

    Dai, Ting-ting; Shan, Chang-ji; Dong, Yan-shou

    2017-09-01

    As a new subject, the research of complex networks has attracted the attention of researchers from different disciplines. Community structure is one of the key structures of complex networks, so it is a very important task to analyze the community structure of complex networks accurately. In this paper, we study the problem of extracting the community structure of complex networks, and propose a continuous neural network (CNN) algorithm. It is proved that for any given initial value, the continuous neural network algorithm converges to the eigenvector of the maximum eigenvalue of the network modularity matrix. Therefore, according to the stability of the evolution of the network symbol will be able to get two community structure.

  14. Structural Analysis of Complex Networks

    CERN Document Server

    Dehmer, Matthias

    2011-01-01

    Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science,

  15. Whole-brain voxel-based morphometry of white matter in medial temporal lobe epilepsy

    Energy Technology Data Exchange (ETDEWEB)

    Yu Aihong [Department of Radiology, Xuanwu Hospital, Capital University of Medical Sciences, Beijing 100053 (China); Li Kuncheng [Department of Radiology, Xuanwu Hospital, Capital University of Medical Sciences, Beijing 100053 (China)], E-mail: Likuncheng@vip.sina.com; Li Lin; Shan Baoci [Institute of High Energy Physics, Chinese Academy of Sciences (China); Wang Yuping; Xue Sufang [Department of Neurology, Xuanwu Hospital, Capital University of Medical Sciences (China)

    2008-01-15

    Purpose: The purpose of this study was to analyze whole-brain white matter changes in medial temporal lobe epilepsy (MTLE). Materials and methods: We studied 23 patients with MTLE and 13 age- and sex-matched healthy control subjects using voxel-based morphometry (VBM) on T1-weighted 3D datasets. The seizure focus was right sided in 11 patients and left sided in 12. The data were collected on a 1.5 T MR system and analyzed by SPM 99 to generate white matter density maps. Results: Voxel-based morphometry revealed diffusively reduced white matter in MTLE prominently including bilateral frontal lobes, bilateral temporal lobes and corpus callosum. White matter reduction was also found in the bilateral cerebellar hemispheres in the left MTLE group. Conclusion: VBM is a simple and automated approach that is able to identify diffuse whole-brain white matter reduction in MTLE.

  16. On the topological structure of multinationals network

    Science.gov (United States)

    Joyez, Charlie

    2017-05-01

    This paper uses a weighted network analysis to examine the structure of multinationals' implantation countries network. Based on French firm-level dataset of multinational enterprises (MNEs) the network analysis provides information on each country position in the network and in internationalization strategies of French MNEs through connectivity preferences among the nodes. The paper also details network-wide features and their recent evolution toward a more decentralized structure. While much has been said on international trade network, this paper shows that multinational firms' studies would also benefit from network analysis, notably by investigating the sensitivity of the network construction to firm heterogeneity.

  17. Management of patients with ≥4 brain metastases using stereotactic radiosurgery boost after whole brain irradiation.

    Science.gov (United States)

    Dincoglan, Ferrat; Sager, Omer; Gamsiz, Hakan; Uysal, Bora; Demiral, Selcuk; Oysul, Kaan; Sirin, Sait; Caglan, Ayca; Beyzadeoglu, Murat

    2014-01-01

    Brain metastases are a prevalent consequence of systemic cancer, and patients suffering from brain metastases usually present with multiple metastatic lesions. An overwhelming majority of the available literature assessing the role of stereotactic radiosurgery in brain metastasis management includes patients with up to 4 metastases. Given the significant benefit of stereotactic radiosurgery for the treatment of 1 to 3 brain metastases, we evaluated the use of stereotactic radiosurgery boost after whole brain irradiation in the management of patients with ≥4 brain metastases. In this retrospective analysis, outcomes of 50 patients who underwent linear accelerator-based stereotactic radiosurgery boost within 4 to 6 weeks of whole brain irradiation for ≥4 brain metastases were assessed in terms of local control, overall survival, primary involved organ, recursive partitioning analysis class and Karnofsky performance status at the time of stereotactic radiosurgery, number of lesions, age, status of the primary cancer (controlled vs uncontrolled), presence of extracranial disease and toxicity. Fifty patients with ≥4 brain metastases were treated using linear accelerator-based stereotactic radiosurgery boost after whole brain irradiation between April 1998 and April 2013. Mean and median number of intracranial lesions was 6.02 and 6, respectively. Median lesion volume was 10.9 cc (range, 0.05-32.6). Median survival time after radiosurgery was 10.1 months (range, 1-25). Status of the primary cancer (controlled vs uncontrolled), recursive partitioning analysis class, Karnofsky performance status, and extracranial metastasis showed statistically significant correlations with overall survival (P stereotactic radiosurgery included temporary edema (n = 14, 28%), hemiparesis (n = 1, 2%), seizure (n = 1, 2%), leukoencephalopathy (n = 2, 4%), and radiation necrosis (n = 6, 12%). Linear accelerator-based stereotactic radiosurgery boost within 4 to 6 weeks after whole brain

  18. Whole brain CT perfusion in acute anterior circulation ischemia: coverage size matters

    Energy Technology Data Exchange (ETDEWEB)

    Emmer, B.J. [Erasmus Medical Centre, Department of Radiology, Postbus 2040, Rotterdam (Netherlands); Rijkee, M.; Walderveen, M.A.A. van [Leiden University Medical Centre, Department of Radiology, Leiden (Netherlands); Niesten, J.M.; Velthuis, B.K. [University Medical Centre Utrecht, Department of Radiology, Utrecht (Netherlands); Wermer, M.J.H. [Leiden University Medical Centre, Department of Neurology, Leiden (Netherlands)

    2014-12-15

    Our aim was to compare infarct core volume on whole brain CT perfusion (CTP) with several limited coverage sizes (i.e., 3, 4, 6, and 8 cm), as currently used in routine clinical practice. In total, 40 acute ischemic stroke patients with non-contrast CT (NCCT) and CTP imaging of anterior circulation ischemia were included. Imaging was performed using a 320-multislice CT. Average volumes of infarct core of all simulated partial coverage sizes were calculated. Infarct core volume of each partial brain coverage was compared with infarct core volume of whole brain coverage and expressed using a percentage. To determine the optimal starting position for each simulated CTP coverage, the percentage of infarct coverage was calculated for every possible starting position of the simulated partial coverage in relation to Alberta Stroke Program Early CT Score in Acute Stroke Triage (ASPECTS 1) level. Whole brain CTP coverage further increased the percentage of infarct core volume depicted by 10 % as compared to the 8-cm coverage when the bottom slice was positioned at the ASPECTS 1 level. Optimization of the position of the region of interest (ROI) in 3 cm, 4 cm, and 8 cm improved the percentage of infarct depicted by 4 % for the 8-cm, 7 % for the 4-cm, and 13 % for the 3-cm coverage size. This study shows that whole brain CTP is the optimal coverage for CTP with a substantial improvement in accuracy in quantifying infarct core size. In addition, our results suggest that the optimal position of the ROI in limited coverage depends on the size of the coverage. (orig.)

  19. Treatment of brain metastases of renal cell cancer with combined hypofractionated stereotactic radiotherapy and whole brain radiotherapy with hippocampal sparing.

    Science.gov (United States)

    Vrána, David; Študentová, Hana; Matzenauer, Marcel; Vlachová, Zuzana; Cwiertka, Karel; Gremlica, David; Kalita, Ondřej

    2016-06-01

    Renal cell cancer patients with brain metastatic disease generally have poor prognosis. Treatment options include surgery, radiotherapy, targeted therapy or best supportive care with respect to disease burden, patient preference and performance status. In the present case report the radiotherapy technique combining whole brain radiotherapy with hippocampal sparing (hippocampal avoidance whole brain radiotherapy HA-WBRT) and hypofractionated stereotactic radiotherapy (SRT) of the brain metastases is performed in a patient with metastatic renal cell carcinoma. HA-WBRT was administered to 30 Gy in 10 fractions with sparing of the hippocampal structures and SRT of 21 Gy in 3 fractions to brain metastases which has preceded the HA-WBRT. Two single arc volumetric modulated arc radiotherapy (VMAT) plans were prepared using Monaco planning software. The HA-WBRT treatment plan achieved the following results: D2=33.91 Gy, D98=25.20 Gy, D100=14.18 Gy, D50=31.26 Gy. The homogeneity index was calculated as a deduction of the minimum dose in 2% and 98% of the planning target volume (PTV), divided by the minimum dose in 50% of the PTV. The maximum dose to the hippocampus was 17.50 Gy and mean dose was 11.59 Gy. The following doses to organs at risk (OAR) were achieved: Right opticus Dmax, 31.96 Gy; left opticus Dmax, 30.96 Gy; chiasma D max, 32,76 Gy. The volume of PTV for stereotactic radiotherapy was 3,736 cm3, with coverage D100=20.95 Gy and with only 0.11% of the PTV being irradiated to dose below the prescribed dose. HA-WBRT with SRT represents a feasible technique for radiotherapy of brain metastatic disease, however this technique is considerably demanding on departmental equipment and staff time/experience.

  20. Distributed Structure-Searchable Toxicity Database Network

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Distributed Structure-Searchable Toxicity (DSSTox) Database Network provides a public forum for search and publishing downloadable, structure-searchable,...

  1. The death of whole-brain death: the plague of the disaggregators, somaticists, and mentalists.

    Science.gov (United States)

    Veatch, Robert M

    2005-08-01

    In its October 2001 issue, this journal published a series of articles questioning the Whole-Brain-based definition of death. Much of the concern focused on whether somatic integration-a commonly understood basis for the whole-brain death view-can survive the brain's death. The present article accepts that there are insurmountable problems with whole-brain death views, but challenges the assumption that loss of somatic integration is the proper basis for pronouncing death. It examines three major themes. First, it accepts the claim of the "disaggregators" that some behaviors traditionally associated with death can be unbundled, but argues that other behaviors (including organ procurement) must continue to be associated. Second, it rejects the claims of the "somaticists," that the integration of the body is critical, arguing instead for equating death with the irreversible loss of "embodied consciousness," that is, the loss of integration of bodily and mental function. Third, it defends higher-brain views against the charge that they are necessarily "mentalist," that is, that they equate death with losing some mental function such as consciousness or personhood. It argues, instead, for the integration of bodily and mental function as the critical feature of human life and that its irreversible loss constitutes death.

  2. Developmental venous anomalies: appearance on whole-brain CT digital subtraction angiography and CT perfusion

    Energy Technology Data Exchange (ETDEWEB)

    Hanson, Eric H. [Advanced Medical Imaging and Genetics (Amigenics), Las Vegas, NV (United States); Touro University Nevada College of Osteopathic Medicine, Henderson, NV (United States); University of Nevada Las Vegas, Department of Health Physics and Diagnostic Sciences, 4505 Maryland Parkway, Box 453037, Las Vegas, NV (United States); Amigenics, Inc, Las Vegas, NV (United States); Roach, Cayce J. [Advanced Medical Imaging and Genetics (Amigenics), Las Vegas, NV (United States); University of Nevada Las Vegas, School of Life Sciences, Las Vegas, NV (United States); Ringdahl, Erik N. [University of Nevada Las Vegas, Department of Psychology, Las Vegas, NV (United States); Wynn, Brad L. [Family Medicine Spokane, Spokane, WA (United States); DeChancie, Sean M.; Mann, Nathan D. [Touro University Nevada College of Osteopathic Medicine, Henderson, NV (United States); Diamond, Alan S. [CHW Nevada Imaging Company, Nevada Imaging Centers, Spring Valley, Las Vegas, NV (United States); Orrison, William W. [Touro University Nevada College of Osteopathic Medicine, Henderson, NV (United States); University of Nevada Las Vegas, Department of Health Physics and Diagnostic Sciences, 4505 Maryland Parkway, Box 453037, Las Vegas, NV (United States); CHW Nevada Imaging Company, Nevada Imaging Centers, Spring Valley, Las Vegas, NV (United States); University of Nevada School of Medicine, Department of Medical Education, Reno, NV (United States)

    2011-05-15

    Developmental venous anomalies (DVA) consist of dilated intramedullary veins that converge into a large collecting vein. The appearance of these anomalies was evaluated on whole-brain computed tomography (CT) digital subtraction angiography (DSA) and CT perfusion (CTP) studies. CT data sets of ten anonymized patients were retrospectively analyzed. Five patients had evidence of DVA and five age- and sex-matched controls were without known neurovascular abnormalities. CT angiograms, CT arterial-venous views, 4-D CT DSA and CTP maps were acquired on a dynamic volume imaging protocol on a 320-detector row CT scanner. Whole-brain CTP parameters were evaluated for cerebral blood flow (CBF), cerebral blood volume (CBV), time to peak (TTP), mean transit time (MTT), and delay. DSA was utilized to visualize DVA anatomy. Radiation dose was recorded from the scanner console. Increased CTP values were present in the DVA relative to the unaffected contralateral hemisphere of 48%, 32%, and 26%; and for the control group with matched hemispheric comparisons of 2%, -10%, and 9% for CBF, CBV, and MTT, respectively. Average effective radiation dose was 4.4 mSv. Whole-brain DSA and CTP imaging can demonstrate a characteristic appearance of altered DVA hemodynamic parameters and capture the anomalies in superior cortices of the cerebrum and the cerebellum. Future research may identify the rare subsets of patients at increased risk of adverse outcomes secondary to the altered hemodynamics to facilitate tailored imaging surveillance and application of appropriate preventive therapeutic measures. (orig.)

  3. Social structure of Facebook networks

    Science.gov (United States)

    Traud, Amanda L.; Mucha, Peter J.; Porter, Mason A.

    2012-08-01

    We study the social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes-gender, class year, major, high school, and residence-at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on user characteristics. We thereby examine the relative importance of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.

  4. Exploring the Community Structure of Complex Networks

    OpenAIRE

    Drago, Carlo

    2016-01-01

    Regarding complex networks, one of the most relevant problems is to understand and to explore community structure. In particular it is important to define the network organization and the functions associated to the different network partitions. In this context, the idea is to consider some new approaches based on interval data in order to represent the different relevant network components as communities. The method is also useful to represent the network community structure, especially the ...

  5. Activating whole brain® innovation: A means of nourishing multiple intelligence in higher education

    Directory of Open Access Journals (Sweden)

    Ann-Louise De Boer

    2015-11-01

    Full Text Available The interconnectedness of the constructs ‘whole brain® thinking’ and ‘multiple intelligence’ forms the epicentre of this article. We depart from the premise that when whole brain® thinking is activated multiple intelligence can be nourished. When this becomes evident in a higher education practice it can be claimed that such a practice is innovative. Whole brain® thinking that informs intelligence and vice versa is inevitable when it comes to facilitating learning with a view to promoting quality learning in the context of higher education. If higher education is concerned about the expectations of industry and the world of work there is no other option as to prepare students in such a way that they develo as holistic – whole brained and intelligent – employers, employees and entrepreneurs who take responsibility for maximising their full potential. Becoming a self-regulated professional and being reflexive are some of the attributes of the 21st century which should be cultivated in all students. Research on whole brain® thinking and multiple intelligence shows that these human attributes form an integral part of one’s interaction with life – one’s environment and especially people as integral part of the environment. This focus on people highlights the need for developing soft skills within every curriculum. The epistemological underpinning of our reporting of experience in practice and research of the application of the principals of the constructs is meta- reflective in nature. Instead of a typical traditional stance to research we do not report on the numerous sets of data obtained over a period of more than 15 years. Our approach is that of a meta-reflective narrative as most of the studies we were involved in and still are, are reflective as it is most often than not action research-driven. And action research is a reflective process. We report on evidence-based practice that includes fields of specialisation such as

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

    Science.gov (United States)

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

    2017-01-01

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

  7. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Abstract. Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from different domains may vary quite significantly. As there is an interplay between network architecture and dynamics, structure plays an impor- tant role in communication and ...

  8. True Nature of Supply Network Communication Structure

    Directory of Open Access Journals (Sweden)

    Lokhman Hakim bin Osman

    2016-04-01

    Full Text Available Globalization of world economy has altered the definition of organizational structure. Global supply chain can no longer be viewed as an arm-length structure. It has become more complex. The complexity demands deeper research and understanding. This research analyzed a structure of supply network in an attempt to elucidate the true structure of the supply network. Using the quantitative Social Network Analysis methodology, findings of this study indicated that, the structure of the supply network differs depending on the types of network relations. An important implication of these findings would be a more focus resource management upon network relationship development that is based on firms’ positions in the different network structure. This research also contributes to the various strategies of effective and efficient supply chain management.

  9. Core-Periphery Structure in Networks

    OpenAIRE

    Rombach, M. Puck; Porter, Mason A.; Fowler, James H.; Mucha, Peter J

    2012-01-01

    Intermediate-scale (or `meso-scale') structures in networks have received considerable attention, as the algorithmic detection of such structures makes it possible to discover network features that are not apparent either at the local scale of nodes and edges or at the global scale of summary statistics. Numerous types of meso-scale structures can occur in networks, but investigations of such features have focused predominantly on the identification and study of community structure. In this p...

  10. Prognostic factors for outcomes after whole-brain irradiation of brain metastases from relatively radioresistant tumors: a retrospective analysis

    NARCIS (Netherlands)

    Meyners, Thekla; Heisterkamp, Christine; Kueter, Jan-Dirk; Veninga, Theo; Stalpers, Lukas J. A.; Schild, Steven E.; Rades, Dirk

    2010-01-01

    This study investigated potential prognostic factors in patients treated with whole-brain irradiation (WBI) alone for brain metastases from relatively radioresistant tumors such as malignant melanoma, renal cell carcinoma, and colorectal cancer. Additionally, a potential benefit from escalating the

  11. Prognostic factors for outcomes after whole-brain irradiation of brain metastases from relatively radioresistant tumors: a retrospective analysis

    NARCIS (Netherlands)

    Meyners, T.; Heisterkamp, C.; Kueter, J.D.; Veninga, T.; Stalpers, L.J.A.; Schild, S.E.; Rades, D.

    2010-01-01

    Background: This study investigated potential prognostic factors in patients treated with whole-brain irradiation (WBI) alone for brain metastases from relatively radioresistant tumors such as malignant melanoma, renal cell carcinoma, and colorectal cancer. Additionally, a potential benefit from

  12. Global Electricity Trade Network: Structures and Implications

    Science.gov (United States)

    Ji, Ling; Jia, Xiaoping; Chiu, Anthony S. F.; Xu, Ming

    2016-01-01

    Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions. PMID:27504825

  13. Global Electricity Trade Network: Structures and Implications.

    Science.gov (United States)

    Ji, Ling; Jia, Xiaoping; Chiu, Anthony S F; Xu, Ming

    2016-01-01

    Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions.

  14. Taxonomies of networks from community structure

    Science.gov (United States)

    Onnela, Jukka-Pekka; Fenn, Daniel J.; Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

    2012-09-01

    The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

  15. Robustness and structure of complex networks

    Science.gov (United States)

    Shao, Shuai

    This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks

  16. Changes in Brain Structural Networks and Cognitive Functions in Testicular Cancer Patients Receiving Cisplatin-Based Chemotherapy

    NARCIS (Netherlands)

    Amidi, Ali; Hosseini, S. M.Hadi; Leemans, Alexander; Kesler, Shelli R.; Agerbæk, Mads; Wu, Lisa M.; Zachariae, Robert

    2017-01-01

    Background: Cisplatin-based chemotherapy may have neurotoxic effects within the central nervous system. The aims of this study were 1) to longitudinally investigate the impact of cisplatin-based chemotherapy on whole-brain networks in testicular cancer patients undergoing treatment and 2) to explore

  17. Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study.

    Science.gov (United States)

    Xie, Hua; Calhoun, Vince D; Gonzalez-Castillo, Javier; Damaraju, Eswar; Miller, Robyn; Bandettini, Peter A; Mitra, Sunanda

    2017-05-23

    Functional connectivity (FC) has been widely used to study the functional organization of temporally correlated and spatially distributed brain regions. Recent studies of FC dynamics, quantified by windowed correlations, provide new insights to analyze dynamic, context-dependent reconfiguration of brain networks. A set of reoccurring whole-brain connectivity patterns at rest, referred to as FC states, have been identified, hypothetically reflecting underlying cognitive processes or mental states. We posit that the mean FC information for a given subject represents a significant contribution to the group-level FC dynamics. We show that the subject-specific FC profile, termed as FC individuality, can be removed to increase sensitivity to cognitively relevant FC states. To assess the impact of the FC individuality and task-specific FC modulation on the group-level FC dynamics analysis, we generate and analyze group studies of four subjects engaging in four cognitive conditions (rest, simple math, two-back memory, and visual attention task). We also propose a model to quantitatively evaluate the effect of two factors, namely, subject-specific and task-specific modulation on FC dynamics. We show that FC individuality is a predominant factor in group-level FC variability, and the embedded cognitively relevant FC states are clearly visible after removing the individual's connectivity profile. Our results challenge the current understanding of FC states and emphasize the importance of individual heterogeneity in connectivity dynamics analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Immunization of networks with community structure

    Energy Technology Data Exchange (ETDEWEB)

    Masuda, Naoki [Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656 (Japan); PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012 (Japan)], E-mail: masuda@mist.i.u-tokyo.ac.jp

    2009-12-15

    In this study, an efficient method to immunize modular networks (i.e. networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, protection against intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.

  19. Airline network structure in competitive market

    Directory of Open Access Journals (Sweden)

    Babić Danica D.

    2014-01-01

    Full Text Available Airline's network is the key element of its business strategy and selected network structure will not have influence only on the airline's costs but could gain some advantage in revenues, too. Network designing implies that an airline has to make decisions about markets that it will serve and how to serve those markets. Network choice raises the following questions for an airline: a what markets to serve, b how to serve selected markets, c what level of service to offer, d what are the benefits/cost of the that decisions and e what is the influence of the competition. We analyzed the existing airline business models and corresponding network structure. The paper highlights the relationship between the network structures and the airline business strategies. Using a simple model we examine the relationship between the network structure and service quality in deregulated market.

  20. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Stea, Diego; Soda, Giuseppe; Pedersen, Torben

    2016-01-01

    outcomes often assumes that different network structures embody specific individual behaviors. This paper challenges the widespread assumption that dense, heavily bonded network structures imply a collaborative attitude on the part of network actors. We propose that collaboration can also be contextual......Network research has yet to determine whether bonding ties or bridging ties are more beneficial for individual creativity, but the debate has mostly overlooked the organizational context in which such ties are formed. In particular, the causal chain connecting network structures and individual...... and exogenous to a network’s structural characteristics, such that it moderates the effects of both dense and brokered networks on individual creativity. Specifically, we argue that knowledge acquisition and, in turn, individual creativity are more likely when an individual’s network position has a good fit...

  1. Abnormal Brain Network Organization in Body Dysmorphic Disorder

    Science.gov (United States)

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

    2013-01-01

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

  2. PARALLEL ALGORITHM FOR BAYESIAN NETWORK STRUCTURE LEARNING

    Directory of Open Access Journals (Sweden)

    S. A. Arustamov

    2013-03-01

    Full Text Available The article deals with implementation of a scalable parallel algorithm for structure learning of Bayesian network. Comparative analysis of sequential and parallel algorithms is done.

  3. Sensitive Dependence of Optimal Network Dynamics on Network Structure

    Directory of Open Access Journals (Sweden)

    Takashi Nishikawa

    2017-11-01

    Full Text Available The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important long-standing problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here, we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, using diffusively coupled systems as examples, we demonstrate that the stability of a dynamical state can exhibit sensitivity to unweighted structural perturbations (i.e., link removals and node additions for undirected optimal networks and to weighted perturbations (i.e., small changes in link weights for directed optimal networks. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of undirected optimal networks and the prevalence of eigenvector degeneracy in directed optimal networks. These findings establish a unified characterization of networks optimized for dynamical stability, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, network diffusion, and several other network processes. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.

  4. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Among all biological networks studied here, the undirected structure of neuronal networks not only possesses the small-world property but the same is also expressed remarkably to a higher degree compared to any randomly generated network which possesses the same degree sequence. A relatively high percentage of ...

  5. Network quotients: structural skeletons of complex systems.

    Science.gov (United States)

    Xiao, Yanghua; MacArthur, Ben D; Wang, Hui; Xiong, Momiao; Wang, Wei

    2008-10-01

    A defining feature of many large empirical networks is their intrinsic complexity. However, many networks also contain a large degree of structural repetition. An immediate question then arises: can we characterize essential network complexity while excluding structural redundancy? In this article we utilize inherent network symmetry to collapse all redundant information from a network, resulting in a coarse graining which we show to carry the essential structural information of the "parent" network. In the context of algebraic combinatorics, this coarse-graining is known as the "quotient." We systematically explore the theoretical properties of network quotients and summarize key statistics of a variety of "real-world" quotients with respect to those of their parent networks. In particular, we find that quotients can be substantially smaller than their parent networks yet typically preserve various key functional properties such as complexity (heterogeneity and hub vertices) and communication (diameter and mean geodesic distance), suggesting that quotients constitute the essential structural skeletons of their parent networks. We summarize with a discussion of potential uses of quotients in analysis of biological regulatory networks and ways in which using quotients can reduce the computational complexity of network algorithms.

  6. Structure of triadic relations in multiplex networks

    Science.gov (United States)

    Cozzo, Emanuele; Kivelä, Mikko; De Domenico, Manlio; Solé-Ribalta, Albert; Arenas, Alex; Gómez, Sergio; Porter, Mason A.; Moreno, Yamir

    2015-07-01

    Recent advances in the study of networked systems have highlighted that our interconnected world is composed of networks that are coupled to each other through different ‘layers’ that each represent one of many possible subsystems or types of interactions. Nevertheless, it is traditional to aggregate multilayer networks into a single weighted network in order to take advantage of existing tools. This is admittedly convenient, but it is also extremely problematic, as important information can be lost as a result. It is therefore important to develop multilayer generalizations of network concepts. In this paper, we analyze triadic relations and generalize the idea of transitivity to multiplex networks. By focusing on triadic relations, which yield the simplest type of transitivity, we generalize the concept and computation of clustering coefficients to multiplex networks. We show how the layered structure of such networks introduces a new degree of freedom that has a fundamental effect on transitivity. We compute multiplex clustering coefficients for several real multiplex networks and illustrate why one must take great care when generalizing standard network concepts to multiplex networks. We also derive analytical expressions for our clustering coefficients for ensemble averages of networks in a family of random multiplex networks. Our analysis illustrates that social networks have a strong tendency to promote redundancy by closing triads at every layer and that they thereby have a different type of multiplex transitivity from transportation networks, which do not exhibit such a tendency. These insights are invisible if one only studies aggregated networks.

  7. Whole brain white matter changes revealed by multiple diffusion metrics in multiple sclerosis: A TBSS study

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Yaou, E-mail: asiaeurope80@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Duan, Yunyun, E-mail: xiaoyun81.love@163.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); He, Yong, E-mail: yong.h.he@gmail.com [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China); Yu, Chunshui, E-mail: csyuster@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Wang, Jun, E-mail: jun_wang@bnu.edu.cn [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China); Huang, Jing, E-mail: sainthj@126.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Ye, Jing, E-mail: jingye.2007@yahoo.com.cn [Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Parizel, Paul M., E-mail: paul.parizel@ua.ac.be [Department of Radiology, Antwerp University Hospital and University of Antwerp, Wilrijkstraat 10, 2650 Edegem, 8 Belgium (Belgium); Li, Kuncheng, E-mail: kunchengli55@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Shu, Ni, E-mail: nshu55@gmail.com [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China)

    2012-10-15

    Objective: To investigate whole brain white matter changes in multiple sclerosis (MS) by multiple diffusion indices, we examined patients with diffusion tensor imaging and utilized tract-based spatial statistics (TBSS) method to analyze the data. Methods: Forty-one relapsing-remitting multiple sclerosis (RRMS) patients and 41 age- and gender-matched normal controls were included in this study. Diffusion weighted images were acquired by employing a single-shot echo planar imaging sequence on a 1.5 T MR scanner. Voxel-wise analyses of multiple diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were performed with TBSS. Results: The MS patients had significantly decreased FA (9.11%), increased MD (8.26%), AD (3.48%) and RD (13.17%) in their white matter skeletons compared with the controls. Through TBSS analyses, we found abnormal diffusion changes in widespread white matter regions in MS patients. Specifically, decreased FA, increased MD and increased RD were involved in whole-brain white matter, while several regions exhibited increased AD. Furthermore, white matter regions with significant correlations between the diffusion metrics and the clinical variables (the EDSS scores, disease durations and white matter lesion loads) in MS patients were identified. Conclusion: Widespread white matter abnormalities were observed in MS patients revealed by multiple diffusion metrics. The diffusion changes and correlations with clinical variables were mainly attributed to increased RD, implying the predominant role of RD in reflecting the subtle pathological changes in MS.

  8. Voxel-based analysis of whole brain FLAIR at 3T detects focal cortical dysplasia.

    Science.gov (United States)

    Focke, Niels K; Symms, Mark R; Burdett, Jane L; Duncan, John S

    2008-05-01

    Focal Cortical Dysplasia (FCD) is an important cause for pharmacoresistant epilepsy that can be treated surgically. The identification of the abnormal cortex on standard MRI can be difficult and computational techniques have been developed to increase sensitivity. In this study we evaluate the potential of a novel whole-brain voxel-based technique using normalized FLAIR signal intensity (nFSI) at 3 Tesla. Twenty-five patients with neuroradiologically reported FCD were included and compared to 25 healthy control subjects using Statistical Parametric Mapping (SPM5). T2 FLAIR scans were intensity normalized and each individual patient was compared against the control group. Each control subject was compared against the remaining control group. SPM correctly identified the FCD in 88% of cases (22/25) with only one false positive finding in the control group. In all but one of these cases the FCD was the most significant finding in the whole brain. All three missing cases could be detected at lower threshold levels but this would give rise to more false positive findings and thus reduce specificity. We present a novel technique that uses standard clinical T2 FLAIR scans to automatically detect FCDs. It can give supplementary information to the established T1-based automated techniques and could be useful for additional screening test, to complement the visual reading and clinical interpretation of MRI scans.

  9. Multimodality 3D Superposition and Automated Whole Brain Tractography: Comprehensive Printing of the Functional Brain.

    Science.gov (United States)

    Konakondla, Sanjay; Brimley, Cameron J; Sublett, Jesna Mathew; Stefanowicz, Edward; Flora, Sarah; Mongelluzzo, Gino; Schirmer, Clemens M

    2017-09-29

    Whole brain tractography using diffusion tensor imaging (DTI) sequences can be used to map cerebral connectivity; however, this can be time-consuming due to the manual component of image manipulation required, calling for the need for a standardized, automated, and accurate fiber tracking protocol with automatic whole brain tractography (AWBT). Interpreting conventional two-dimensional (2D) images, such as computed tomography (CT) and magnetic resonance imaging (MRI), as an intraoperative three-dimensional (3D) environment is a difficult task with recognized inter-operator variability. Three-dimensional printing in neurosurgery has gained significant traction in the past decade, and as software, equipment, and practices become more refined, trainee education, surgical skills, research endeavors, innovation, patient education, and outcomes via valued care is projected to improve. We describe a novel multimodality 3D superposition (MMTS) technique, which fuses multiple imaging sequences alongside cerebral tractography into one patient-specific 3D printed model. Inferences on cost and improved outcomes fueled by encouraging patient engagement are explored.

  10. Sources and implications of whole-brain fMRI signals in humans.

    Science.gov (United States)

    Power, Jonathan D; Plitt, Mark; Laumann, Timothy O; Martin, Alex

    2017-02-01

    Whole-brain fMRI signals are a subject of intense interest: variance in the global fMRI signal (the spatial mean of all signals in the brain) indexes subject arousal, and psychiatric conditions such as schizophrenia and autism have been characterized by differences in the global fMRI signal. Further, vigorous debates exist on whether global signals ought to be removed from fMRI data. However, surprisingly little research has focused on the empirical properties of whole-brain fMRI signals. Here we map the spatial and temporal properties of the global signal, individually, in 1000+ fMRI scans. Variance in the global fMRI signal is strongly linked to head motion, to hardware artifacts, and to respiratory patterns and their attendant physiologic changes. Many techniques used to prepare fMRI data for analysis fail to remove these uninteresting kinds of global signal fluctuations. Thus, many studies include, at the time of analysis, prominent global effects of yawns, breathing changes, and head motion, among other signals. Such artifacts will mimic dynamic neural activity and will spuriously alter signal covariance throughout the brain. Methods capable of isolating and removing global artifactual variance while preserving putative "neural" variance are needed; this paper adopts no position on the topic of global signal regression. Published by Elsevier Inc.

  11. Pencilbeam irradiation technique for whole brain radiotherapy: technical and biological challenges in a small animal model.

    Science.gov (United States)

    Schültke, Elisabeth; Trippel, Michael; Bräuer-Krisch, Elke; Renier, Michel; Bartzsch, Stefan; Requardt, Herwig; Döbrössy, Máté D; Nikkhah, Guido

    2013-01-01

    We have conducted the first in-vivo experiments in pencilbeam irradiation, a new synchrotron radiation technique based on the principle of microbeam irradiation, a concept of spatially fractionated high-dose irradiation. In an animal model of adult C57 BL/6J mice we have determined technical and physiological limitations with the present technical setup of the technique. Fifty-eight animals were distributed in eleven experimental groups, ten groups receiving whole brain radiotherapy with arrays of 50 µm wide beams. We have tested peak doses ranging between 172 Gy and 2,298 Gy at 3 mm depth. Animals in five groups received whole brain radiotherapy with a center-to-center (ctc) distance of 200 µm and a peak-to-valley ratio (PVDR) of ∼ 100, in the other five groups the ctc was 400 µm (PVDR ∼ 400). Motor and memory abilities were assessed during a six months observation period following irradiation. The lower dose limit, determined by the technical equipment, was at 172 Gy. The LD50 was about 1,164 Gy for a ctc of 200 µm and higher than 2,298 Gy for a ctc of 400 µm. Age-dependent loss in motor and memory performance was seen in all groups. Better overall performance (close to that of healthy controls) was seen in the groups irradiated with a ctc of 400 µm.

  12. Amygdala and whole brain activity to emotional faces distinguishes major depressive disorder and bipolar disorder

    Science.gov (United States)

    Fournier, Jay C.; Keener, Matthew T.; Almeida, Jorge; Kronhaus, Dina M.; Phillips, Mary L.

    2013-01-01

    Objectives It can be clinically difficult to distinguish depressed individuals with bipolar disorder (BD) and major depressive disorder (MDD). To examine potential biomarkers of difference between the two disorders, the current study examined differences in the functioning of emotion processing neural regions during a dynamic emotional faces task. Methods During functional magnetic resonance imaging, healthy control adults (HC) (n = 29) and depressed adults with MDD (n = 30) and BD (n = 22) performed an implicit emotional-faces task in which they identified a color label superimposed on neutral faces that dynamically morphed into one of four emotional faces (angry, fearful, sad, happy). We compared neural activation between the groups in an amygdala region-of-interest and at the whole brain level. Results Adults with MDD showed significantly greater activity than adults with BD in the left amygdala to the anger condition (p = 0.01). Results of whole brain analyses (at p emotional faces. Those with BD showed greater activity during mood-congruent (i.e., sad) faces, whereas, those with MDD showed greater activity for mood-incongruent (i.e., fear, anger, and happy) faces. Such findings may reflect markers of differences between BD and MDD depression in underlying pathophysiological processes. PMID:23911154

  13. Amygdala and whole-brain activity to emotional faces distinguishes major depressive disorder and bipolar disorder.

    Science.gov (United States)

    Fournier, Jay C; Keener, Matthew T; Almeida, Jorge; Kronhaus, Dina M; Phillips, Mary L

    2013-11-01

    It can be clinically difficult to distinguish depressed individuals with bipolar disorder (BD) and major depressive disorder (MDD). To examine potential biomarkers of difference between the two disorders, the current study examined differences in the functioning of emotion-processing neural regions during a dynamic emotional faces task. During functional magnetic resonance imaging, healthy control adults (HC) (n = 29) and depressed adults with MDD (n = 30) and BD (n = 22) performed an implicit emotional-faces task in which they identified a color label superimposed on neutral faces that dynamically morphed into one of four emotional faces (angry, fearful, sad, happy). We compared neural activation between the groups in an amygdala region-of-interest and at the whole-brain level. Adults with MDD showed significantly greater activity than adults with BD in the left amygdala to the anger condition (p = 0.01). Results of whole-brain analyses (at p depressed adults with BD and MDD in the processing of emerging emotional faces. Those with BD showed greater activity during mood-congruent (i.e., sad) faces, whereas those with MDD showed greater activity for mood-incongruent (i.e., fear, anger, and happy) faces. Such findings may reflect markers of differences between BD and MDD depression in underlying pathophysiological processes. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  14. Network repair based on community structure

    Science.gov (United States)

    Wang, Tianyu; Zhang, Jun; Sun, Xiaoqian; Wandelt, Sebastian

    2017-06-01

    Real-world complex systems are often fragile under disruptions. Accordingly, research on network repair has been studied intensively. Recently proposed efficient strategies for network disruption, based on collective influence, call for more research on efficient network repair strategies. Existing strategies are often designed to repair networks with local information only. However, the absence of global information impedes the creation of efficient repairs. Motivated by this limitation, we propose a concept of community-level repair, which leverages the community structure of the network during the repair process. Moreover, we devise a general framework of network repair, with in total six instances. Evaluations on real-world and random networks show the effectiveness and efficiency of the community-level repair approaches, compared to local and random repairs. Our study contributes to a better understanding of repair processes, and reveals that exploitation of the community structure improves the repair process on a disrupted network significantly.

  15. Exploring biological network structure with clustered random networks

    Directory of Open Access Journals (Sweden)

    Bansal Shweta

    2009-12-01

    Full Text Available Abstract Background Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions and the extent of clustering (the tendency for a set of three nodes to be interconnected are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in

  16. Exploring biological network structure with clustered random networks.

    Science.gov (United States)

    Bansal, Shweta; Khandelwal, Shashank; Meyers, Lauren Ancel

    2009-12-09

    Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics.Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural

  17. A precision 3D conformal treatment technique in rats: Application to whole-brain radiotherapy with hippocampal avoidance.

    Science.gov (United States)

    Yoon, Suk W; Cramer, Christina K; Miles, Devin A; Reinsvold, Michael H; Joo, Kyeung M; Kirsch, David G; Oldham, Mark

    2017-08-24

    -to-agreement of 2 mm and dose difference of ±3% at 91.7% gamma passing rate (passing criteria of γ 3D-printing technology was developed, implemented, and validated. A workflow was developed to generate accurate 3D-printed blocks from registered high-resolution rat MRI atlas structures. Although hippocampus was spared with this technique, whole-brain target coverage was suboptimal, indicating that non-coplanar beams and IMRT capability may be required to meet stringent dose criteria associated with current human RTOG trials. © 2017 American Association of Physicists in Medicine.

