Please use this identifier to cite or link to this item: http://repositorio.unifesp.br/handle/11600/36649
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dc.contributor.authorSato, João Ricardo
dc.contributor.authorTakahashi, Daniel Yasumasa
dc.contributor.authorHoexter, Marcelo Queiroz [UNIFESP]
dc.contributor.authorMassirer, Katlin Brauer
dc.contributor.authorFujita, André
dc.date.accessioned2016-01-24T14:32:08Z-
dc.date.available2016-01-24T14:32:08Z-
dc.date.issued2013-08-15
dc.identifierhttp://dx.doi.org/10.1016/j.neuroimage.2013.03.035
dc.identifier.citationNeuroimage. San Diego: Academic Press Inc Elsevier Science, v. 77, p. 44-51, 2013.
dc.identifier.issn1053-8119
dc.identifier.urihttp://repositorio.unifesp.br/handle/11600/36649-
dc.description.abstractThe application of graph analysis methods to the topological organization of brain connectivity has been a useful tool in the characterization of brain related disorders. However, the availability of tools, which enable researchers to investigate functional brain networks, is still a major challenge. Most of the studies evaluating brain images are based on centrality and segregation measurements of complex networks. in this study, we applied the concept of graph spectral entropy (GSE) to quantify the complexity in the organization of brain networks. in addition, to enhance interpretability, we also combined graph spectral clustering to investigate the topological organization of sub-network's modules. We illustrate the usefulness of the proposed approach by comparing brain networks between attention deficit hyperactivity disorder (ADHD) patients and the brain networks of typical developing (TD) controls. the main findings highlighted that GSE involving sub-networks comprising the areas mostly bilateral pre and post central cortex, superior temporal gyrus, and inferior frontal gyri were statistically different (p-value = 0.002) between ADHD patients and TO controls. in the same conditions, the other conventional graph descriptors (betweenness centrality, clustering coefficient, and shortest path length) commonly used to identify connectivity abnormalities did not show statistical significant difference. We conclude that analysis of topological organization of brain sub-networks based on GSE can identify networks between brain regions previously unobserved to be in association with ADHD. (C) 2013 Elsevier Inc. All rights reserved.en
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipPew Latin American Fellowship
dc.format.extent44-51
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofNeuroimage
dc.rightsAcesso aberto
dc.subjectADHDen
dc.subjectGraphen
dc.subjectSpectral analysisen
dc.subjectEntropyen
dc.subjectfMRIen
dc.titleMeasuring network's entropy in ADHD: A new approach to investigate neuropsychiatric disordersen
dc.typeArtigo
dc.rights.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.contributor.institutionFed Univ ABC
dc.contributor.institutionPrinceton Univ
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.description.affiliationFed Univ ABC, Ctr Math Computat & Cognit, BR-09210170 Santo Andre, SP, Brazil
dc.description.affiliationPrinceton Univ, Dept Psychol, Princeton, NJ 08540 USA
dc.description.affiliationPrinceton Univ, Neurosci Inst, Princeton, NJ 08540 USA
dc.description.affiliationUniversidade Federal de São Paulo, Dept Psychiat, Lab Interdisciplinar Neurociencias Clin, São Paulo, Brazil
dc.description.affiliationUniv Estadual Campinas, Ctr Mol Biol & Genet Engn, BR-13083875 Campinas, SP, Brazil
dc.description.affiliationUniv São Paulo, Dept Comp Sci, Inst Math & Stat, BR-05508090 São Paulo, Brazil
dc.description.affiliationUnifespUniversidade Federal de São Paulo, Dept Psychiat, Lab Interdisciplinar Neurociencias Clin, São Paulo, Brazil
dc.identifier.fileWOS000320073900004.pdf
dc.identifier.doi10.1016/j.neuroimage.2013.03.035
dc.description.sourceWeb of Science
dc.identifier.wosWOS:000320073900004
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