title: Inter-regional cortical thickness correlations are associated with autistic symptoms: a machine-learning approach.
abstract: The investigation of neural substrates of autism spectrum disorder using neuroimaging has been the focus of recent literature. In addition, machine-learning approaches have also been used to extract relevant information from neuroimaging data. There are only few studies directly exploring the inter-regional structural relationships to identify and characterize neuropsychiatric disorders. In this study, we concentrate on addressing two issues: (i) a novel approach to extract individual subject features from inter-regional thickness correlations based on structural magnetic resonance imaging (MRI); (ii) using these features in a machine-learning framework to obtain individual subject prediction of a severity scores based on neurobiological criteria rather than behavioral information. In a sample of 82 autistic patients, we have shown that structural covariances among several brain regions are associated with the presence of the autistic symptoms. In addition, we also demonstrated that structural relationships from the left hemisphere are more relevant than the ones from the right. Finally, we identified several brain areas containing relevant information, such as frontal and temporal regions. This study provides evidence for the usefulness of this new tool to characterize neuropsychiatric disorders.
authors.affiliation: Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Rua Santa Adélia, 166, Bairro Bangu, CEP 09.210-170 Santo André, SP, Brazil. joao.sato@ufabc.edu.br
authors.forename: João Ricardo
authors.initials: JR
authors.lastname: Sato
authors.forename: Marcelo Queiroz
authors.initials: MQ
authors.lastname: Hoexter
authors.forename: Pedro Paulo de Magalhães
authors.initials: PP
authors.lastname: Oliveira
authors.forename: Michael John
authors.initials: MJ
authors.lastname: Brammer
authors.forename: Declan
authors.initials: D
authors.lastname: Murphy
authors.forename: Christine
authors.initials: C
authors.lastname: Ecker
mesh_terms: D000293:Adolescent; D000328:Adult; D001185:Artificial Intelligence; D001321:Autistic Disorder / Q000473:pathology*; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D010364:Pattern Recognition, Visual; D011237:Predictive Value of Tests; D012720:Severity of Illness Index; D055815:Young Adult