  18. STRUCTURE AND COOPTATION IN ORGANIZATION NETWORK

    Directory of Open Access Journals (Sweden)

    Valéria Riscarolli

    2007-10-01

    Full Text Available Business executive are rethinking business concept, based on horizontalization principles. As so, most organizational functions are outsourced, leading the enterprise to build business through a network of organizations. Here we study the case of Cia Hering’s network of organizations, a leader in knit apparel segment in Latin America (IEMI, 2004, looking at the network’s structure and levels of cooptation. A theoretical model was used using Quinn et al. (2001 “sun ray” network structure as basis to analyze the case study. Main results indicate higher degree of structural conformity, but incipient degree of coopetation in the network.

  19. Network structure of inter-industry flows

    Science.gov (United States)

    McNerney, James; Fath, Brian D.; Silverberg, Gerald

    2013-12-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 45 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.

  20. Network structure of inter-industry flows

    CERN Document Server

    McNerney, James; Silverberg, Gerald

    2012-01-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.

  1. Network structure of inter-industry flows

    OpenAIRE

    McNerney, J.; Fath, B.D.; G. Silverberg

    2012-01-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, servic...

  2. Network Structure of Inter-Industry Flows

    NARCIS (Netherlands)

    McNerney, J.; Fath, B.D.; Silverberg, G.P.

    2015-01-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 45 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community

  3. Optimized null model for protein structure networks.

    Science.gov (United States)

    Milenković, Tijana; Filippis, Ioannis; Lappe, Michael; Przulj, Natasa

    2009-06-26

    Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by

  4. Optimized null model for protein structure networks.

    Directory of Open Access Journals (Sweden)

    Tijana Milenković

    Full Text Available Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model

  5. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... prediction performance of the learning based approaches and other widely used link prediction approaches in 14 networks ranging from medium size to large networks with more than a million nodes. While link prediction is typically well above chance for all networks, we find that the learning based mixed...... membership stochastic block model of Airoldi et al., performs well and often best in our experiments. The added complexity of the LD model improves link predictions for four of the 14 networks....

  6. Whole brain C-arm computed tomography parenchymal blood volume measurements.

    Science.gov (United States)

    Kamran, Mudassar; Byrne, James V

    2016-04-01

    C-arm flat detector computed tomography (FDCT) parenchymal blood volume (PBV) imaging in the neuro-interventional suite is a new technique for which detailed whole brain measurements have not been previously reported. This study aims to create a catalogue of PBV measurements for various anatomical regions encompassing the whole brain, using a three-dimensional volume-of-interest (3D-VOI) analysis. We acquired and analysed 30 C-arm FDCT datasets from 26 patients with aneurysmal subarachnoid haemorrhage (SAH), as part of a prospective study comparing C-arm computed tomography (CT) PBV with magnetic resonance perfusion-weighted imaging (MR-PWI). We calculated the PBV values for various brain regions with an automated analysis, using 58 pre-defined atlas-based 3D-VOIs encompassing the whole brain. VOIs partially or completely overlapping regions of magnetic resonance diffusion weighted imaging (MR-DWI) abnormality or magnetic resonance cerebral blood flow (MR-CBF) asymmetry were excluded from the analysis. Of the 30 C-arm CT PBV datasets, 14 (54%; 12 patients) had areas of restricted diffusion, the majority of which were focal. The PBV values for the cerebral cortex and cerebral white matter were 4.01 ± 0.47 (mean ± SD) and 3.01 ± 0.39 ml per 100 ml. Lobar PBV values were: frontal lobe 4.2 ± 0.8, temporal lobe 4.2 ± 0.9, parietal lobe 3.9 ± 0.7 and occipital lobe 4.3 ± 0.8 ml/100 ml. The basal ganglia and brainstem PBV values were 3.4 ± 0.7 and 4.6 ± 0.6 ml/100 ml, respectively. Compared with the typical reference cerebral blood volume (CBV) values reported in the literature for Positron Emission Tomography (PET), the PBV values were relatively high for the white matter and relatively low for the cortical grey matter. The reported catalogue of PBV values for various brain regions would be useful to inform future studies and could be used in clinical practice, when interpreting PBV maps. © The Author(s) 2016.

  7. Network structure and travel time perception.

    Science.gov (United States)

    Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

    2013-01-01

    The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.

  8. 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...

  9. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  10. Exploring the structural regularities in networks

    CERN Document Server

    Shen, Hua-Wei; Guo, Jia-Feng

    2011-01-01

    In this paper, we consider the problem of exploring structural regularities of networks by dividing the nodes of a network into groups such that the members of each group have similar patterns of connections to other groups. Specifically, we propose a general statistical model to describe network structure. In this model, group is viewed as hidden or unobserved quantity and it is learned by fitting the observed network data using the expectation-maximization algorithm. Compared with existing models, the most prominent strength of our model is the high flexibility. This strength enables it to possess the advantages of existing models and overcomes their shortcomings in a unified way. As a result, not only broad types of structure can be detected without prior knowledge of what type of intrinsic regularities exist in the network, but also the type of identified structure can be directly learned from data. Moreover, by differentiating outgoing edges from incoming edges, our model can detect several types of stru...

  11. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Stea, Diego; Soda, Giuseppe; Pedersen, Torben

    2016-01-01

    and exogenous to a network’s structural characteristics, such that it moderates the effects of both dense and brokered networks on individual creativity. Specifically, we argue that knowledge acquisition and, in turn, individual creativity are more likely when an individual’s network position has a good fit...... with the network’s organizational context. Thus, actors in dense network structures acquire more knowledge and eventually become more creative in organizational contexts where collaboration is high. Conversely, brokers who arbitrage information across disconnected network contacts acquire more valuable knowledge...

  12. Whole-brain ex-vivo quantitative MRI of the cuprizone mouse model

    Directory of Open Access Journals (Sweden)

    Tobias C. Wood

    2016-11-01

    Full Text Available Myelin is a critical component of the nervous system and a major contributor to contrast in Magnetic Resonance (MR images. However, the precise contribution of myelination to multiple MR modalities is still under debate. The cuprizone mouse is a well-established model of demyelination that has been used in several MR studies, but these have often imaged only a single slice and analysed a small region of interest in the corpus callosum. We imaged and analyzed the whole brain of the cuprizone mouse ex-vivo using high-resolution quantitative MR methods (multi-component relaxometry, Diffusion Tensor Imaging (DTI and morphometry and found changes in multiple regions, including the corpus callosum, cerebellum, thalamus and hippocampus. The presence of inflammation, confirmed with histology, presents difficulties in isolating the sensitivity and specificity of these MR methods to demyelination using this model.

  13. The Whole-Brain “Global” Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism

    Science.gov (United States)

    Thompson, Garth J.; Grimmer, Timo; Drzezga, Alexander; Herman, Peter

    2016-01-01

    Abstract The evolution of functional magnetic resonance imaging to resting state (R-fMRI) allows measurement of changes in brain networks attributed to state changes, such as in neuropsychiatric diseases versus healthy controls. Since these networks are observed by comparing normalized R-fMRI signals, it is difficult to determine the metabolic basis of such group differences. To investigate the metabolic basis of R-fMRI network differences within a normal range, eyes open versus eyes closed in healthy human subjects was used. R-fMRI was recorded simultaneously with fluoro-deoxyglucose positron emission tomography (FDG-PET). Higher baseline FDG was observed in the eyes open state. Variance-based metrics calculated from R-fMRI did not match the baseline shift in FDG. Functional connectivity density (FCD)-based metrics showed a shift similar to the baseline shift of FDG, however, this was lost if R-fMRI “nuisance signals” were regressed before FCD calculation. Average correlation with the mean R-fMRI signal across the whole brain, generally regarded as a “nuisance signal,” also showed a shift similar to the baseline of FDG. Thus, despite lacking a baseline itself, changes in whole-brain correlation may reflect changes in baseline brain metabolism. Conversely, variance-based metrics may remain similar between states due to inherent region-to-region differences overwhelming the differences between normal physiological states. As most previous studies have excluded the spatial means of R-fMRI metrics from their analysis, this work presents the first evidence of a potential R-fMRI biomarker for baseline shifts in quantifiable metabolism between brain states. PMID:27029438

  14. Whole brain 3D T2-weighted BOLD fMRI at 7T

    Science.gov (United States)

    Hua, Jun; Qin, Qin; van Zijl, Peter C. M.; Pekar, James J.; Jones, Craig K.

    2014-01-01

    Purpose A new acquisition scheme for T2-weighted spin-echo BOLD fMRI is introduced. Methods It employs a T2-preparation module to induce BOLD contrast, followed by a single-shot 3D fast gradient-echo readout with short TE. It differs from most spin-echo BOLD sequences in that BOLD contrast is generated before the readout, which eliminates the “dead time” due to long TE required for T2 contrast, and substantially improves acquisition efficiency. This approach, termed “3D T2prep-GRE”, was implemented at 7T with a typical spatial (2.5×2.5×2.5mm3) and temporal (TR=2.3s) resolution for fMRI and whole-brain coverage (55 slices), and compared with the widely used 2D spin-echo EPI sequence. Results In fMRI experiments of simultaneous visual/motor activities, 3D T2prep-GRE showed minimal distortion and little signal dropout across the whole brain. Its lower power deposition allowed greater spatial coverage (55 versus 17 slices with identical TR, resolution and power level), temporal SNR (60% higher) and CNR (35% higher) efficiency than 2D spin-echo EPI. It also showed smaller T2* contamination. Conclusion This approach is expected to be useful for ultra-high field fMRI, especially for regions near air cavities. The concept of using T2-preparation to generate BOLD contrast can be combined with many other sequences at any field strength. PMID:24338901

  15. Do patients with very few brain metastases from breast cancer benefit from whole-brain radiotherapy in addition to radiosurgery?

    Science.gov (United States)

    Rades, Dirk; Huttenlocher, Stefan; Hornung, Dagmar; Blanck, Oliver; Schild, Steven E; Fischer, Dorothea

    2014-12-04

    An important issue in palliative radiation oncology is the whether whole-brain radiotherapy should be added to radiosurgery when treating a limited number of brain metastases. To optimize personalized treatment of cancer patients with brain metastases, the value of whole-brain radiotherapy should be described separately for each tumor entity. This study investigated the role of whole-brain radiotherapy added to radiosurgery in breast cancer patients. Fifty-eight patients with 1-3 brain metastases from breast cancer were included in this retrospective study. Of these patients, 30 were treated with radiosurgery alone and 28 with radiosurgery plus whole-brain radiotherapy. Both groups were compared for local control of the irradiated metastases, freedom from new brain metastases and survival. Furthermore, eight additional factors were analyzed including dose of radiosurgery, age at radiotherapy, Eastern Cooperative Oncology Group (ECOG) performance score, number of brain metastases, maximum diameter of all brain metastases, site of brain metastases, extra-cranial metastases and the time from breast cancer diagnosis to radiotherapy. The treatment regimen had no significant impact on local control in the univariate analysis (p=0.59). Age ≤59 years showed a trend towards improved local control on univariate (p=0.066) and multivariate analysis (p=0.07). On univariate analysis, radiosurgery plus whole-brain radiotherapy (p=0.040) and ECOG 0-1 (p=0.012) showed positive associations with freedom from new brain metastases. Both treatment regimen (p=0.039) and performance status (p=0.028) maintained significance on multivariate analysis. ECOG 0-1 was positively correlated with survival on univariate analysis (pbreast cancer patients with few brain metastases, radiosurgery plus whole-brain radiotherapy resulted in significantly better freedom from new brain metastases than radiosurgery alone. However, this advantage did not lead to significantly better survival.

  16. Novel MRI methodology to detect human whole-brain connectivity changes after ingestion of fructose or glucose

    Science.gov (United States)

    Tsao, Sinchai; Wilkins, Bryce; Page, Kathleen A.; Singh, Manbir

    2012-03-01

    A novel MRI protocol has been developed to investigate the differential effects of glucose or fructose consumption on whole-brain functional brain connectivity. A previous study has reported a decrease in the fMRI blood oxygen level dependent (BOLD) signal of the hypothalamus following glucose ingestion, but due to technical limitations, was restricted to a single slice covering the hypothalamus, and thus unable to detect whole-brain connectivity. In another previous study, a protocol was devised to acquire whole-brain fMRI data following food intake, but only after restricting image acquisition to an MR sampling or repetition time (TR) of 20s, making the protocol unsuitable to detect functional connectivity above 0.025Hz. We have successfully implemented a continuous 36-min, 40 contiguous slices, whole-brain BOLD acquisition protocol on a 3T scanner with TR=4.5s to ensure detection of up to 0.1Hz frequencies for whole-brain functional connectivity analysis. Human data were acquired first with ingestion of water only, followed by a glucose or fructose drink within the scanner, without interrupting the scanning. Whole-brain connectivity was analyzed using standard correlation methodology in the 0.01-0.1 Hz range. The correlation coefficient differences between fructose and glucose ingestion among targeted regions were converted to t-scores using the water-only correlation coefficients as a null condition. Results show a dramatic increase in the hypothalamic connectivity to the hippocampus, amygdala, insula, caudate and the nucleus accumben for fructose over glucose. As these regions are known to be key components of the feeding and reward brain circuits, these results suggest a preference for fructose ingestion.

  17. Information transfer in community structured multiplex networks

    Science.gov (United States)

    Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

    2015-08-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  18. Information transfer in community structured multiplex networks

    Directory of Open Access Journals (Sweden)

    Albert eSolé Ribalta

    2015-08-01

    Full Text Available The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.. The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  19. A combined solenoid-surface RF coil for high-resolution whole-brain rat imaging on a 3.0 Tesla clinical MR scanner.

    Science.gov (United States)

    Underhill, Hunter R; Yuan, Chun; Hayes, Cecil E

    2010-09-01

    Rat brain models effectively simulate a multitude of human neurological disorders. Improvements in coil design have facilitated the wider utilization of rat brain models by enabling the utilization of clinical MR scanners for image acquisition. In this study, a novel coil design, subsequently referred to as the rat brain coil, is described that exploits and combines the strengths of both solenoids and surface coils into a simple, multichannel, receive-only coil dedicated to whole-brain rat imaging on a 3.0 T clinical MR scanner. Compared with a multiturn solenoid mouse body coil, a 3-cm surface coil, a modified Helmholtz coil, and a phased-array surface coil, the rat brain coil improved signal-to-noise ratio by approximately 72, 61, 78, and 242%, respectively. Effects of the rat brain coil on amplitudes of static field and radiofrequency field uniformity were similar to each of the other coils. In vivo, whole-brain images of an adult male rat were acquired with a T(2)-weighted spin-echo sequence using an isotropic acquisition resolution of 0.25 x 0.25 x 0.25 mm(3) in 60.6 min. Multiplanar images of the in vivo rat brain with identification of anatomic structures are presented. Improvement in signal-to-noise ratio afforded by the rat brain coil may broaden experiments that utilize clinical MR scanners for in vivo image acquisition. 2010 Wiley-Liss, Inc.

  20. Information transfer in community structured multiplex networks

    CERN Document Server

    Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex

    2015-01-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer ...

  1. The Influence Of Implementation Brain-Friendly Learning Through The Whole Brain Teaching To Students’ Response and Creative Character In Learning Mathematics

    OpenAIRE

    Winarso, Widodo; Karimah, Siti Asri

    2017-01-01

    This study aims to determine whether the application of brain-friendly learning through whole brain teaching gives a positive effect on the creative character of students, to know the response of the students against the application of brain-friendly learning through whole brain teaching, and to find out if the student response against the application of brain-friendly learning through whole brain teaching correlates positively with the creative character of students in learning mathematics. ...

  2. Industrial entrepreneurial network: Structural and functional analysis

    Science.gov (United States)

    Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.

    2016-12-01

    Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.

  3. THE COMMERCIAL BANK AS NETWORK STRUCTURE

    Directory of Open Access Journals (Sweden)

    D. O. Dyl

    2010-05-01

    Full Text Available The article examines the problems of the modern enterprise as a network structure that meets the increasing processes of globalization and the rise of postmodern trends. The definition of the term «a network of commercial bank» and the main characteristics of such a definition are given.

  4. Learning Bayesian Network Model Structure from Data

    National Research Council Canada - National Science Library

    Margaritis, Dimitris

    2003-01-01

    In this thesis I address the important problem of the determination of the structure of directed statistical models, with the widely used class of Bayesian network models as a concrete vehicle of my ideas...

  5. Nonparametric inference of network structure and dynamics

    Science.gov (United States)

    Peixoto, Tiago P.

    The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among

  6. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Soda, Giuseppe; Stea, Diego; Pedersen, Torben

    2017-01-01

    attitude on the part of the embedded actors and propose that the level of collaboration in a network can be independent from that network’s structural characteristics, such that it moderates the effects of closed and brokering network positions on the acquisition of knowledge that supports creativity....... Individuals embedded in closed networks acquire more knowledge and become more creative when the level of collaboration in their network is high. Brokers who arbitrage information across disconnected contacts acquire more knowledge and become more creative when collaboration is low. An analysis of employee...

  7. In vivo quantitative whole-brain diffusion tensor imaging analysis of APP/PS1 transgenic mice using voxel-based and atlas-based methods

    Energy Technology Data Exchange (ETDEWEB)

    Qin, Yuan-Yuan [Huazhong University of Science and Technology, Department of Radiology, Tongji Hospital, Tongji Medical College, Wuhan (China); The Johns Hopkins University School of Medicine, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, MD (United States); Li, Mu-Wei; Oishi, Kenichi [The Johns Hopkins University School of Medicine, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, MD (United States); Zhang, Shun; Zhang, Yan; Zhao, Ling-Yun; Zhu, Wen-Zhen [Huazhong University of Science and Technology, Department of Radiology, Tongji Hospital, Tongji Medical College, Wuhan (China); Lei, Hao [Chinese Academy of Sciences, Wuhan Center for Magnetic Resonance, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Wuhan (China)

    2013-08-15

    Diffusion tensor imaging (DTI) has been applied to characterize the pathological features of Alzheimer's disease (AD) in a mouse model, although little is known about whether these features are structure specific. Voxel-based analysis (VBA) and atlas-based analysis (ABA) are good complementary tools for whole-brain DTI analysis. The purpose of this study was to identify the spatial localization of disease-related pathology in an AD mouse model. VBA and ABA quantification were used for the whole-brain DTI analysis of nine APP/PS1 mice and wild-type (WT) controls. Multiple scalar measurements, including fractional anisotropy (FA), trace, axial diffusivity (DA), and radial diffusivity (DR), were investigated to capture the various types of pathology. The accuracy of the image transformation applied for VBA and ABA was evaluated by comparing manual and atlas-based structure delineation using kappa statistics. Following the MR examination, the brains of the animals were analyzed for microscopy. Extensive anatomical alterations were identified in APP/PS1 mice, in both the gray matter areas (neocortex, hippocampus, caudate putamen, thalamus, hypothalamus, claustrum, amygdala, and piriform cortex) and the white matter areas (corpus callosum/external capsule, cingulum, septum, internal capsule, fimbria, and optic tract), evidenced by an increase in FA or DA, or both, compared to WT mice (p < 0.05, corrected). The average kappa value between manual and atlas-based structure delineation was approximately 0.8, and there was no significant difference between APP/PS1 and WT mice (p > 0.05). The histopathological changes in the gray matter areas were confirmed by microscopy studies. DTI did, however, demonstrate significant changes in white matter areas, where the difference was not apparent by qualitative observation of a single-slice histological specimen. This study demonstrated the structure-specific nature of pathological changes in APP/PS1 mouse, and also showed the

  8. The Deep Structure of Organizational Online Networking

    DEFF Research Database (Denmark)

    Trier, Matthias; Richter, Alexander

    2015-01-01

    While research on organizational online networking recently increased significantly, most studies adopt quantitative research designs with a focus on the consequences of social network configurations. Very limited attention is paid to comprehensive theoretical conceptions of the complex phenomenon...... of organizational online networking. We address this gap by adopting a theoretical framework of the deep structure of organizational online networking with a focus on their emerging meaning for the employees. We apply and assess the framework in a qualitative case study of a large-scale implementation...... of a corporate social network site (SNS) in a global organization. We reveal organizational online networking as a multi-dimensional phenomenon with multiplex relationships that are unbalanced, primarily consist of weak ties and are subject to temporal change. Further, we identify discourse drivers...

  9. Modelling the structure of complex networks

    DEFF Research Database (Denmark)

    Herlau, Tue

    networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex......A complex network is a systems in which a discrete set of units interact in a quantifiable manner. Representing systems as complex networks have become increasingly popular in a variety of scientific fields including biology, social sciences and economics. Parallel to this development complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

  10. Fundamental structures of dynamic social networks

    DEFF Research Database (Denmark)

    Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann

    2016-01-01

    , and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals...... and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection...... a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework...

  11. Efficiency and prognosis of whole brain irradiation combined with precise radiotherapy on triple-negative breast cancer

    Directory of Open Access Journals (Sweden)

    Xinhong Wu

    2013-01-01

    Conclusion: After whole brain irradiation followed by IMRT or 3DCRT treatment, TN phenotype breast cancer patients with intracranial metastasis had high objective response rates but shorter survival time. With respect to survival in breast cancer patients with intracranial metastasis, the TN phenotype represents a significant adverse prognostic factor.

  12. Comparison of short-course versus long-course whole-brain radiotherapy in the treatment of brain metastases

    NARCIS (Netherlands)

    Rades, Dirk; Bohlen, Guenther; Dunst, Juergen; Lohynska, Radka; Veninga, Theo; Stalpers, Lukas; Schild, Steven E.; Dahm-Daphi, Jochen

    2008-01-01

    Whole-brain radiotherapy (WBRT) is the most common treatment for brain metastases. Most of these patients have a poor survival prognosis. Therefore, a short radiation program is preferred, if it provides a similar outcome as longer programs. This study compares 20 Gy in five fractions (treatment

  13. Structural measures for multiplex networks.

    Science.gov (United States)

    Battiston, Federico; Nicosia, Vincenzo; Latora, Vito

    2014-03-01

    Many real-world complex systems consist of a set of elementary units connected by relationships of different kinds. All such systems are better described in terms of multiplex networks, where the links at each layer represent a different type of interaction between the same set of nodes rather than in terms of (single-layer) networks. In this paper we present a general framework to describe and study multiplex networks, whose links are either unweighted or weighted. In particular, we propose a series of measures to characterize the multiplexicity of the systems in terms of (i) basic node and link properties such as the node degree, and the edge overlap and reinforcement, (ii) local properties such as the clustering coefficient and the transitivity, and (iii) global properties related to the navigability of the multiplex across the different layers. The measures we introduce are validated on a genuinely multiplex data set of Indonesian terrorists, where information among 78 individuals are recorded with respect to mutual trust, common operations, exchanged communications, and business relationships.

  14. The evaluation of lens absorbed dose according to the optimold for whole brain radiation therapy

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Yong Mo; Park, Byoung Suk; Ahn, Jong Ho; Song, Ki Won [Dept. of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

    2014-06-15

    In the current whole brain Radiation Therapy, Optimold was used to immobilize the head. However, skin dose was increased about 22% due to the scattering radiation by the Optimold. Since the minimum dose causing cataracts was 2 Gy, it could be seen that the effects were large especially on the lens. Therefore, in the whole brain Radiation Therapy, it was to compare and to evaluate the lens absorbed dose according to the presence of Optimold in the eyeball part. In order to compare and to evaluate the lens absorbed dose according to the presence of Optimold in the eyeball part, the Optimold mask was made up to 5 mm bolus on the part of the eye lens in the human model phantom (Anderson Rando Phantom, USA). In the practice treatment, to measure the lens dose, the simulation therapy was processed by placing the GafChromic EBT3 film under bolus, and after the treatment plan was set up through the treatment planning system (Pinnacle, PHILIPS, USA), the treatments were measured repeatedly three times in the same way. After removing the Optimold mask in the eyeball part, it was measured in the same way as above. After scanning the film and measuring the dose by using the Digital Flatbed Scanner (Expression 10000XL, EPSON, USA), the doses were compared and evaluated according to the presence of Optimold mask in the eyeball part. When there was the Optimold mask in the eyeball part, it was measured at 10.2cGy ± 1.5 in the simulation therapy, and at 24.8cGy ± 2.7 in the treatment, and when the Optimold mask was removed in the eye part, it was measured at 12.9cGy ± 2.2 in the simulation therapy, and at 17.6cGy ± 1.5 in the treatment. In case of removing the Optimold mask in the eyeball part, the dose was increased approximately 3cGy in the simulation therapy and was reduced approximately 7cGy in the treatment in comparison to the case that the Optimold mask was not removed. During the whole treatment, since the lens absorbed dose was reduced about 27%, the chance to cause

  15. Controlling congestion on complex networks: fairness, efficiency and network structure.

    Science.gov (United States)

    Buzna, Ľuboš; Carvalho, Rui

    2017-08-22

    We consider two elementary (max-flow and uniform-flow) and two realistic (max-min fairness and proportional fairness) congestion control schemes, and analyse how the algorithms and network structure affect throughput, the fairness of flow allocation, and the location of bottleneck edges. The more realistic proportional fairness and max-min fairness algorithms have similar throughput, but path flow allocations are more unequal in scale-free than in random regular networks. Scale-free networks have lower throughput than their random regular counterparts in the uniform-flow algorithm, which is favoured in the complex networks literature. We show, however, that this relation is reversed on all other congestion control algorithms for a region of the parameter space given by the degree exponent γ and average degree 〈k〉. Moreover, the uniform-flow algorithm severely underestimates the network throughput of congested networks, and a rich phenomenology of path flow allocations is only present in the more realistic α-fair family of algorithms. Finally, we show that the number of paths passing through an edge characterises the location of a wide range of bottleneck edges in these algorithms. Such identification of bottlenecks could provide a bridge between the two fields of complex networks and congestion control.

  16. Colony-stimulating factor 1 receptor blockade prevents fractionated whole-brain irradiation-induced memory deficits.

    Science.gov (United States)

    Feng, Xi; Jopson, Timothy D; Paladini, Maria Serena; Liu, Sharon; West, Brian L; Gupta, Nalin; Rosi, Susanna

    2016-08-30

    Primary central nervous system (CNS) neoplasms and brain metastases are routinely treated with whole-brain radiation. Long-term survival occurs in many patients, but their quality of life is severely affected by the development of cognitive deficits, and there is no treatment to prevent these adverse effects. Neuroinflammation, associated with activation of brain-resident microglia and infiltrating monocytes, plays a pivotal role in loss of neurological function and has been shown to be associated with acute and long-term effects of brain irradiation. Colony-stimulating factor 1 receptor (CSF-1R) signaling is essential for the survival and differentiation of microglia and monocytes. Here, we tested the effects of CSF-1R blockade by PLX5622 on cognitive function in mice treated with three fractions of 3.3 Gy whole-brain irradiation. Young adult C57BL/6J mice were given three fractions of 3.3 Gy whole-brain irradiation while they were on diet supplemented with PLX5622, and the effects on periphery monocyte accumulation, microglia numbers, and neuronal functions were assessed. The mice developed hippocampal-dependent cognitive deficits at 1 and 3 months after they received fractionated whole-brain irradiation. The impaired cognitive function correlated with increased number of periphery monocyte accumulation in the CNS and decreased dendritic spine density in hippocampal granule neurons. PLX5622 treatment caused temporary reduction of microglia numbers, inhibited monocyte accumulation in the brain, and prevented radiation-induced cognitive deficits. Blockade of CSF-1R by PLX5622 prevents fractionated whole-brain irradiation-induced memory deficits. Therapeutic targeting of CSF-1R may provide a new avenue for protection from radiation-induced memory deficits.

  17. Structure and function of complex brain networks

    Science.gov (United States)

    Sporns, Olaf

    2013-01-01

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

  18. Whole-brain grey matter density predicts balance stability irrespective of age and protects older adults from falling.

    Science.gov (United States)

    Boisgontier, Matthieu P; Cheval, Boris; van Ruitenbeek, Peter; Levin, Oron; Renaud, Olivier; Chanal, Julien; Swinnen, Stephan P

    2016-03-01

    Functional and structural imaging studies have demonstrated the involvement of the brain in balance control. Nevertheless, how decisive grey matter density and white matter microstructural organisation are in predicting balance stability, and especially when linked to the effects of ageing, remains unclear. Standing balance was tested on a platform moving at different frequencies and amplitudes in 30 young and 30 older adults, with eyes open and with eyes closed. Centre of pressure variance was used as an indicator of balance instability. The mean density of grey matter and mean white matter microstructural organisation were measured using voxel-based morphometry and diffusion tensor imaging, respectively. Mixed-effects models were built to analyse the extent to which age, grey matter density, and white matter microstructural organisation predicted balance instability. Results showed that both grey matter density and age independently predicted balance instability. These predictions were reinforced when the level of difficulty of the conditions increased. Furthermore, grey matter predicted balance instability beyond age and at least as consistently as age across conditions. In other words, for balance stability, the level of whole-brain grey matter density is at least as decisive as being young or old. Finally, brain grey matter appeared to be protective against falls in older adults as age increased the probability of losing balance in older adults with low, but not moderate or high grey matter density. No such results were observed for white matter microstructural organisation, thereby reinforcing the specificity of our grey matter findings. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. How structure determines correlations in neuronal networks.

    Directory of Open Access Journals (Sweden)

    Volker Pernice

    2011-05-01

    Full Text Available Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.

  20. Structural Connectivity Networks of Transgender People

    NARCIS (Netherlands)

    Hahn, Andreas; Kranz, Georg S; Küblböck, Martin; Kaufmann, Ulrike; Ganger, Sebastian; Hummer, Allan; Seiger, Rene; Spies, Marie; Winkler, Dietmar; Kasper, Siegfried; Windischberger, Christian; Swaab, Dick F; Lanzenberger, Rupert

    2015-01-01

    Although previous investigations of transsexual people have focused on regional brain alterations, evaluations on a network level, especially those structural in nature, are largely missing. Therefore, we investigated the structural connectome of 23 female-to-male (FtM) and 21 male-to-female (MtF)

  1. A Topological Perspective of Neural Network Structure

    Science.gov (United States)

    Sizemore, Ann; Giusti, Chad; Cieslak, Matthew; Grafton, Scott; Bassett, Danielle

    The wiring patterns of white matter tracts between brain regions inform functional capabilities of the neural network. Indeed, densely connected and cyclically arranged cognitive systems may communicate and thus perform distinctly. However, previously employed graph theoretical statistics are local in nature and thus insensitive to such global structure. Here we present an investigation of the structural neural network in eight healthy individuals using persistent homology. An extension of homology to weighted networks, persistent homology records both circuits and cliques (all-to-all connected subgraphs) through a repetitive thresholding process, thus perceiving structural motifs. We report structural features found across patients and discuss brain regions responsible for these patterns, finally considering the implications of such motifs in relation to cognitive function.

  2. Whole-Brain Monosynaptic Afferent Inputs to Basal Forebrain Cholinergic System

    Directory of Open Access Journals (Sweden)

    Rongfeng Hu

    2016-10-01

    Full Text Available The basal forebrain cholinergic system (BFCS robustly modulates many important behaviors, such as arousal, attention, learning and memory, through heavy projections to cortex and hippocampus. However, the presynaptic partners governing BFCS activity still remain poorly understood. Here, we utilized a recently developed rabies virus-based cell-type-specific retrograde tracing system to map the whole-brain afferent inputs of the BFCS. We found that the BFCS receives inputs from multiple cortical areas, such as orbital frontal cortex, motor cortex, and insular cortex, and that the BFCS also receives dense inputs from several subcortical nuclei related to motivation and stress, including lateral septum (LS, central amygdala (CeA, paraventricular nucleus of hypothalamus (PVH, dorsal raphe (DRN and parabrachial nucleus (PBN. Interestingly, we found that the BFCS receives inputs from the olfactory areas and the entorhinal-hippocampal system. These results greatly expand our knowledge about the connectivity of the mouse BFCS and provided important preliminary indications for future exploration of circuit function.

  3. Neurocognitive function impairment after whole brain radiotherapy for brain metastases: actual assessment

    Directory of Open Access Journals (Sweden)

    Tallet Agnes V

    2012-05-01

    Full Text Available Abstract Whole brain radiation therapy (WBRT is an effective treatment in brain metastases and, when combined with local treatments such as surgery and stereotactic radiosurgery, gives the best brain control. Nonetheless, WBRT is often omitted after local treatment due to its potential late neurocognitive effects. Publications on radiation-induced neurotoxicity have used different assessment methods, time to assessment, and definition of impairment, thus making it difficult to accurately assess the rate and magnitude of the neurocognitive decline that can be expected. In this context, and to help therapeutic decision making, we have conducted this literature review, with the aim of providing an average incidence, magnitude and time to occurrence of radio-induced neurocognitive decline. We reviewed all English language published articles on neurocognitive effects of WBRT for newly diagnosed brain metastases or with a preventive goal in adult patients, with any methodology (MMSE, battery of neurcognitive tests with which baseline status was provided. We concluded that neurocognitive decline is predominant at 4 months, strongly dependant on brain metastases control, partially solved at later time, graded 1 on a SOMA-LENT scale (only 8% of grade 2 and more, insufficiently assessed in long-term survivors, thus justifying all efforts to reduce it through irradiation modulation.

  4. A novel ex vivo method for measuring whole brain metabolism in model systems.

    Science.gov (United States)

    Neville, Kathryn E; Bosse, Timothy L; Klekos, Mia; Mills, John F; Weicksel, Steven E; Waters, James S; Tipping, Marla

    2018-02-15

    Many neuronal and glial diseases have been associated with changes in metabolism. Therefore, metabolic reprogramming has become an important area of research to better understand disease at the cellular level, as well as to identify targets for treatment. Model systems are ideal for interrogating metabolic questions in a tissue dependent context. However, while new tools have been developed to study metabolism in cultured cells there has been less progress towards studies in vivo and ex vivo. We have developed a method using newly designed tissue restraints to adapt the Agilent XFe96 metabolic analyzer for whole brain analysis. These restraints create a chamber for Drosophila brains and other small model system tissues to reside undisrupted, while still remaining in the zone for measurements by sensor probes. This method generates reproducible oxygen consumption and extracellular acidification rate data for Drosophila larval and adult brains. Single brains are effectively treated with inhibitors and expected metabolic readings are observed. Measuring metabolic changes, such as glycolytic rate, in transgenic larval brains demonstrates the potential for studying how genotype affects metabolism. Current methodology either utilizes whole animal chambers to measure respiration, not allowing for targeted tissue analysis, or uses technically challenging MRI technology for in vivo analysis that is not suitable for smaller model systems. This new method allows for novel metabolic investigation of intact brains and other tissues ex vivo in a quick, and simplistic way with the potential for large-scale studies. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  5. Whole brain magnetization transfer histogram analysis of pediatric acute lymphoblastic leukemia patients receiving intrathecal methotrexate therapy

    Energy Technology Data Exchange (ETDEWEB)

    Yamamoto, Akira [Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto-shi Kyoto 606-8507 (Japan)]. E-mail: yakira@kuhp.kyoto-u.ac.jp; Miki, Yukio [Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto-shi Kyoto 606-8507 (Japan)]. E-mail: mikiy@kuhp.kyoto-u.ac.jp; Adachi, Souichi [Department of Pediatrics, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto-shi Kyoto 606-8507 (Japan)]. E-mail: sadachi@kuhp.kyoto-u.ac.jp (and others)

    2006-03-15

    Background and purpose: The purpose of this prospective study was to evaluate the hypothesis that magnetization transfer ratio (MTR) histogram analysis of the whole brain could detect early and subtle brain changes nonapparent on conventional magnetic resonance imaging (MRI) in children with acute lymphoblastic leukemia (ALL) receiving methotrexate (MTX) therapy. Materials and methods: Subjects in this prospective study comprised 10 children with ALL (mean age, 6 years; range, 0-16 years). In addition to conventional MRI, magnetization transfer images were obtained before and after intrathecal and intravenous MTX therapy. MTR values were calculated and plotted as a histogram, and peak height and location were calculated. Differences in peak height and location between pre- and post-MTX therapy scans were statistically analyzed. Conventional MRI was evaluated for abnormal signal area in white matter. Results: MTR peak height was significantly lower on post-MTX therapy scans than on pre-MTX therapy scans (p = 0.002). No significant differences in peak location were identified between pre- and post-chemotherapy imaging. No abnormal signals were noted in white matter on either pre- or post-MTX therapy conventional MRI. Conclusions: This study demonstrates that MTR histogram analysis allows better detection of early and subtle brain changes in ALL patients who receive MTX therapy than conventional MRI.

  6. Whole-brain mapping of neuronal activity in the learned helplessness model of depression

    Directory of Open Access Journals (Sweden)

    Yongsoo eKim

    2016-02-01

    Full Text Available Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing helpless behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing resilient behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

  7. Whole-Brain Susceptibility-Weighted Thrombus Imaging in Stroke: Fragmented Thrombi Predict Worse Outcome.

    Science.gov (United States)

    Gratz, P P; Schroth, G; Gralla, J; Mattle, H P; Fischer, U; Jung, S; Mordasini, P; Hsieh, K; Verma, R K; Weisstanner, C; El-Koussy, M

    2015-07-01

    The prevalence and clinical importance of primarily fragmented thrombi in patients with acute ischemic stroke remains elusive. Whole-brain SWI was used to detect multiple thrombus fragments, and their clinical significance was analyzed. Pretreatment SWI was analyzed for the presence of a single intracranial thrombus or multiple intracranial thrombi. Associations with baseline clinical characteristics, complications, and clinical outcome were studied. Single intracranial thrombi were detected in 300 (92.6%), and multiple thrombi, in 24 of 324 patients (7.4%). In 23 patients with multiple thrombi, all thrombus fragments were located in the vascular territory distal to the primary occluding thrombus; in 1 patient, thrombi were found both in the anterior and posterior circulation. Only a minority of thrombus fragments were detected on TOF-MRA, first-pass gadolinium-enhanced MRA, or DSA. Patients with multiple intracranial thrombi presented with more severe symptoms (median NIHSS scores, 15 versus 11; P = .014) and larger ischemic areas (median DWI ASPECTS, 5 versus 7; P = .006); good collaterals, rated on DSA, were fewer than those in patients with a single thrombus (21.1% versus 44.2%, P = .051). The presence of multiple thrombi was a predictor of unfavorable outcome at 3 months (P = .040; OR, 0.251; 95% CI, 0.067-0.939). Patients with multiple intracranial thrombus fragments constitute a small subgroup of patients with stroke with a worse outcome than patients with single thrombi. © 2015 by American Journal of Neuroradiology.

  8. Colony stimulating factor-1 receptor as a treatment for cognitive deficits postfractionated whole-brain irradiation

    Directory of Open Access Journals (Sweden)

    Susanna Rosi

    2017-01-01

    Full Text Available Whole-brain irradiation (WBI is commonly used to treat primary tumors of the central nervous systems tumors as well as brain metastases. While this technique has increased survival among brain tumor patients, the side effects of including a decline in cognitive abilities that are generally progressive. In an effort to combat WBI side effects, researchers explored the treatment of colony stimulating factor-1 receptor (CSF-1R inhibitor. Data show that when a CSF-1R inhibitor is administered with fractionated WBI treatment, there is a decline in the number of resident and peripheral mononuclear phagocytes, a decrease in dendritic spine loss and a reduction in functional and memory deficits. CSFR-1R inhibitors have displayed promising results as an effective counter-treatment for WBI-induced deficits. Further research is required to optimize treatment strategies, establish a treatment timeline and gain a better understanding of the long-term side effects of targeting CSF-1R as a treatment strategy for WBI symptoms. This paper is a review article. Referred literature in this paper has been listed in the references section. The datasets supporting the conclusions of this article are available online by searching various databases, including PubMed. Some original points in this article come from the laboratory practice in our research center and the authors' experiences.

