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Gray Matter Alterations in Young Children with Autism Spectrum Disorders: Comparing Morphometry at the Voxel and Regional Level
Author(s) -
Gori Ilaria,
Giuliano Alessia,
Muratori Filippo,
Saviozzi Irene,
Oliva Piernicola,
Tancredi Raffaella,
Cosenza Angela,
Tosetti Michela,
Calderoni Sara,
Retico Alessandra
Publication year - 2015
Publication title -
journal of neuroimaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.822
H-Index - 64
eISSN - 1552-6569
pISSN - 1051-2284
DOI - 10.1111/jon.12280
Subject(s) - autism , autism spectrum disorder , medicine , voxel , receiver operating characteristic , voxel based morphometry , cohort , neuroimaging , brain morphometry , artificial intelligence , magnetic resonance imaging , pathology , psychiatry , computer science , radiology , white matter
BACKGROUND AND PURPOSE Sophisticated 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). METHODS The 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. RESULTS The 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. CONCLUSIONS Despite 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.