Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning

Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning

Author Sato, Joao Ricardo Autor UNIFESP Google Scholar
Biazoli, Claudinei Eduardo, Jr. Google Scholar
Salum, Giovanni Abrahao Google Scholar
Gadelha, Ary Autor UNIFESP Google Scholar
Crossley, Nicolas Google Scholar
Vieira, Gilson Google Scholar
Zugman, Andre Autor UNIFESP Google Scholar
Picon, Felipe Almeida Google Scholar
Pan, Pedro Mario Autor UNIFESP Google Scholar
Hoexter, Marcelo Queiroz Autor UNIFESP Google Scholar
Amaro, Edson, Jr. Google Scholar
Anes, Mauricio Google Scholar
Moura, Luciana Monteiro Autor UNIFESP Google Scholar
Gomes Del'Aquilla, Marco Antonio Autor UNIFESP Google Scholar
Mcguire, Philip Google Scholar
Rohde, Luis Augusto Google Scholar
Miguel, Euripedes Constantino Google Scholar
Jackowski, Andrea Parolin Autor UNIFESP Google Scholar
Bressan, Rodrigo Affonseca Autor UNIFESP Google Scholar
Abstract Objectives: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity.In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM).Methods: We applied this approach to resting-state fMRI data from 622 children and adolescents. Eigenvector centrality (EVC) of nodes from positive- and negative-task networks were extracted from each subject and used as input to an OC-SVM to label individual brain networks as typical or atypical. We hypothesised that classification of these subjects regarding the pattern of brain connectivity would predict the level of psychopathology.Results: Subjects with atypical brain network organisation had higher levels of psychopathology (p<0.001). There was a greater EVC in the typical group at the bilateral posterior cingulate and bilateral posterior temporal cortices

and significant decreases in EVC at left temporal pole.Conclusions: The combination of graph theory methods and an OC-SVM is a promising method to characterise neurodevelopment, and may be useful to understand the deviations leading to mental disorders.
Keywords Connectivity
children
psychopathology
machine learning
fMRI
xmlui.dri2xhtml.METS-1.0.item-coverage Abingdon
Language English
Sponsor Sao Paulo Research Foundation - FAPESP [2013/10498-6, 2013/00506-1, 2013/08531-5]
CAPES
CNPq, Brazil
CNPq [573974/2008-0]
FAPESP [2008/57896-8, 2013/16864-4]
CAPES-Brazil
CAPES/FAPERGS
Grant number FAPESP [2013/10498-6, 2013/00506-1, 2013/08531-5]
CAPES
CNPq, Brazil
CNPq [573974/2008-0]
FAPESP [2008/57896-8, 2013/16864-4]
Date 2018
Published in World Journal Of Biological Psychiatry. Abingdon, v. 19, n. 2, p. 119-129, 2018.
ISSN 1562-2975 (Sherpa/Romeo, impact factor)
Publisher Taylor & Francis Ltd
Extent 119-129
Origin http://dx.doi.org/10.1080/15622975.2016.1274050
Access rights Closed access
Type Article
Web of Science ID WOS:000424124800005
URI https://repositorio.unifesp.br/handle/11600/53885

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