  9. Cognitive dysfunction and histological findings in adult rats one year after whole brain irradiation

    Energy Technology Data Exchange (ETDEWEB)

    Akiyama, Katsuhiko; Tanaka, Ryuichi; Sato, Mitsuya; Takeda, Norio [Niigata Univ. (Japan). Brain Research Inst.

    2001-12-01

    Cognitive dysfunction and histological changes in the brain were investigated following irradiation in 20 Fischer 344 rats aged 6 months treated with whole brain irradiation (WBR) (25 Gy/single dose), and compared with the same number of sham-irradiated rats as controls. Performance of the Morris water maze task and the passive avoidance task were examined one year after WBR. Finally, histological and immunohistochemical examinations using antibodies to myelin basic protein (MBP), glial fibrillary acidic protein (GFAP), and neurofilament (NF) were performed of the rat brains. The irradiated rats continued to gain weight 7 months after WBR whereas the control rats stopped gaining weight. Cognitive functions in both the water maze task and the passive avoidance task were lower in the irradiated rats than in the control rats. Brain damage consisting of demyelination only or with necrosis was found mainly in the body of the corpus callosum and the parietal white matter near the corpus callosum in the irradiated rats. Immunohistochemical examination of the brains without necrosis found MBP-positive fibers were markedly decreased in the affected areas by irradiation; NF-positive fibers were moderately decreased and irregularly dispersed in various shapes in the affected areas; and GFAP-positive fibers were increased, with gliosis in those areas. These findings are similar to those in clinically accelerated brain aging in conditions such as Alzheimer's disease, Binswanger's disease, and multiple sclerosis. (author)

  10. Overall Survival After Whole-Brain Radiation Therapy for Intracerebral Metastases from Testicular Cancer.

    Science.gov (United States)

    Rades, Dirk; Dziggel, Liesa; Veninga, Theo; Bajrovic, Amira; Schild, Steven E

    2016-09-01

    To identify predictors and develop a score for overall survival of patients with intracerebral metastasis from testicular cancer. Whole-brain radiation therapy program, age, Karnofsky performance score (KPS), number of intracerebral metastases, number of other metastatic sites and time between testicular cancer diagnosis and radiation therapy were analyzed for their association with overall survival in eight patients. KPS of 80-90% was significantly associated with better overall survival (p=0.006), one or no other metastatic sites showed a trend for a better outcome (p=0.10). The following scores were assigned: KPS 60-70%=0 points, KPS 80-90%=1 point, ≥2 other metastatic sites=0 points, 0-1 other metastatic sites=1 point. Two groups, with 0 and with 1-2 points, were formed. Overall survival rates were 33% vs. 100% at 6 months and 0% vs. 100% at 12 months (p=0.006), respectively. A simple instrument enabling physicians to judge the overall survival of patients with intracerebral metastasis from testicular cancer is provided. Copyright© 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

  11. Outcome after whole brain radiotherapy alone in intracranial leptomeningeal carcinomatosis from solid tumors

    Energy Technology Data Exchange (ETDEWEB)

    Gani, C.; Mueller, A.C.; Eckert, F.; Schroeder, C.; Bamberg, M.; Berger, B. [Univ. of Tuebingen (Germany). Dept. of Radiation Oncology; Bender, B. [Univ. of Tuebingen (Germany). Dept. of Diagnostics and Interventional Neuroradiology; Pantazis, G. [Univ. of Tuebingen (Germany). Dept. of Neuropathology

    2012-02-15

    The purpose of the present study was to investigate outcome after whole brain radiotherapy (WBRT) alone as a palliative treatment without concomitant chemotherapy for intracranial leptomeningeal carcinomatosis (LMC). Overall survival and treatment response were retrospectively analyzed in 27 consecutive patients with LMC from breast and lung cancer. All patients had evidence of intracranial manifestations of LMC. Seven potential prognostic factors were evaluated. Median overall survival (OS) for the entire group was 8.1 weeks. OS rates after 6 and 12 months were 26% and 15%, respectively. Improvement of neurological deficits was observed in 3 patients. In 3 of 4 patients with follow-up MRI studies, a decreased size of contrast-enhanced lesions was observed. Prognostic factors for improved OS on univariate analysis were absence of cranial nerve dysfunction, Karnofsky Performance Score (KPS) > 60%, and time interval > 35 months between the initial diagnosis of malignant disease and development of LMC. On multivariate analysis, absence of cranial nerve dysfunction remained the only significant prognosticator for OS (median 3.7 vs. 19.4 weeks, p < 0.001). WBRT alone is an effective palliative treatment for patients unfit/unsuitable for chemotherapy and low performance status suffering from intracranial LMC. However, prognostic factors should be considered in order to identify patients who are likely to benefit from WBRT. (orig.)

  12. Pathology of fractionated whole-brain irradiation in rhesus monkeys ( Macaca mulatta ).

    Science.gov (United States)

    Hanbury, David B; Robbins, Mike E; Bourland, J Daniel; Wheeler, Kenneth T; Peiffer, Ann M; Mitchell, Erin L; Daunais, James B; Deadwyler, Samuel A; Cline, J Mark

    2015-03-01

    Fractionated whole-brain irradiation (fWBI), used to treat brain metastases, often leads to neurologic injury and cognitive impairment. The cognitive effects of irradiation in nonhuman primates (NHP) have been previously published; this report focuses on corresponding neuropathologic changes that could have served as the basis for those effects in the same study. Four rhesus monkeys were exposed to 40 Gy of fWBI [5 Gy × 8 fraction (fx), 2 fx/week for four weeks] and received anatomical MRI prior to, and 14 months after fWBI. Neurologic and histologic sequelae were studied posthumously. Three of the NHPs underwent cognitive assessments, and each exhibited radiation-induced impairment associated with various degrees of vascular and inflammatory neuropathology. Two NHPs had severe multifocal necrosis of the forebrain, midbrain and brainstem. Histologic and MRI findings were in agreement, and the severity of cognitive decrement previously reported corresponded to the degree of observed pathology in two of the animals. In response to fWBI, the NHPs showed pathology similar to humans exposed to radiation and show comparable cognitive decline. These results provide a basis for implementing NHPs to examine and treat adverse cognitive and neurophysiologic sequelae of radiation exposure in humans.

  13. Structure formation in active networks

    CERN Document Server

    Köhler, Simone; Bausch, Andreas R

    2011-01-01

    Structure formation and constant reorganization of the actin cytoskeleton are key requirements for the function of living cells. Here we show that a minimal reconstituted system consisting of actin filaments, crosslinking molecules and molecular-motor filaments exhibits a generic mechanism of structure formation, characterized by a broad distribution of cluster sizes. We demonstrate that the growth of the structures depends on the intricate balance between crosslinker-induced stabilization and simultaneous destabilization by molecular motors, a mechanism analogous to nucleation and growth in passive systems. We also show that the intricate interplay between force generation, coarsening and connectivity is responsible for the highly dynamic process of structure formation in this heterogeneous active gel, and that these competing mechanisms result in anomalous transport, reminiscent of intracellular dynamics.

  14. Identifying community structure in complex networks

    Science.gov (United States)

    Shao, Chenxi; Duan, Yubing

    2015-07-01

    A wide variety of applications could be formulated to resolve the problem of finding all communities from a given network, ranging from social and biological network analysis to web mining and searching. In this study, we propose the concept of virtual attractive strength between each pair of node in networks, and then give the definition of community structure based on the proposed attractive strength. Furthermore, we present a community detection method by moving vertices to the clusters that produce the largest attractive strengths to them until the division of network reaches unchanged. Experimental results on synthetic and real networks indicate that the proposed approach has favorite effectiveness and fast convergence speed, which provides an efficient method for exploring and analyzing complex systems.

  15. Diffusion tractography and graph theory analysis reveal the disrupted rich-club organization of white matter structural networks in early Tourette Syndrome children

    Science.gov (United States)

    Wen, Hongwei; Liu, Yue; Wang, Shengpei; Zhang, Jishui; Peng, Yun; He, Huiguang

    2017-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. At present, the topological disruptions of the whole brain white matter (WM) structural networks remain poorly understood in TS children. Considering the unique position of the topologically central role of densely interconnected brain hubs, namely the rich club regions, therefore, we aimed to investigate whether the rich club regions and their related connections would be particularly vulnerable in early TS children. In our study, we used diffusion tractography and graph theoretical analyses to explore the rich club structures in 44 TS children and 48 healthy children. The structural networks of TS children exhibited significantly increased normalized rich club coefficient, suggesting that TS is characterized by increased structural integrity of this centrally embedded rich club backbone, potentially resulting in increased global communication capacity. In addition, TS children showed a reorganization of rich club regions, as well as significantly increased density and decreased number in feeder connections. Furthermore, the increased rich club coefficients and feeder connections density of TS children were significantly positively correlated to tic severity, indicating that TS may be characterized by a selective alteration of the structural connectivity of the rich club regions, tending to have higher bridging with non-rich club regions, which may increase the integration among tic-related brain circuits with more excitability but less inhibition for information exchanges between highly centered brain regions and peripheral areas. In all, our results suggest the disrupted rich club organization in early TS children and provide structural insights into the brain networks.

  16. Community Structure in Online Collegiate Social Networks

    Science.gov (United States)

    Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

    2009-03-01

    Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

  17. Impulsivity and the modular organization of resting-state neural networks.

    Science.gov (United States)

    Davis, F Caroline; Knodt, Annchen R; Sporns, Olaf; Lahey, Benjamin B; Zald, David H; Brigidi, Bart D; Hariri, Ahmad R

    2013-06-01

    Impulsivity is a complex trait associated with a range of maladaptive behaviors, including many forms of psychopathology. Previous research has implicated multiple neural circuits and neurotransmitter systems in impulsive behavior, but the relationship between impulsivity and organization of whole-brain networks has not yet been explored. Using graph theory analyses, we characterized the relationship between impulsivity and the functional segregation ("modularity") of the whole-brain network architecture derived from resting-state functional magnetic resonance imaging (fMRI) data. These analyses revealed remarkable differences in network organization across the impulsivity spectrum. Specifically, in highly impulsive individuals, regulatory structures including medial and lateral regions of the prefrontal cortex were isolated from subcortical structures associated with appetitive drive, whereas these brain areas clustered together within the same module in less impulsive individuals. Further exploration of the modular organization of whole-brain networks revealed novel shifts in the functional connectivity between visual, sensorimotor, cortical, and subcortical structures across the impulsivity spectrum. The current findings highlight the utility of graph theory analyses of resting-state fMRI data in furthering our understanding of the neurobiological architecture of complex behaviors.

  18. Impulsivity and the Modular Organization of Resting-State Neural Networks

    Science.gov (United States)

    Davis, F. Caroline; Knodt, Annchen R.; Sporns, Olaf; Lahey, Benjamin B.; Zald, David H.; Brigidi, Bart D.; Hariri, Ahmad R.

    2013-01-01

    Impulsivity is a complex trait associated with a range of maladaptive behaviors, including many forms of psychopathology. Previous research has implicated multiple neural circuits and neurotransmitter systems in impulsive behavior, but the relationship between impulsivity and organization of whole-brain networks has not yet been explored. Using graph theory analyses, we characterized the relationship between impulsivity and the functional segregation (“modularity”) of the whole-brain network architecture derived from resting-state functional magnetic resonance imaging (fMRI) data. These analyses revealed remarkable differences in network organization across the impulsivity spectrum. Specifically, in highly impulsive individuals, regulatory structures including medial and lateral regions of the prefrontal cortex were isolated from subcortical structures associated with appetitive drive, whereas these brain areas clustered together within the same module in less impulsive individuals. Further exploration of the modular organization of whole-brain networks revealed novel shifts in the functional connectivity between visual, sensorimotor, cortical, and subcortical structures across the impulsivity spectrum. The current findings highlight the utility of graph theory analyses of resting-state fMRI data in furthering our understanding of the neurobiological architecture of complex behaviors. PMID:22645253

  19. Rumor propagation on networks with community structure

    Science.gov (United States)

    Zhang, Ruixia; Li, Deyu

    2017-10-01

    In this paper, based on growth and preferential attachment mechanism, we give a network generation model aiming at generating networks with community structure. There are three characteristics for the networks generated by the generation model. The first is that the community sizes can be nonuniform. The second is that there are bridge hubs in each community. The third is that the strength of community structure is adjustable. Next, we investigate rumor propagation behavior on the generated networks by performing Monte Carlo simulations to reveal the influence of bridge hubs, nonuniformity of community sizes and the strength of community structure on the dynamic behavior of the rumor propagation. We find that bridge hubs have outstanding performance in propagation speed and propagation size, and larger modularity can reduce rumor propagation. Furthermore, when the decay rate of rumor spreading β is large, the final density of the stiflers is larger if the rumor originates in larger community. Additionally, when on networks with different strengths of community structure, rumor propagation exhibits greater difference in the density of stiflers and in the peak prevalence if the decay rate β is larger.

  20. Structural systems identification of genetic regulatory networks.

    Science.gov (United States)

    Xiong, Hao; Choe, Yoonsuck

    2008-02-15

    Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit. In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints. The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.

  1. Structural health monitoring using wireless sensor networks

    Science.gov (United States)

    Sreevallabhan, K.; Nikhil Chand, B.; Ramasamy, Sudha

    2017-11-01

    Monitoring and analysing health of large structures like bridges, dams, buildings and heavy machinery is important for safety, economical, operational, making prior protective measures, and repair and maintenance point of view. In recent years there is growing demand for such larger structures which in turn make people focus more on safety. By using Microelectromechanical Systems (MEMS) Accelerometer we can perform Structural Health Monitoring by studying the dynamic response through measure of ambient vibrations and strong motion of such structures. By using Wireless Sensor Networks (WSN) we can embed these sensors in wireless networks which helps us to transmit data wirelessly thus we can measure the data wirelessly at any remote location. This in turn reduces heavy wiring which is a cost effective as well as time consuming process to lay those wires. In this paper we developed WSN based MEMS-accelerometer for Structural to test the results in the railway bridge near VIT University, Vellore campus.

  2. Nicotine increases brain functional network efficiency.

    Science.gov (United States)

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

    2012-10-15

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

  3. Efficacy and toxicity of whole brain radiotherapy in patients with multiple cerebral metastases from malignant melanoma

    Directory of Open Access Journals (Sweden)

    Hauswald Henrik

    2012-08-01

    Full Text Available Abstract Background To retrospectively access outcome and toxicity of whole brain radiotherapy (WBRT in patients with multiple brain metastases (BM from malignant melanoma (MM. Patients and methods Results of 87 patients (median age 58 years; 35 female, 52 male treated by WBRT for BM of MM between 2000 and 2011 were reviewed. Total dose applied was either 30 Gy in 10 fractions (n = 56 or 40 Gy in 20 fractions (n = 31. All but 9 patients suffered from extra-cerebral metastases. Prior surgical resection of BM was performed in 18 patients, salvage stereotactic radiosurgery in 13 patients. Results Mean follow-up was 8 months (range, 0–57 months, the 6- and 12-months overall-(OS survival rates were 29.2% and 16.5%, respectively. The median OS was 3.5 months. In cerebral follow-up imaging 6 (11 patients showed a complete (partial remission, while 11 (17 patients had stable disease (intra-cerebral tumor progression. In comparison of total dose, the group treated with 40 Gy in 20 fractions achieved a significant longer OS (p = 0.003, median 3.1 vs. 5.6 months. Furthermore, DS-GPA score (p  Conclusion Treatment of BM from MM with WBRT is tolerated well and some remissions of BM could be achieved. An advantage for higher treatment total doses was seen. However, outcome is non-satisfying, and further improvements in treatment of BM from MM are warranted.

  4. Scalp Dose Evaluation According Radiation Therapy Technique of Whole Brain Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Jang, Joon Yung; Park, Soo Yun; Kim, Jong Sik; Choi, Byeong Gi; Song, Gi Won [Dept. of Radiation Oncology, Samsung Medical Center, Seoul (Korea, Republic of)

    2011-09-15

    Opposing portal irradiation with helmet field shape that has been given to a patient with brain metastasis can cause excess dose in patient's scalp, resulting in hair loss. For this reason, this study is to quantitatively analyze scalp dose for effective prevention of hair loss by comparing opposing portal irradiation with scalp-shielding shape and tomotherapy designed to protect patient's scalp with conventional radiation therapy. Scalp dose was measured by using three therapies (HELMET, MLC, TOMO) after five thermo-luminescence dosimeters were positioned along center line of frontal lobe by using RANDO Phantom. Scalp dose and change in dose distribution were compared and analyzed with DVH after radiation therapy plan was made by using Radiation Treatment Planning System (Pinnacle3, Philips Medical System, USA) and 6 MV X-ray (Clinac 6EX, VARIAN, USA). When surface dose of scalp by using thermo-luminescence dosimeters was measured, it was revealed that scalp dose decreased by average 87.44% at each point in MLC technique and that scalp dose decreased by average 88.03% at each point in TOMO compared with HELMET field therapy. In addition, when percentage of volume (V95%, V100%, V105% of prescribed dose) was calculated by using Dose Volume Histogram (DVH) in order to evaluate the existence or nonexistence of hotspot in scalp as to three therapies (HELMET, MLC, TOMO), it was revealed that MLC technique and TOMO plan had good dose coverage and did not have hot spot. Reducing hair loss of a patient who receives whole brain radiotherapy treatment can make a contribution to improve life quality of the patient. It is expected that making good use of opposing portal irradiation with scalp-shielding shape and tomotherapy to protect scalp of a patient based on this study will reduce hair loss of a patient.

  5. Changes in Imaging and Cognition in Juvenile Rats After Whole-Brain Irradiation

    Energy Technology Data Exchange (ETDEWEB)

    Brown, Robert J.; Jun, Brandon J. [Division of Molecular and Cellular Oncology, Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California (United States); Advanced Imaging Laboratory, Department of Radiology, Children' s Hospital Los Angeles, Los Angeles, California (United States); Rudi Schulte Research Institute, Santa Barbara, California (United States); Cushman, Jesse D. [Department of Psychology, University of California, Los Angeles, Los Angeles, California (United States); Nguyen, Christine; Beighley, Adam H.; Blanchard, Johnny; Iwamoto, Kei; Schaue, Dorthe [Division of Molecular and Cellular Oncology, Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California (United States); Harris, Neil G. [UCLA Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA Center for the Health Sciences, Los Angeles, California (United States); Jentsch, James D. [Department of Psychology, University of California, Los Angeles, Los Angeles, California (United States); Bluml, Stefan [Advanced Imaging Laboratory, Department of Radiology, Children' s Hospital Los Angeles, Los Angeles, California (United States); Rudi Schulte Research Institute, Santa Barbara, California (United States); McBride, William H., E-mail: wmcbride@mednet.ucla.edu [Division of Molecular and Cellular Oncology, Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California (United States)

    2016-10-01

    Purpose: In pediatric cancer survivors treated with whole-brain irradiation (WBI), long-term cognitive deficits and morbidity develop that are poorly understood and for which there is no treatment. We describe similar cognitive defects in juvenile WBI rats and correlate them with alterations in diffusion tensor imaging and magnetic resonance spectroscopy (MRS) during brain development. Methods and Materials: Juvenile Fischer rats received clinically relevant fractionated doses of WBI or a high-dose exposure. Diffusion tensor imaging and MRS were performed at the time of WBI and during the subacute (3-month) and late (6-month) phases, before behavioral testing. Results: Fractional anisotropy in the splenium of the corpus callosum increased steadily over the study period, reflecting brain development. WBI did not alter the subacute response, but thereafter there was no further increase in fractional anisotropy, especially in the high-dose group. Similarly, the ratios of various MRS metabolites to creatine increased over the study period, and in general, the most significant changes after WBI were during the late phase and with the higher dose. The most dramatic changes observed were in glutamine-creatine ratios that failed to increase normally between 3 and 6 months after either radiation dose. WBI did not affect the ambulatory response to novel open field testing in the subacute phase, but locomotor habituation was impaired and anxiety-like behaviors increased. As for cognitive measures, the most dramatic impairments were in novel object recognition late after either dose of WBI. Conclusions: The developing brains of juvenile rats given clinically relevant fractionated doses of WBI show few abnormalities in the subacute phase but marked late cognitive alterations that may be linked with perturbed MRS signals measured in the corpus callosum. This pathomimetic phenotype of clinically relevant cranial irradiation effects may be useful for modeling, mechanistic

  6. Cerebral white matter injury and damage to myelin sheath following whole-brain ischemia.

    Science.gov (United States)

    Chen, Yingzhu; Yi, Qiong; Liu, Gang; Shen, Xue; Xuan, Lihui; Tian, Ye

    2013-02-07

    Myelin sheath, either in white matter or in other regions of brain, is vulnerable to ischemia. The specific events involved in the progression of ischemia in white matter have not yet been elucidated. The aim of this study was to determine histopathological alterations in cerebral white matter and levels of myelin basic protein (MBP) in ischemia-injured brain tissue during the acute and subacute phases of central nervous injury following whole-brain ischemia. The whole cerebral ischemia model (four-vessel occlusion (4-VO)) was established in adult Sprague-Dawley rats and MBP gene expression and protein levels in the brain tissue were measured using reverse transcription-polymerase chain reaction and enzyme-linked immunosorbent assay (ELISA) at 2 days, 4 days, 7 days, 14 days, and 28 days following ischemia. Demyelination was determined by Luxol fast blue myelin staining, routine histopathological staining, and electron microscopy in injured brain tissue. Results showed that edema, vascular dilation, focal necrosis, demyelination, adjacent reactive gliosis and inflammation occurred 7 days after ischemia in HE staining and recovered to control levels at 28 days. The absence of Luxol fast blue staining and vacuolation was clearly visible at 7 days, 14 days, and 28 days. Semiquantitative analysis showed that the transparency of myelin had decreased significantly by 7 days, 14 days, and 28 days. Demyelination and ultrastructual changes were detected 7 days after ischemia. The relative levels of MBP mRNA decreased 2 days after ischemia and this trend continued throughout the remaining four points in time. The MBP levels measured using ELISA also decreased significantly at 2 days and 4 days, but they recovered by 7 days and returned to control levels by 14 days. These results suggest that the impact of ischemia on cerebral white matter is time-sensitive and that different effects may follow different courses over time. Copyright © 2012 Elsevier B.V. All rights reserved.

  7. Diagnostic accuracy of whole-brain CT perfusion in the detection of acute infratentorial infarctions

    Energy Technology Data Exchange (ETDEWEB)

    Bollwein, Christine; Sommer, Wieland H.; Thierfelder, Kolja M.; Reiser, Maximilian F. [Ludwig-Maximilians-University Hospital of Munich, Institute for Clinical Radiology, Munich (Germany); Plate, Annika; Straube, Andreas; Baumgarten, Louisa von [Ludwig-Maximilians-University Hospital of Munich, Department of Neurology, Munich (Germany); Janssen, Hendrik [South Nuremberg Hospital, Department of Neuroradiology, Nuremberg (Germany)

    2016-11-15

    Although the diagnostic performance of whole-brain computed tomographic perfusion (WB-CTP) in the detection of supratentorial infarctions is well established, its value in the detection of infratentorial strokes remains less well defined. We examined its diagnostic accuracy in the detection of infratentorial infarctions and compared it to nonenhanced computed tomography (NECT), aiming to identify factors influencing its detection rate. Out of a cohort of 1380 patients who underwent WB-CTP due to suspected stroke, we retrospectively included all patients with MRI-confirmed infratentorial strokes and compared it to control patients without infratentorial strokes. Two blinded readers evaluated NECT and four different CTP maps independently for the presence and location of infratentorial ischemic perfusion deficits. The study was designed as a retrospective case-control study and included 280 patients (cases/controls = 1/3). WB-CTP revealed a greater diagnostic sensitivity than NECT (41.4 vs. 17.1 %, P = 0.003). The specificity, however, was comparable (93.3 vs. 95.0 %). Mean transit time (MTT) and time to drain (TTD) were the most sensitive (41.4 and 40.0 %) and cerebral blood volume (CBV) the most specific (99.5 %) perfusion maps. Infarctions detected using WB-CTP were significantly larger than those not detected (15.0 vs. 2.2 ml; P = 0.0007); infarct location, however, did not influence the detection rate. The detection of infratentorial infarctions can be improved by assessing WB-CTP as part of the multimodal stroke workup. However, it remains a diagnostic challenge, especially small volume infarctions in the brainstem are likely to be missed. (orig.)

  8. Incidence of Leukoencephalopathy After Whole-Brain Radiation Therapy for Brain Metastases

    Energy Technology Data Exchange (ETDEWEB)

    Ebi, Junko, E-mail: junkoe@fmu.ac.jp [Department of Radiology, Fukushima Medical University, Fukushima (Japan); Sato, Hisashi; Nakajima, Masaru; Shishido, Fumio [Department of Radiology, Fukushima Medical University, Fukushima (Japan)

    2013-04-01

    Purpose: To evaluate the incidence of leukoencephalopathy after whole-brain radiation therapy (WBRT) in patients with brain metastases. Methods and Materials: We retrospectively reviewed 111 patients who underwent WBRT for brain metastases from April 2001 through March 2008 and had evaluable computed tomography (CT) and/or magnetic resonance imaging (MRI) at least 1 month after completion of WBRT. We evaluated the leukoencephalopathy according to the Common Terminology Criteria for Adverse Events, version 3.0. The patients who had brain tumor recurrence after WBRT were censored at the last follow-up CT or MRI without recurrence. To evaluate the risk factors for leukoencephalopathy, bivariate analysis was performed using a logistic regression analysis adjusted for follow-up time. Factors included in the analysis were age, gender, dose fractionation, 5-fluorouracil, methotrexate, cisplatin, and other chemotherapeutic agents. Results: The median age of the 111 patients was 60.0 years (range, 23-89 years). The median follow-up was 3.8 months (range, 1.0-38.1 months). Leukoencephalopathy developed in 23 of the 111 patients. Grades 1, 2, and 3 were observed in 8, 7, and 8 patients, respectively. The incidence was 34.4% (11 of 32), 42.9% (6 of 14), 66.7% (2 of 3), and 100% (2 of 2) of the patients who were followed up for ≥6, ≥12, ≥24, and ≥36 months, respectively. In the bivariate analysis, older age (≥65 years) was significantly correlated with higher risk of leukoencephalopathy (odds ratio 3.31; 95% confidence interval 1.15-9.50; P=.03). Conclusions: The incidence of leukoencephalopathy after WBRT was 34.4% with ≥6 months follow-up, and increased with longer follow-up. Older age was a significant risk factor. The schedule of WBRT for patients with brain metastases should be carefully determined, especially for favorable patients.

  9. Distinctive Structural and Effective Connectivity Changes of Semantic Cognition Network across Left and Right Mesial Temporal Lobe Epilepsy Patients

    Science.gov (United States)

    Fan, Xiaotong; Shang, Kun; Wang, Xiaocui; Wang, Peipei; Shan, Yongzhi; Lu, Jie

    2016-01-01

    Occurrence of language impairment in mesial temporal lobe epilepsy (mTLE) patients is common and left mTLE patients always exhibit a primary problem with access to names. To explore different neuropsychological profiles between left and right mTLE patients, the study investigated both structural and effective functional connectivity changes within the semantic cognition network between these two groups and those from normal controls. We found that gray matter atrophy of left mTLE patients was more severe than that of right mTLE patients in the whole brain and especially within the semantic cognition network in their contralateral hemisphere. It suggested that seizure attacks were rather targeted than random for patients with hippocampal sclerosis (HS) in the dominant hemisphere. Functional connectivity analysis during resting state fMRI revealed that subregions of the anterior temporal lobe (ATL) in the left HS patients were no longer effectively connected. Further, we found that, unlike in right HS patients, increased causal linking between ipsilateral regions in the left HS epilepsy patients cannot make up for their decreased contralateral interaction. It suggested that weakened contralateral connection and disrupted effective interaction between subregions of the unitary, transmodal hub of the ATL may be the primary cause of anomia in the left HS patients. PMID:28018680

  10. Robustness in Weighted Networks with Cluster Structure

    Directory of Open Access Journals (Sweden)

    Yi Zheng

    2014-01-01

    Full Text Available The vulnerability of complex systems induced by cascade failures revealed the comprehensive interaction of dynamics with network structure. The effect on cascade failures induced by cluster structure was investigated on three networks, small-world, scale-free, and module networks, of which the clustering coefficient is controllable by the random walk method. After analyzing the shifting process of load, we found that the betweenness centrality and the cluster structure play an important role in cascading model. Focusing on this point, properties of cascading failures were studied on model networks with adjustable clustering coefficient and fixed degree distribution. In the proposed weighting strategy, the path length of an edge is designed as the product of the clustering coefficient of its end nodes, and then the modified betweenness centrality of the edge is calculated and applied in cascade model as its weights. The optimal region of the weighting scheme and the size of the survival components were investigated by simulating the edge removing attack, under the rule of local redistribution based on edge weights. We found that the weighting scheme based on the modified betweenness centrality makes all three networks have better robustness against edge attack than the one based on the original betweenness centrality.

  11. An Evaluation Framework for Large-Scale Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Knudsen, Thomas Phillip; Madsen, Ole Brun

    2004-01-01

    An evaluation framework for large-scale network structures is presented, which facilitates evaluations and comparisons of different physical network structures. A number of quantitative and qualitative parameters are presented, and their importance to networks discussed. Choosing a network...... is closed by an example of how the framework can be used. The framework supports network planners in decision-making and researchers in evaluation and development of network structures....

  12. THE GOVERNANCE STRUCTURE OF COOPERATIVE NETWORKS

    National Research Council Canada - National Science Library

    Rosileia Milagres

    2014-01-01

    .... The analysis shows that the governance structure is influenced by the objectives established, the partners' experience, the types of knowledge and the context where network is inserted. The case highlights the importance of learning during the process, but, although present, it can be negatively influenced by the context and the possibility of future partnerships.

  13. Social Network Structures among Groundnut Farmers

    Science.gov (United States)

    Thuo, Mary; Bell, Alexandra A.; Bravo-Ureta, Boris E.; Okello, David K.; Okoko, Evelyn Nasambu; Kidula, Nelson L.; Deom, C. Michael; Puppala, Naveen

    2013-01-01

    Purpose: Groundnut farmers in East Africa have experienced declines in production despite research and extension efforts to increase productivity. This study examined how social network structures related to acquisition of information about new seed varieties and productivity among groundnut farmers in Uganda and Kenya.…

  14. Structural network efficiency predicts conversion to dementia

    NARCIS (Netherlands)

    Tuladhar, A.; van Uden, I.W.M.; Rutten-Jacobs, L.C.A.; van der Holst, H.; van Norden, A.; de Laat, K.; Dijk, E.; Claassen, J.A.H.R.; Kessels, R.P.C.; Markus, H.S.; Norris, David Gordon; de Leeuw, F.E.

    2016-01-01

    Objective: To examine whether structural network connectivity at baseline predicts incident all-cause dementia in a prospective hospital-based cohort of elderly participants with MRI evidence of small vessel disease (SVD). Methods: A total of 436 participants from the Radboud University Nijmegen

  15. Structures and Statistics of Citation Networks

    Science.gov (United States)

    2011-05-01

    assignment procedure ( QAP ) (14) and its regression counterpart MRQAP (15) have been used to detect structural significance and compare networks in...Correcting Codes. Hamming, R.W. 2, s.l. : Bell System Technical Journal, 1950, Vol. 29, pp. 147--160. 14. QAP Partialling as a Test of Spuriousness* 1

  16. Distinct functional networks within the cerebellum and their relation to cortical systems assessed with independent component analysis.

    Science.gov (United States)

    Dobromyslin, Vitaly I; Salat, David H; Fortier, Catherine B; Leritz, Elizabeth C; Beckmann, Christian F; Milberg, William P; McGlinchey, Regina E

    2012-05-01

    Cerebellar functional circuitry has been examined in several prior studies using resting fMRI data and seed-based procedures, as well as whole-brain independent component analysis (ICA). Here, we hypothesized that ICA applied to functional data from the cerebellum exclusively would provide increased sensitivity for detecting cerebellar networks compared to previous approaches. Consistency of group-level networks was assessed in two age- and sex-matched groups of twenty-five subjects each. Cerebellum-only ICA was compared to the traditional whole-brain ICA procedure to examine the potential gain in sensitivity of the novel method. In addition to replicating a number of previously identified cerebellar networks, the current approach revealed at least one network component that was not apparent with the application of whole brain ICA. These results demonstrate the gain in sensitivity attained through specifying the cerebellum as a target structure with regard to the identification of robust and reliable networks. The use of similar procedures could be important in further expanding on previously defined patterns of cerebellar functional anatomy, as well as provide information about unique networks that have not been explored in prior work. Such information may prove crucial for understanding the cognitive and behavioral importance of the cerebellum in health and disease. Published by Elsevier Inc.

  17. Information diffusion in structured online social networks

    Science.gov (United States)

    Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

    2015-05-01

    Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

  18. Tensegrity II. How structural networks influence cellular information processing networks

    Science.gov (United States)

    Ingber, Donald E.

    2003-01-01

    The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.

  19. A Robust Method for Inferring Network Structures.

    Science.gov (United States)

    Yang, Yang; Luo, Tingjin; Li, Zhoujun; Zhang, Xiaoming; Yu, Philip S

    2017-07-12

    Inferring the network structure from limited observable data is significant in molecular biology, communication and many other areas. It is challenging, primarily because the observable data are sparse, finite and noisy. The development of machine learning and network structure study provides a great chance to solve the problem. In this paper, we propose an iterative smoothing algorithm with structure sparsity (ISSS) method. The elastic penalty in the model is introduced for the sparse solution, identifying group features and avoiding over-fitting, and the total variation (TV) penalty in the model can effectively utilize the structure information to identify the neighborhood of the vertices. Due to the non-smoothness of the elastic and structural TV penalties, an efficient algorithm with the Nesterov's smoothing optimization technique is proposed to solve the non-smooth problem. The experimental results on both synthetic and real-world networks show that the proposed model is robust against insufficient data and high noise. In addition, we investigate many factors that play important roles in identifying the performance of ISSS.

  20. Network structure of multivariate time series

    Science.gov (United States)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-01

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  1. The fundamental structures of dynamic social networks

    CERN Document Server

    Sekara, Vedran; Lehmann, Sune

    2015-01-01

    Networks provide a powerful mathematical framework for analyzing the structure and dynamics of complex systems (1-3). The study of group behavior has deep roots in the social science literature (4,5) and community detection is a central part of modern network science. Network communities have been found to be highly overlapping and organized in a hierarchical structure (6-9). Recent technological advances have provided a toolset for measuring the detailed social dynamics at scale (10,11). In spite of great progress, a quantitative description of the complex temporal behavior of social groups-with dynamics spanning from minute-by-minute changes to patterns expressed on the timescale of years-is still absent. Here we uncover a class of fundamental structures embedded within highly dynamic social networks. On the shortest time-scale, we find that social gatherings are fluid, with members coming and going, but organized via a stable core of individuals. We show that cores represent social contexts (9), with recur...

  2. Scalable brain network construction on white matter fibers

    Science.gov (United States)

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

    2011-03-01

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

  3. Risk of dry eye syndrome in patients treated with whole-brain radiotherapy.

    Science.gov (United States)

    Nanda, Tavish; Wu, Cheng-Chia; Campbell, Ashley A; Bathras, Ryan M; Jani, Ashish; Kazim, Michael; Wang, Tony J C

    2017-08-04

    With improvements in systemic therapy, patients with cancer treated with whole-brain radiotherapy (WBRT) are living long enough to develop late toxicities, including dry eye syndrome. In general practice, dose to the lacrimal gland (LG) is not constrained (maximum constraint <40 Gy) in WBRT. The purpose of this study was to measure dose to the LG in WBRT and determine methods for reducing radiation exposure. We conducted a retrospective review of 70 3-dimensional (3D) conformal plans; thirty-six plans with a radiation prescription of 30 Gy in 10 fractions and 34 plans with a prescription of 37.5 Gy in 15 fractions. LGs were contoured in accordance with Freedman and Sidani (2015). Biological effective dose (BED)3 maximum constraints were calculated from 40 Gy and 20 Gy to be 32.17 Gy (30 Gy) and 36.70 Gy (37.5 Gy). Both regimens demonstrated supraorbital blocking by 3 methods: T1, bordering the supraorbital ridge; T2, no contact with supraorbital ridge; and T3, coverage of the supraorbital ridge. Mean dose for the plans with a 30-Gy prescription and the plans with a 37.5-Gy prescription was 27.5 Gy and 35.2 Gy, respectively (p ≤ 0.0001). BED3 maximum constraint (Dmax) was violated 16 of 26 (61.5%) in T1 (average Dmax: 32.2 Gy), 13 of 28 (46.4%) in T2 (average Dmax: 32.1 Gy), and 5 of 18 (27.8%) in T3 (average Dmax: 31.8 Gy) for the 30-Gy prescription. Dmax was violated in 32 of 32 (100%) in T1 (average Dmax: 40.1 Gy), 22 of 22 (100%) in T2 (average Dmax: 40.3 Gy), and 14 of 14 (100%) in T3 (average Dmax: 39.4) for the 37.5 Gy prescription. Average Dmax for the 37.5-Gy prescription was highly significant in favor of T3 (p = 0.0098). Patients who receive WBRT may develop dry eye syndrome as a late toxicity. Constraints are commonly violated with a prescription of 37.5 Gy. Methods to reduce dose include T3 supraorbital blocking, an easily implementable change that may dramatically improve patient quality of life. Copyright © 2017

  4. Methods and applications for detecting structure in complex networks

    Science.gov (United States)

    Leicht, Elizabeth A.

