Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine.

Journal: Brain and behavior
Published Date: Jul 15, 2021

Abstract

OBJECTIVE: Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects.

Authors

  • Letizia Squarcina
    UOC Psychiatry, Azienda Ospedaliera Universitaria Integrata Verona (AOUI), Italy; InterUniversity Centre for Behavioural Neurosciences (ICBN), University of Verona, Verona, Italy.
  • Guido Nosari
    Department of Pathophysiology and Transplantation, University of Milan, Via Festa del Perdono, 7, 20122 Milan, Italy.
  • Riccardo Marin
    Department of Informatics, University of Verona, Verona, Italy.
  • Umberto Castellani
    Department of Informatics, University of Verona, Verona, Italy.
  • Marcella Bellani
    UOC Psychiatry, Azienda Ospedaliera Universitaria Integrata Verona (AOUI), Italy; InterUniversity Centre for Behavioural Neurosciences (ICBN), University of Verona, Verona, Italy.
  • Carolina Bonivento
    IRCCS "E. Medea", Polo Friuli Venezia Giulia, San Vito al Tagliamento (PN), Italy.
  • Franco Fabbro
    Department of Medicine, University of Udine, Udine, Italy.
  • Massimo Molteni
  • Paolo Brambilla
    Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy; Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, TX, USA. Electronic address: paolo.brambilla1@unimi.it.

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