title: Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism.
abstract: Although typically identified in early childhood, the social communication symptoms and adaptive behavior deficits that are characteristic of autism spectrum disorder (ASD) persist throughout the lifespan. Despite this persistence, even individuals without cooccurring intellectual disability show substantial heterogeneity in outcomes. Previous studies have found various behavioral assessments [such as intelligence quotient (IQ), early language ability, and baseline autistic traits and adaptive behavior scores] to be predictive of outcome, but most of the variance in functioning remains unexplained by such factors. In this study, we investigated to what extent functional brain connectivity measures obtained from resting-state functional connectivity MRI (rs-fcMRI) could predict the variance left unexplained by age and behavior (follow-up latency and baseline autistic traits and adaptive behavior scores) in two measures of outcome--adaptive behaviors and autistic traits at least 1 y postscan (mean follow-up latency = 2 y, 10 mo). We found that connectivity involving the so-called salience network (SN), default-mode network (DMN), and frontoparietal task control network (FPTCN) was highly predictive of future autistic traits and the change in autistic traits and adaptive behavior over the same time period. Furthermore, functional connectivity involving the SN, which is predominantly composed of the anterior insula and the dorsal anterior cingulate, predicted reliable improvement in adaptive behaviors with 100% sensitivity and 70.59% precision. From rs-fcMRI data, our study successfully predicted heterogeneity in outcomes for individuals with ASD that was unaccounted for by simple behavioral metrics and provides unique evidence for networks underlying long-term symptom abatement.
authors.affiliation: Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892; mplitt@stanford.edu alexmartin@mail.nih.gov.
authors.forename: Mark
authors.initials: M
authors.lastname: Plitt
authors.affiliation: Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892;
authors.forename: Kelly Anne
authors.initials: KA
authors.lastname: Barnes
authors.affiliation: Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892; Department of Speech and Hearing Sciences, George Washington University, Washington, DC 20052;
authors.forename: Gregory L
authors.initials: GL
authors.lastname: Wallace
authors.affiliation: Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892; Center for Autism Spectrum Disorders, Children's National Health System, Washington, DC 20010.
authors.forename: Lauren
authors.initials: L
authors.lastname: Kenworthy
authors.affiliation: Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892; mplitt@stanford.edu alexmartin@mail.nih.gov.
authors.forename: Alex
authors.initials: A
authors.lastname: Martin
mesh_terms: D000293:Adolescent; D000328:Adult; D000067877:Autism Spectrum Disorder / Q000503:physiopathology*; D001519:Behavior; D001921:Brain / Q000502:physiology; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000503:physiopathology; D002648:Child; D002675:Child, Preschool; D003071:Cognition; D005260:Female; D005500:Follow-Up Studies; D006179:Gyrus Cinguli / Q000503:physiopathology; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D009434:Neural Pathways / Q000502:physiology; D012044:Regression Analysis; D015203:Reproducibility of Results; D013997:Time Factors; D055815:Young Adult
keywords: ASD; adaptive behavior; autism; machine learning; rs-fMRI