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

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

Author Sato, João Ricardo Google Scholar
Takahashi, Daniel Yasumasa Google Scholar
Hoexter, Marcelo Queiroz Autor UNIFESP Google Scholar
Massirer, Katlin Brauer Google Scholar
Fujita, André Google Scholar
Institution Fed Univ ABC
Princeton Univ
Universidade Federal de São Paulo (UNIFESP)
Universidade Estadual de Campinas (UNICAMP)
Universidade de São Paulo (USP)
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.
Keywords ADHD
Graph
Spectral analysis
Entropy
fMRI
Language English
Sponsor Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Pew Latin American Fellowship
Date 2013-08-15
Published in Neuroimage. San Diego: Academic Press Inc Elsevier Science, v. 77, p. 44-51, 2013.
ISSN 1053-8119 (Sherpa/Romeo, impact factor)
Publisher Elsevier B.V.
Extent 44-51
Origin http://dx.doi.org/10.1016/j.neuroimage.2013.03.035
Access rights Open access Open Access
Type Article
Web of Science ID WOS:000320073900004
URI http://repositorio.unifesp.br/handle/11600/36649

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