• Ilaria Gori, Alessia Giuliano, Filippo Muratori, Irene Saviozzi, Piernicola Oliva, Raffaella Tancredi, Angela Cosenza, Michela Tosetti, Sara Calderoni, and Alessandra Retico.
• Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy.
• J Neuroimaging. 2015 Nov 1; 25 (6): 866-74.

Background And PurposeSophisticated algorithms to infer disease diagnosis, pathology progression and patient outcome are increasingly being developed to analyze brain MRI data. They have been successfully implemented in a variety of diseases and are currently investigated in the field of neuropsychiatric disorders, including autism spectrum disorder (ASD). We aim to test the ability to predict ASD from subtle morphological changes in structural magnetic resonance imaging (sMRI).MethodsThe analysis of sMRI of a cohort of male ASD children and controls matched for age and nonverbal intelligence quotient (NVIQ) has been carried out with two widely used preprocessing software packages (SPM and Freesurfer) to extract brain morphometric information at different spatial scales. Then, support vector machines have been implemented to classify the brain features and to localize which brain regions contribute most to the ASD-control separation.ResultsThe features extracted from the gray matter subregions provide the best classification performance, reaching an area under the receiver operating characteristic curve (AUC) of 74%. This value is enhanced to 80% when considering only subjects with NVIQ over 70.ConclusionsDespite the subtle impact of ASD on brain morphology and a limited cohort size, results from sMRI-based classifiers suggest a consistent network of altered brain regions.Copyright © 2015 by the American Society of Neuroimaging.

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