title: Gray Matter Alterations in Young Children with Autism Spectrum Disorders: Comparing Morphometry at the Voxel and Regional Level.
abstract: OBJECTIVE
  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.
authors.affiliation: Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy.|Dipartimento di Chimica e Farmacia, Università di Sassari, Italy.
authors.forename: Ilaria
authors.initials: I
authors.lastname: Gori
authors.affiliation: Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy.|Dipartimento di Chimica e Farmacia, Università di Sassari, Italy.|Dipartimento di Fisica, Università di Pisa, Italy.
authors.forename: Alessia
authors.initials: A
authors.lastname: Giuliano
authors.affiliation: IRCCS Fondazione Stella Maris, Pisa, Italy.|Dipartimento di Medicina Clinica e Sperimentale, Università of Pisa, Italy.
authors.forename: Filippo
authors.initials: F
authors.lastname: Muratori
authors.affiliation: IRCCS Fondazione Stella Maris, Pisa, Italy.
authors.forename: Irene
authors.initials: I
authors.lastname: Saviozzi
authors.affiliation: Dipartimento di Chimica e Farmacia, Università di Sassari, Italy.|Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari, Italy.
authors.forename: Piernicola
authors.initials: P
authors.lastname: Oliva
authors.affiliation: IRCCS Fondazione Stella Maris, Pisa, Italy.
authors.forename: Raffaella
authors.initials: R
authors.lastname: Tancredi
authors.affiliation: IRCCS Fondazione Stella Maris, Pisa, Italy.
authors.forename: Angela
authors.initials: A
authors.lastname: Cosenza
authors.affiliation: IRCCS Fondazione Stella Maris, Pisa, Italy.
authors.forename: Michela
authors.initials: M
authors.lastname: Tosetti
authors.affiliation: IRCCS Fondazione Stella Maris, Pisa, Italy.
authors.forename: Sara
authors.initials: S
authors.lastname: Calderoni
authors.affiliation: Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy.
authors.forename: Alessandra
authors.initials: A
authors.lastname: Retico
mesh_terms: D000067877:Autism Spectrum Disorder / Q000000981:diagnostic imaging* / Q000473:pathology; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D002675:Child, Preschool; D066128:Gray Matter / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D009929:Organ Size
keywords: Autism spectrum disorders; classification; feature extraction; machine learning; magnetic resonance imaging; support vector machines