    The use of networks to represent systems of interacting components is now common in many fields including the biological, physical, and social sciences. Network models are widely applicable due to their relatively simple framework of vertices and edges. Network structure, patterns of connection between vertices, impacts both the functioning of networks and processes occurring on networks. However, many aspects of network structure are still poorly understood. This dissertation presents a set of network analysis methods and applications to real-world as well as simulated networks. The methods are divided into two main types: linear algebra formulations and probabilistic mixture model techniques. Network models lend themselves to compact mathematical representation as matrices, making linear algebra techniques useful probes of network structure. We present methods for the detection of two distinct, but related, network structural forms. First, we derive a measure of vertex similarity based upon network structure. The method builds on existing ideas concerning calculation of vertex similarity, but generalizes and extends the scope to large networks. Second, we address the detection of communities or modules in a specific class of networks, directed networks. We propose a method for detecting community structure in directed networks, which is an extension of a community detection method previously only known for undirected networks. Moving away from linear algebra formulations, we propose two methods for network structure detection based on probabilistic techniques. In the first method, we use the machinery of the expectation-maximization (EM) algorithm to probe patterns of connection among vertices in static networks. The technique allows for the detection of a broad range of types of structure in networks. The second method focuses on time evolving networks. We propose an application of the EM algorithm to evolving networks that can reveal significant structural

  5. Structural determinants of criticality in biological networks.

    Science.gov (United States)

    Valverde, Sergi; Ohse, Sebastian; Turalska, Malgorzata; West, Bruce J; Garcia-Ojalvo, Jordi

    2015-01-01

    Many adaptive evolutionary systems display spatial and temporal features, such as long-range correlations, typically associated with the critical point of a phase transition in statistical physics. Empirical and theoretical studies suggest that operating near criticality enhances the functionality of biological networks, such as brain and gene networks, in terms for instance of information processing, robustness, and evolvability. While previous studies have explained criticality with specific system features, we still lack a general theory of critical behavior in biological systems. Here we look at this problem from the complex systems perspective, since in principle all critical biological circuits have in common the fact that their internal organization can be described as a complex network. An important question is how self-similar structure influences self-similar dynamics. Modularity and heterogeneity, for instance, affect the location of critical points and can be used to tune the system toward criticality. We review and discuss recent studies on the criticality of neuronal and genetic networks, and discuss the implications of network theory when assessing the evolutionary features of criticality.

  6. Structural Determinants of Criticality in Biological Networks

    Directory of Open Access Journals (Sweden)

    Sergi eValverde

    2015-05-01

    Full Text Available Many adaptive evolutionary systems display spatial and temporal features, such as long-range correlations, typically associated with the critical point of a phase transition in statistical physics. Empirical and theoretical studies suggest that operating near criticality enhances the functionality of biological networks, such as brain and gene networks, in terms for instance of information processing, robustness and evolvability. While previous studies have explained criticality with specific system features, we still lack a general theory of critical behaviour in biological systems. Here we look at this problem from the complex systems perspective, since in principle all critical biological circuits have in common the fact that their internal organisation can be described as a complex network. An important question is how self-similar structure influences self-similar dynamics. Modularity and heterogeneity, for instance, affect the location of critical points and can be used to tune the system towards criticality. We review and discuss recent studies on the criticality of neuronal and genetic networks, and discuss the implications of network theory when assessing the evolutionary features of criticality.

  7. Community structure in the phonological network

    Directory of Open Access Journals (Sweden)

    Cynthia S. Q. Siew

    2013-08-01

    Full Text Available Community structure, which refers to the presence of densely connected groups within a larger network, is a common feature of several real-world networks from a variety of domains such as the human brain, social networks of hunter-gatherers and business organizations, and the World Wide Web (Porter et al., 2009. Using a community detection technique known as the Louvain optimization method, 17 communities were extracted from the giant component of the phonological network described in Vitevitch (2008. Additional analyses comparing the lexical and phonological characteristics of words in these communities against words in randomly generated communities revealed several novel discoveries. Larger communities tend to consist of short, frequent words of high degree and low age of acquisition ratings, and smaller communities tend to consist of longer, less frequent words of low degree and high age of acquisition ratings. Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities. Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935. The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.

  8. Structure and mechanics of aegagropilae fiber network.

    Science.gov (United States)

    Verhille, Gautier; Moulinet, Sébastien; Vandenberghe, Nicolas; Adda-Bedia, Mokhtar; Le Gal, Patrice

    2017-05-02

    Fiber networks encompass a wide range of natural and manmade materials. The threads or filaments from which they are formed span a wide range of length scales: from nanometers, as in biological tissues and bundles of carbon nanotubes, to millimeters, as in paper and insulation materials. The mechanical and thermal behavior of these complex structures depends on both the individual response of the constituent fibers and the density and degree of entanglement of the network. A question of paramount importance is how to control the formation of a given fiber network to optimize a desired function. The study of fiber clustering of natural flocs could be useful for improving fabrication processes, such as in the paper and textile industries. Here, we use the example of aegagropilae that are the remains of a seagrass (Posidonia oceanica) found on Mediterranean beaches. First, we characterize different aspects of their structure and mechanical response, and second, we draw conclusions on their formation process. We show that these natural aggregates are formed in open sea by random aggregation and compaction of fibers held together by friction forces. Although formed in a natural environment, thus under relatively unconstrained conditions, the geometrical and mechanical properties of the resulting fiber aggregates are quite robust. This study opens perspectives for manufacturing complex fiber network materials.

  9. Structure of the human chromosome interaction network.

    Directory of Open Access Journals (Sweden)

    Sergio Sarnataro

    Full Text Available New Hi-C technologies have revealed that chromosomes have a complex network of spatial contacts in the cell nucleus of higher organisms, whose organisation is only partially understood. Here, we investigate the structure of such a network in human GM12878 cells, to derive a large scale picture of nuclear architecture. We find that the intensity of intra-chromosomal interactions is power-law distributed. Inter-chromosomal interactions are two orders of magnitude weaker and exponentially distributed, yet they are not randomly arranged along the genomic sequence. Intra-chromosomal contacts broadly occur between epigenomically homologous regions, whereas inter-chromosomal contacts are especially associated with regions rich in highly expressed genes. Overall, genomic contacts in the nucleus appear to be structured as a network of networks where a set of strongly individual chromosomal units, as envisaged in the 'chromosomal territory' scenario derived from microscopy, interact with each other via on average weaker, yet far from random and functionally important interactions.

  10. Improving the Robustness of Complex Networks with Preserving Community Structure

    Science.gov (United States)

    Yang, Yang; Li, Zhoujun; Chen, Yan; Zhang, Xiaoming; Wang, Senzhang

    2015-01-01

    Complex networks are everywhere, such as the power grid network, the airline network, the protein-protein interaction network, and the road network. The networks are ‘robust yet fragile’, which means that the networks are robust against random failures but fragile under malicious attacks. The cascading failures, system-wide disasters and intentional attacks on these networks are deserving of in-depth study. Researchers have proposed many solutions to improve the robustness of these networks. However whilst many solutions preserve the degree distribution of the networks, little attention is paid to the community structure of these networks. We argue that the community structure of a network is a defining characteristic of a network which identifies its functionality and thus should be preserved. In this paper, we discuss the relationship between robustness and the community structure. Then we propose a 3-step strategy to improve the robustness of a network, while retaining its community structure, and also its degree distribution. With extensive experimentation on representative real-world networks, we demonstrate that our method is effective and can greatly improve the robustness of networks, while preserving community structure and degree distribution. Finally, we give a description of a robust network, which is useful not only for improving robustness, but also for designing robust networks and integrating networks. PMID:25674786

  11. Community structure in introductory physics course networks

    CERN Document Server

    Traxler, Adrienne L

    2015-01-01

    Student-to-student interactions are foundational to many active learning environments, but are most often studied using qualitative methods. Network analysis tools provide a quantitative complement to this picture, allowing researchers to describe the social interactions of whole classrooms as systems. Past results from introductory physics courses have suggested a sharp division in the formation of social structure between large lecture sections and small studio classroom environments. Extending those results, this study focuses on calculus-based introductory physics courses at a large public university with a heavily commuter and nontraditional student population. Community detection network methods are used to characterize pre- and post-course collaborative structure in several sections, and differences are considered between small and large classes. These results are compared with expectations from earlier findings, and comment on implications for instruction and further study.

  12. Finding local community structure in networks

    Science.gov (United States)

    Clauset, Aaron

    2005-08-01

    Although the inference of global community structure in networks has recently become a topic of great interest in the physics community, all such algorithms require that the graph be completely known. Here, we define both a measure of local community structure and an algorithm that infers the hierarchy of communities that enclose a given vertex by exploring the graph one vertex at a time. This algorithm runs in time O(k2d) for general graphs when d is the mean degree and k is the number of vertices to be explored. For graphs where exploring a new vertex is time consuming, the running time is linear, O(k) . We show that on computer-generated graphs the average behavior of this technique approximates that of algorithms that require global knowledge. As an application, we use this algorithm to extract meaningful local clustering information in the large recommender network of an online retailer.

  13. Hierarchical Neural Network Structures for Phoneme Recognition

    CERN Document Server

    Vasquez, Daniel; Minker, Wolfgang

    2013-01-01

    In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a  Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.

  14. Walk modularity and community structure in networks

    OpenAIRE

    Mehrle, David; Strosser, Amy; Harkin, Anthony

    2014-01-01

    Modularity maximization has been one of the most widely used approaches in the last decade for discovering community structure in networks of practical interest in biology, computing, social science, statistical mechanics, and more. Modularity is a quality function that measures the difference between the number of edges found within clusters minus the number of edges one would statistically expect to find based on random chance. We present a natural generalization of modularity based on the ...

  15. A placebo-controlled, randomized phase II study of maintenance enzastaurin following whole brain radiation therapy in the treatment of brain metastases from lung cancer

    DEFF Research Database (Denmark)

    Grønberg, Bjørn H; Ciuleanu, Tudor; Fløtten, Øystein

    2012-01-01

    Enzastaurin is a protein kinase C inhibitor with anti-tumor activity. This study was designed to determine if maintenance enzastaurin improved the outcome of whole brain radiotherapy (WBRT) in lung cancer (LC) patients with brain metastases (BMs)....

  16. Structure and dynamics of core-periphery networks

    CERN Document Server

    Csermely, Peter; Wu, Ling-Yun; Uzzi, Brian

    2013-01-01

    Recent studies uncovered important core/periphery network structures characterizing complex sets of cooperative and competitive interactions between network nodes, be they proteins, cells, species or humans. Better characterization of the structure, dynamics and function of core/periphery networks is a key step of our understanding cellular functions, species adaptation, social and market changes. Here we summarize the current knowledge of the structure and dynamics of "traditional" core/periphery networks, rich-clubs, nested, bow-tie and onion networks. Comparing core/periphery structures with network modules, we discriminate between global and local cores. The core/periphery network organization lies in the middle of several extreme properties, such as random/condensed structures, clique/star configurations, network symmetry/asymmetry, network assortativity/disassortativity, as well as network hierarchy/anti-hierarchy. These properties of high complexity together with the large degeneracy of core pathways e...

  17. Complex network perspective on structure and function of ...

    Indian Academy of Sciences (India)

    , uncovering complex network structure and function from these networks is becoming one of the most important topics in system biology. This work aims at studying the structure and function of Staphylococcus aureus (S. aureus) metabolic ...

  18. Increased Stability and Breakdown of Brain Effective Connectivity During Slow-Wave Sleep: Mechanistic Insights from Whole-Brain Computational Modelling

    OpenAIRE

    Jobst, Beatrice M; Hindriks, Rikkert; Laufs, Helmut; Tagliazucchi, E; Hahn, Gerald; Ponce-Alvarez, Adrián; Stevner, Angus B. A.; Kringelbach, Morten L.; Deco, Gustavo

    2017-01-01

    Recent research has found that the human sleep cycle is characterised by changes in spatiotemporal patterns of brain activity. Yet, we are still missing a mechanistic explanation of the local neuronal dynamics underlying these changes. We used whole-brain computational modelling to study the differences in global brain functional connectivity and synchrony of fMRI activity in healthy humans during wakefulness and slow-wave sleep. We applied a whole-brain model based on the normal form of a su...

  19. Whole-brain hippocampal sparing radiation therapy: Volume-modulated arc therapy vs intensity-modulated radiation therapy case study

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Katrina, E-mail: Trinabena23@gmail.com; Lenards, Nishele; Holson, Janice

    2016-04-01

    The hippocampus is responsible for memory and cognitive function. An ongoing phase II clinical trial suggests that sparing dose to the hippocampus during whole-brain radiation therapy can help preserve a patient's neurocognitive function. Progressive research and advancements in treatment techniques have made treatment planning more sophisticated but beneficial for patients undergoing treatment. The aim of this study is to evaluate and compare hippocampal sparing whole-brain (HS-WB) radiation therapy treatment planning techniques using volume-modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT). We randomly selected 3 patients to compare different treatment techniques that could be used for reducing dose to the hippocampal region. We created 2 treatment plans, a VMAT and an IMRT, from each patient's data set and planned on the Eclipse 11.0 treatment planning system (TPS). A total of 6 plans (3 IMRT and 3 VMAT) were created and evaluated for this case study. The physician contoured the hippocampus as per the Radiation Therapy Oncology Group (RTOG) 0933 protocol atlas. The organs at risk (OR) were contoured and evaluated for the plan comparison, which included the spinal cord, optic chiasm, the right and left eyes, lenses, and optic nerves. Both treatment plans produced adequate coverage on the planning target volume (PTV) while significantly reducing dose to the hippocampal region. The VMAT treatment plans produced a more homogenous dose distribution throughout the PTV while decreasing the maximum point dose to the target. However, both treatment techniques demonstrated hippocampal sparing when irradiating the whole brain.

  20. Structural host-microbiota interaction networks.

    Science.gov (United States)

    Guven-Maiorov, Emine; Tsai, Chung-Jung; Nussinov, Ruth

    2017-10-01

    Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.

  1. 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.

  2. Investigation of whole-brain white matter identifies altered water mobility in the pathogenesis of high-altitude headache

    OpenAIRE

    Lawley, Justin S; Oliver, Samuel J; Mullins, Paul G; Macdonald, Jamie H

    2013-01-01

    Elevated brain water is a common finding in individuals with severe forms of altitude illness. However, the location, nature, and a causative link between brain edema and symptoms of acute mountain sickness such as headache remains unknown. We examined indices of brain white matter water mobility in 13 participants after 2 and 10 hours in normoxia (21% O2) and hypoxia (12% O2) using magnetic resonance imaging. Using a whole-brain analysis (tract-based spatial statistics (TBSS)), mean diffusiv...

  3. Higher-order structure and epidemic dynamics in clustered networks.

    Science.gov (United States)

    Ritchie, Martin; Berthouze, Luc; House, Thomas; Kiss, Istvan Z

    2014-05-07

    Clustering is typically measured by the ratio of triangles to all triples regardless of whether open or closed. Generating clustered networks, and how clustering affects dynamics on networks, is reasonably well understood for certain classes of networks (Volz et al., 2011; Karrer and Newman, 2010), e.g. networks composed of lines and non-overlapping triangles. In this paper we show that it is possible to generate networks which, despite having the same degree distribution and equal clustering, exhibit different higher-order structure, specifically, overlapping triangles and other order-four (a closed network motif composed of four nodes) structures. To distinguish and quantify these additional structural features, we develop a new network metric capable of measuring order-four structure which, when used alongside traditional network metrics, allows us to more accurately describe a network׳s topology. Three network generation algorithms are considered: a modified configuration model and two rewiring algorithms. By generating homogeneous networks with equal clustering we study and quantify their structural differences, and using SIS (Susceptible-Infected-Susceptible) and SIR (Susceptible-Infected-Recovered) dynamics we investigate computationally how differences in higher-order structure impact on epidemic threshold, final epidemic or prevalence levels and time evolution of epidemics. Our results suggest that characterising and measuring higher-order network structure is needed to advance our understanding of the impact of network topology on dynamics unfolding on the networks. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. Structure Learning in Power Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-01-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  5. The complex channel networks of bone structure

    CERN Document Server

    Costa, Luciano da Fontoura; Beletti, Marcelo E

    2006-01-01

    Bone structure in mammals involves a complex network of channels (Havers and Volkmann channels) required to nourish the bone marrow cells. This work describes how three-dimensional reconstructions of such systems can be obtained and represented in terms of complex networks. Three important findings are reported: (i) the fact that the channel branching density resembles a power law implies the existence of distribution hubs; (ii) the conditional node degree density indicates a clear tendency of connection between nodes with degrees 2 and 4; and (iii) the application of the recently introduced concept of hierarchical clustering coefficient allows the identification of typical scales of channel redistribution. A series of important biological insights is drawn and discussed

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

    Science.gov (United States)

    Spiegler, Andreas; Hansen, Enrique C A; Bernard, Christophe; McIntosh, Anthony R; Jirsa, Viktor K

    2016-01-01

    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.

  7. The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

    Directory of Open Access Journals (Sweden)

    Rutger Goekoop

    Full Text Available INTRODUCTION: Human personality is described preferentially in terms of factors (dimensions found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. AIM: To directly compare the ability of network community detection (NCD and principal component factor analysis (PCA to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R. METHODS: 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. RESULTS: At facet level, NCS showed a best match (96.2% with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80% with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. CONCLUSION: We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

  8. The Network Structure of Human Personality According to the NEO-PI-R: Matching Network Community Structure to Factor Structure

    Science.gov (United States)

    Goekoop, Rutger; Goekoop, Jaap G.; Scholte, H. Steven

    2012-01-01

    Introduction Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. Aim To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). Methods 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. Results At facet level, NCS showed a best match (96.2%) with a ‘confirmatory’ 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with ‘confirmatory’ 5-FS and ‘exploratory’ 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. Conclusion We present the first optimized network graph of personality traits according to the NEO-PI-R: a ‘Personality Web’. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network. PMID:23284713

  9. The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

    Science.gov (United States)

    Goekoop, Rutger; Goekoop, Jaap G; Scholte, H Steven

    2012-01-01

    Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. At facet level, NCS showed a best match (96.2%) with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

  10. Indian-ink perfusion based method for reconstructing continuous vascular networks in whole mouse brain.

    Directory of Open Access Journals (Sweden)

    Songchao Xue

    Full Text Available The topology of the cerebral vasculature, which is the energy transport corridor of the brain, can be used to study cerebral circulatory pathways. Limited by the restrictions of the vascular markers and imaging methods, studies on cerebral vascular structure now mainly focus on either observation of the macro vessels in a whole brain or imaging of the micro vessels in a small region. Simultaneous vascular studies of arteries, veins and capillaries have not been achieved in the whole brain of mammals. Here, we have combined the improved gelatin-Indian ink vessel perfusion process with Micro-Optical Sectioning Tomography for imaging the vessel network of an entire mouse brain. With 17 days of work, an integral dataset for the entire cerebral vessels was acquired. The voxel resolution is 0.35×0.4×2.0 µm(3 for the whole brain. Besides the observations of fine and complex vascular networks in the reconstructed slices and entire brain views, a representative continuous vascular tracking has been demonstrated in the deep thalamus. This study provided an effective method for studying the entire macro and micro vascular networks of mouse brain simultaneously.

  11. Information diversity in structure and dynamics of simulated neuronal networks.

    Science.gov (United States)

    Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Nykter, Matti; Kesseli, Juha; Ruohonen, Keijo; Yli-Harja, Olli; Linne, Marja-Leena

    2011-01-01

    Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.

  12. Information Diversity in Structure and Dynamics of Simulated Neuronal Networks

    Directory of Open Access Journals (Sweden)

    Tuomo eMäki-Marttunen

    2011-06-01

    Full Text Available Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance (NCD. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviours are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses.We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.

  13. Stable Matching with Incomplete Information in Structured Networks

    OpenAIRE

    Ling, Ying; Wan, Tao; Qin, Zengchang

    2015-01-01

    In this paper, we investigate stable matching in structured networks. Consider case of matching in social networks where candidates are not fully connected. A candidate on one side of the market gets acquaintance with which one on the heterogeneous side depends on the structured network. We explore four well-used structures of networks and define the social circle by the distance between each candidate. When matching within social circle, we have equilibrium distinguishes from each other sinc...

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

    Science.gov (United States)

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

    2017-08-01

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

  15. Altered white matter integrity in whole brain and segments of corpus callosum, in young social drinkers with binge drinking pattern.

    Science.gov (United States)

    Smith, Kathleen W; Gierski, Fabien; Andre, Judith; Dowell, Nicholas G; Cercignani, Mara; Naassila, Mickaël; Duka, Theodora

    2017-03-01

    Binge drinking is associated with impaired cognitive functioning, but the relationship of cognitive impairments and white matter integrity is less known. We used diffusion tensor imaging (DTI) to investigate the relationships of binge drinking, whole brain white matter integrity and cognitive performance during young adulthood (18 to 25 years), a period of continued brain development in two sessions 1 year apart. Binge drinkers (n = 20) and non-binge drinkers (n = 20) underwent DTI and completed measures of spatial working memory and motor impulsivity. Fractional anisotropy (FA), a measure derived from DTI, was estimated from whole brain and from five segments of the corpus callosum (CC): prefrontal, premotor/supplementary motor, motor, (SMA) sensory and parietal/temporal/occipital (PTO). FA was lower for binge than for non-binge men but not women at Session 1 and 2 for all measurements except for FA in the motor segment, which was significantly increased from Session 1 to Session 2. Lower FA in the prefrontal and PTO CC segments was associated with higher binge score, whereas lower FA in all five segments was associated with greater drug use in men and worse spatial working memory both in men and women. These findings extend the literature by showing that in early adulthood, binge drinking and drug use are linked with degradations in neural white matter and that compromised white matter at this period of brain development is linked with impaired cognitive functioning. © 2015 Society for the Study of Addiction.

  16. Efficiency and prognosis of whole brain irradiation combined with precise radiotherapy on triple-negative breast cancer.

    Science.gov (United States)

    Wu, Xinhong; Luo, Bo; Wei, Shaozhong; Luo, Yan; Feng, Yaojun; Xu, Juan; Wei, Wei

    2013-11-01

    To investigate the treatment efficiency of whole brain irradiation combined with precise radiotherapy on triple-negative (TN) phenotype breast cancer patients with brain metastases and their survival times. A total of 112 metastatic breast cancer patients treated with whole brain irradiation and intensity modulated radiotherapy (IMRT) or 3D conformal radiotherapy (3DCRT) were analyzed. Thirty-seven patients were of TN phenotype. Objective response rates were compared. Survival times were estimated by using the Kaplan-Meier method. Log-rank test was used to compare the survival time difference between the TN and non-TN groups. Potential prognostic factors were determined by using a Cox proportional hazard regression model. The efficiency of radiotherapy treatment on TN and non-TN phenotypes was 96.2% and 97%, respectively. TN phenotype was associated with worse survival times than non-TN phenotype after radiotherapy (6.9 months vs. 17 months) (P brain irradiation followed by IMRT or 3DCRT treatment, TN phenotype breast cancer patients with intracranial metastasis had high objective response rates but shorter survival time. With respect to survival in breast cancer patients with intracranial metastasis, the TN phenotype represents a significant adverse prognostic factor.

  17. [NOVEL STRATEGY IN THE RADIOTHERAPY OF METASTATIC BRAIN TUMORS: SIMULTANEOUS WHOLE BRAIN RADIOTHERAPY AND INTEGRATED STEREOTACTIC RADIOSURGERY].

    Science.gov (United States)

    Kalincsák, Judit; László, Zoltán; Sebestyén, Zsolt; Kovács, Péter; Horváth, Zsolt; Dóczi, Tamás; Mangel László

    2015-11-30

    Treatment of central nervous system (CNS) tumors has always played an important role in development of radiotherapy techniques. Precise patient immobilisation, non-coplanar field arrangement, conformal treatment, arc therapy, radiosurgery, application of image fusion to radiation planning or re-irradiation were first introduced into clinical routine in the treatment of brain tumors. A modern multifunctional radiation instrument, Novalis TX has been installed at the University of Pécs two years ago. New methods, such as real time 3D image guided therapy, dynamic arc therapy and ultra-conformity offer further progress in treatment of CNS tumors. Whole brain irradiation and simultaneous fractionated stereotactic radiosurgery or integrated boost seem to be an optimal method in the treatment of not only soliter or oligo, but even a higher number (4-9) and not typically radiosensitive brain metastases. The new treatment strategy is illustrated by presentation of four case histories. Treatment protocol was completed in all cases. Treatment period of 1.5 to 3 weeks, and treatment time of only a few minutes were not stressful for the patients. A quite remarkable clinical improvement as to general condition of the patients was experienced in three cases. Follow-up images confirmed either remission or a stable disease. Simultaneous whole brain radiotherapy and integrated stereotactic radiosurgery is a reproducible, safe method that offers an effective irradiation with delivery of definitive dosage even in cases with radio-insensitive brain metastasis.

  18. Fundamental structures of dynamic social networks

    DEFF Research Database (Denmark)

    Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann

    2016-01-01

    unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit...... a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework......Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships...

  19. Dynamics and control of diseases in networks with community structure.

    Directory of Open Access Journals (Sweden)

    Marcel Salathé

    2010-04-01

    Full Text Available The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc. depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

  20. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    1995-01-01

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  1. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  2. Assessment of the structural brain network reveals altered connectivity in children with unilateral cerebral palsy due to periventricular white matter lesions.

    Science.gov (United States)

    Pannek, Kerstin; Boyd, Roslyn N; Fiori, Simona; Guzzetta, Andrea; Rose, Stephen E

    2014-01-01

    Cerebral palsy (CP) is a term to describe the spectrum of disorders of impaired motor and sensory function caused by a brain lesion occurring early during development. Diffusion MRI and tractography have been shown to be useful in the study of white matter (WM) microstructure in tracts likely to be impacted by the static brain lesion. The purpose of this study was to identify WM pathways with altered connectivity in children with unilateral CP caused by periventricular white matter lesions using a whole-brain connectivity approach. Data of 50 children with unilateral CP caused by periventricular white matter lesions (5-17 years; manual ability classification system [MACS] I = 25/II = 25) and 17 children with typical development (CTD; 7-16 years) were analysed. Structural and High Angular Resolution Diffusion weighted Images (HARDI; 64 directions, b = 3000 s/mm(2)) were acquired at 3 T. Connectomes were calculated using whole-brain probabilistic tractography in combination with structural parcellation of the cortex and subcortical structures. Connections with altered fractional anisotropy (FA) in children with unilateral CP compared to CTD were identified using network-based statistics (NBS). The relationship between FA and performance of the impaired hand in bimanual tasks (Assisting Hand Assessment-AHA) was assessed in connections that showed significant differences in FA compared to CTD. FA was reduced in children with unilateral CP compared to CTD. Seven pathways, including the corticospinal, thalamocortical, and fronto-parietal association pathways were identified simultaneously in children with left and right unilateral CP. There was a positive relationship between performance of the impaired hand in bimanual tasks and FA within the cortico-spinal and thalamo-cortical pathways (r(2) = 0.16-0.44; p < 0.05). This study shows that network-based analysis of structural connectivity can identify alterations in FA in unilateral CP, and that these

  3. How structure determines correlations in neuronal networks

    National Research Council Canada - National Science Library

    Pernice, Volker; Staude, Benjamin; Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

    Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting...

  4. Towards structural controllability of local-world networks

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Shiwen, E-mail: sunsw80@126.com [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China); Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China)

    2016-05-20

    Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.

  5. Social inheritance can explain the structure of animal social networks

    Science.gov (United States)

    Ilany, Amiyaal; Akçay, Erol

    2016-01-01

    The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance. PMID:27352101

  6. Social inheritance can explain the structure of animal social networks.

    Science.gov (United States)

    Ilany, Amiyaal; Akçay, Erol

    2016-06-28

    The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance.

  7. PROSPECTS OF REGIONAL NETWORK STRUCTURES IN THE SOUTHERN FEDERAL DISTRICT

    Directory of Open Access Journals (Sweden)

    I. V. Morozov

    2014-01-01

    Full Text Available The article reveals the possibility of the Southern Federal District to form regional network structures. The prospects for the formation of networks in the region in relation to the Olympic Winter Games Sochi 2014.

  8. Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

    OpenAIRE

    Mucha, Peter J; Richardson, Thomas; Macon, Kevin; Porter, Mason A.; Onnela, Jukka-Pekka

    2009-01-01

    Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly-connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that con...

  9. A Decomposition Algorithm for Learning Bayesian Network Structures from Data

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Cordero Hernandez, Jorge

    2008-01-01

    It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

  10. Sparse and shrunken estimates of MRI networks in the brain and their influence on network properties

    DEFF Research Database (Denmark)

    Romero-Garcia, Rafael; Clemmensen, Line Katrine Harder

    2014-01-01

    Estimation of morphometric relationships between cortical regions is a widely used approach to identify and characterize structural connectivity. The elevated number of regions that can be considered in a whole-brain correlation analysis might lead to overfitted models. However, the overfitting can...... approaches showed more stable results with a relative low variance at the expense of a little bias. Interestingly, topological properties as local and global efficiency estimated in networks constructed from traditional non-regularized correlations also showed higher variability when compared to those from...

  11. Whole brain volume changes and its correlation with clinical symptom severity in patients with schizophrenia: A DARTEL-based VBM study.

    Directory of Open Access Journals (Sweden)

    Gwang-Won Kim

    Full Text Available The purpose of this study was to evaluate gray matter (GM and white matter (WM volume alterations in whole-brain structures in patients with schizophrenia and healthy controls using voxel-based morphometry (VBM, and further to assess the correlation between GM and WM volume variations and symptom severity in schizophrenia. A total of 22 patients with schizophrenia and 22 age-matched healthy controls participated. Magnetic resonance image data were processed using SPM8 software with diffeomorphic anatomical registration via an exponentiated Lie algebra (DARTEL algorithm. Patients with schizophrenia exhibited significantly decreased GM volumes of the insula, superior temporal gyrus (STG, gyrus rectus, and anterior cingulate cortex (ACC compared with healthy controls. The GM volumes of the STG and gyrus rectus were negatively correlated with the positive scales on the Positive and Negative Syndrome Scale (PANSS and those of the STG and ACC were negatively correlated with the negative scales. The durations of illness in schizophrenia were negatively correlated with the GM volumes of the insula, STG, and ACC. Patients with schizophrenia exhibited significantly decreased WM volumes of the superior frontal gyrus, inferior temporal gyrus, and STG. The WM volumes of the STG were negatively correlated with the duration of illness. Our findings suggest that GM and WM volume abnormalities in the STG are associated with the psychopathology of schizophrenia.

  12. Whole brain volume changes and its correlation with clinical symptom severity in patients with schizophrenia: A DARTEL-based VBM study.

    Science.gov (United States)

    Kim, Gwang-Won; Kim, Yun-Hyeon; Jeong, Gwang-Woo

    2017-01-01

    The purpose of this study was to evaluate gray matter (GM) and white matter (WM) volume alterations in whole-brain structures in patients with schizophrenia and healthy controls using voxel-based morphometry (VBM), and further to assess the correlation between GM and WM volume variations and symptom severity in schizophrenia. A total of 22 patients with schizophrenia and 22 age-matched healthy controls participated. Magnetic resonance image data were processed using SPM8 software with diffeomorphic anatomical registration via an exponentiated Lie algebra (DARTEL) algorithm. Patients with schizophrenia exhibited significantly decreased GM volumes of the insula, superior temporal gyrus (STG), gyrus rectus, and anterior cingulate cortex (ACC) compared with healthy controls. The GM volumes of the STG and gyrus rectus were negatively correlated with the positive scales on the Positive and Negative Syndrome Scale (PANSS) and those of the STG and ACC were negatively correlated with the negative scales. The durations of illness in schizophrenia were negatively correlated with the GM volumes of the insula, STG, and ACC. Patients with schizophrenia exhibited significantly decreased WM volumes of the superior frontal gyrus, inferior temporal gyrus, and STG. The WM volumes of the STG were negatively correlated with the duration of illness. Our findings suggest that GM and WM volume abnormalities in the STG are associated with the psychopathology of schizophrenia.

  13. Whole-brain radiation fails to boost intracerebral gefitinib concentration in patients with brain metastatic non-small cell lung cancer: a self-controlled, pilot study.

    Science.gov (United States)

    Fang, Luo; Sun, Xiaojiang; Song, Yu; Zhang, Yiwen; Li, Fanzhu; Xu, Yaping; Ma, Shenglin; Lin, Nengming

    2015-10-01

    Whole-brain radiation therapy (WBRT) is generally considered as an efficient strategy to improve blood-brain barrier (BBB) permeability by damaging BBB structure and is therefore, used as a promising pretreatment of chemotherapy. However, the impact of radiotherapy on leaky BBB is still controversial for the reason that BBB of metastatic brain tumor lesion had been breached by tumor metastasizing. Herein, we conducted a self-controlled study to evaluate the effect of WBRT on the permeability of BBB in non-small cell lung cancer (NSCLC) patients with brain metastases (BM). A prospective self-controlled research was performed. Radiation-naive NSCLC patients with BM were enrolled and treated with gefitinib for 2 weeks, and then concurrently combined with WBRT for 2 weeks. Plasma and cerebrospinal fluid (CSF) before and after WBRT were collected on day 15 and 29 after the initiation of gefitinib treatment. The concentrations of gefitinib in these samples were measured by HPLC. Three patients were enrolled and evaluated. The concentrations of gefitinib in plasma and CSF pre-WBRT were comparable to those of post-WBRT. Consequently, no significant change was noted in the CSF-to-plasma ratios of gefitinib before and after WBRT (2.79 ± 1.47 vs. 2.35 ± 1.74 %, p = 0.123). The WBRT may not affect the BBB permeability by determining the concentration of gefitinib in NSCLC patients with BM.

  14. Global diffusion tensor imaging derived metrics differentiate glioblastoma multiforme vs. normal brains by using discriminant analysis: introduction of a novel whole-brain approach

    Science.gov (United States)

    Roldan-Valadez, Ernesto; Rios, Camilo; Cortez-Conradis, David; Favila, Rafael; Moreno-Jimenez, Sergio

    2014-01-01

    Background Histological behavior of glioblastoma multiforme suggests it would benefit more from a global rather than regional evaluation. A global (whole-brain) calculation of diffusion tensor imaging (DTI) derived tensor metrics offers a valid method to detect the integrity of white matter structures without missing infiltrated brain areas not seen in conventional sequences. In this study we calculated a predictive model of brain infiltration in patients with glioblastoma using global tensor metrics. Methods Retrospective, case and control study; 11 global DTI-derived tensor metrics were calculated in 27 patients with glioblastoma multiforme and 34 controls: mean diffusivity, fractional anisotropy, pure isotropic diffusion, pure anisotropic diffusion, the total magnitude of the diffusion tensor, linear tensor, planar tensor, spherical tensor, relative anisotropy, axial diffusivity and radial diffusivity. The multivariate discriminant analysis of these variables (including age) with a diagnostic test evaluation was performed. Results The simultaneous analysis of 732 measures from 12 continuous variables in 61 subjects revealed one discriminant model that significantly differentiated normal brains and brains with glioblastoma: Wilks’ λ = 0.324, χ2 (3) = 38.907, p < .001. The overall predictive accuracy was 92.7%. Conclusions We present a phase II study introducing a novel global approach using DTI-derived biomarkers of brain impairment. The final predictive model selected only three metrics: axial diffusivity, spherical tensor and linear tensor. These metrics might be clinically applied for diagnosis, follow-up, and the study of other neurological diseases. PMID:24991202

  15. Phenology drives mutualistic network structure and diversity

    NARCIS (Netherlands)

    Encinas Viso, Francisco; Revilla, Tomas A; Etienne, Rampal S.

    Several network properties have been identified as determinants of the stability and complexity of mutualistic networks. However, it is unclear which mechanisms give rise to these network properties. Phenology seems important, because it shapes the topology of mutualistic networks, but its effects

  16. Analysis of Ego Network Structure in Online Social Networks

    OpenAIRE

    Arnaboldi, Valerio; Conti, Marco; Passarella, Andrea; Pezzoni, Fabio

    2012-01-01

    Results about offline social networks demonstrated that the social relationships that an individual (ego) maintains with other people (alters) can be organised into different groups according to the ego network model. In this model the ego can be seen as the centre of a series of layers of increasing size. Social relationships between ego and alters in layers close to ego are stronger than those belonging to more external layers. Online Social Networks are becoming a fundamental medium for hu...

  17. A hierarchical method for whole-brain connectivity-based parcellation

    OpenAIRE

    Moreno-Dominguez, D.; Anwander, A.; Knösche, T.

    2014-01-01

    In modern neuroscience there is general agreement that brain function relies on networks and that connectivity is therefore of paramount importance for brain function. Accordingly, the delineation of functional brain areas on the basis of diffusion magnetic resonance imaging (dMRI) and tractography may lead to highly relevant brain maps. Existing methods typically aim to find a predefined number of areas and/or are limited to small regions of grey matter. However, it is in general not likely ...

  18. Structural equation models from paths to networks

    CERN Document Server

    Westland, J Christopher

    2015-01-01

    This compact reference surveys the full range of available structural equation modeling (SEM) methodologies.  It reviews applications in a broad range of disciplines, particularly in the social sciences where many key concepts are not directly observable.  This is the first book to present SEM’s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method.  This book also surveys the emerging path and network approaches that complement and enhance SEM, and that will grow in importance in the near future.  SEM’s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists.  Latent variable theory and application are comprehensively explained, and methods are presented for extending their power, including guidelines for data preparation, sample size calculation, and the special treatment of Likert scale data.  Tables of software, methodologies and fit st...

  19. Evaluation of group-specific, whole-brain atlas generation using Volume-based Template Estimation (VTE): application to normal and Alzheimer's populations.

    Science.gov (United States)

    Zhang, Yajing; Zhang, Jiangyang; Hsu, Johnny; Oishi, Kenichi; Faria, Andreia V; Albert, Marilyn; Miller, Michael I; Mori, Susumu

    2014-01-01

    MRI-based human brain atlases, which serve as a common coordinate system for image analysis, play an increasingly important role in our understanding of brain anatomy, image registration, and segmentation. Study-specific brain atlases are often obtained from one of the subjects in a study or by averaging the images of all participants after linear or non-linear registration. The latter approach has the advantage of providing an unbiased anatomical representation of the study population. But, the image contrast is influenced by both inherent MR contrasts and residual anatomical variability after the registration; in addition, the topology of the brain structures cannot reliably be preserved. In this study, we demonstrated a population-based template-creation approach, which is based on Bayesian template estimation on a diffeomorphic random orbit model. This approach attempts to define a population-representative template without the cross-subject intensity averaging; thus, the topology of the brain structures is preserved. It has been tested for segmented brain structures, such as the hippocampus, but its validity on whole-brain MR images has not been examined. This paper validates and evaluates this atlas generation approach, i.e., Volume-based Template Estimation (VTE). Using datasets from normal subjects and Alzheimer's patients, quantitative measurements of sub-cortical structural volumes, metric distance, displacement vector, and Jacobian were examined to validate the group-averaged shape features of the VTE. In addition to the volume-based quantitative analysis, the preserved brain topology of the VTE allows surface-based analysis within the same atlas framework. This property was demonstrated by analyzing the registration accuracy of the pre- and post-central gyri. The proposed method achieved registration accuracy within 1mm for these population-preserved cortical structures in an elderly population. Published by Elsevier Inc.

  20. Epidemic spreading on complex networks with community structures

    CERN Document Server

    Stegehuis, Clara; van Leeuwaarden, Johan S H

    2016-01-01

    Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities and the vertex degrees. These models show that community structure is an important determinant of the behavior of percolation processes on networks, such as information diffusion or virus spreading: the community structure can both \\textit{enforce} as well as \\textit{inhibit} diffusion processes. Our models further show that it is the mesoscopic set of communities that matters. The exact internal structures of communities barely influence the behavior of percolation processes across networks. This insensitivity is likely due to the relative denseness of the communities.

