Please use this identifier to cite or link to this item:
Title: Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning
Authors: Sato, Joao Ricardo [UNIFESP]
Biazoli, Claudinei Eduardo, Jr.
Salum, Giovanni Abrahao
Gadelha, Ary [UNIFESP]
Crossley, Nicolas
Vieira, Gilson
Zugman, Andre [UNIFESP]
Picon, Felipe Almeida
Pan, Pedro Mario [UNIFESP]
Hoexter, Marcelo Queiroz [UNIFESP]
Amaro, Edson, Jr.
Anes, Mauricio
Moura, Luciana Monteiro [UNIFESP]
Gomes Del'Aquilla, Marco Antonio [UNIFESP]
Mcguire, Philip
Rohde, Luis Augusto
Miguel, Euripedes Constantino
Jackowski, Andrea Parolin [UNIFESP]
Bressan, Rodrigo Affonseca [UNIFESP]
Keywords: Connectivity
machine learning
Issue Date: 2018
Publisher: Taylor & Francis Ltd
Citation: World Journal Of Biological Psychiatry. Abingdon, v. 19, n. 2, p. 119-129, 2018.
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.
ISSN: 1562-2975
Other Identifiers:
Appears in Collections:Artigo

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.