title: Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.
abstract: This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) were selected from the multi-center Functional Connectome Project. Regional gray matter volume and cortical thickness increased, whereas white matter volume decreased in ASD compared to controls. Small-world network analysis of quantitative MRI data demonstrated decreased global efficiency based on gray matter cortical thickness but not with functional connectivity MRI (fcMRI) or volumetry. An integrative model of 22 quantitative imaging features was used for classification and prediction of phenotypic features that included the autism diagnostic observation schedule, the revised autism diagnostic interview, and intelligence quotient scores. Among the 22 imaging features, four (caudate volume, caudate-cortical functional connectivity and inferior frontal gyrus functional connectivity) were found to be highly informative, markedly improving classification and prediction accuracy when compared with the single imaging features. This approach could potentially serve as a biomarker in prognosis, diagnosis, and monitoring disease progression.
authors.affiliation: Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Yongxia
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Research Imaging Institute, Departments of Ophthalmology, Radiology, Physiology, University of Texas Health Science Center, South Texas Veterans Health Care System, Department of Veterans Affairs, San Antonio, Texas, United States of America.
authors.forename: Fang
authors.initials: F
authors.lastname: Yu
authors.affiliation: Research Imaging Institute, Departments of Ophthalmology, Radiology, Physiology, University of Texas Health Science Center, South Texas Veterans Health Care System, Department of Veterans Affairs, San Antonio, Texas, United States of America.
authors.forename: Timothy
authors.initials: T
authors.lastname: Duong
mesh_terms: D000293:Adolescent; D001185:Artificial Intelligence; D016022:Case-Control Studies; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000175:diagnosis*; D063132:Connectome; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male