Measuring network's entropy in ADHD: A new approach to investigate neuropsychiatric disorders

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dc.contributor.author Sato, João Ricardo
dc.contributor.author Takahashi, Daniel Yasumasa
dc.contributor.author Hoexter, Marcelo Queiroz [UNIFESP]
dc.contributor.author Massirer, Katlin Brauer
dc.contributor.author Fujita, André
dc.date.accessioned 2016-01-24T14:32:08Z
dc.date.available 2016-01-24T14:32:08Z
dc.date.issued 2013-08-15
dc.identifier http://dx.doi.org/10.1016/j.neuroimage.2013.03.035
dc.identifier.citation Neuroimage. San Diego: Academic Press Inc Elsevier Science, v. 77, p. 44-51, 2013.
dc.identifier.issn 1053-8119
dc.identifier.uri http://repositorio.unifesp.br/handle/11600/36649
dc.description.abstract The 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.sponsorship Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorship Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorship Pew Latin American Fellowship
dc.format.extent 44-51
dc.language.iso eng
dc.publisher Elsevier B.V.
dc.relation.ispartof Neuroimage
dc.rights Acesso aberto
dc.subject ADHD en
dc.subject Graph en
dc.subject Spectral analysis en
dc.subject Entropy en
dc.subject fMRI en
dc.title Measuring network's entropy in ADHD: A new approach to investigate neuropsychiatric disorders en
dc.type Artigo
dc.rights.license http://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.contributor.institution Fed Univ ABC
dc.contributor.institution Princeton Univ
dc.contributor.institution Universidade Federal de São Paulo (UNIFESP)
dc.contributor.institution Universidade Estadual de Campinas (UNICAMP)
dc.contributor.institution Universidade de São Paulo (USP)
dc.description.affiliation Fed Univ ABC, Ctr Math Computat & Cognit, BR-09210170 Santo Andre, SP, Brazil
dc.description.affiliation Princeton Univ, Dept Psychol, Princeton, NJ 08540 USA
dc.description.affiliation Princeton Univ, Neurosci Inst, Princeton, NJ 08540 USA
dc.description.affiliation Universidade Federal de São Paulo, Dept Psychiat, Lab Interdisciplinar Neurociencias Clin, São Paulo, Brazil
dc.description.affiliation Univ Estadual Campinas, Ctr Mol Biol & Genet Engn, BR-13083875 Campinas, SP, Brazil
dc.description.affiliation Univ São Paulo, Dept Comp Sci, Inst Math & Stat, BR-05508090 São Paulo, Brazil
dc.description.affiliationUnifesp Universidade Federal de São Paulo, Dept Psychiat, Lab Interdisciplinar Neurociencias Clin, São Paulo, Brazil
dc.identifier.file WOS000320073900004.pdf
dc.identifier.doi 10.1016/j.neuroimage.2013.03.035
dc.description.source Web of Science
dc.identifier.wos WOS:000320073900004



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