  1. Distance metric learning for complex networks: Towards size-independent comparison of network structures

    Science.gov (United States)

    Aliakbary, Sadegh; Motallebi, Sadegh; Rashidian, Sina; Habibi, Jafar; Movaghar, Ali

    2015-02-01

    Real networks show nontrivial topological properties such as community structure and long-tail degree distribution. Moreover, many network analysis applications are based on topological comparison of complex networks. Classification and clustering of networks, model selection, and anomaly detection are just some applications of network comparison. In these applications, an effective similarity metric is needed which, given two complex networks of possibly different sizes, evaluates the amount of similarity between the structural features of the two networks. Traditional graph comparison approaches, such as isomorphism-based methods, are not only too time consuming but also inappropriate to compare networks with different sizes. In this paper, we propose an intelligent method based on the genetic algorithms for integrating, selecting, and weighting the network features in order to develop an effective similarity measure for complex networks. The proposed similarity metric outperforms state of the art methods with respect to different evaluation criteria.

  2. Rationale for the Use of Upfront Whole Brain Irradiation in Patients with Brain Metastases from Breast Cancer

    Directory of Open Access Journals (Sweden)

    Agnes V. Tallet

    2014-05-01

    Full Text Available Breast cancer is the second most common cause of brain metastases and deserves particular attention in relation to current prolonged survival of patients with metastatic disease. Advances in both systemic therapies and brain local treatments (surgery and stereotactic radiosurgery have led to a reappraisal of brain metastases management. With respect to this, the literature review presented here was conducted in an attempt to collect medical evidence-based data on the use of whole-brain radiotherapy for the treatment of brain metastases from breast cancer. In addition, this study discusses here the potential differences in outcomes between patients with brain metastases from breast cancer and those with brain metastases from other primary malignancies and the potential implications within a treatment strategy.

  3. Rationale for the use of upfront whole brain irradiation in patients with brain metastases from breast cancer.

    Science.gov (United States)

    Tallet, Agnes V; Azria, David; Le Rhun, Emilie; Barlesi, Fabrice; Carpentier, Antoine F; Gonçalves, Antony; Taillibert, Sophie; Dhermain, Frédéric; Spano, Jean-Philippe; Metellus, Philippe

    2014-05-08

    Breast cancer is the second most common cause of brain metastases and deserves particular attention in relation to current prolonged survival of patients with metastatic disease. Advances in both systemic therapies and brain local treatments (surgery and stereotactic radiosurgery) have led to a reappraisal of brain metastases management. With respect to this, the literature review presented here was conducted in an attempt to collect medical evidence-based data on the use of whole-brain radiotherapy for the treatment of brain metastases from breast cancer. In addition, this study discusses here the potential differences in outcomes between patients with brain metastases from breast cancer and those with brain metastases from other primary malignancies and the potential implications within a treatment strategy.

  4. A mathematical model for networks with structures in the mesoscale

    OpenAIRE

    Criado, Regino; Flores, Julio; Gacia Del Amo, Alejandro Jose; Gómez, Jesus; Romance, Miguel

    2011-01-01

    Abstract The new concept of multilevel network is introduced in order to embody some topological properties of complex systems with structures in the mesoscale which are not completely captured by the classical models. This new model, which generalizes the hyper-network and hyper-structure models, fits perfectly with several real-life complex systems, including social and public transportation networks. We present an analysis of the structural properties of the mu...

  5. Whole brain MP2RAGE-based mapping of the longitudinal relaxation time at 9.4T.

    Science.gov (United States)

    Hagberg, G E; Bause, J; Ethofer, T; Ehses, P; Dresler, T; Herbert, C; Pohmann, R; Shajan, G; Fallgatter, A; Pavlova, M A; Scheffler, K

    2017-01-01

    Mapping of the longitudinal relaxation time (T1) with high accuracy and precision is central for neuroscientific and clinical research, since it opens up the possibility to obtain accurate brain tissue segmentation and gain myelin-related information. An ideal, quantitative method should enable whole brain coverage within a limited scan time yet allow for detailed sampling with sub-millimeter voxel sizes. The use of ultra-high magnetic fields is well suited for this purpose, however the inhomogeneous transmit field potentially hampers its use. In the present work, we conducted whole brain T1 mapping based on the MP2RAGE sequence at 9.4T and explored potential pitfalls for automated tissue classification compared with 3T. Data accuracy and T2-dependent variation of the adiabatic inversion efficiency were investigated by single slice T1 mapping with inversion recovery EPI measurements, quantitative T2 mapping using multi-echo techniques and simulations of the Bloch equations. We found that the prominent spatial variation of the transmit field at 9.4T (yielding flip angles between 20% and 180% of nominal values) profoundly affected the result of image segmentation and T1 mapping. These effects could be mitigated by correcting for both flip angle and inversion efficiency deviations. Based on the corrected T1 maps, new, 'flattened', MP2RAGE contrast images were generated, that were no longer affected by variations of the transmit field. Unlike the uncorrected MP2RAGE contrast images acquired at 9.4T, these flattened images yielded image segmentations comparable to 3T, making bias-field correction prior to image segmentation and tissue classification unnecessary. In terms of the T1 estimates at high field, the proposed correction methods resulted in an improved precision, with test-retest variability below 1% and a coefficient-of-variation across 25 subjects below 3%. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Treatment planning and 3D dose verification of whole brain radiation therapy with hippocampal avoidance in rats

    Science.gov (United States)

    Yoon, S. W.; Miles, D.; Cramer, C.; Reinsvold, M.; Kirsch, D.; Oldham, M.

    2017-05-01

    Despite increasing use of stereotactic radiosurgery, whole brain radiotherapy (WBRT) continues to have a therapeutic role in a selected subset of patients. Selectively avoiding the hippocampus during such treatment (HA-WBRT) emerged as a strategy to reduce the cognitive morbidity associated with WBRT and gave rise to a recently published the phase II trial (RTOG 0933) and now multiple ongoing clinical trials. While conceptually hippocampal avoidance is supported by pre-clinical evidence showing that the hippocampus plays a vital role in memory, there is minimal pre-clinic data showing that selectively avoiding the hippocampus will reduce radiation-induced cognitive decline. Largely the lack of pre-clinical evidence can be attributed to the technical hurdles associated with delivering precise conformal treatment the rat brain. In this work we develop a novel conformal HA-WBRT technique for Wistar rats, utilizing a 225kVp micro-irradiator with precise 3D-printed radiation blocks designed to spare hippocampus while delivering whole brain dose. The technique was verified on rodent-morphic Presage® 3D dosimeters created from micro-CT scans of Wistar rats with Duke Large Field-of-View Optical Scanner (DLOS) at 1mm isotropic voxel resolution. A 4-field box with parallel opposed AP-PA and two lateral opposed fields was explored with conformal hippocampal sparing aided by 3D-printed radiation blocks. The measured DVH aligned reasonably well with that calculated from SmART Plan Monte Carlo simulations with simulated blocks for 4-field HA-WBRT with both demonstrating hippocampal sparing of 20% volume receiving less than 30% the prescription dose.

  7. Individualized prediction of schizophrenia based on the whole-brain pattern of altered white matter tract integrity.

    Science.gov (United States)

    Chen, Yu-Jen; Liu, Chih-Min; Hsu, Yung-Chin; Lo, Yu-Chun; Hwang, Tzung-Jeng; Hwu, Hai-Gwo; Lin, Yi-Tin; Tseng, Wen-Yih Isaac

    2018-01-01

    A schizophrenia diagnosis relies on characteristic symptoms identified by trained physicians, and is thus prone to subjectivity. This study developed a procedure for the individualized prediction of schizophrenia based on whole-brain patterns of altered white matter tract integrity. The study comprised training (108 patients and 144 controls) and testing (60 patients and 60 controls) groups. Male and female participants were comparable in each group and were analyzed separately. All participants underwent diffusion spectrum imaging of the head, and the data were analyzed using the tract-based automatic analysis method to generate a standardized two-dimensional array of white matter tract integrity, called the connectogram. Unique patterns in the connectogram that most accurately identified schizophrenia were systematically reviewed in the training group. Then, the diagnostic performance of the patterns was individually verified in the testing group by using receiver-operating characteristic curve analysis. The performance was high in men (accuracy = 0.85) and satisfactory in women (accuracy = 0.75). In men, the pattern was located in discrete fiber tracts, as has been consistently reported in the literature; by contrast, the pattern was widespread over all tracts in women. These distinct patterns suggest that there is a higher variability in the microstructural alterations in female patients than in male patients. The individualized prediction of schizophrenia is feasible based on the different whole-brain patterns of tract integrity. The optimal masks and their corresponding regions in the fiber tracts could serve as potential imaging biomarkers for schizophrenia. Hum Brain Mapp 39:575-587, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  8. Whole brain-based analysis of regional white matter tract alterations in rare motor neuron diseases by diffusion tensor imaging.

    Science.gov (United States)

    Unrath, Alexander; Müller, Hans-Peter; Riecker, Axel; Ludolph, Albert C; Sperfeld, Anne-Dorte; Kassubek, Jan

    2010-11-01

    Different motor neuron disorders (MNDs) are mainly defined by the clinical presentation based on the predominance of upper or lower motor neuron impairment and the course of the disease. Magnetic resonance imaging (MRI) mostly serves as a tool to exclude other pathologies, but novel approaches such as diffusion tensor imaging (DTI) have begun to add information on the underlying pathophysiological processes of these disorders in vivo. The present study was designed to investigate three different rare MNDs, i.e., primary lateral sclerosis (PLS, N = 25), hereditary spastic paraparesis (HSP, N = 24), and X-linked spinobulbar muscular atrophy (X-SBMA, N = 20), by use of whole-brain-based DTI analysis in comparison with matched controls. This analysis of white matter (WM) impairment revealed widespread and characteristic patterns of alterations within the motor system with a predominant deterioration of the corticospinal tract (CST) in HSP and PLS patients according to the clinical presentation and also in patients with X-SBMA to a lesser degree, but also WM changes in projections to the limbic system and within distinct areas of the corpus callosum (CC), the latter both for HSP and PLS. In summary, DTI was able to define a characteristic WM pathoanatomy in motor and extra-motor brain areas, such as the CC and the limbic projectional system, for different MNDs via whole brain-based FA assessment and quantitative fiber tracking. Future advanced MRI-based investigations might help to provide a fingerprint-identification of MNDs. © 2010 Wiley-Liss, Inc.

  9. Whole-brain three-dimensional T2-weighted BOLD functional magnetic resonance imaging at 7 Tesla.

    Science.gov (United States)

    Hua, Jun; Qin, Qin; van Zijl, Peter C M; Pekar, James J; Jones, Craig K

    2014-12-01

    A new acquisition scheme for T2-weighted spin-echo BOLD fMRI is introduced. It uses a T2-preparation module to induce blood-oxygenation-level-dependent (BOLD) contrast, followed by a single-shot three-dimensional (3D) fast gradient-echo readout with short echo time (TE). It differs from most spin-echo BOLD sequences in that BOLD contrast is generated before the readout, which eliminates the "dead time" due to long TE required for T2 contrast, and substantially improves acquisition efficiency. This approach, termed "3D T2prep-GRE," was implemented at 7 Tesla (T) with a typical spatial (2.5 × 2.5 × 2.5 mm(3) ) and temporal (TR = 2.3 s) resolution for functional MRI (fMRI) and whole-brain coverage (55 slices), and compared with the widely used 2D spin-echo EPI sequence. In fMRI experiments of simultaneous visual/motor activities, 3D T2prep-GRE showed minimal distortion and little signal dropout across the whole brain. Its lower power deposition allowed greater spatial coverage (55 versus 17 slices with identical TR, resolution and power level), temporal SNR (60% higher) and CNR (35% higher) efficiency than 2D spin-echo EPI. It also showed smaller T2* contamination. This approach is expected to be useful for ultra-high field fMRI, especially for regions near air cavities. The concept of using T2-preparation to generate BOLD contrast can be combined with many other sequences at any field strength. © 2013 Wiley Periodicals, Inc.

  10. Quality of life and symptoms control in brain metastasis after palliative whole brain radiotherapy using two different protocols.

    Science.gov (United States)

    Akhtar, Muhammad Sohail; Kousar, Farzana; Fatmi, Shahab; Jabeen, Kaukab; Akhtar, Kalsoom

    2012-05-01

    To compare the quality of life and symptomatic improvement after palliative radiotherapy to brain metastasis using two different treatment protocols. Comparative study. Bahawalpur Institute of Nuclear Medicine and Oncology, Bahawalpur, from January 2009 to November 2010. Patients presenting with brain metastasis referred to Bahawalpur Institute of Nuclear Medicine and Oncology, Bahawalpur for whole brain radiotherapy (WBRT) were included. Patients were divided in two groups. In group A WBRT 30 Gys in 10 fractions were given. While in group B 30 Gys in 15 fractions to whole brain followed by 20 Gys in 10 fractions boost to primary metastatic site with 2 cm margins were given. Follow-up was done at 1 and 3 months. A total of 46 patients with brain metastasis were enrolled with median Karnofsky performance score 50. Median age was 64 years. Most common presenting symptoms were headache, weakness, balance problem and early fatigability. Fifty percent of patients had improvement in their presenting symptoms after completion of palliative radiotherapy at one month and three months follow-up. There was a statistically significant improvement in headache, nausea or vomiting, focal weakness, dizziness, balance problem and problems with smell, hearing and tingling sensation in group B patients as compared to group A. More controlled and better quality of life was observed in patient given 30 Gys in 15 fractions followed by a boost of 20 fractions to primary metastatic site versus WBRT with 30 Gys in 10 fractions and in patients with metastatic sites are less than three and having difference not more than 2 cm apart between two metastatic sites.

  11. High-resolution whole-brain DCE-MRI using constrained reconstruction: Prospective clinical evaluation in brain tumor patients

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yi, E-mail: yiguo@usc.edu; Zhu, Yinghua; Lingala, Sajan Goud; Nayak, Krishna [Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California 90089 (United States); Lebel, R. Marc [GE Healthcare, Calgary, Alberta AB T2P 1G1 (Canada); Shiroishi, Mark S.; Law, Meng [Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California 90033 (United States)

    2016-05-15

    Purpose: To clinically evaluate a highly accelerated T1-weighted dynamic contrast-enhanced (DCE) MRI technique that provides high spatial resolution and whole-brain coverage via undersampling and constrained reconstruction with multiple sparsity constraints. Methods: Conventional (rate-2 SENSE) and experimental DCE-MRI (rate-30) scans were performed 20 minutes apart in 15 brain tumor patients. The conventional clinical DCE-MRI had voxel dimensions 0.9 × 1.3 × 7.0 mm{sup 3}, FOV 22 × 22 × 4.2 cm{sup 3}, and the experimental DCE-MRI had voxel dimensions 0.9 × 0.9 × 1.9 mm{sup 3}, and broader coverage 22 × 22 × 19 cm{sup 3}. Temporal resolution was 5 s for both protocols. Time-resolved images and blood–brain barrier permeability maps were qualitatively evaluated by two radiologists. Results: The experimental DCE-MRI scans showed no loss of qualitative information in any of the cases, while achieving substantially higher spatial resolution and whole-brain spatial coverage. Average qualitative scores (from 0 to 3) were 2.1 for the experimental scans and 1.1 for the conventional clinical scans. Conclusions: The proposed DCE-MRI approach provides clinically superior image quality with higher spatial resolution and coverage than currently available approaches. These advantages may allow comprehensive permeability mapping in the brain, which is especially valuable in the setting of large lesions or multiple lesions spread throughout the brain.

  12. Evolving networks-Using past structure to predict the future

    Science.gov (United States)

    Shang, Ke-ke; Yan, Wei-sheng; Small, Michael

    2016-08-01

    Many previous studies on link prediction have focused on using common neighbors to predict the existence of links between pairs of nodes. More broadly, research into the structural properties of evolving temporal networks and temporal link prediction methods have recently attracted increasing attention. In this study, for the first time, we examine the use of links between a pair of nodes to predict their common neighbors and analyze the relationship between the weight and the structure in static networks, evolving networks, and in the corresponding randomized networks. We propose both new unweighted and weighted prediction methods and use six kinds of real networks to test our algorithms. In unweighted networks, we find that if a pair of nodes connect to each other in the current network, they will have a higher probability to connect common nodes both in the current and the future networks-and the probability will decrease with the increase of the number of neighbors. Furthermore, we find that the original networks have their particular structure and statistical characteristics which benefit link prediction. In weighted networks, the prediction algorithm performance of networks which are dominated by human factors decrease with the decrease of weight and are in general better in static networks. Furthermore, we find that geographical position and link weight both have significant influence on the transport network. Moreover, the evolving financial network has the lowest predictability. In addition, we find that the structure of non-social networks has more robustness than social networks. The structure of engineering networks has both best predictability and also robustness.

  13. Network nestedness as generalized core-periphery structures

    CERN Document Server

    Lee, Sang Hoon

    2016-01-01

    The concept of nestedness, in particular for ecological and economical networks, has been introduced as a structural characteristic of real interacting systems. We suggest that the nestedness is in fact another way to express a mesoscale network property called the core-periphery structure. With real ecological mutualistic networks and synthetic model networks, we reveal the strong correlation between the nestedness and core-peripheriness, by defining the network-level measures for nestedness and core-peripheriness in case of weighted and bipartite networks. However, at the same time, via more sophisticated null-model analysis, we also discover that the degree (the number of connected neighbors of a node) distribution poses quite severe restrictions on the possible nestedness and core-peripheriness parameter space. Therefore, there must exist structurally interwoven properties in more fundamental levels of network formation, behind this seemingly obvious relation between nestedness and core-periphery structur...

  14. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  15. Kinematic Structural Modelling in Bayesian Networks

    Science.gov (United States)

    Schaaf, Alexander; de la Varga, Miguel; Florian Wellmann, J.

    2017-04-01

    We commonly capture our knowledge about the spatial distribution of distinct geological lithologies in the form of 3-D geological models. Several methods exist to create these models, each with its own strengths and limitations. We present here an approach to combine the functionalities of two modeling approaches - implicit interpolation and kinematic modelling methods - into one framework, while explicitly considering parameter uncertainties and thus model uncertainty. In recent work, we proposed an approach to implement implicit modelling algorithms into Bayesian networks. This was done to address the issues of input data uncertainty and integration of geological information from varying sources in the form of geological likelihood functions. However, one general shortcoming of implicit methods is that they usually do not take any physical constraints into consideration, which can result in unrealistic model outcomes and artifacts. On the other hand, kinematic structural modelling intends to reconstruct the history of a geological system based on physically driven kinematic events. This type of modelling incorporates simplified, physical laws into the model, at the cost of a substantial increment of usable uncertain parameters. In the work presented here, we show an integration of these two different modelling methodologies, taking advantage of the strengths of both of them. First, we treat the two types of models separately, capturing the information contained in the kinematic models and their specific parameters in the form of likelihood functions, in order to use them in the implicit modelling scheme. We then go further and combine the two modelling approaches into one single Bayesian network. This enables the direct flow of information between the parameters of the kinematic modelling step and the implicit modelling step and links the exclusive input data and likelihoods of the two different modelling algorithms into one probabilistic inference framework. In

  16. Reverse Logistics Network Structures and Design

    NARCIS (Netherlands)

    M. Fleischmann (Moritz)

    2001-01-01

    textabstractLogistics network design is commonly recognized as a strategic supply chain issue of prime importance. The location of production facilities, storage concepts, and transportation strategies are major determinants of supply chain performance. This chapter considers logistics network

  17. The structure of replicating kinetoplast DNA networks

    OpenAIRE

    1993-01-01

    Kinetoplast DNA (kDNA), the mitochondrial DNA of Crithidia fasciculata and related trypanosomatids, is a network containing approximately 5,000 covalently closed minicircles which are topologically interlocked. kDNA synthesis involves release of covalently closed minicircles from the network, and, after replication of the free minicircles, reattachment of the nicked or gapped progeny minicircles to the network periphery. We have investigated this process by electron microscopy of networks at ...

  18. Structural Antecedents of Corporate Network Evolution

    NARCIS (Netherlands)

    F.H. Wijen (Frank); N. Noorderhaven (Niels); W. Vanhaverbeke (Wim)

    2011-01-01

    textabstractAbstract: While most network studies adopt a static view, we argue that corporate social networks are subject to endogenous dynamics of cognitive path dependence and self-reinforcing power relations. Over time, these dynamics drive corporate networks to become increasingly focused (i.e.,

  19. Health and the Structure of Adolescent Social Networks

    Science.gov (United States)

    Haas, Steven A.; Schaefer, David R.; Kornienko, Olga

    2010-01-01

    Much research has explored the role of social networks in promoting health through the provision of social support. However, little work has examined how social networks themselves may be structured by health. This article investigates the link between individuals' health and the characteristics of their social network positions.We first develop…

  20. Stable and emergent network topologies : A structural approach

    NARCIS (Netherlands)

    Herman Monsuur

    2007-01-01

    Economic, social and military networks have at least one thing in common: they change over time. For various reasons, nodes form and terminate links, thereby rearranging the network. In this paper, we present a structural network mechanism that formalizes a possible incentive that guides nodes in

  1. 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. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  2. Structural Behavioral Study on the General Aviation Network Based on Complex Network

    Science.gov (United States)

    Zhang, Liang; Lu, Na

    2017-12-01

    The general aviation system is an open and dissipative system with complex structures and behavioral features. This paper has established the system model and network model for general aviation. We have analyzed integral attributes and individual attributes by applying the complex network theory and concluded that the general aviation network has influential enterprise factors and node relations. We have checked whether the network has small world effect, scale-free property and network centrality property which a complex network should have by applying degree distribution of functions and proved that the general aviation network system is a complex network. Therefore, we propose to achieve the evolution process of the general aviation industrial chain to collaborative innovation cluster of advanced-form industries by strengthening network multiplication effect, stimulating innovation performance and spanning the structural hole path.

  3. The National Biomedical Communications Network as a Developing Structure *

    Science.gov (United States)

    Davis, Ruth M.

    1971-01-01

    The National Biomedical Communications Network has evolved both from a set of conceptual recommendations over the last twelve years and an accumulation of needs manifesting themselves in the requests of members of the medical community. With a short history of three years this network and its developing structure have exhibited most of the stresses of technology interfacing with customer groups, and of a structure attempting to build itself upon many existing fragmentary unconnected segments of a potentially viable resourcesharing capability. In addition to addressing these topics, the paper treats a design appropriate to any network devoted to information transfer in a special interest user community. It discusses fundamentals of network design, highlighting that network structure most appropriate to a national information network. Examples are given of cost analyses of information services and certain conjectures are offered concerning the roles of national networks. PMID:5542912

  4. Random field Ising model and community structure in complex networks

    Science.gov (United States)

    Son, S.-W.; Jeong, H.; Noh, J. D.

    2006-04-01

    We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)

  5. Structural factoring approach for analyzing stochastic networks

    Science.gov (United States)

    Hayhurst, Kelly J.; Shier, Douglas R.

    1991-01-01

    The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.

  6. Exploring community structure in biological networks with random graphs.

    Science.gov (United States)

    Sah, Pratha; Singh, Lisa O; Clauset, Aaron; Bansal, Shweta

    2014-06-25

    Community structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system's functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge. Here, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks. Our model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.

  7. Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

    Science.gov (United States)

    Hosseini, S. M. Hadi; Kesler, Shelli R.

    2013-01-01

    In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672

  8. Topological properties of four networks in protein structures

    Science.gov (United States)

    Min, Seungsik; Kim, Kyungsik; Chang, Ki-Ho; Ha, Deok-Ho; Lee, Jun-Ho

    2017-11-01

    In this paper, we investigate the complex networks of interacting amino acids in protein structures. The cellular networks and their random controls are treated for the four threshold distances between atoms. The numerical simulation and analysis are relevant to the topological properties of the complex networks in the structural classification of proteins, and we mainly estimate the network's metrics from the resultant network. The cellular network is shown to exhibit a small-world feature regardless of their structural class. The protein structure presents the positive assortative coefficients, when the topological property is described as a tendency for connectivity of high-degree nodes. We particularly show that both the modularity and the small-wordness are significantly followed the increasing function against nodes.

  9. Measuring the robustness of network community structure using assortativity

    Science.gov (United States)

    Shizuka, Daizaburo; Farine, Damien R.

    2016-01-01

    The existence of discrete social clusters, or ‘communities’, is a common feature of social networks in human and nonhuman animals. The level of such community structure in networks is typically measured using an index of modularity, Q. While modularity quantifies the degree to which individuals associate within versus between social communities and provides a useful measure of structure in the social network, it assumes that the network has been well sampled. However, animal social network data is typically subject to sampling errors. In particular, the associations among individuals are often not sampled equally, and animal social network studies are often based on a relatively small set of observations. Here, we extend an existing framework for bootstrapping network metrics to provide a method for assessing the robustness of community assignment in social networks using a metric we call community assortativity (rcom). We use simulations to demonstrate that modularity can reliably detect the transition from random to structured associations in networks that differ in size and number of communities, while community assortativity accurately measures the level of confidence based on the detectability of associations. We then demonstrate the use of these metrics using three publicly available data sets of avian social networks. We suggest that by explicitly addressing the known limitations in sampling animal social network, this approach will facilitate more rigorous analyses of population-level structural patterns across social systems. PMID:26949266

  10. Exploring network structure, dynamics, and function using networkx

    Energy Technology Data Exchange (ETDEWEB)

    Hagberg, Aric [Los Alamos National Laboratory; Swart, Pieter [Los Alamos National Laboratory; S Chult, Daniel [COLGATE UNIV

    2008-01-01

    NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility mades NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for eash exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdoes-Renyi, Small World, and Barabasi-Albert models, are included. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.

  11. Diminished whole-brain but enhanced peri-sylvian connectivity in absolute pitch musicians.

    Science.gov (United States)

    Jäncke, Lutz; Langer, Nicolas; Hänggi, Jürgen

    2012-06-01

    Several anatomical studies have identified specific anatomical features within the peri-sylvian brain system of absolute pitch (AP) musicians. In this study we used graph theoretical analysis of cortical thickness covariations (as indirect indicator of connectivity) to examine whether AP musicians differ from relative pitch musicians and nonmusicians in small-world network characteristics. We measured "local connectedness" (local clustering = γ), "global efficiency of information transfer" (path length = λ), "small-worldness" (σ = γ/λ), and "degree" centrality as measures of connectivity. Although all groups demonstrated typical small-world features, AP musicians showed significant small-world alterations. "Degree" as a measure of interconnectedness was globally significantly decreased in AP musicians. These differences let us suggest that AP musicians demonstrate diminished neural integration (less connections) among distant brain regions. In addition, AP musicians demonstrated significantly increased local connectivity in peri-sylvian language areas of which the planum temporale, planum polare, Heschl's gyrus, lateral aspect of the superior temporal gyrus, STS, pars triangularis, and pars opercularis were hub regions. All of these brain areas are known to be involved in higher-order auditory processing, working or semantic memory processes. Taken together, whereas AP musicians demonstrate decreased global interconnectedness, the local connectedness in peri-sylvian brain areas is significantly higher than for relative pitch musicians and nonmusicians.

  12. Comparing Community Structure to Characteristics in Online Collegiate Social Networks

    OpenAIRE

    Traud, Amanda L.; Kelsic, Eric D.; Mucha, Peter J; Porter, Mason A.

    2008-01-01

    We study the structure of social networks of students by examining the graphs of Facebook "friendships" at five American universities at a single point in time. We investigate each single-institution network's community structure and employ graphical and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of self-identified user characteristics (residence, class year, major, and high school). We review the bas...

  13. Tensor Spectral Clustering for Partitioning Higher-order Network Structures.

    Science.gov (United States)

    Benson, Austin R; Gleich, David F; Leskovec, Jure

    2015-01-01

    Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.

  14. Network versus portfolio structure in financial systems

    Science.gov (United States)

    Kobayashi, Teruyoshi

    2013-10-01

    The question of how to stabilize financial systems has attracted considerable attention since the global financial crisis of 2007-2009. Recently, Beale et al. [Proc. Natl. Acad. Sci. USA 108, 12647 (2011)] demonstrated that higher portfolio diversity among banks would reduce systemic risk by decreasing the risk of simultaneous defaults at the expense of a higher likelihood of individual defaults. In practice, however, a bank default has an externality in that it undermines other banks’ balance sheets. This paper explores how each of these different sources of risk, simultaneity risk and externality, contributes to systemic risk. The results show that the allocation of external assets that minimizes systemic risk varies with the topology of the financial network as long as asset returns have negative correlations. In the model, a well-known centrality measure, PageRank, reflects an appropriately defined “infectiveness” of a bank. An important result is that the most infective bank needs not always to be the safest bank. Under certain circumstances, the most infective node should act as a firewall to prevent large-scale collective defaults. The introduction of a counteractive portfolio structure will significantly reduce systemic risk.

  15. Wireless sensor networks for structural health monitoring

    CERN Document Server

    Cao, Jiannong

    2016-01-01

    This brief covers the emerging area of wireless sensor network (WSN)-based structural health monitoring (SHM) systems, and introduces the authors’ WSN-based platform called SenetSHM. It helps the reader differentiate specific requirements of SHM applications from other traditional WSN applications, and demonstrates how these requirements are addressed by using a series of systematic approaches. The brief serves as a practical guide, explaining both the state-of-the-art technologies in domain-specific applications of WSNs, as well as the methodologies used to address the specific requirements for a WSN application. In particular, the brief offers instruction for problem formulation and problem solving based on the authors’ own experiences implementing SenetSHM. Seven concise chapters cover the development of hardware and software design of SenetSHM, as well as in-field experiments conducted while testing the platform. The brief’s exploration of the SenetSHM platform is a valuable feature for civil engine...

  16. Online Social Networks: Essays on Membership, Privacy, and Structure

    NARCIS (Netherlands)

    Hofstra, B.

    2017-01-01

    The structure of social networks is crucial for obtaining social support, for meaningful connections to unknown social groups, and to overcome prejudice. Yet, we know little about the structure of social networks beyond those contacts that stand closest to us. This lack of knowledge results from a

  17. Robustness and modular structure in networks

    DEFF Research Database (Denmark)

    Bagrow, James P.; Lehmann, Sune; Ahn, Yong-Yeol

    2015-01-01

    Complex networks have recently attracted much interest due to their prevalence in nature and our daily lives [1, 2]. A critical property of a network is its resilience to random breakdown and failure [3-6], typically studied as a percolation problem [7-9] or by modeling cascading failures[10....... If overlapping modular organization plays a role in overall functionality, networks may be far more vulnerable than predicted by conventional percolation theory....

  18. Stochastic margin-based structure learning of Bayesian network classifiers.

    Science.gov (United States)

    Pernkopf, Franz; Wohlmayr, Michael

    2013-02-01

    The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers. Furthermore, greedy hill-climbing and simulated annealing search heuristics are applied to determine the classifier structures. In the experiments, we demonstrate the advantages of maximum margin optimized Bayesian network structures in terms of classification performance compared to traditionally used discriminative structure learning methods. Stochastic simulated annealing requires less score evaluations than greedy heuristics. Additionally, we compare generative and discriminative parameter learning on both generatively and discriminatively structured Bayesian network classifiers. Margin-optimized Bayesian network classifiers achieve similar classification performance as support vector machines. Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.

  19. Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease.

    Science.gov (United States)

    Moradi, Elaheh; Hallikainen, Ilona; Hänninen, Tuomo; Tohka, Jussi

    2017-01-01

    Rey's Auditory Verbal Learning Test (RAVLT) is a powerful neuropsychological tool for testing episodic memory, which is widely used for the cognitive assessment in dementia and pre-dementia conditions. Several studies have shown that an impairment in RAVLT scores reflect well the underlying pathology caused by Alzheimer's disease (AD), thus making RAVLT an effective early marker to detect AD in persons with memory complaints. We investigated the association between RAVLT scores (RAVLT Immediate and RAVLT Percent Forgetting) and the structural brain atrophy caused by AD. The aim was to comprehensively study to what extent the RAVLT scores are predictable based on structural magnetic resonance imaging (MRI) data using machine learning approaches as well as to find the most important brain regions for the estimation of RAVLT scores. For this, we built a predictive model to estimate RAVLT scores from gray matter density via elastic net penalized linear regression model. The proposed approach provided highly significant cross-validated correlation between the estimated and observed RAVLT Immediate (R = 0.50) and RAVLT Percent Forgetting (R = 0.43) in a dataset consisting of 806 AD, mild cognitive impairment (MCI) or healthy subjects. In addition, the selected machine learning method provided more accurate estimates of RAVLT scores than the relevance vector regression used earlier for the estimation of RAVLT based on MRI data. The top predictors were medial temporal lobe structures and amygdala for the estimation of RAVLT Immediate and angular gyrus, hippocampus and amygdala for the estimation of RAVLT Percent Forgetting. Further, the conversion of MCI subjects to AD in 3-years could be predicted based on either observed or estimated RAVLT scores with an accuracy comparable to MRI-based biomarkers.

  20. Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease

    Directory of Open Access Journals (Sweden)

    Elaheh Moradi

    2017-01-01

    Full Text Available Rey's Auditory Verbal Learning Test (RAVLT is a powerful neuropsychological tool for testing episodic memory, which is widely used for the cognitive assessment in dementia and pre-dementia conditions. Several studies have shown that an impairment in RAVLT scores reflect well the underlying pathology caused by Alzheimer's disease (AD, thus making RAVLT an effective early marker to detect AD in persons with memory complaints. We investigated the association between RAVLT scores (RAVLT Immediate and RAVLT Percent Forgetting and the structural brain atrophy caused by AD. The aim was to comprehensively study to what extent the RAVLT scores are predictable based on structural magnetic resonance imaging (MRI data using machine learning approaches as well as to find the most important brain regions for the estimation of RAVLT scores. For this, we built a predictive model to estimate RAVLT scores from gray matter density via elastic net penalized linear regression model. The proposed approach provided highly significant cross-validated correlation between the estimated and observed RAVLT Immediate (R = 0.50 and RAVLT Percent Forgetting (R = 0.43 in a dataset consisting of 806 AD, mild cognitive impairment (MCI or healthy subjects. In addition, the selected machine learning method provided more accurate estimates of RAVLT scores than the relevance vector regression used earlier for the estimation of RAVLT based on MRI data. The top predictors were medial temporal lobe structures and amygdala for the estimation of RAVLT Immediate and angular gyrus, hippocampus and amygdala for the estimation of RAVLT Percent Forgetting. Further, the conversion of MCI subjects to AD in 3-years could be predicted based on either observed or estimated RAVLT scores with an accuracy comparable to MRI-based biomarkers.

  1. Topological effects of network structure on long-term social network dynamics in a wild mammal.

    Science.gov (United States)

    Ilany, Amiyaal; Booms, Andrew S; Holekamp, Kay E

    2015-07-01

    Social structure influences ecological processes such as dispersal and invasion, and affects survival and reproductive success. Recent studies have used static snapshots of social networks, thus neglecting their temporal dynamics, and focused primarily on a limited number of variables that might be affecting social structure. Here, instead we modelled effects of multiple predictors of social network dynamics in the spotted hyena, using observational data collected during 20 years of continuous field research in Kenya. We tested the hypothesis that the current state of the social network affects its long-term dynamics. We employed stochastic agent-based models that allowed us to estimate the contribution of multiple factors to network changes. After controlling for environmental and individual effects, we found that network density and individual centrality affected network dynamics, but that social bond transitivity consistently had the strongest effects. Our results emphasise the significance of structural properties of networks in shaping social dynamics. © 2015 John Wiley & Sons Ltd/CNRS.

  2. Experimental Study Comparing a Traditional Approach to Performance Appraisal Training to a Whole-Brain Training Method at C.B. Fleet Laboratories

    Science.gov (United States)

    Selden, Sally; Sherrier, Tom; Wooters, Robert

    2012-01-01

    The purpose of this study is to examine the effects of a new approach to performance appraisal training. Motivated by split-brain theory and existing studies of cognitive information processing and performance appraisals, this exploratory study examined the effects of a whole-brain approach to training managers for implementing performance…

  3. Increased Stability and Breakdown of Brain Effective Connectivity During Slow-Wave Sleep : Mechanistic Insights from Whole-Brain Computational Modelling

    NARCIS (Netherlands)

    Jobst, Beatrice M; Hindriks, Rikkert; Laufs, Helmut; Tagliazucchi, E.; Hahn, Gerald; Ponce-Alvarez, Adrián; Stevner, Angus B A; Kringelbach, Morten L; Deco, Gustavo

    2017-01-01

    Recent research has found that the human sleep cycle is characterised by changes in spatiotemporal patterns of brain activity. Yet, we are still missing a mechanistic explanation of the local neuronal dynamics underlying these changes. We used whole-brain computational modelling to study the

  4. Fast and Sequence-Adaptive Whole-Brain Segmentation Using Parametric Bayesian Modeling

    DEFF Research Database (Denmark)

    Puonti, Oula; Iglesias, Juan Eugenio; Van Leemput, Koen

    2016-01-01

    Quantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate...... the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable...... accuracy to state-of-the-art methods on T1-weighted scans while being one to two orders of magnitude faster. The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data....

  5. Early diagnosis of dementia based on intersubject whole-brain dissimilarities

    DEFF Research Database (Denmark)

    Klein, S.; Loog, M.; Lijn, F. van der

    2010-01-01

    and gender matched healthy controls were selected. Each subject was registered to all other subjects, using a nonrigid registration algorithm. Based on statistics of the deformation field in the brain, a dissimilarity measure was calculated between each pair of subjects, yielding a 58×58 dissimilarity matrix......This article studies the possibility of detecting dementia in an early stage, using nonrigid registration of MR brain scans in combination with dissimilarity-based pattern recognition techniques. Instead of focussing on the shape of a single brain structure, we take into account the shape...... differences within the entire brain. Imaging data was obtained from a longitudinal, population based study of the elderly. A set of 29 subjects was identified, who were asymptomatic at the time of scanning, but were diagnosed as having dementia within 0.7 to 5 years after the scan, and a set of 29 age...

