Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning.
World J Biol Psychiatry
; 19(2): 119-129, 2018 Mar.
Article
en En
| MEDLINE
| ID: mdl-28635541
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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Corteza Cerebral
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Desarrollo Infantil
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Desarrollo del Adolescente
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Máquina de Vectores de Soporte
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Conectoma
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Trastornos Mentales
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Red Nerviosa
Tipo de estudio:
Diagnostic_studies
/
Risk_factors_studies
Límite:
Adolescent
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Child
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Female
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Humans
/
Male
Idioma:
En
Año:
2018
Tipo del documento:
Article