  6. Medical Image Processing for Fully Integrated Subject Specific Whole Brain Mesh Generation

    Directory of Open Access Journals (Sweden)

    Chih-Yang Hsu

    2015-05-01

    Full Text Available Currently, anatomically consistent segmentation of vascular trees acquired with magnetic resonance imaging requires the use of multiple image processing steps, which, in turn, depend on manual intervention. In effect, segmentation of vascular trees from medical images is time consuming and error prone due to the tortuous geometry and weak signal in small blood vessels. To overcome errors and accelerate the image processing time, we introduce an automatic image processing pipeline for constructing subject specific computational meshes for entire cerebral vasculature, including segmentation of ancillary structures; the grey and white matter, cerebrospinal fluid space, skull, and scalp. To demonstrate the validity of the new pipeline, we segmented the entire intracranial compartment with special attention of the angioarchitecture from magnetic resonance imaging acquired for two healthy volunteers. The raw images were processed through our pipeline for automatic segmentation and mesh generation. Due to partial volume effect and finite resolution, the computational meshes intersect with each other at respective interfaces. To eliminate anatomically inconsistent overlap, we utilized morphological operations to separate the structures with a physiologically sound gap spaces. The resulting meshes exhibit anatomically correct spatial extent and relative positions without intersections. For validation, we computed critical biometrics of the angioarchitecture, the cortical surfaces, ventricular system, and cerebrospinal fluid (CSF spaces and compared against literature values. Volumina and surface areas of the computational mesh were found to be in physiological ranges. In conclusion, we present an automatic image processing pipeline to automate the segmentation of the main intracranial compartments including a subject-specific vascular trees. These computational meshes can be used in 3D immersive visualization for diagnosis, surgery planning with haptics

  7. Patterns of relapse and late toxicity after resection and whole-brain radiotherapy for solitary brain metastases

    Energy Technology Data Exchange (ETDEWEB)

    Nieder, C.; Schnabel, K. [Univ. Hospital, Homburg/Saar (Germany). Dept. of Radiotherapy; Schwerdtfeger, K.; Steudel, W.I. [Univ. Hospital, Homburg/Saar (Germany). Dept. of Neurosurgery

    1998-05-01

    From a total of 66 patients, 52 received 10 x 3 Gy and 10 were treated with 20 x 2 Gy whole-brain radiotherapy after resection of their brain metastases. The actuarial probablity of relapse was 27% and 55% after 1 and 2 year(s), respectively. The local relapse rate (at the original site of resected brain metastases) was rather high for melanoma, non-breast adenocarcinoma, and squamous-cell carcinoma. No local relapse occurred in breast cancer and small-cell carcinoma. Failure elsewhere in the brain seemed to be influenced by extracranial disease activity. Size of brain metastases and total dose showed no correlation with relapse rate. Occurrence of brain relapse was not associated with a reduced survival time, because 10/15 patients who developed a relapse received salvage therapy. Of the patients, 11 had symptoms of late radiation toxicity (the actuarial probability was 42% after 2 years). Most results of surgical and radiosurgical studies are comparable to ours. Several randomized trials investigate surgical resection versus radiosurgery, as well as the effects of additional whole-brain radiotherapy in order to define the treatment of choice. Some data support the adjuvant application of 10 x 3 Gy over 2 weeks as a reasonable compromise when local control, toxicity, and treatment time have to be considered. (orig./MG) [Deutsch] Nach der Resektion der Hirnmetastase erhielten 52 von 66 Patienten eine Ganzhirnbestrahlung mit zehn Fraktionen von 3 Gy in zwei Wochen und zehn eine solche mit 20 Fraktionen von 2 Gy in vier Wochen. Die Kaplan-Meier-Analyse ergab eine Rezidivrate von insgesamt 27% nach einem bzw. 55% nach zwei Jahren. Rezidive im Bereich der resezierten Metastase wurden am haeufigsten bei Melanomen, Adenokarzinomen (mit Ausnahme der Mammakarzinome) und Plattenepithelkarzinomen beobachtet. Dagegen traten bei Mammakarzinomen und kleinzelligen Karzinomen keine solchen Rezidive auf. Das Auftreten von Hirnmetastasen anderer Lokalisation schien vom

  8. Clinical features of brain metastases in breast cancer: an implication for hippocampal-sparing whole-brain radiation therapy

    Directory of Open Access Journals (Sweden)

    Wu S

    2016-12-01

    Full Text Available San-Gang Wu,1,* Jia-Yuan Sun,2,* Qin Tong,3 Feng-Yan Li,2 Zhen-Yu He2 1Department of Radiation Oncology, Xiamen Cancer Hospital, The First Affiliated Hospital of Xiamen University, Xiamen, 2Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 3Department of Radiation Oncology, The First Affiliated Hospital of University of South China, Hengyang, People’s Republic of China *These authors contributed equally to this work Objective: The objectives of this study were to describe the distribution of brain metastases (BM in breast cancer patients and investigate the risk factors for perihippocampal metastases (PHM. Patients and methods: Retrospective analysis of the clinicopathological characteristics and patterns of BM was performed. Associations between clinicopathological characteristics and PHM (the hippocampus plus 5 mm margin were evaluated using logistic regression analyses. Results: A total of 1,356 brain metastatic lesions were identified in 192 patients. Patients with 1–3 BM, 4–9 BM, and ≥10 BM accounted for 63.0%, 18.8%, and 18.2%, respectively. There were only 7 (3.6% patients with hippocampal metastases (HM and 14 (7.3% patients with PHM. On logistic regression, the number of BM was an independent risk factor for PHM. Patients with ≥10 BM had a significantly higher risk of PHM compared with those with <10 BM. Breast cancer subtype (BCS was not associated with PHM. The number of BM was significantly correlated with various BCSs. Patients with hormone receptor (HR+/human epidermal growth factor receptor 2 (HER2+, HR-/HER2+, and HR-/HER2- subtypes had a higher probability of ≥10 BM, relative to patients with an HR+/HER2- subtype. Conclusion: Our study suggests that a low incidence of PHM may be acceptable to perform hippocampal-sparing whole-brain radiation therapy for breast cancer patients

  9. An efficient Volumetric Arc Therapy treatment planning approach for hippocampal-avoidance whole-brain radiation therapy (HA-WBRT)

    Energy Technology Data Exchange (ETDEWEB)

    Shen, Jin [Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY (United States); Bender, Edward [Department of Medical Physics, University of Wisconsin, Madison, WI (United States); Yaparpalvi, Ravindra; Kuo, Hsiang-Chi; Basavatia, Amar; Hong, Linda; Bodner, William; Garg, Madhur K.; Kalnicki, Shalom [Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY (United States); Tomé, Wolfgang A., E-mail: wtome@montefiore.org [Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY (United States); Department of Medical Physics, University of Wisconsin, Madison, WI (United States)

    2015-10-01

    An efficient and simple class solution is proposed for hippocampal-avoidance whole-brain radiation therapy (HA-WBRT) planning using the Volumetric Arc Therapy (VMAT) delivery technique following the NRG Oncology protocol NRG-CC001 treatment planning guidelines. The whole-brain planning target volume (PTV) was subdivided into subplanning volumes that lie in plane and out of plane with the hippocampal-avoidance volume. To further improve VMAT treatment plans, a partial-field dual-arc technique was developed. Both the arcs were allowed to overlap on the in-plane subtarget volume, and in addition, one arc covered the superior out-of-plane sub-PTV, while the other covered the inferior out-of-plane subtarget volume. For all plans (n = 20), the NRG-CC001 protocol dose-volume criteria were met. Mean values of volumes for the hippocampus and the hippocampal-avoidance volume were 4.1 cm{sup 3} ± 1.0 cm{sup 3} and 28.52 cm{sup 3} ± 3.22 cm{sup 3}, respectively. For the PTV, the average values of D{sub 2%} and D{sub 98%} were 36.1 Gy ± 0.8 Gy and 26.2 Gy ± 0.6 Gy, respectively. The hippocampus D{sub 100%} mean value was 8.5 Gy ± 0.2 Gy and the maximum dose was 15.7 Gy ± 0.3 Gy. The corresponding plan quality indices were 0.30 ± 0.01 (homogeneity index), 0.94 ± 0.01 (target conformality), and 0.75 ± 0.02 (confirmation number). The median total monitor unit (MU) per fraction was 806 MU (interquartile range [IQR]: 792 to 818 MU) and the average beam total delivery time was 121.2 seconds (IQR: 120.6 to 121.35 seconds). All plans passed the gamma evaluation using the 5-mm, 4% criteria, with γ > 1 of not more than 9.1% data points for all fields. An efficient and simple planning class solution for HA-WBRT using VMAT has been developed that allows all protocol constraints of NRG-CC001 to be met.

  10. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates ...

  11. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    Corresponding author. E-mail: Kiran.Kolwankar@gmail.com. Abstract. We study the effect of learning dynamics on network topology. Firstly, a network of dis- crete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the ...

  12. Wireless Sensor Networks : Structure and Algorithms

    NARCIS (Netherlands)

    van Dijk, T.C.|info:eu-repo/dai/nl/304841293

    2014-01-01

    In this thesis we look at various problems in wireless networking. First we consider two problems in physical-model networks. We introduce a new model for localisation. The model is based on a range-free model of radio transmissions. The first scheme is randomised and we analyse its expected

  13. Spatial Structure and Scaling of Agricultural Networks

    CERN Document Server

    Sousa, Daniel

    2016-01-01

    Considering agricultural landscapes as networks can provide information about spatial connectivity relevant for a wide range of applications including pollination, pest management, and ecology. Global agricultural networks are well-described by power law rank-size distributions. However, regional analyses capture only a subset of the total global network. Most analyses are regional. In this paper, we seek to address the following questions: Does the globally observed scale-free property of agricultural networks hold over smaller spatial domains? Can similar properties be observed at kilometer to meter scales? We analyze 9 intensively cultivated Landsat scenes on 5 continents with a wide range of vegetation distributions. We find that networks of vegetation fraction within the domain of each of these Landsat scenes exhibit substantial variability - but still possess similar scaling properties to the global distribution of agriculture. We also find similar results using a 39 km2 IKONOS image. To illustrate an a...

  14. Structure and properties of triolein-based polyurethane networks.

    Science.gov (United States)

    Zlatanić, Alisa; Petrović, Zoran S; Dusek, Karel

    2002-01-01

    Polyurethane networks based on vegetable oils have very heterogeneous composition, and it is difficult to find a close correlation between their structure and properties. To establish benchmark structure-properties relationships, we have prepared model polyurethane networks based on triolein and 4,4'-diphenylmethane diisocyanate (MDI). Cross-linking in the middle of fatty acid chains leaves significant parts of the triglyceride as dangling chains. To examine their effect on properties, we have synthesized another polyurethane network using triolein without dangling chains (removed by metathesis). The structure of polyols was studied in detail since it affects the structure of polyurethane networks. The network structure was analyzed from swelling and mechanical measurements and by applying network and rubber elasticity theories. The cross-linking density in both networks was found to be close to theoretical. The triolein-based model network displayed modulus (around 6 MPa), tensile strength (8.7 MPa), and elongation at break (136%), characteristic of hard rubbers. Glass transition temperatures of the networks from triolein and its metathesis analogue were 25 and 31.5 degrees C, respectively.

  15. The relevance of network micro-structure for neural dynamics

    Directory of Open Access Journals (Sweden)

    Volker ePernice

    2013-06-01

    Full Text Available The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previousstudies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neuronsin recurrent networks. However, typically very simple random network models are considered in such studies. Here weuse a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much morevariable than commonly used network models, and which therefore promise to sample the space of recurrent networks ina more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology insimulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive datasetof networks and neuronal simulations we assess statistical relations between features of the network structure and the spikingactivity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics ofboth single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistentrelations between activity characteristics like spike-train irregularity or correlations and network properties, for example thedistributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that itis possible to estimate structural characteristics of the network from activity data. We also assess higher order correlationsof spiking activity in the various networks considered here, and find that their occurrence strongly depends on the networkstructure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpretspike train recordings from neural circuits.

  16. Adapting Bayes Network Structures to Non-stationary Domains

    DEFF Research Database (Denmark)

    Nielsen, Søren Holbech; Nielsen, Thomas Dyhre

    2006-01-01

    When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit observations, as they are read from a database, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN...

  17. Adapting Bayes Network Structures to Non-stationary Domains

    DEFF Research Database (Denmark)

    Nielsen, Søren Holbech; Nielsen, Thomas Dyhre

    2008-01-01

    When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN...

  18. Social Network Analysis of a Supply Network Structural Investigation of the South Korean Automotive Industry

    OpenAIRE

    Kim, Jin-Baek

    2015-01-01

    Part 3: Knowledge Based Production Management; International audience; In this paper, we analyzed the structure of the South Korean automotive industry using social network analysis (SNA) metrics. Based on the data collected from 275 companies, a social network model of the supply network was constructed. Centrality measures in the SNA field were used to interpret the result and identify key companies. The results show that SNA metrics can be useful to understand the structure of a supply net...

  19. Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks

    Science.gov (United States)

    Ma, Chuang; Bao, Zhong-Kui; Zhang, Hai-Feng

    2017-10-01

    So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an adaptive fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is adaptively determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.

  20. The value of whole-brain CT perfusion imaging and CT angiography using a 320-slice CT scanner in the diagnosis of MCI and AD patients.

    Science.gov (United States)

    Zhang, Bo; Gu, Guo-Jun; Jiang, Hong; Guo, Yi; Shen, Xing; Li, Bo; Zhang, Wei

    2017-06-02

    To validate the value of whole-brain computed tomography perfusion (CTP) and CT angiography (CTA) in the diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). Whole-brain CTP and four-dimensional CT angiography (4D-CTA) images were acquired in 30 MCI, 35 mild AD patients, 35 moderate AD patients, 30 severe AD patients and 50 normal controls (NC). Cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time to peak (TTP), and correlation between CTP and 4D-CTA were analysed. Elevated CBF in the left frontal and temporal cortex was found in MCI compared with the NC group. However, TTP was increased in the left hippocampus in mild AD patients compared with NC. In moderate and severe AD patients, hypoperfusion was found in multiple brain areas compared with NC. Finally, we found that the extent of arterial stenosis was negatively correlated with CBF in partial cerebral cortex and hippocampus, and positively correlated with TTP in these areas of AD and MCI patients. Our findings suggest that whole-brain CTP and 4D-CTA could serve as a diagnostic modality in distinguishing MCI and AD, and predicting conversion from MCI based on TTP of left hippocampus. • Whole-brain perfusion using the full 160-mm width of 320 detector rows • Provide clinical experience of 320-row CT in cerebrovascular disorders of Alzheimer's disease • Initial combined 4D CTA-CTP data analysed perfusion and correlated with CT angiography • Whole-brain CTP and 4D-CTA have high value for monitoring MCI to AD progression • TTP in the left hippocampus may predict the transition from MCI to AD.

  1. Euler Elastica regularized Logistic Regression for whole-brain decoding of fMRI data.

    Science.gov (United States)

    Zhang, Chuncheng; Yao, Li; Song, Sutao; Wen, Xiaotong; Zhao, Xiaojie; Long, Zhiying

    2017-09-25

    Multivariate pattern analysis (MVPA) methods have been widely applied to functional magnetic resonance imaging (fMRI) data to decode brain states. Due to the "high features, low samples" in fMRI data, machine learning methods have been widely regularized using various regularizations to avoid overfitting. Both total variation (TV) using the gradients of images and Euler's elastica (EE) using the gradient and the curvature of images are the two popular regulations with spatial structures. In contrast to TV, EE regulation is able to overcome the disadvantage of TV regulation that favored piecewise constant images over piecewise smooth images. In this study, we introduced EE to fMRI-based decoding for the first time and proposed the EE regularized multinomial logistic regression (EELR) algorithm for multi-class classification. We performed experimental tests on both simulated and real fMRI data to investigate the feasibility and robustness of EELR. The performance of EELR was compared with sparse logistic regression (SLR) and TV regularized LR (TVLR). The results showed that EELR was more robustness to noises and showed significantly higher classification performance than TVLR and SLR. Moreover, the forward models and weights patterns revealed that EELR detected larger brain regions that were discriminative to each task and activated by each task than TVLR. The results suggest that EELR not only performs well in brain decoding but also reveals meaningful discriminative and activation patterns. This study demonstrated that EELR showed promising potential in brain decoding and discriminative/activation pattern detection.

  2. Completely random measures for modelling block-structured sparse networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Schmidt, Mikkel Nørgaard; Mørup, Morten

    2016-01-01

    Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks...... [2014] proposed the use of a different notion of exchangeability due to Kallenberg [2006] and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re......-introduce the use of block-structure for network models obeying allenberg’s notion of exchangeability and thereby obtain a model which admits the inference of block-structure and edge inhomogeneity. We derive a simple expression for the likelihood and an efficient sampling method. The obtained model...

  3. Whole-brain white matter disruption in semantic and nonfluent variants of primary progressive aphasia.

    Science.gov (United States)

    Schwindt, Graeme C; Graham, Naida L; Rochon, Elizabeth; Tang-Wai, David F; Lobaugh, Nancy J; Chow, Tiffany W; Black, Sandra E

    2013-04-01

    Semantic (svPPA) and nonfluent (nfPPA) variants of primary progressive aphasia are associated with distinct patterns of cortical atrophy and underlying pathology. Little is known, however, about their contrasting spread of white matter disruption and how this relates to grey matter (GM) loss. We undertook a structural MRI study to investigate this relationship. We used diffusion tensor imaging, tract-based spatial statistics, and voxel-based morphometry to examine fractional anisotropy (FA) and directional diffusivities in nine patients with svPPA and nine patients with nfPPA, and compared them to 16 matched controls after accounting for global GM atrophy. Significant differences in topography of white matter changes were found, with more ventral involvement in svPPA patients and more widespread frontal involvement in nfPPA individuals. However, each group had both ventral and dorsal tract changes, and both showed spread of diffusion abnormalities beyond sites of local atrophy. There was a clear dissociation in sensitivity of diffusion tensor imaging measures between groups. SvPPA patients showed widespread changes in FA and radial diffusivity, whereas changes in axial diffusivity were more restricted and proximal to sites of GM atrophy. NfPPA patients showed isolated changes in FA, but widespread axial and radial diffusivity changes. These findings reveal the extent of white matter disruption in these variants of PPA after accounting for GM loss. Further, they suggest that differences in the relative sensitivity of diffusion metrics may reflect differences in the nature of underlying white matter pathology in these two subtypes. Copyright © 2011 Wiley Periodicals, Inc.

  4. A whole brain atlas with sub-parcellation of cortical gyri using resting fMRI

    Science.gov (United States)

    Joshi, Anand A.; Choi, Soyoung; Sonkar, Gaurav; Chong, Minqi; Gonzalez-Martinez, Jorge; Nair, Dileep; Shattuck, David W.; Damasio, Hanna; Leahy, Richard M.

    2017-02-01

    The new hybrid-BCI-DNI atlas is a high-resolution MPRAGE, single-subject atlas, constructed using both anatomical and functional information to guide the parcellation of the cerebral cortex. Anatomical labeling was performed manually on coronal single-slice images guided by sulcal and gyral landmarks to generate the original (non-hybrid) BCI-DNI atlas. Functional sub-parcellations of the gyral ROIs were then generated from 40 minimally preprocessed resting fMRI datasets from the HCP database. Gyral ROIs were transferred from the BCI-DNI atlas to the 40 subjects using the HCP grayordinate space as a reference. For each subject, each gyral ROI was subdivided using the fMRI data by applying spectral clustering to a similarity matrix computed from the fMRI time-series correlations between each vertex pair. The sub-parcellations were then transferred back to the original cortical mesh to create the subparcellated hBCI-DNI atlas with a total of 67 cortical regions per hemisphere. To assess the stability of the gyral subdivisons, a separate set of 60 HCP datasets were processed as follows: 1) coregistration of the structural scans to the hBCI-DNI atlas; 2) coregistration of the anatomical BCI-DNI atlas without functional subdivisions, followed by sub-parcellation of each subject's resting fMRI data as described above. We then computed consistency between the anatomically-driven delineation of each gyral subdivision and that obtained per subject using individual fMRI data. The gyral sub-parcellations generated by atlas-based registration show variable but generally good overlap of the confidence intervals with the resting fMRI-based subdivisions. These consistency measures will provide a quantitative measure of reliability of each subdivision to users of the atlas.

  5. A survival score for patients with brain metastases from less radiosensitive tumors treated with whole-brain radiotherapy alone

    Energy Technology Data Exchange (ETDEWEB)

    Dziggel, L.; Rades, D. [University Hospital Schleswig-Holstein, Department of Radiation Oncology, Luebeck (Germany); Segedin, B.; Podvrsnik, N.H.; Oblak, I. [Institute of Oncology, Division of Radiation Oncology, Ljubljana (Slovenia); Schild, S.E. [Mayo Clinic Scottsdale, Department of Radiation Oncology, Scottsdale, Arizona (United States)

    2014-01-15

    This study aimed to develop and validate a scoring system to predict the survival of patients receiving whole-brain radiotherapy (WBRT) alone for brain metastases from less radiosensitive tumors. The study included data from 176 patients with brain metastasis from renal cell carcinoma, malignant melanoma or colorectal cancer. Patients were divided into a test group (N=88) and a validation group (N=88). In the multivariate analysis of the test group, age, Karnofsky Performance Status and extracranial metastasis were significantly associated with survival. These three factors were included in the scoring system. The score for each factor was determined by dividing the 6-month survival rate (in %) by 10. The total score represented the sum of the three scores. According to the total scores - which ranged from 5 to14 points - three prognostic groups were created. The 6-month survival rates in the test group were 11% for 5-8 points (N=47, group A), 38% for 9-11 points (N=29, group B) and 83% for 12-14 points (N=12, group C). In the validation group the 6-month survival rates were 12, 31 and 75%, respectively. Comparisons between the prognostic groups A, B and C of the test group with those of the validation group did not reveal any significant differences. The new scoring system based on three independent prognostic factors can help to estimate the survival of patients with brain metastases from a less radiosensitive tumor. The score appears to be valid and reproducible. (orig.)

  6. Examination of the predictive factors of the response to whole brain radiotherapy for brain metastases from lung cancer using MRI.

    Science.gov (United States)

    Aoki, Shuri; Kanda, Tomonori; Matsutani, Noriyuki; Seki, Nobuhiko; Kawamura, Masafumi; Furui, Shigeru; Yamashita, Hideomi

    2017-07-01

    Previous studies have been conducted on the prognostic factors for overall survival in patients with brain metastases (BMs) following whole brain radiotherapy (WBRT). However, there have been a small number of studies regarding the prognostic factors for the response of tumor to WBRT. The aim of the present study was to identify the predictive factors for the response to WBRT from the point of view of reduction of tumor using magnetic resonance imaging. A retrospective analysis of 62 patients with BMs from primary lung cancer treated with WBRT was undertaken. The effects of the following factors on the response to WBRT were evaluated: Age; sex; performance status; lactate dehydrogenase; pathology; existence of extracranial metastases; activity of extracranial disease; chemo-history; chest radiotherapy history; treatment term; γ-knife radiotherapy; diffusion weighted image signal intensity; tumor diameter; extent of edema and the edema/tumor (E/T) ratio. The association between the reduction of tumors and clinical factors was evaluated using logistic regression analysis. Ppredictive factors for the reduction of tumor. The following 3 factors were significantly associated with the response of tumors to WBRT: The presence of SCLC; an E/T ratio of ≥1.5; and the presence of extracranial metastases. The E/T ratio is a novel index that provides a simple and easy predictive method for use in a clinical setting.

  7. Whole-brain in-vivo measurements of the axonal g-ratio in a group of 37 healthy volunteers

    Directory of Open Access Journals (Sweden)

    Siawoosh eMohammadi

    2015-11-01

    Full Text Available The g-ratio, quantifying the ratio between the inner and outer diameters of a fiber, is an important microstructural characteristic of fiber pathways and is functionally related to conduction velocity. We introduce a novel method for estimating the MR g-ratio non-invasively across the whole brain using high-fidelity magnetization transfer (MT imaging and single-shell diffusion MRI. These methods enabled us to map the MR g-ratio in vivo across the brain’s prominent fiber pathways in a group of 37 healthy volunteers and to estimate the inter-subject variability. Effective correction of susceptibility-related distortion artifacts was essential before combining the MT and diffusion data, in order to reduce partial volume and edge artifacts. The MR g-ratio is in good qualitative agreement with histological findings despite the different resolution and spatial coverage of MRI and histology. The MR g-ratio holds promise as an important non-invasive biomarker due to its microstructural and functional relevance in neurodegeneration.

  8. A Case of Brain Metastases from Breast Cancer Treated with Whole-Brain Radiotherapy and Eribulin Mesylate

    Directory of Open Access Journals (Sweden)

    Carsten Nieder

    2012-01-01

    Full Text Available Patients with triple receptor-negative breast cancer often develop aggressive metastatic disease, which also might involve the brain. In many cases, systemic and local treatment is needed. It is important to consider the toxicity of chemo- and radiotherapy, especially when newly approved drugs become available. Randomised studies leading to drug approval often exclude patients with newly diagnosed brain metastases. Here we report our initial experience with eribulin mesylate and whole-brain radiotherapy (WBRT in a heavily pretreated patient with multiple brain, lung, and bone metastases from triple receptor-negative breast cancer. Eribulin mesylate was given after 4 previous lines for metastatic disease. Two weeks after the initial dose, that is, during the first cycle, the patient was diagnosed with 5 brain metastases with a maximum size of approximately 4.5 cm. She continued chemotherapy and received concomitant WBRT with 10 fractions of 3 Gy. After 3 cycles of eribulin mesylate, treatment was discontinued because of newly diagnosed liver metastases and progression in the lungs. No unexpected acute toxicity was observed. The only relevant adverse reactions were haematological events after the third cycle (haemoglobin 9.5 g/dL, leukocytes 3.1×109/L. The patient died from respiratory failure 18.5 months from diagnosis of metastatic disease, and 2.7 months from diagnosis of brain metastases. To the best of our knowledge, this is the first report on combined WBRT and eribulin mesylate.

  9. Whole-Brain In-vivo Measurements of the Axonal G-Ratio in a Group of 37 Healthy Volunteers.

    Science.gov (United States)

    Mohammadi, Siawoosh; Carey, Daniel; Dick, Fred; Diedrichsen, Joern; Sereno, Martin I; Reisert, Marco; Callaghan, Martina F; Weiskopf, Nikolaus

    2015-01-01

    The g-ratio, quantifying the ratio between the inner and outer diameters of a fiber, is an important microstructural characteristic of fiber pathways and is functionally related to conduction velocity. We introduce a novel method for estimating the MR g-ratio non-invasively across the whole brain using high-fidelity magnetization transfer (MT) imaging and single-shell diffusion MRI. These methods enabled us to map the MR g-ratio in vivo across the brain's prominent fiber pathways in a group of 37 healthy volunteers and to estimate the inter-subject variability. Effective correction of susceptibility-related distortion artifacts was essential before combining the MT and diffusion data, in order to reduce partial volume and edge artifacts. The MR g-ratio is in good qualitative agreement with histological findings despite the different resolution and spatial coverage of MRI and histology. The MR g-ratio holds promise as an important non-invasive biomarker due to its microstructural and functional relevance in neurodegeneration.

  10. Do patients with a limited number of brain metastases need whole-brain radiotherapy in addition to radiosurgery?

    Energy Technology Data Exchange (ETDEWEB)

    Rades, D. [University Hospital Schleswig-Holstein, Luebeck (Germany). Dept. of Radiation Oncology; Schild, S.E. [Mayo Clinic, Scottsdale, AZ (United States). Dept. of Radiation Oncology

    2012-08-15

    Background: About 40% of patients with brain metastases have a very limited number of lesions and may be candidates for radiosurgery. Radiosurgery alone is superior to whole-brain radiotherapy (WBRT) alone for control of treated and new brain metastases. In patients with a good performance status, radiosurgery also resulted in better survival. However, the question is whether the results of radiosurgery alone can be further improved with additional WBRT. Methods: Information for this review was compiled by searching the PubMed and MEDLINE databases. Very important published meeting abstracts were also considered. Results: Based on both retrospective and prospective studies, the addition of WBRT to radiosurgery improved control of treated and new brain metastases but not survival. However, because a recurrence within the brain has a negative impact on neurocognitive function, it is important to achieve long-term control of brain metastases. Conclusion: The addition of WBRT provides significant benefits. Further randomized studies including adequate assessment of neurocognitive function and a follow-up period of at least 2 years are needed to help customize the treatment for individual patients. (orig.)

  11. Using multiple imputation to efficiently correct cerebral MRI whole brain lesion and atrophy data in patients with multiple sclerosis.

    Science.gov (United States)

    Chua, Alicia S; Egorova, Svetlana; Anderson, Mark C; Polgar-Turcsanyi, Mariann; Chitnis, Tanuja; Weiner, Howard L; Guttmann, Charles R G; Bakshi, Rohit; Healy, Brian C

    2015-10-01

    Automated segmentation of brain MRI scans into tissue classes is commonly used for the assessment of multiple sclerosis (MS). However, manual correction of the resulting brain tissue label maps by an expert reader remains necessary in many cases. Since automated segmentation data awaiting manual correction are "missing", we proposed to use multiple imputation (MI) to fill-in the missing manually-corrected MRI data for measures of normalized whole brain volume (brain parenchymal fraction-BPF) and T2 hyperintense lesion volume (T2LV). Automated and manually corrected MRI measures from 1300 patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB) were identified. Simulation studies were conducted to assess the performance of MI with missing data both missing completely at random and missing at random. An imputation model including the concurrent automated data as well as clinical and demographic variables explained a high proportion of the variance in the manually corrected BPF (R(2)=0.97) and T2LV (R(2)=0.89), demonstrating the potential to accurately impute the missing data. Further, our results demonstrate that MI allows for the accurate estimation of group differences with little to no bias and with similar precision compared to an analysis with no missing data. We believe that our findings provide important insights for efficient correction of automated MRI measures to obviate the need to perform manual correction on all cases. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Prognostic factors for outcomes after whole-brain irradiation of brain metastases from relatively radioresistant tumors: a retrospective analysis

    Directory of Open Access Journals (Sweden)

    Schild Steven E

    2010-10-01

    Full Text Available Abstract Background This study investigated potential prognostic factors in patients treated with whole-brain irradiation (WBI alone for brain metastases from relatively radioresistant tumors such as malignant melanoma, renal cell carcinoma, and colorectal cancer. Additionally, a potential benefit from escalating the radiation dose was investigated. Methods Data from 220 patients were retrospectively analyzed for overall survival and local control. Nine potential prognostic factors were evaluated: tumor type, WBI schedule, age, gender, Karnofsky performance score, number of brain metastases, extracerebral metastases, interval from diagnosis of cancer to WBI, and recursive partitioning analysis (RPA class. Results Survival rates at 6 and 12 months were 32% and 19%, respectively. In the multivariate analysis, WBI doses >30 Gy (p = 0.038, KPS ≥70 (p Conclusions Improved outcomes were associated with WBI doses >30 Gy, better performance status, fewer brain metastases, lack of extracerebral metastases, and lower RPA class. Patients receiving WBI alone appear to benefit from WBI doses >30 Gy. However, such a benefit is limited to RPA class 1 or 2 patients.

  13. Whole brain analysis of postmortem density changes of grey and white matter on computed tomography by statistical parametric mapping

    Energy Technology Data Exchange (ETDEWEB)

    Nishiyama, Yuichi; Mori, Hiroshi; Katsube, Takashi; Kitagaki, Hajime [Shimane University Faculty of Medicine, Department of Radiology, Izumo-shi, Shimane (Japan); Kanayama, Hidekazu; Tada, Keiji; Yamamoto, Yasushi [Shimane University Hospital, Department of Radiology, Izumo-shi, Shimane (Japan); Takeshita, Haruo [Shimane University Faculty of Medicine, Department of Legal Medicine, Izumo-shi, Shimane (Japan); Kawakami, Kazunori [Fujifilm RI Pharma, Co., Ltd., Tokyo (Japan)

    2017-06-15

    This study examined the usefulness of statistical parametric mapping (SPM) for investigating postmortem changes on brain computed tomography (CT). This retrospective study included 128 patients (23 - 100 years old) without cerebral abnormalities who underwent unenhanced brain CT before and after death. The antemortem CT (AMCT) scans and postmortem CT (PMCT) scans were spatially normalized using our original brain CT template, and postmortem changes of CT values (in Hounsfield units; HU) were analysed by the SPM technique. Compared with AMCT scans, 58.6 % and 98.4 % of PMCT scans showed loss of the cerebral sulci and an unclear grey matter (GM)-white matter (WM) interface, respectively. SPM analysis revealed a significant decrease in cortical GM density within 70 min after death on PMCT scans, suggesting cytotoxic brain oedema. Furthermore, there was a significant increase in the density of the WM, lenticular nucleus and thalamus more than 120 min after death. The SPM technique demonstrated typical postmortem changes on brain CT scans, and revealed that the unclear GM-WM interface on early PMCT scans is caused by a rapid decrease in cortical GM density combined with a delayed increase in WM density. SPM may be useful for assessment of whole brain postmortem changes. (orig.)

  14. Positive selection in ASPM is correlated with cerebral cortex evolution across primates but not with whole-brain size.

    Science.gov (United States)

    Ali, Farhan; Meier, Rudolf

    2008-11-01

    The rapid increase of brain size is a key event in human evolution. Abnormal spindle-like microcephaly associated (ASPM) is discussed as a major candidate gene for explaining the exceptionally large brain in humans but ASPM's role remains controversial. Here we use codon-specific models and a comparative approach to test this candidate gene that was initially identified in Homo-chimp comparisons. We demonstrate that accelerated evolution of ASPM (omega = 4.7) at 16 amino acid sites occurred in 9 primate lineages with major changes in relative cerebral cortex size. However, ASPM's evolution is not correlated with major changes in relative whole-brain or cerebellum sizes. Our results suggest that a single candidate gene such as ASPM can influence a specific component of the brain across large clades through changes in a few amino acid sites. We furthermore illustrate the power of using continuous phenotypic variability across primates to rigorously test candidate genes that have been implicated in the evolution of key human traits.

  15. Optimizing and Understanding Network Structure for Diffusion

    OpenAIRE

    Zhang, Yao

    2017-01-01

    Given a population contact network and electronic medical records of patients, how to distribute vaccines to individuals to effectively control a flu epidemic? Similarly, given the Twitter following network and tweets, how to choose the best communities/groups to stop rumors from spreading? How to find the best accounts that bridge celebrities and ordinary users? These questions are related to diffusion (aka propagation) phenomena. Diffusion can be treated as a behavior of spreading contagion...

  16. Reverse Logistics Network Structures and Design

    OpenAIRE

    Fleischmann, Moritz

    2001-01-01

    textabstractLogistics network design is commonly recognized as a strategic supply chain issue of prime importance. The location of production facilities, storage concepts, and transportation strategies are major determinants of supply chain performance. This chapter considers logistics network design for the particular case of closed-loop supply chains. We highlight key issues that companies are facing when deciding upon the logistics implementation of a product recovery initiative. In partic...

  17. Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks

    Energy Technology Data Exchange (ETDEWEB)

    Gallagher, B; Eliassi-Rad, T

    2007-10-22

    We address the problem of classification in a partially labeled network (a.k.a. within-network classification), with an emphasis on tasks in which we have very few labeled instances to start with. Recent work has demonstrated the utility of collective classification (i.e., simultaneous inferences over class labels of related instances) in this general problem setting. However, the performance of collective classification algorithms can be adversely affected by the sparseness of labels in real-world networks. We show that on several real-world data sets, collective classification appears to offer little advantage in general and hurts performance in the worst cases. In this paper, we explore a complimentary approach to within-network classification that takes advantage of network structure. Our approach is motivated by the observation that real-world networks often provide a great deal more structural information than attribute information (e.g., class labels). Through experiments on supervised and semi-supervised classifiers of network data, we demonstrate that a small number of structural features can lead to consistent and sometimes dramatic improvements in classification performance. We also examine the relative utility of individual structural features and show that, in many cases, it is a combination of both local and global network structure that is most informative.

  18. Supervised neural networks for the classification of structures.

    Science.gov (United States)

    Sperduti, A; Starita, A

    1997-01-01

    Standard neural networks and statistical methods are usually believed to be inadequate when dealing with complex structures because of their feature-based approach. In fact, feature-based approaches usually fail to give satisfactory solutions because of the sensitivity of the approach to the a priori selection of the features, and the incapacity to represent any specific information on the relationships among the components of the structures. However, we show that neural networks can, in fact, represent and classify structured patterns. The key idea underpinning our approach is the use of the so called "generalized recursive neuron", which is essentially a generalization to structures of a recurrent neuron. By using generalized recursive neurons, all the supervised networks developed for the classification of sequences, such as backpropagation through time networks, real-time recurrent networks, simple recurrent networks, recurrent cascade correlation networks, and neural trees can, on the whole, be generalized to structures. The results obtained by some of the above networks (with generalized recursive neurons) on the classification of logic terms are presented.

  19. Joint Modelling of Structural and Functional Brain Networks

    DEFF Research Database (Denmark)

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

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

  20. Structure of Retail Services in the Brazilian Hosting Network

    Directory of Open Access Journals (Sweden)

    Claudio Zancan

    2015-08-01

    Full Text Available this research has identified Brazilian hosting networks through infrastructure services indicators that it was sold to tourists in organizations that form these networks. The theory consulted the discussion of structural techniques present in Social Network Analysis. The study has three stages: documental research, creation of Tourism database and interviews. The results identified three networks with the highest expression in Brazil formed by hotels, lodges, and resorts. Different char-acteristics of infrastructure and services were observed between hosting networks. Future studies suggest a comparative analysis of structural indicators present in other segments of tourism services, as well as the existing international influ-ence on the development of the Brazilian hosting networks.

  1. Self-organization in neural networks - Applications in structural optimization

    Science.gov (United States)

    Hajela, Prabhat; Fu, B.; Berke, Laszlo

    1993-01-01

    The present paper discusses the applicability of ART (Adaptive Resonance Theory) networks, and the Hopfield and Elastic networks, in problems of structural analysis and design. A characteristic of these network architectures is the ability to classify patterns presented as inputs into specific categories. The categories may themselves represent distinct procedural solution strategies. The paper shows how this property can be adapted in the structural analysis and design problem. A second application is the use of Hopfield and Elastic networks in optimization problems. Of particular interest are problems characterized by the presence of discrete and integer design variables. The parallel computing architecture that is typical of neural networks is shown to be effective in such problems. Results of preliminary implementations in structural design problems are also included in the paper.

  2. Structural dimensions of knowledge-action networks for sustainability

    Science.gov (United States)

    Tischa A. Munoz; B.B. Cutts

    2016-01-01

    Research on the influence of social network structure over flows of knowledge in support of sustainability governance and action has recently flourished. These studies highlight three challenges to evaluating knowledge-action networks: first, defining boundaries; second, characterizing power distributions; and third, identifying obstacles to knowledge sharing and...

  3. Structural and Infrastructural Underpinnings of International R&D Networks

    DEFF Research Database (Denmark)

    Niang, Mohamed; Sørensen, Brian Vejrum

    2009-01-01

    This paper explores the process of globally distributing R&D activities with an emphasis on the effects of network maturity. It discusses emerging configurations by asking how the structure and infrastructure of international R&D networks evolve along with the move from a strong R&D center...

  4. The National Biomedical Communications Network as a Developing Structure.

    Science.gov (United States)

    Davis, Ruth M.

    The National Biomedical Communications Network has evolved both from a set of conceptual recommendations over the last twelve years and an accumulation of needs manifesting themselves in the requests of members of the medical community. With a short history of three years this Network and its developing structure have exhibited most of the…

  5. Information Propagation in Complex Networks : Structures and Dynamics

    NARCIS (Netherlands)

    Märtens, M.

    2018-01-01

    This thesis is a contribution to a deeper understanding of how information propagates and what this process entails. At its very core is the concept of the network: a collection of nodes and links, which describes the structure of the systems under investigation. The network is a mathematical model

  6. Chinese lexical networks: The structure, function and formation

    Science.gov (United States)

    Li, Jianyu; Zhou, Jie; Luo, Xiaoyue; Yang, Zhanxin

    2012-11-01

    In this paper Chinese phrases are modeled using complex networks theory. We analyze statistical properties of the networks and find that phrase networks display some important features: not only small world and the power-law distribution, but also hierarchical structure and disassortative mixing. These statistical traits display the global organization of Chinese phrases. The origin and formation of such traits are analyzed from a macroscopic Chinese culture and philosophy perspective. It is interesting to find that Chinese culture and philosophy may shape the formation and structure of Chinese phrases. To uncover the structural design principles of networks, network motif patterns are studied. It is shown that they serve as basic building blocks to form the whole phrase networks, especially triad 38 (feed forward loop) plays a more important role in forming most of the phrases and other motifs. The distinct structure may not only keep the networks stable and robust, but also be helpful for information processing. The results of the paper can give some insight into Chinese language learning and language acquisition. It strengthens the idea that learning the phrases helps to understand Chinese culture. On the other side, understanding Chinese culture and philosophy does help to learn Chinese phrases. The hub nodes in the networks show the close relationship with Chinese culture and philosophy. Learning or teaching the hub characters, hub-linking phrases and phrases which are meaning related based on motif feature should be very useful and important for Chinese learning and acquisition.

  7. Whole Brain Learning.

    Science.gov (United States)

    Hatcher, Margaret

    1983-01-01

    The educational implications of recent brain research suggest that schools should emphasize activities that balance right and left hemisphere functions in order to encourage students' creativity. Some techniques currently in favor for achieving balance are synectics, multisensory and experiential learning, creative thinking methods, and the…

  8. A whole brain volumetric approach in overweight/obese children: Examining the association with different physical fitness components and academic performance. The ActiveBrains project.

    Science.gov (United States)

    Esteban-Cornejo, Irene; Cadenas-Sanchez, Cristina; Contreras-Rodriguez, Oren; Verdejo-Roman, Juan; Mora-Gonzalez, Jose; Migueles, Jairo H; Henriksson, Pontus; Davis, Catherine L; Verdejo-Garcia, Antonio; Catena, Andrés; Ortega, Francisco B

    2017-10-01

    Obesity, as compared to normal weight, is associated with detectable structural differences in the brain. To the best of our knowledge, no previous study has examined the association of physical fitness with gray matter volume in overweight/obese children using whole brain analyses. Thus, the aim of this study was to examine the association between the key components of physical fitness (i.e. cardiorespiratory fitness, speed-agility and muscular fitness) and brain structural volume, and to assess whether fitness-related changes in brain volumes are related to academic performance in overweight/obese children. A total of 101 overweight/obese children aged 8-11 years were recruited from Granada, Spain. The physical fitness components were assessed following the ALPHA health-related fitness test battery. T1-weighted images were acquired with a 3.0 T S Magnetom Tim Trio system. Gray matter tissue was calculated using Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL). Academic performance was assessed by the Batería III Woodcock-Muñoz Tests of Achievement. All analyses were controlled for sex, peak high velocity offset, parent education, body mass index and total brain volume. The statistical threshold was calculated with AlphaSim and further Hayasaka adjusted to account for the non-isotropic smoothness of structural images. The main results showed that higher cardiorespiratory fitness was related to greater gray matter volumes (P academic performance (β ranging from 0.211 to 0.352; all P academic performance (β ranging from 0.217 to 0.296; both P academic performance. Importantly, the identified associations of fitness and gray matter volume were different for each fitness component. These findings suggest that increases in cardiorespiratory fitness and speed-agility may positively influence the development of distinctive brain regions and academic indicators, and thus counteract the harmful effect of overweight and obesity on

  9. Mesoscopic structure conditions the emergence of cooperation on social networks

    Energy Technology Data Exchange (ETDEWEB)

    Lozano, S.; Arenas, A.; Sanchez, A.

    2008-12-01

    We study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data. We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates. Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.

  10. Neural Network Algorithm for Prediction of Secondary Protein Structure

    National Research Council Canada - National Science Library

    Zikrija Avdagic; Elvir Purisevic; Emir Buza; Zlatan Coralic

    2009-01-01

    .... In this paper we describe the method and results of using CB513 as a dataset suitable for development of artificial neural network algorithms for prediction of secondary protein structure with MATLAB...

  11. Neural network definitions of highly predictable protein secondary structure classes

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A. [Los Alamos National Lab., NM (United States)]|[Santa Fe Inst., NM (United States); Steeg, E. [Toronto Univ., ON (Canada). Dept. of Computer Science; Farber, R. [Los Alamos National Lab., NM (United States)

    1994-02-01

    We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been a popular method to attempt to predict these conventional secondary structure classes. Accuracy has been disappointingly low. The algorithm presented here uses neural networks to similtaneously examine both sequence and structure data, and to evolve new classes of secondary structure that can be predicted from sequence with significantly higher accuracy than the conventional classes. These new classes have both similarities to, and differences with the conventional alpha helix, beta strand and coil.

  12. Structural Changes in Online Discussion Networks

    DEFF Research Database (Denmark)

    Yang, Yang; Medaglia, Rony

    2014-01-01

    Social networking platforms in China provide a hugely interesting and relevant source for understanding dynamics of online discussions in a unique socio-cultural and institutional environment. This paper investigates the evolution of patterns of similar-minded and different-minded interactions over...

  13. Whole-brain structural connectivity in dyskinetic cerebral palsy and its association with motor and cognitive function

    NARCIS (Netherlands)

    Ballester-Plané, Júlia; Schmidt, Ruben; Laporta-Hoyos, Olga; Junqué, Carme; Vázquez, Élida; Delgado, Ignacio; Zubiaurre-Elorza, Leire; Macaya, Alfons; Póo, Pilar; Toro, Esther; de Reus, Marcel A.|info:eu-repo/dai/nl/413970728; van den Heuvel, Martijn P.|info:eu-repo/dai/nl/304820466; Pueyo, Roser

    2017-01-01

    Dyskinetic cerebral palsy (CP) has long been associated with basal ganglia and thalamus lesions. Recent evidence further points at white matter (WM) damage. This study aims to identify altered WM pathways in dyskinetic CP from a standardized, connectome-based approach, and to assess

  14. The effects of traffic structure on application and network performance

    CERN Document Server

    Aikat, Jay; Smith, F Donelson

    2012-01-01

    Over the past three decades, the Internet's rapid growth has spurred the development of new applications in mobile computing, digital music, online video, gaming and social networks. These applications rely heavily upon various underlying network protocols and mechanisms to enable, maintain and enhance their Internet functionalityThe Effects of Traffic Structure on Application and Network Performance provides the necessary tools for maximizing the network efficiency of any Internet application, and presents ground-breaking research that will influence how these applications are built in the fu

  15. Repeat Courses of Stereotactic Radiosurgery (SRS), Deferring Whole-Brain Irradiation, for New Brain Metastases After Initial SRS

    Energy Technology Data Exchange (ETDEWEB)

    Shultz, David B.; Modlin, Leslie A.; Jayachandran, Priya; Von Eyben, Rie; Gibbs, Iris C. [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California (United States); Choi, Clara Y.H. [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California (United States); Department of Radiation Oncology, Santa Clara Valley Medical Center, San Jose, California (United States); Chang, Steven D.; Harsh, Griffith R.; Li, Gordon; Adler, John R. [Department of Neurosurgery, Stanford University School of Medicine, Stanford, California (United States); Hancock, Steven L. [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California (United States); Soltys, Scott G., E-mail: sgsoltys@stanford.edu [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California (United States)

    2015-08-01

    Purpose: To report the outcomes of repeat stereotactic radiosurgery (SRS), deferring whole-brain radiation therapy (WBRT), for distant intracranial recurrences and identify factors associated with prolonged overall survival (OS). Patients and Methods: We retrospectively identified 652 metastases in 95 patients treated with 2 or more courses of SRS for brain metastases, deferring WBRT. Cox regression analyzed factors predictive for OS. Results: Patients had a median of 2 metastases (range, 1-14) treated per course, with a median of 2 courses (range, 2-14) of SRS per patient. With a median follow-up after first SRS of 15 months (range, 3-98 months), the median OS from the time of the first and second course of SRS was 18 (95% confidence interval [CI] 15-24) and 11 months (95% CI 6-17), respectively. On multivariate analysis, histology, graded prognostic assessment score, aggregate tumor volume (but not number of metastases), and performance status correlated with OS. The 1-year cumulative incidence, with death as a competing risk, of local failure was 5% (95% CI 4-8%). Eighteen (24%) of 75 deaths were from neurologic causes. Nineteen patients (20%) eventually received WBRT. Adverse radiation events developed in 2% of SRS sites. Conclusion: Multiple courses of SRS, deferring WBRT, for distant brain metastases after initial SRS, seem to be a safe and effective approach. The graded prognostic assessment score, updated at each course, and aggregate tumor volume may help select patients in whom the deferral of WBRT might be most beneficial.

  16. Microstructural changes of whole brain in patients with comitant strabismus: evidence from a diffusion tensor imaging study

    Directory of Open Access Journals (Sweden)

    Huang X

    2016-08-01

    Full Text Available Xin Huang,1,2,* Hai-Jun Li,3,* Ying Zhang,1 De-Chang Peng,3 Pei-Hong Hu,1 Yu-Lin Zhong,1 Fu-Qing Zhou,3 Yi Shao1 1Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, 2Department of Ophthalmology, The First People’s Hospital of Jiujiang City, Jiujiang, 3Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China*These authors contributed equally to this work Objective: The aim of this study was to investigate the fractional anisotropy (FA and mean diffusivity (MD using a diffusion tensor imaging technique and whole-brain voxel-based analysis in patients with comitant strabismus.Patients and methods: A total of 19 (nine males and ten females patients with comitant strabismus and 19 age-, sex-, and education-matched healthy controls (HCs underwent magnetic resonance imaging examination. Imaging data were analyzed using two-sample t-tests to identify group differences in FA and MD values. Patients with comitant strabismus were distinguishable from HCs by receiver operating characteristic curves.Results: Compared with HCs, patients with comitant strabismus exhibited significantly decreased FA values in the brain regions of the left superior temporal gyrus and increased values in the bilateral medial frontal gyrus, right globus pallidus/brainstem, and bilateral precuneus. Meanwhile, MD value was significantly reduced in the brain regions of the bilateral cerebellum posterior lobe and left middle frontal gyrus but increased in the brain regions of the right middle frontal gyrus and left anterior cingulate.Conclusion: These results suggest significant brain abnormalities in comitant strabismus, which may underlie the pathologic mechanisms of fusion defects and ocular motility disorders in patients with comitant strabismus. Keywords: comitant strabismus, diffusion tensor imaging, mean diffusivity, fractional anisotropy, resting state

  17. Postoperative Stereotactic Radiosurgery Without Whole-Brain Radiation Therapy for Brain Metastases: Potential Role of Preoperative Tumor Size

    Energy Technology Data Exchange (ETDEWEB)

    Hartford, Alan C., E-mail: Alan.C.Hartford@Hitchcock.org [Section of Radiation Oncology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire (United States); Paravati, Anthony J. [Section of Radiation Oncology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire (United States); Spire, William J. [Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire (United States); Li, Zhongze [Biostatistics Shared Resource, Norris Cotton Cancer Center, Lebanon, New Hampshire (United States); Jarvis, Lesley A. [Section of Radiation Oncology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire (United States); Fadul, Camilo E. [Section of Hematology/Oncology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire (United States); Rhodes, C. Harker [Department of Pathology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire (United States); Erkmen, Kadir [Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire (United States); Friedman, Jonathan [Department of Surgery, Texas A and M College of Medicine, College Station, Texas (United States); Gladstone, David J. [Section of Radiation Oncology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire (United States); Hug, Eugen B. [ProCure, New York, New York (United States); Roberts, David W.; Simmons, Nathan E. [Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire (United States)

    2013-03-01

    Purpose: Radiation therapy following resection of a brain metastasis increases the probability of disease control at the surgical site. We analyzed our experience with postoperative stereotactic radiosurgery (SRS) as an alternative to whole-brain radiotherapy (WBRT), with an emphasis on identifying factors that might predict intracranial disease control and overall survival (OS). Methods and Materials: We retrospectively reviewed all patients through December 2008, who, after surgical resection, underwent SRS to the tumor bed, deferring WBRT. Multiple factors were analyzed for time to intracranial recurrence (ICR), whether local recurrence (LR) at the surgical bed or “distant” recurrence (DR) in the brain, for time to WBRT, and for OS. Results: A total of 49 lesions in 47 patients were treated with postoperative SRS. With median follow-up of 9.3 months (range, 1.1-61.4 months), local control rates at the resection cavity were 85.5% at 1 year and 66.9% at 2 years. OS rates at 1 and 2 years were 52.5% and 31.7%, respectively. On univariate analysis (preoperative) tumors larger than 3.0 cm exhibited a significantly shorter time to LR. At a cutoff of 2.0 cm, larger tumors resulted in significantly shorter times not only for LR but also for DR, ICR, and salvage WBRT. While multivariate Cox regressions showed preoperative size to be significant for times to DR, ICR, and WBRT, in similar multivariate analysis for OS, only the graded prognostic assessment proved to be significant. However, the number of intracranial metastases at presentation was not significantly associated with OS nor with other outcome variables. Conclusions: Larger tumor size was associated with shorter time to recurrence and with shorter time to salvage WBRT; however, larger tumors were not associated with decrements in OS, suggesting successful salvage. SRS to the tumor bed without WBRT is an effective treatment for resected brain metastases, achieving local control particularly for tumors up to

  18. Multi-institutional Nomogram Predicting Survival Free From Salvage Whole Brain Radiation After Radiosurgery in Patients With Brain Metastases

    Energy Technology Data Exchange (ETDEWEB)

    Gorovets, Daniel [Department of Radiation Oncology, Rhode Island Hospital, Brown University Warren Alpert Medical School, Providence, Rhode Island (United States); Department of Radiation Oncology, Perlmutter Cancer Center, NYU School of Medicine, New York, New York (United States); Ayala-Peacock, Diandra [Department of Radiation Oncology, Wake Forest School of Medicine, Winston-Salem, North Carolina (United States); Department of Radiation Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee (United States); Tybor, David J. [Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, Massachusetts (United States); Rava, Paul [Department of Radiation Oncology, Rhode Island Hospital, Brown University Warren Alpert Medical School, Providence, Rhode Island (United States); Department of Radiation Oncology, UMass Memorial Medical Center, University of Massachusetts School of Medicine, Worcester, Massachusetts (United States); Ebner, Daniel [Department of Radiation Oncology, Rhode Island Hospital, Brown University Warren Alpert Medical School, Providence, Rhode Island (United States); Cielo, Deus; Norén, Georg [Department of Neurosurgery, Rhode Island Hospital, Brown University Warren Alpert Medical School, Providence, Rhode Island (United States); Wazer, David E. [Department of Radiation Oncology, Rhode Island Hospital, Brown University Warren Alpert Medical School, Providence, Rhode Island (United States); Chan, Michael [Department of Radiation Oncology, Wake Forest School of Medicine, Winston-Salem, North Carolina (United States); Hepel, Jaroslaw T., E-mail: jhepel@lifespan.org [Department of Radiation Oncology, Rhode Island Hospital, Brown University Warren Alpert Medical School, Providence, Rhode Island (United States)

    2017-02-01

    Purpose: Optimal patient selection for stereotactic radiosurgery (SRS) as the initial treatment for brain metastases is complicated and controversial. This study aimed to develop a nomogram that predicts survival without salvage whole brain radiation therapy (WBRT) after upfront SRS. Methods and Materials: Multi-institutional data were analyzed from 895 patients with 2095 lesions treated with SRS without prior or planned WBRT. Cox proportional hazards regression model was used to identify independent pre-SRS predictors of WBRT-free survival, which were integrated to build a nomogram that was subjected to bootstrap validation. Results: Median WBRT-free survival was 8 months (range, 0.1-139 months). Significant independent predictors for inferior WBRT-free survival were age (hazard ratio [HR] 1.1 for each 10-year increase), HER2(−) breast cancer (HR 1.6 relative to other histologic features), colorectal cancer (HR 1.4 relative to other histologic features), increasing number of brain metastases (HR 1.09, 1.32, 1.37, and 1.87 for 2, 3, 4, and 5+ lesions, respectively), presence of neurologic symptoms (HR 1.26), progressive systemic disease (HR 1.35), and increasing extracranial disease burden (HR 1.31 for oligometastatic and HR 1.56 for widespread). Additionally, HER2(+) breast cancer (HR 0.81) and melanoma (HR 1.11) trended toward significance. The independently weighted hazard ratios were used to create a nomogram to display estimated probabilities of 6-month and 12-month WBRT-free survival with a corrected Harrell's C concordance statistic of 0.62. Conclusions: Our nomogram can be used at initial evaluation to help select patients best suited for upfront SRS for brain metastases while reducing expense and morbidity in patients who derive minimal or no benefit.

  19. Prognostic factors in patients with brain metastasis from non-small cell lung cancer treated with whole-brain radiotherapy.

    Science.gov (United States)

    Harada, Hideyuki; Asakura, Hirofumi; Ogawa, Hirofumi; Mori, Keita; Takahashi, Toshiaki; Nakasu, Yoko; Nishimura, Tetsuo

    2016-01-01

    The purpose of this study was to evaluate the prognostic factors associated with overall survival (OS) in nonsmall cell lung cancer (NSCLC) patients with brain metastasis who received whole-brain radiotherapy (WBRT). This study included 264 consecutive NSCLC patients with brain metastasis who received WBRT. Patients with leptomeningeal metastasis and those who underwent craniotomy or stereotactic radiotherapy before WBRT were excluded. The evaluated prognostic factors for OS included gender, neurological deficit, histology, epidermal growth factor receptor (EGFR) mutation status, previous cytotoxic chemotherapy, previous EGFR-tyrosine kinase inhibitor treatment, recursive partitioning analysis (RPA) class, and diagnosis-specific graded prognostic assessment (DS-GPA) score. All factors with a P < 0.05 in univariate analysis were entered into multivariate analysis using Cox regression and a confidence interval of 99%. Two hundred thirty patients had died, 14 patients were alive, and 20 patients were lost to follow-up. The median follow-up time was 20.9 months. The median survival time was 5.5 months (95% confidence interval; 4.8-6.3). Univariate analysis showed that gender, neurological deficit, histology, EGFR mutation status, RPA class, and DS-GPA score were significant prognostic factors for OS. In multivariate analysis, RPA class and histology were found to be significant prognostic factors for OS, with P values of 0.0039 and 0.0014, respectively. RPA Class I or II (Karnofsky Performance Status ≥70) and adenocarcinoma histology were associated with longer OS. These factors should be taken into account when considering indication for WBRT.

  20. Whole brain radiation-induced impairments in learning and memory are time-sensitive and reversible by systemic hypoxia.

    Directory of Open Access Journals (Sweden)

    Junie P Warrington

    Full Text Available Whole brain radiation therapy (WBRT is commonly used for treatment of primary and metastatic brain tumors; however, cognitive impairment occurs in 40-50% of brain tumor survivors. The etiology of the cognitive impairment following WBRT remains elusive. We recently reported that radiation-induced cerebrovascular rarefaction within hippocampal subregions could be completely reversed by systemic hypoxia. However, the effects of this intervention on learning and memory have not been reported. In this study, we assessed the time-course for WBRT-induced impairments in contextual and spatial learning and the capacity of systemic hypoxia to reverse WBRT-induced deficits in spatial memory. A clinical fractionated series of 4.5Gy WBRT was administered to mice twice weekly for 4 weeks, and after various periods of recovery, behavioral analyses were performed. To study the effects of systemic hypoxia, mice were subjected to 11% (hypoxia or 21% oxygen (normoxia for 28 days, initiated 1 month after the completion of WBRT. Our results indicate that WBRT induces a transient deficit in contextual learning, disruption of working memory, and progressive impairment of spatial learning. Additionally, systemic hypoxia completely reversed WBRT-induced impairments in learning and these behavioral effects as well as increased vessel density persisted for at least 2 months following hypoxia treatment. Our results provide critical support for the hypothesis that cerebrovascular rarefaction is a key component of cognitive impairment post-WBRT and indicate that processes of learning and memory, once thought to be permanently impaired after WBRT, can be restored.

  1. Diagnostic performance of whole brain volume perfusion CT in intra-axial brain tumors: Preoperative classification accuracy and histopathologic correlation

    Energy Technology Data Exchange (ETDEWEB)

    Xyda, Argyro, E-mail: argyro.xyda@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany); Department of Radialogy, University Hospital of Heraklion, Voutes, 71110 Heraklion, Crete (Greece); Haberland, Ulrike, E-mail: ulrike.haberland@siemens.com [Siemens AG Healthcare Sector, Computed Tomography, Siemensstr. 1, 91301 Forchheim (Germany); Klotz, Ernst, E-mail: ernst.klotz@siemens.com [Siemens AG Healthcare Sector, Computed Tomography, Siemensstr. 1, 91301 Forchheim (Germany); Jung, Klaus, E-mail: kjung1@uni-goettingen.de [Department of Medical Statistics, Georg-August University, Humboldtallee 32, 37073 Goettingen (Germany); Bock, Hans Christoph, E-mail: cbock@gmx.de [Department of Neurosurgery, Johannes Gutenberg University Hospital of Mainz, Langenbeckstraße 1, 55101 Mainz (Germany); Schramm, Ramona, E-mail: ramona.schramm@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany); Knauth, Michael, E-mail: michael.knauth@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany); Schramm, Peter, E-mail: p.schramm@med.uni-goettingen.de [Department of Neuroradiology, Georg-August University, University Hospital of Goettingen, Robert-Koch Strasse 40, 37075 Goettingen (Germany)

    2012-12-15

    Background: To evaluate the preoperative diagnostic power and classification accuracy of perfusion parameters derived from whole brain volume perfusion CT (VPCT) in patients with cerebral tumors. Methods: Sixty-three patients (31 male, 32 female; mean age 55.6 ± 13.9 years), with MRI findings suspected of cerebral lesions, underwent VPCT. Two readers independently evaluated VPCT data. Volumes of interest (VOIs) were marked circumscript around the tumor according to maximum intensity projection volumes, and then mapped automatically onto the cerebral blood volume (CBV), flow (CBF) and permeability Ktrans perfusion datasets. A second VOI was placed in the contra lateral cortex, as control. Correlations among perfusion values, tumor grade, cerebral hemisphere and VOIs were evaluated. Moreover, the diagnostic power of VPCT parameters, by means of positive and negative predictive value, was analyzed. Results: Our cohort included 32 high-grade gliomas WHO III/IV, 18 low-grade I/II, 6 primary cerebral lymphomas, 4 metastases and 3 tumor-like lesions. Ktrans demonstrated the highest sensitivity, specificity and positive predictive value, with a cut-off point of 2.21 mL/100 mL/min, for both the comparisons between high-grade versus low-grade and low-grade versus primary cerebral lymphomas. However, for the differentiation between high-grade and primary cerebral lymphomas, CBF and CBV proved to have 100% specificity and 100% positive predictive value, identifying preoperatively all the histopathologically proven high-grade gliomas. Conclusion: Volumetric perfusion data enable the hemodynamic assessment of the entire tumor extent and provide a method of preoperative differentiation among intra-axial cerebral tumors with promising diagnostic accuracy.

  2. Accelerated whole brain intracranial vessel wall imaging using black blood fast spin echo with compressed sensing (CS-SPACE).

    Science.gov (United States)

    Zhu, Chengcheng; Tian, Bing; Chen, Luguang; Eisenmenger, Laura; Raithel, Esther; Forman, Christoph; Ahn, Sinyeob; Laub, Gerhard; Liu, Qi; Lu, Jianping; Liu, Jing; Hess, Christopher; Saloner, David

    2017-12-05

    Develop and optimize an accelerated, high-resolution (0.5 mm isotropic) 3D black blood MRI technique to reduce scan time for whole-brain intracranial vessel wall imaging. A 3D accelerated T 1 -weighted fast-spin-echo prototype sequence using compressed sensing (CS-SPACE) was developed at 3T. Both the acquisition [echo train length (ETL), under-sampling factor] and reconstruction parameters (regularization parameter, number of iterations) were first optimized in 5 healthy volunteers. Ten patients with a variety of intracranial vascular disease presentations (aneurysm, atherosclerosis, dissection, vasculitis) were imaged with SPACE and optimized CS-SPACE, pre and post Gd contrast. Lumen/wall area, wall-to-lumen contrast ratio (CR), enhancement ratio (ER), sharpness, and qualitative scores (1-4) by two radiologists were recorded. The optimized CS-SPACE protocol has ETL 60, 20% k-space under-sampling, 0.002 regularization factor with 20 iterations. In patient studies, CS-SPACE and conventional SPACE had comparable image scores both pre- (3.35 ± 0.85 vs. 3.54 ± 0.65, p = 0.13) and post-contrast (3.72 ± 0.58 vs. 3.53 ± 0.57, p = 0.15), but the CS-SPACE acquisition was 37% faster (6:48 vs. 10:50). CS-SPACE agreed with SPACE for lumen/wall area, ER measurements and sharpness, but marginally reduced the CR. In the evaluation of intracranial vascular disease, CS-SPACE provides a substantial reduction in scan time compared to conventional T 1 -weighted SPACE while maintaining good image quality.

  3. Community structure from spectral properties in complex networks

    Science.gov (United States)

    Servedio, V. D. P.; Colaiori, F.; Capocci, A.; Caldarelli, G.

    2005-06-01

    We analyze the spectral properties of complex networks focusing on their relation to the community structure, and develop an algorithm based on correlations among components of different eigenvectors. The algorithm applies to general weighted networks, and, in a suitably modified version, to the case of directed networks. Our method allows to correctly detect communities in sharply partitioned graphs, however it is useful to the analysis of more complex networks, without a well defined cluster structure, as social and information networks. As an example, we test the algorithm on a large scale data-set from a psychological experiment of free word association, where it proves to be successful both in clustering words, and in uncovering mental association patterns.

  4. Ranking influential nodes in complex networks with structural holes

    Science.gov (United States)

    Hu, Ping; Mei, Ting

    2018-01-01

    Ranking influential nodes in complex networks is of great theoretical and practical significance to ensure the safe operations of networks. In view of the important role structural hole nodes usually play in information spreading in complex networks, we propose a novel ranking method of influential nodes using structural holes called E-Burt method, which can be applied to weighted networks. This method fully takes into account the total connectivity strengths of the node in its local scope, the number of the connecting edges and the distributions of the total connectivity strengths on its connecting edges. The simulation results on the susceptible-infectious-recovered (SIR) dynamics suggest that the proposed E-Burt method can rank influential nodes more effectively and accurately in complex networks.

  5. Exponential random graph models for networks with community structure.

    Science.gov (United States)

    Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian

    2013-09-01

    Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.

  6. Maps of random walks on complex networks reveal community structure.

    Science.gov (United States)

    Rosvall, Martin; Bergstrom, Carl T

    2008-01-29

    To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.

  7. Realization of Broadband Matched Filter Structures Based on Dual Networks

    Directory of Open Access Journals (Sweden)

    M. Gerding

    2005-01-01

    Full Text Available This paper deals with the basic electrical properties of dual networks and with their application in broadband matched filter structures. Starting with the main characteristics and different realization methods of dual networks, a filter structure is presented, which is based on a combination of dual networks and which provides a broadband matched input and two decoupled output ports. This filter synthesis focuses on the design of high pass filters, which are suitable to be used as differentiating stages in electrical pulse generators as a part of the so-called pulse shaping network. In order to achieve a proper pulse shape and for the prevention of multiple reflections between the switching circuit and the differentiating network, a broadband matched filter is a basic requirement.

  8. The structure of complex networks theory and applications

    CERN Document Server

    Estrada, Ernesto

    2012-01-01

    This book deals with the analysis of the structure of complex networks by combining results from graph theory, physics, and pattern recognition. The book is divided into two parts. 11 chapters are dedicated to the development of theoretical tools for the structural analysis of networks, and 7 chapters are illustrating, in a critical way, applications of these tools to real-world scenarios. The first chapters provide detailed coverage of adjacency and metric and topologicalproperties of networks, followed by chapters devoted to the analysis of individual fragments and fragment-based global inva

  9. Modeling Temporal Evolution and Multiscale Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

    2013-01-01

    -point model to account for the temporal evolution of each vertex. We demonstrate that our model is able to infer time-varying multiscale structure in synthetic as well as three real world time-evolving complex networks. Our modeling of the temporal evolution of hierarchies brings new insights......Many real-world networks exhibit both temporal evolution and multiscale structure. We propose a model for temporally correlated multifurcating hierarchies in complex networks which jointly capture both effects. We use the Gibbs fragmentation tree as prior over multifurcating trees and a change...

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

    Directory of Open Access Journals (Sweden)

    Stavros I. Dimitriadis

    2017-12-01

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

  11. Structure and Evolution of the Foreign Exchange Networks

    Science.gov (United States)

    Kwapień, J.; Gworek, S.; Drożdż, S.

    2009-01-01

    We investigate topology and temporal evolution of the foreign currency exchange market viewed from a weighted network perspective. Based on exchange rates for a set of 46 currencies (including precious metals), we construct different representations of the FX network depending on a choice of the base currency. Our results show that the network structure is not stable in time, but there are main clusters of currencies, which persist for a long period of time despite the fact that their size and content are variable. We find a long-term trend in the network's evolution which affects the USD and EUR nodes. In all the network representations, the USD node gradually loses its centrality, while, on contrary, the EUR node has become slightly more central than it used to be in its early years. Despite this directional trend, the overall evolution of the network is noisy.

  12. Neural network structure for navigation using potential fields

    Science.gov (United States)

    Plumer, Edward S.

    1992-01-01

    A hybrid-network method for obstacle avoidance in the truck-backing system of D. Nguyen and B. Widrow (1989) is presented. A neural network technique for vehicle navigation and control in the presence of obstacles has been developed. A potential function which peaks at the surface of obstacles and has its minimum at the proper vehicle destination is computed using a network structure. The field is guaranteed not to have spurious local minima and does not have the property of flattening-out far from the goal. A feedforward neural network is used to control the steering of the vehicle using local field information. The network is trained in an obstacle-free space to follow the negative gradient of the field, after which the network is able to control and navigate the truck to its target destination in a space of obstacles which may be stationary or movable.

  13. Fragmented Romanian Sociology: Growth and Structure of the Collaboration Network

    Science.gov (United States)

    Hâncean, Marian-Gabriel; Perc, Matjaž; Vlăsceanu, Lazăr

    2014-01-01

    Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common. PMID:25409180

  14. Fragmented Romanian sociology: growth and structure of the collaboration network.

    Directory of Open Access Journals (Sweden)

    Marian-Gabriel Hâncean

    Full Text Available Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common.

  15. Network structure classification and features of water distribution systems

    Science.gov (United States)

    Giustolisi, Orazio; Simone, Antonietta; Ridolfi, Luca

    2017-04-01

    The network connectivity structure of water distribution systems (WDSs) represents the domain where hydraulic processes occur, driving the emerging behavior of such systems, for example with respect to robustness and vulnerability. In complex network theory (CNT), a common way of classifying the network structure and connectivity is the association of the nodal degree distribution to specific probability distribution models, and during the last decades, researchers classified many real networks using the Poisson or Pareto distributions. In spite of the fact that degree-based network classification could play a crucial role to assess WDS vulnerability, this task is not easy because the network structure of WDSs is strongly constrained by spatial characteristics of the environment where they are constructed. The consequence of these spatial constraints is that the nodal degree spans very small ranges in WDSs hindering a reliable classification by the standard approach based on the nodal degree distribution. This work investigates the classification of the network structure of 22 real WDSs, built in different environments, demonstrating that the Poisson distribution generally models the degree distributions very well. In order to overcome the problem of the reliable classification based on the standard nodal degree, we define the "neighborhood" degree, equal to the sum of the nodal degrees of the nearest topological neighbors (i.e., the adjacent nodes). This definition of "neighborhood" degree is consistent with the fact that the degree of a single node is not significant for analysis of WDSs.

  16. Fragmented Romanian sociology: growth and structure of the collaboration network.

    Science.gov (United States)

    Hâncean, Marian-Gabriel; Perc, Matjaž; Vlăsceanu, Lazăr

    2014-01-01

    Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common.

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2017-03-01

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

  19. Unifying Inference of Meso-Scale Structures in Networks.

    Directory of Open Access Journals (Sweden)

    Birkan Tunç

    Full Text Available Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities of the brain, as well as its auxiliary characteristics (core-periphery.

  20. Unifying Inference of Meso-Scale Structures in Networks.

    Science.gov (United States)

    Tunç, Birkan; Verma, Ragini

    2015-01-01

    Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).

  1. Conversation practices and network structure in Twitter

    DEFF Research Database (Denmark)

    Rossi, Luca; Magnani, Matteo

    2012-01-01

    the participation in the same hashtag based conversation change the follower list of the participants? Is it possible to point out specific social behaviors that would produce a major gain of followers? Our conclusions are based on real data concerning the popular TV show Xfactor, that largely used Twitter......The public by default nature of Twitter messages, together with the adoption of the #hashtag convention led, in few years, to the creation of a digital space able to host worldwide conversation on almost every kind of topic. From major TV shows to Natural disasters there is no contemporary event...... that does not have its own #hashtag to gather together the ongoing Twitter conversation. These topical discussions take place outside of the Twitter network made of followers and friends. Nevertheless this topical network is where many of the most studied phenomena take place. Therefore Twitter based...

  2. Predicting network structure using unlabeled interaction information

    OpenAIRE

    Nasim, Mehwish; Brandes, Ulrik

    2014-01-01

    We are interested in the question whether interactions in online social networks (OSNs) can serve as a proxy for more persistent social relation. With Facebook as the context of our analysis, we look at commenting on wall posts as a form of interaction, and friendship ties as social relations. Findings from a pretest suggest that others’ joint commenting patterns on someone’s status posts are indeed indicative of friendship ties between them, independent of the contents. This would have impli...

  3. Network structure impacts global commodity trade growth and resilience

    Science.gov (United States)

    Rovenskaya, Elena; Fath, Brian D.

    2017-01-01

    Global commodity trade networks are critical to our collective sustainable development. Their increasing interconnectedness pose two practical questions: (i) Do the current network configurations support their further growth? (ii) How resilient are these networks to economic shocks? We analyze the data of global commodity trade flows from 1996 to 2012 to evaluate the relationship between structural properties of the global commodity trade networks and (a) their dynamic growth, as well as (b) the resilience of their growth with respect to the 2009 global economic shock. Specifically, we explore the role of network efficiency and redundancy using the information theory-based network flow analysis. We find that, while network efficiency is positively correlated with growth, highly efficient systems appear to be less resilient, losing more and gaining less growth following an economic shock. While all examined networks are rather redundant, we find that network redundancy does not hinder their growth. Moreover, systems exhibiting higher levels of redundancy lose less and gain more growth following an economic shock. We suggest that a strategy to support making global trade networks more efficient via, e.g., preferential trade agreements and higher specialization, can promote their further growth; while a strategy to increase the global trade networks’ redundancy via e.g., more abundant free-trade agreements, can improve their resilience to global economic shocks. PMID:28207790

  4. Structural Quality of Service in Large-Scale Networks

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup

    , telephony and data. To meet the requirements of the different applications, and to handle the increased vulnerability to failures, the ability to design robust networks providing good Quality of Service is crucial. However, most planning of large-scale networks today is ad-hoc based, leading to highly......Digitalization has created the base for co-existence and convergence in communications, leading to an increasing use of multi service networks. This is for example seen in the Fiber To The Home implementations, where a single fiber is used for virtually all means of communication, including TV...... complex networks lacking predictability and global structural properties. The thesis applies the concept of Structural Quality of Service to formulate desirable global properties, and it shows how regular graph structures can be used to obtain such properties....

  5. The value of whole-brain CT perfusion imaging and CT angiography using a 320-slice CT scanner in the diagnosis of MCI and AD patients

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Bo; Gu, Guo-jun; Jiang, Hong; Guo, Yi [Medical School of Tongji University, Department of Medical Imaging, Tongji Hospital, Shanghai (China); Shen, Xing [Traditional Chinese Hospital, Department of Radiology, Kun Shan, Jiangsu Province (China); Li, Bo; Zhang, Wei [Medical School of Jiaotong University, Department of Medical Imaging, Renji Hospital, Shanghai (China)

    2017-11-15

    To validate the value of whole-brain computed tomography perfusion (CTP) and CT angiography (CTA) in the diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). Whole-brain CTP and four-dimensional CT angiography (4D-CTA) images were acquired in 30 MCI, 35 mild AD patients, 35 moderate AD patients, 30 severe AD patients and 50 normal controls (NC). Cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time to peak (TTP), and correlation between CTP and 4D-CTA were analysed. Elevated CBF in the left frontal and temporal cortex was found in MCI compared with the NC group. However, TTP was increased in the left hippocampus in mild AD patients compared with NC. In moderate and severe AD patients, hypoperfusion was found in multiple brain areas compared with NC. Finally, we found that the extent of arterial stenosis was negatively correlated with CBF in partial cerebral cortex and hippocampus, and positively correlated with TTP in these areas of AD and MCI patients. Our findings suggest that whole-brain CTP and 4D-CTA could serve as a diagnostic modality in distinguishing MCI and AD, and predicting conversion from MCI based on TTP of left hippocampus. (orig.)

  6. Voxel-based analysis of whole-brain effects of age and gender on dopamine transporter SPECT imaging in healthy subjects

    Energy Technology Data Exchange (ETDEWEB)

    Eusebio, Alexandre; Azulay, Jean-Philippe [APHM, Hopital de la Timone, Service de Neurologie et Pathologie du Mouvement, Marseille (France); CNRS, Aix-Marseille Univ, Institut de Neurosciences de la Timone, Marseille (France); Ceccaldi, Mathieu [APHM, Hopital de la Timone, Service de Neurologie et de Neuropsychologie, Marseille (France); Aix-Marseille Univ, UMR Inserm 1106, Institut de Neurosciences des Systemes, Marseille (France); Girard, Nadine [APHM, Hopital de la Timone, Service de Neuroradiologie diagnostique et interventionnelle, Marseille (France); Mundler, Olivier [APHM, Hopital de la Timone, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Aix-Marseille Univ, CERIMED, Marseille (France); Guedj, Eric [CNRS, Aix-Marseille Univ, Institut de Neurosciences de la Timone, Marseille (France); APHM, Hopital de la Timone, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Aix-Marseille Univ, CERIMED, Marseille (France)

    2012-11-15

    Several studies have shown age- and gender-related differences in striatal dopamine transporter (DaT) binding. These studies were based on a striatal region on interest approach that may have underestimated these effects and could not evaluate extrastriatal regions. Our aim was to determine the effects at the voxel level of age and gender on whole-brain DaT distribution using [{sup 123}I]FP-CIT SPECT in healthy subjects. We performed a whole-brain [{sup 123}I]FP-CIT SPECT voxel-based analysis using SPM8 and a standardized normalization template (p < 0.05, corrected using the false discovery rate method) in 51 healthy subjects aged from 21 to 79 years. We found an age-related DaT binding decrease in the striatum, anterior cingulate/medial frontal cortices and insulo-opercular cortices. Also DaT binding ratios were higher in women than men in the striatum and opercular cortices. This study showed both striatal and extrastriatal age-related and gender-related differences in DaT binding in healthy subjects using a whole-brain voxel-based non-a priori approach. These differences highlight the need for careful age and gender matching in DaT analyses of neuropsychiatric disorders. (orig.)

  7. Design and implementation of embedded 8-channel receive-only arrays for whole-brain MRI and fMRI of conscious awake marmosets.

    Science.gov (United States)

    Papoti, Daniel; Yen, Cecil Chern-Chyi; Hung, Chia-Chun; Ciuchta, Jennifer; Leopold, David A; Silva, Afonso C

    2017-07-01

    The common marmoset (Callithrix jacchus) is a New World primate of increasing interest to neuroscience and in translational brain research. The present work describes the design and implementation of individualized 8-channel receive-only radiofrequency (RF) coil arrays that provide whole-brain coverage and allow anatomical and functional MRI experiments in conscious, awake marmosets. The coil arrays were designed with their elements embedded inside individualized restraint helmets. The size, geometry, and arrangement of the coil elements were optimized to allow whole-brain coverage. Coil-to-coil decoupling was achieved by a combination of geometric decoupling and low input impedance preamplifiers. The performance of the embedded arrays was compared against that of one 8-channel receive-only array built to fit the external surface of the helmets. Three individualized helmets with embedded coil arrays were built for three marmosets. Whole-brain coverage was achieved with high sensitivity extending over the entire cortex. Visual stimulation of conscious awake marmosets elicited robust BOLD fMRI responses in both primary and higher order visual areas of the occipitotemporal cortex. The high sensitivity provided by embedded receive-only coil arrays allows both anatomical and functional MRI data to be obtained with high spatial resolution in conscious, awake marmosets. Magn Reson Med 78:387-398, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

  8. Some structural determinants of Pavlovian conditioning in artificial neural networks.

    Science.gov (United States)

    Sánchez, José M; Galeazzi, Juan M; Burgos, José E

    2010-05-01

    This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks' structure and functioning are described by a strongly bottom-up model that takes into account the roles of hippocampal and dopaminergic systems in conditioning. Neuroanatomical features were simulated as generic structural or architectural features of neural networks. We focused on the number of units per hidden layer and connectivity. The effect of the number of units per hidden layer was investigated through simulations of resistance to extinction in fully connected networks. Large networks were more resistant to extinction than small networks, a stochastic effect of the asynchronous random procedure used in the simulator to update activations and weights. These networks did not simulate second-order conditioning because weight competition prevented conditioning to a stimulus after conditioning to another. Partially connected networks simulated second-order conditioning and devaluation of the second-order stimulus after extinction of a similar first-order stimulus. Similar stimuli were simulated as nonorthogonal input-vectors. Copyright (c) 2009 Elsevier B.V. All rights reserved.

  9. A local immunization strategy for networks with overlapping community structure

    Science.gov (United States)

    Taghavian, Fatemeh; Salehi, Mostafa; Teimouri, Mehdi

    2017-02-01

    Since full coverage treatment is not feasible due to limited resources, we need to utilize an immunization strategy to effectively distribute the available vaccines. On the other hand, the structure of contact network among people has a significant impact on epidemics of infectious diseases (such as SARS and influenza) in a population. Therefore, network-based immunization strategies aim to reduce the spreading rate by removing the vaccinated nodes from contact network. Such strategies try to identify more important nodes in epidemics spreading over a network. In this paper, we address the effect of overlapping nodes among communities on epidemics spreading. The proposed strategy is an optimized random-walk based selection of these nodes. The whole process is local, i.e. it requires contact network information in the level of nodes. Thus, it is applicable to large-scale and unknown networks in which the global methods usually are unrealizable. Our simulation results on different synthetic and real networks show that the proposed method outperforms the existing local methods in most cases. In particular, for networks with strong community structures, high overlapping membership of nodes or small size communities, the proposed method shows better performance.

  10. Structure of Small World Innovation Network and Learning Performance

    Directory of Open Access Journals (Sweden)

    Shuang Song

    2014-01-01

    Full Text Available This paper examines the differences of learning performance of 5 MNCs (multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality. We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter distance between the knowledge of the individual and the reality denotes a lower (higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

  11. Impact of constrained rewiring on network structure and node dynamics.

    Science.gov (United States)

    Rattana, P; Berthouze, L; Kiss, I Z

    2014-11-01

    In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic. We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network's initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence.

  12. 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.

  13. Comparison and validation of community structures in complex networks

    Science.gov (United States)

    Gustafsson, Mika; Hörnquist, Michael; Lombardi, Anna

    2006-07-01

    The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information. Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

  14. Comparing and Selecting Generalized Double Ring Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Knudsen, Thomas Phillip; Madsen, Ole Brun

    2004-01-01

    N2R(p;q) network structures were introduced recently as a generalization of double rings, and they were shown to be superior compared to double rings in terms of average distance and diameter. For a given number of nodes, there is only one double ring, but often more different N2R(p;q) structures...

  15. Bayesian network structure learning using chaos hybrid genetic algorithm

    Science.gov (United States)

    Shen, Jiajie; Lin, Feng; Sun, Wei; Chang, KC

    2012-06-01

    A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.

  16. Structural parameter identifiability analysis for dynamic reaction networks

    DEFF Research Database (Denmark)

    Davidescu, Florin Paul; Jørgensen, Sten Bay

    2008-01-01

    where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models....... The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion...... method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters...

  17. Social structures in Russia : cells and networks

    OpenAIRE

    Yefimov, Vladimir

    2001-01-01

    Russian companies heirs of Soviet enterprises are not Western-style companies, a significant difference is that they represent the basic structures of social life in the USSR : cells. The Soviet cellular system itself has deep roots in the history of Russia. The principal social structure of pre-revolutionary Russia was the rural community. In the late 1950s, Soviet society began to move away from the classic model. Cells gradually lose their exclusive role in the functioning of society. New ...

  18. A Phase I Study of Short-Course Accelerated Whole Brain Radiation Therapy for Multiple Brain Metastases

    Energy Technology Data Exchange (ETDEWEB)

    Caravatta, Luciana; Deodato, Francesco; Ferro, Marica [Department of Radiation Oncology, Fondazione di Ricerca e Cura ' Giovanni Paolo II' , Universita Cattolica del S. Cuore, Campobasso (Italy); Macchia, Gabriella, E-mail: gmacchia@rm.unicatt.it [Department of Radiation Oncology, Fondazione di Ricerca e Cura ' Giovanni Paolo II' , Universita Cattolica del S. Cuore, Campobasso (Italy); Massaccesi, Mariangela [Department of Radiation Oncology, Fondazione di Ricerca e Cura ' Giovanni Paolo II' , Universita Cattolica del S. Cuore, Campobasso (Italy); Cilla, Savino [Medical Physics Unit, Fondazione di Ricerca e Cura ' Giovanni Paolo II,' Universita Cattolica del S. Cuore, Campobasso (Italy); Padula, Gilbert D.A. [Department of Radiation Oncology, The Lacks Cancer Center Saint Mary' s Health Care, Grand Rapids, Michigan (United States); Mignogna, Samantha; Tambaro, Rosa [Department of Palliative Therapies, Fondazione di Ricerca e Cura ' Giovanni Paolo II' , Universita Cattolica del S. Cuore, Campobasso (Italy); Carrozza, Francesco [Department of Oncology, A. Cardarelli Hospital, Campobasso (Italy); Flocco, Mariano [Madre Teresa di Calcutta Hospice, Larino (Italy); Cantore, Giampaolo [Department of Neurological Sciences, Istituto Neurologico Mediterraneo Neuromed, Istituto di Ricovero e Cura a Carattere Scientifico, Pozzilli (Italy); Scapati, Andrea [Department of Radiation Oncology, ' San Francesco' Hospital, Nuoro (Italy); Buwenge, Milly [Department of Radiotherapy, Mulago Hospital, Kampala (Uganda); and others

    2012-11-15

    Purpose: To define the maximum tolerated dose (MTD) of a SHort-course Accelerated whole brain RadiatiON therapy (SHARON) in the treatment of patients with multiple brain metastases. Methods and Materials: A phase 1 trial in 4 dose-escalation steps was designed: 12 Gy (3 Gy per fraction), 14 Gy (3.5 Gy per fraction), 16 Gy (4 Gy per fraction), and 18 Gy (4.5 Gy per fraction). Eligibility criteria included patients with unfavorable recursive partitioning analysis (RPA) class > or =2 with at least 3 brain metastases or metastatic disease in more than 3 organ systems, and Eastern Cooperative Oncology Group (ECOG) performance status {<=}3. Treatment was delivered in 2 days with twice-daily fractionation. Patients were treated in cohorts of 6-12 to define the MTD. The dose-limiting toxicity (DLT) was defined as any acute toxicity {>=}grade 3, according to the Radiation Therapy Oncology Group scale. Information on the status of the main neurologic symptoms and quality of life were recorded. Results: Characteristics of the 49 enrolled patients were as follows: male/female, 30/19; median age, 66 years (range, 23-83 years). ECOG performance status was <3 in 46 patients (94%). Fourteen patients (29%) were considered to be in recursive partitioning analysis (RPA) class 3. Grade 1-2 acute neurologic (26.4%) and skin (18.3%) toxicities were recorded. Only 1 patient experienced DLT (neurologic grade 3 acute toxicity). With a median follow-up time of 5 months (range, 1-23 months), no late toxicities have been observed. Three weeks after treatment, 16 of 21 symptomatic patients showed an improvement or resolution of presenting symptoms (overall symptom response rate, 76.2%; confidence interval 0.95: 60.3-95.9%). Conclusions: Short-course accelerated radiation therapy in twice-daily fractions for 2 consecutive days is tolerated up to a total dose of 18 Gy. A phase 2 study has been planned to evaluate the efficacy on overall survival, symptom control, and quality of life indices.

  19. Phase I/II trial of simultaneous whole-brain irradiation and dose-escalating topotecan for brain metastases

    Energy Technology Data Exchange (ETDEWEB)

    Kocher, M.; Eich, H.T.; Semrau, R.; Guener, S.A.; Mueller, R.P. [Dept. of Radiation Oncology, Univ. Hospital, Univ. of Cologne, Cologne (Germany)

    2005-01-01

    Background and purpose: topotecan penetrates the blood-brain barrier and sensitizes tumor cells against radiation. A phase I/II dose-escalating trial of repetitive daily i.v. topotecan application simultaneously with whole-brain irradiation (WBRT) was conducted to estimate toxicity, maximum tolerated dose and survival in patients with inoperable brain metastases. Patients and methods: in 47 patients suffering from previously untreated brain metastases, topotecan was applied on a daily i.v. schedule simultaneously with WBRT (36 Gy/3-Gy fractions). The infusion schedule started at the beginning of WBRT and was discontinued during weekends. Each infusion was completed within 1-2 h before irradiation. In a dose-finding study, topotecan was escalated from 5 x 0.5 mg/m{sup 2}, 8 x 0.5 mg/m{sup 2}, 12 x 0.5 mg/m{sup 2} to 12 x 0.6 mg/m{sup 2}. Results: altogether, 38/47 patients (81%) completed the prescribed schedule. Leukopenia and thrombocytopenia were dose-limiting. Grade 3/4 hematologic toxicity occurred in 5/32 chemonaive patients (16%) and 7/15 patients (47%) with previous chemotherapy. At 12 x 0.6 mg/m{sup 2}, 2/4 patients experienced grade 4 leukopenia/thrombopenia. Nonhematologic toxicities were generally mild to moderate and unrelated to topotecan. Response evaluation was possible in 26/47 patients, overall response rate was 58% (CR [complete remission] 5/26, PR [partial remission] 10/26, NC [no change] 8/26). Median survival amounted to 5.1 months. In 15/42 patients (36%), brain metastases were the dominant cause of death. Conclusion: for a daily topotecan schedule simultaneous to WBRT, the maximum tolerated dose is 12 x 0.5 mg/m{sup 2} in chemonaive patients. For chemo-pretreated patients, daily doses should be reduced to 0.4 mg/m{sup 2}. A phase III trial has now been started to find out whether WBRT + topotecan increases survival compared to WBRT alone. (orig.)

  20. Radiological distribution of brain metastases and its implication for the hippocampus avoidance in whole brain radiotherapy approach.

    Science.gov (United States)

    Han, Yi-Min; Cai, Gang; Chai, Wei-Min; Xu, Cheng; Cao, Lu; Ou, Dan; Chen, Jia-Yi; Kirova, Youlia M

    2017-11-01

    Hippocampus avoidance in whole brain radiotherapy (HA-WBRT) offers the feasibility of less-impaired cognitive function than conventional WBRT. The study aims to assess the radiological distribution of brain metastases (BMs) with relation to the hippocampus and peri-hippocampus region as defined by the RTOG 0933 for better understanding of margin definition in HA-WBRT treatment planning. Consecutive patients with diagnosis of BM from enhanced MRI between March 2011 and July 2016 were analysed. The pre-treatment T1 weighted, T2 weighted, T2 flair, three-dimensional spoiled gradient axial and contrast-enhanced axial cranial MR images of 226 patients are examined. The closest distances between the edge of hippocampus and the margin of tumours on different planes were measured. A total of 226 patients with 1080 visible metastatic sites were reviewed. The origin of the primary tumors was in 72.6% lung (n = 164), in 45 cases (19.9%) breast cancer and in 7.5% other malignancies (n = 17). There were 758 (70.2%) lesions situated beyond the tentorium. The median size of single lesion was 13.9 ± 14.7 mm. Impossible, it seems that more of the patients are with only one lesion, to verify. The hippocampus involvement was found in 3.1% (n = 7, 95% CI 0.01-0.05) within 5 mm, 5.7% (n = 13, 95% CI 0.03-0.09) within 10mm and 8.4% (n = 19, 95% CI 0.05-0.12) within 20 mm. In multivariate analysis, the number 6 BM or higher was found to be an independent risk factor for hippocampal involvement (HI) (OR: 5.2, 5.38 and 3.84 in 5, 10 and 20 mm). This radiological study found that the incidence of hippocampus involvement is low in patients with BM. HA-WBRT can be delivered under the context of complete radiological diagnosis after careful delineation, proper margin definition and individual planning optimization. Advances in knowledge: The incidence of HI in patients with initial diagnosis of BM from solid tumours impacts the radiotherapeutic decision. Our radiological data analysed the

  1. Resistance and Security Index of Networks: Structural Information Perspective of Network Security.

    Science.gov (United States)

    Li, Angsheng; Hu, Qifu; Liu, Jun; Pan, Yicheng

    2016-06-03

    Recently, Li and Pan defined the metric of the K-dimensional structure entropy of a structured noisy dataset G to be the information that controls the formation of the K-dimensional structure of G that is evolved by the rules, order and laws of G, excluding the random variations that occur in G. Here, we propose the notion of resistance of networks based on the one- and two-dimensional structural information of graphs. Given a graph G, we define the resistance of G, written , as the greatest overall number of bits required to determine the code of the module that is accessible via random walks with stationary distribution in G, from which the random walks cannot escape. We show that the resistance of networks follows the resistance law of networks, that is, for a network G, the resistance of G is , where and are the one- and two-dimensional structure entropies of G, respectively. Based on the resistance law, we define the security index of a network G to be the normalised resistance of G, that is, . We show that the resistance and security index are both well-defined measures for the security of the networks.

  2. Structured learning via convolutional neural networks for vehicle detection

    Science.gov (United States)

    Maqueda, Ana I.; del Blanco, Carlos R.; Jaureguizar, Fernando; García, Narciso

    2017-05-01

    One of the main tasks in a vision-based traffic monitoring system is the detection of vehicles. Recently, deep neural networks have been successfully applied to this end, outperforming previous approaches. However, most of these works generally rely on complex and high-computational region proposal networks. Others employ deep neural networks as a segmentation strategy to achieve a semantic representation of the object of interest, which has to be up-sampled later. In this paper, a new design for a convolutional neural network is applied to vehicle detection in highways for traffic monitoring. This network generates a spatially structured output that encodes the vehicle locations. Promising results have been obtained in the GRAM-RTM dataset.

  3. Modeling structure and resilience of the dark network.

    Science.gov (United States)

    De Domenico, Manlio; Arenas, Alex

    2017-02-01

    While the statistical and resilience properties of the Internet are no longer changing significantly across time, the Darknet, a network devoted to keep anonymous its traffic, still experiences rapid changes to improve the security of its users. Here we study the structure of the Darknet and find that its topology is rather peculiar, being characterized by a nonhomogeneous distribution of connections, typical of scale-free networks; very short path lengths and high clustering, typical of small-world networks; and lack of a core of highly connected nodes. We propose a model to reproduce such features, demonstrating that the mechanisms used to improve cybersecurity are responsible for the observed topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet much more resilient than the Internet (used as a benchmark for comparison at a descriptive level) to random failures, targeted attacks, and cascade failures, as a result of adaptive changes in response to the attempts of dismantling the network across time.

  4. Modeling structure and resilience of the dark network

    Science.gov (United States)

    De Domenico, Manlio; Arenas, Alex

    2017-02-01

    While the statistical and resilience properties of the Internet are no longer changing significantly across time, the Darknet, a network devoted to keep anonymous its traffic, still experiences rapid changes to improve the security of its users. Here we study the structure of the Darknet and find that its topology is rather peculiar, being characterized by a nonhomogeneous distribution of connections, typical of scale-free networks; very short path lengths and high clustering, typical of small-world networks; and lack of a core of highly connected nodes. We propose a model to reproduce such features, demonstrating that the mechanisms used to improve cybersecurity are responsible for the observed topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet much more resilient than the Internet (used as a benchmark for comparison at a descriptive level) to random failures, targeted attacks, and cascade failures, as a result of adaptive changes in response to the attempts of dismantling the network across time.

  5. Finding community structure in networks using the eigenvectors of matrices.

    Science.gov (United States)

    Newman, M E J

    2006-09-01

    We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as "modularity" over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.

  6. Offspring social network structure predicts fitness in families.

    Science.gov (United States)

    Royle, Nick J; Pike, Thomas W; Heeb, Philipp; Richner, Heinz; Kölliker, Mathias

    2012-12-22

    Social structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively.

  7. Brain networks that track musical structure.

    Science.gov (United States)

    Janata, Petr

    2005-12-01

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

  8. From Microactions to Macrostructure and Back : A Structurational Approach to the Evolution of Organizational Networks

    NARCIS (Netherlands)

    Whitbred, Robert; Fonti, Fabio; Steglich, Christian; Contractor, Noshir

    Structuration theory (ST) and network analysis are promising approaches for studying the emergence of communication networks. We offer a model that integrates the conceptual richness of structuration with the precision of relevant concepts and mechanisms offered from communication network research.

  9. Dependency structure matrix modelling for stakeholder value networks

    OpenAIRE

    Feng, Wen; Crawley, Edward F.; de Weck, Olivier L.; Keller, Rene; Robinson, Bob

    2010-01-01

    This paper develops a qualitative/quantitative network approach, namely a “Stakeholder Value Network”, to understand the impacts of both direct and indirect relationships between stakeholders on the success of large engineering projects. Specifically, this paper explores the feasibility and benefit of applying the Dependency Structure Matrix (DSM) as the modelling platform for Stakeholder Value Networks. Further, an efficient algorithm is designed for computing indirect stakeholder influence ...

  10. Novel fingerprinting method characterises the necessary and sufficient structural connectivity from deep brain stimulation electrodes for a successful outcome

    Science.gov (United States)

    Fernandes, Henrique M.; Van Hartevelt, Tim J.; Boccard, Sandra G. J.; Owen, Sarah L. F.; Cabral, Joana; Deco, Gustavo; Green, Alex L.; Fitzgerald, James J.; Aziz, Tipu Z.; Kringelbach, Morten L.

    2015-01-01

    Deep brain stimulation (DBS) is a remarkably effective clinical tool, used primarily for movement disorders. DBS relies on precise targeting of specific brain regions to rebalance the oscillatory behaviour of whole-brain neural networks. Traditionally, DBS targeting has been based upon animal models (such as MPTP for Parkinson’s disease) but has also been the result of serendipity during human lesional neurosurgery. There are, however, no good animal models of psychiatric disorders such as depression and schizophrenia, and progress in this area has been slow. In this paper, we use advanced tractography combined with whole-brain anatomical parcellation to provide a rational foundation for identifying the connectivity ‘fingerprint’ of existing, successful DBS targets. This knowledge can then be used pre-surgically and even potentially for the discovery of novel targets. First, using data from our recent case series of cingulate DBS for patients with treatment-resistant chronic pain, we demonstrate how to identify the structural ‘fingerprints’ of existing successful and unsuccessful DBS targets in terms of their connectivity to other brain regions, as defined by the whole-brain anatomical parcellation. Second, we use a number of different strategies to identify the successful fingerprints of structural connectivity across four patients with successful outcomes compared with two patients with unsuccessful outcomes. This fingerprinting method can potentially be used pre-surgically to account for a patient’s individual connectivity and identify the best DBS target. Ultimately, our novel fingerprinting method could be combined with advanced whole-brain computational modelling of the spontaneous dynamics arising from the structural changes in disease, to provide new insights and potentially new targets for hitherto impenetrable neuropsychiatric disorders.

  11. Direct modulation of aberrant brain network connectivity through real-time NeuroFeedback

    Science.gov (United States)

    Kimmich, Sara; Gonzalez-Castillo, Javier; Roopchansingh, Vinai; Popal, Haroon; White, Emily; Gotts, Stephen J; Martin, Alex

    2017-01-01

    The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants’ awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns. PMID:28917059

  12. Motif structure and cooperation in real-world complex networks

    Science.gov (United States)

    Salehi, Mostafa; Rabiee, Hamid R.; Jalili, Mahdi

    2010-12-01

    Networks of dynamical nodes serve as generic models for real-world systems in many branches of science ranging from mathematics to physics, technology, sociology and biology. Collective behavior of agents interacting over complex networks is important in many applications. The cooperation between selfish individuals is one of the most interesting collective phenomena. In this paper we address the interplay between the motifs’ cooperation properties and their abundance in a number of real-world networks including yeast protein-protein interaction, human brain, protein structure, email communication, dolphins’ social interaction, Zachary karate club and Net-science coauthorship networks. First, the amount of cooperativity for all possible undirected subgraphs with three to six nodes is calculated. To this end, the evolutionary dynamics of the Prisoner’s Dilemma game is considered and the cooperativity of each subgraph is calculated as the percentage of cooperating agents at the end of the simulation time. Then, the three- to six-node motifs are extracted for each network. The significance of the abundance of a motif, represented by a Z-value, is obtained by comparing them with some properly randomized versions of the original network. We found that there is always a group of motifs showing a significant inverse correlation between their cooperativity amount and Z-value, i.e. the more the Z-value the less the amount of cooperativity. This suggests that networks composed of well-structured units do not have good cooperativity properties.

  13. Sampling from complex networks with high community structures.

    Science.gov (United States)

    Salehi, Mostafa; Rabiee, Hamid R; Rajabi, Arezo

    2012-06-01

    In this paper, we propose a novel link-tracing sampling algorithm, based on the concepts from PageRank vectors, to sample from networks with high community structures. Our method has two phases; (1) Sampling the closest nodes to the initial nodes by approximating personalized PageRank vectors and (2) Jumping to a new community by using PageRank vectors and unknown neighbors. Empirical studies on several synthetic and real-world networks show that the proposed method improves the performance of network sampling compared to the popular link-based sampling methods in terms of accuracy and visited communities.

  14. Acoustical properties of nonwoven fiber network structures

    Science.gov (United States)

    Tascan, Mevlut

    Sound insulation is one of the most important issues for the automotive and building industries. Because they are porous fibrous structures, textile materials can be used as sound insulating and sound absorbing materials. Very high-density materials such as steel can insulate sound very effectively but these rigid materials reflect most of the sound back to the environment, causing sound pollution. Additionally, because high-density, rigid materials are also heavy and high cost, they cannot be used for sound insulation for the automotive and building industries. Nonwoven materials are more suitable for these industries, and they can also absorb sound in order to decrease sound pollution in the environment. Therefore, nonwoven materials are one of the most important materials for sound insulation and absorption applications materials. Insulation and absorption properties of nonwoven fabrics depend on fiber geometry and fiber arrangement within the fabric structure. Because of their complex structure, it is very difficult to define the microstructure of nonwovens. The structure of nonwovens only has fibers and voids that are filled by air. Because of the complexity of fiber-void geometry, there is still not a very accurate theory or model that defines the structural arrangement. A considerable amount of modeling has been reported in literature [1--19], but most models are not accurate due to the assumptions made. Voids that are covered by fibers are called pores in nonwoven structures and their geometry is very important, especially for the absorption properties of nonwovens. In order to define the sound absorption properties of nonwoven fabrics, individual pore structure and the number of pores per unit thickness of the fabric should be determined. In this research, instead of trying to define pores, the properties of the fibers are investigated and the number of fibers per volume of fabric is taken as a parameter in the theory. Then the effect of the nonwoven

  15. Neural networks for harmonic structure in music perception and action

    OpenAIRE

    Bianco, R.; Novembre, G.; Keller, P. E.; Kim, S G; Scharf, F.; Friederici, A. D.; Villringer, A; Sammler, D.

    2016-01-01

    The ability to predict upcoming structured events based on long-term knowledge and contextual priors is a fundamental principle of human cognition. Tonal music triggers predictive processes based on structural properties of harmony, i.e., regularities defining the arrangement of chords into well-formed musical sequences. While the neural architecture of structure-based predictions during music perception is well described, little is known about the neural networks for analogous predictions in...

  16. Influence and structural balance in social networks

    Science.gov (United States)

    Singh, P.; Sreenivasan, S.; Szymanski, B.; Korniss, G.

    2012-02-01

    Models on structural balance have been studied in the past with links being categorized as friendly or antagonistic [Ref- T. Antal et al., Phys. Rev. E 72, 036121 (2005)]. However no connection between the nature of the links and states of the nodes they connect has been made. We introduce a model which combines the dynamics of the structural balance with spread of social influence. In this model, every node is in one of the three possible states (e.g. leftist, centrist and rightist) [Ref- F. Vazquez, S. Redner, J. Phys A, 37 (2004) 8479-8494] where links between leftists and rightists are antagonistic while all other links are friendly. The evolution of the system is governed by the rules of structural balance and opinion spread takes place as a result of structural balance process. The dynamics can lead the system to a number of fixed points (absorbing states). We study how the initial density of centrists nc affects the dynamics and probabilities of ending up in different absorbing states. We also study the scaling behavior of the expected time to converge to one of the absorbing states as a function of the initial density of centrists and under some variations of our basic model.

  17. Electrical network method for the thermal or structural characterization of a conducting material sample or structure

    Science.gov (United States)

    Ortiz, Marco G.

    1993-01-01

    A method for modeling a conducting material sample or structure system, as an electrical network of resistances in which each resistance of the network is representative of a specific physical region of the system. The method encompasses measuring a resistance between two external leads and using this measurement in a series of equations describing the network to solve for the network resistances for a specified region and temperature. A calibration system is then developed using the calculated resistances at specified temperatures. This allows for the translation of the calculated resistances to a region temperature. The method can also be used to detect and quantify structural defects in the system.

  18. Correlations between community structure and link formation in complex networks.

    Directory of Open Access Journals (Sweden)

    Zhen Liu

    Full Text Available BACKGROUND: Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. METHODOLOGY/PRINCIPAL FINDINGS: Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. CONCLUSIONS/SIGNIFICANCE: Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction.

  19. Correlations between community structure and link formation in complex networks.

    Science.gov (United States)

    Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep

    2013-01-01

    Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction.

  20. Scalable, ultra-resistant structural colors based on network metamaterials

    CERN Document Server

    Galinski, Henning; Dong, Hao; Gongora, Juan S Totero; Favaro, Grégory; Döbeli, Max; Spolenak, Ralph; Fratalocchi, Andrea; Capasso, Federico

    2016-01-01

    Structural colours have drawn wide attention for their potential as a future printing technology for various applications, ranging from biomimetic tissues to adaptive camouflage materials. However, an efficient approach to realise robust colours with a scalable fabrication technique is still lacking, hampering the realisation of practical applications with this platform. Here we develop a new approach based on large scale network metamaterials, which combine dealloyed subwavelength structures at the nanoscale with loss-less, ultra-thin dielectrics coatings. By using theory and experiments, we show how sub-wavelength dielectric coatings control a mechanism of resonant light coupling with epsilon-near-zero (ENZ) regions generated in the metallic network, manifesting the formation of highly saturated structural colours that cover a wide portion of the spectrum. Ellipsometry measurements report the efficient observation of these colours even at angles of $70$ degrees. The network-like architecture of these nanoma...

  1. Structure analysis of growing network based on partial differential equations

    Directory of Open Access Journals (Sweden)

    Junbo JIA

    2016-04-01

    Full Text Available The topological structure is one of the most important contents in the complex network research. Therein the node degree and the degree distribution are the most basic characteristic quantities to describe topological structure. In order to calculate the degree distribution, first of all, the node degree is considered as a continuous variable. Then, according to the Markov Property of growing network, the cumulative distribution function's evolution equation with time can be obtained. Finally, the partial differential equation (PDE model can be established through distortion processing. Taking the growing network with preferential and random attachment mechanism as an example, the PDE model is obtained. The analytic expression of degree distribution is obtained when this model is solved. Besides, the degree function over time is the same as the characteristic line of PDE. At last, the model is simulated. This PDE method of changing the degree distribution calculation into problem of solving PDE makes the structure analysis more accurate.

  2. The interplay between microscopic and mesoscopic structures in complex networks.

    Directory of Open Access Journals (Sweden)

    Jörg Reichardt

    Full Text Available Understanding a complex network's structure holds the key to understanding its function. The physics community has contributed a multitude of methods and analyses to this cross-disciplinary endeavor. Structural features exist on both the microscopic level, resulting from differences between single node properties, and the mesoscopic level resulting from properties shared by groups of nodes. Disentangling the determinants of network structure on these different scales has remained a major, and so far unsolved, challenge. Here we show how multiscale generative probabilistic exponential random graph models combined with efficient, distributive message-passing inference techniques can be used to achieve this separation of scales, leading to improved detection accuracy of latent classes as demonstrated on benchmark problems. It sheds new light on the statistical significance of motif-distributions in neural networks and improves the link-prediction accuracy as exemplified for gene-disease associations in the highly consequential Online Mendelian Inheritance in Man database.

  3. Validation of protein structure models using network similarity score.

    Science.gov (United States)

    Ghosh, Sambit; Gadiyaram, Vasundhara; Vishveshwara, Saraswathi

    2017-09-01

    Accurate structural validation of proteins is of extreme importance in studies like protein structure prediction, analysis of molecular dynamic simulation trajectories and finding subtle changes in very similar structures. The benchmarks for today's structure validation are scoring methods like global distance test-total structure (GDT-TS), TM-score and root mean square deviations (RMSD). However, there is a lack of methods that look at both the protein backbone and side-chain structures at the global connectivity level and provide information about the differences in connectivity. To address this gap, a graph spectral based method (NSS-network similarity score) which has been recently developed to rigorously compare networks in diverse fields, is adopted to compare protein structures both at the backbone and at the side-chain noncovalent connectivity levels. In this study, we validate the performance of NSS by investigating protein structures from X-ray structures, modeling (including CASP models), and molecular dynamics simulations. Further, we systematically identify the local and the global regions of the structures contributing to the difference in NSS, through the components of the score, a feature unique to this spectral based scoring scheme. It is demonstrated that the method can quantify subtle differences in connectivity compared to a reference protein structure and can form a robust basis for protein structure comparison. Additionally, we have also introduced a network-based method to analyze fluctuations in side chain interactions (edge-weights) in an ensemble of structures, which can be an useful tool for the analysis of MD trajectories. © 2017 Wiley Periodicals, Inc.

  4. Structure Learning for Deep Neural Networks Based on Multiobjective Optimization.

    Science.gov (United States)

    Liu, Jia; Gong, Maoguo; Miao, Qiguang; Wang, Xiaogang; Li, Hao

    2017-05-05

    This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible data are modeled with respect to structure based on the products of experts. In order to mitigate the difficulty of estimating the denominator in PoE, the denominator is simplified and taken as another objective, i.e., the connecting sparsity. Moreover, for the consideration of the contradictory nature between the representation ability and the network connecting sparsity, the multiobjective model is established. An improved multiobjective evolutionary algorithm is used to solve this model. Two tricks are designed to decrease the computational cost according to the properties of input data. The experiments on single-layer level, hierarchical level, and application level demonstrate the effectiveness of the proposed algorithm, and the learned structures can improve the performance of deep neural networks.

  5. Network structure detection and analysis of Shanghai stock market

    Directory of Open Access Journals (Sweden)

    Sen Wu

    2015-04-01

    Full Text Available Purpose: In order to investigate community structure of the component stocks of SSE (Shanghai Stock Exchange 180-index, a stock correlation network is built to find the intra-community and inter-community relationship. Design/methodology/approach: The stock correlation network is built taking the vertices as stocks and edges as correlation coefficients of logarithm returns of stock price. It is built as undirected weighted at first. GN algorithm is selected to detect community structure after transferring the network into un-weighted with different thresholds. Findings: The result of the network community structure analysis shows that the stock market has obvious industrial characteristics. Most of the stocks in the same industry or in the same supply chain are assigned to the same community. The correlation of the internal stock prices’ fluctuation is closer than in different communities. The result of community structure detection also reflects correlations among different industries. Originality/value: Based on the analysis of the community structure in Shanghai stock market, the result reflects some industrial characteristics, which has reference value to relationship among industries or sub-sectors of listed companies.

  6. SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

    Science.gov (United States)

    Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T

    2015-02-01

    Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Structure and Controls of the Global Virtual Water Trade Network

    Science.gov (United States)

    Suweis, S. S.

    2011-12-01

    Recurrent or ephemeral water shortages are a crucial global challenge, in particular because of their impacts on food production. The global character of this challenge is reflected in the trade among nations of virtual water, i.e. the amount of water used to produce a given commodity. We build, analyze and model the network describing the transfer of virtual water between world nations for staple food products. We find that all the key features of the network are well described by a model, the fitness model, that reproduces both the topological and weighted properties of the global virtual water trade network, by assuming as sole controls each country's gross domestic product and yearly rainfall on agricultural areas. We capture and quantitatively describe the high degree of globalization of water trade and show that a small group of nations play a key role in the connectivity of the network and in the global redistribution of virtual water. Finally, we illustrate examples of prediction of the structure of the network under future political, economic and climatic scenarios, suggesting that the crucial importance of the countries that trade large volumes of water will be strengthened. Our results show the importance of incorporating a network framework in the study of virtual water trades and provide a model to study the structure and resilience of the GVWTN under future scenarios for social, economic and climate change.

  8. A clustering algorithm for determining community structure in complex networks

    Science.gov (United States)

    Jin, Hong; Yu, Wei; Li, ShiJun

    2018-02-01

    Clustering algorithms are attractive for the task of community detection in complex networks. DENCLUE is a representative density based clustering algorithm which has a firm mathematical basis and good clustering properties allowing for arbitrarily shaped clusters in high dimensional datasets. However, this method cannot be directly applied to community discovering due to its inability to deal with network data. Moreover, it requires a careful selection of the density parameter and the noise threshold. To solve these issues, a new community detection method is proposed in this paper. First, we use a spectral analysis technique to map the network data into a low dimensional Euclidean Space which can preserve node structural characteristics. Then, DENCLUE is applied to detect the communities in the network. A mathematical method named Sheather-Jones plug-in is chosen to select the density parameter which can describe the intrinsic clustering structure accurately. Moreover, every node on the network is meaningful so there were no noise nodes as a result the noise threshold can be ignored. We test our algorithm on both benchmark and real-life networks, and the results demonstrate the effectiveness of our algorithm over other popularity density based clustering algorithms adopted to community detection.

  9. Theory of Mind and the Whole Brain Functional Connectivity: Behavioral and Neural Evidences with the Amsterdam Resting State Questionnaire.

    Science.gov (United States)

    Marchetti, Antonella; Baglio, Francesca; Costantini, Isa; Dipasquale, Ottavia; Savazzi, Federica; Nemni, Raffaello; Sangiuliano Intra, Francesca; Tagliabue, Semira; Valle, Annalisa; Massaro, Davide; Castelli, Ilaria

    2015-01-01

    A topic of common interest to psychologists and philosophers is the spontaneous flow of thoughts when the individual is awake but not involved in cognitive demands. This argument, classically referred to as the "stream of consciousness" of James, is now known in the psychological literature as "Mind-Wandering." Although of great interest, this construct has been scarcely investigated so far. Diaz et al. (2013) created the Amsterdam Resting State Questionnaire (ARSQ), composed of 27 items, distributed in seven factors: discontinuity of mind, theory of mind (ToM), self, planning, sleepiness, comfort, and somatic awareness. The present study aims at: testing psychometric properties of the ARSQ in a sample of 670 Italian subjects; exploring the neural correlates of a subsample of participants (N = 28) divided into two groups on the basis of the scores obtained in the ToM factor. Results show a satisfactory reliability of the original factional structure in the Italian sample. In the subjects with a high mean in the ToM factor compared to low mean subjects, functional MRI revealed: a network (48 nodes) with higher functional connectivity (FC) with a dominance of the left hemisphere; an increased within-lobe FC in frontal and insular lobes. In both neural and behavioral terms, our results support the idea that the mind, which does not rest even when explicitly asked to do so, has various and interesting mentalistic-like contents.

  10. Whole brain irradiation in case of brain metastases in from 2005 to 2011 in the clinic for nuclear medicine of the university hospital Freiburg; Ganzhirnbestrahlung bei Hirnmetastasen von 2005 bis 2011 in der Klinik fuer Strahlenheilkunde des Universitaetsklinikums Freiburg

    Energy Technology Data Exchange (ETDEWEB)

    Hintz, Mandy

    2017-10-01

    Brain metastases are the largest group of brain tumors. Their occurrence influences the overall survival and the quality of life. The retrospective study deals with the overall survival, the local tumor control and the prognostic factors of patients treated with whole brain irradiation. The data were evaluated using multivariate analysis. Whole brain irradiation has shown to be an efficient therapy option for patients with brain metastases and has the possibility to improve the overall progress-free survival and the symptom control.

  11. Emergence of Slow-Switching Assemblies in Structured Neuronal Networks.

    Directory of Open Access Journals (Sweden)

    Michael T Schaub

    2015-07-01

    Full Text Available Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics can impact on spatio-temporal neural activity and constrain the resulting dynamics.

  12. The structure and resilience of financial market networks.

    Science.gov (United States)

    Peron, Thomas Kaue Dal'Maso; Costa, Luciano da Fontoura; Rodrigues, Francisco A

    2012-03-01

    Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience.

  13. Emergence of Slow-Switching Assemblies in Structured Neuronal Networks.

    Science.gov (United States)

    Schaub, Michael T; Billeh, Yazan N; Anastassiou, Costas A; Koch, Christof; Barahona, Mauricio

    2015-07-01

    Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated