Distributed Intrinsic Functional Connectivity Patterns Predict Diagnostic Status in Large Autism Cohort.

ABSTRACT: Diagnosis of autism spectrum disorder (ASD) currently relies on behavioral observations because brain markers are unknown. Machine learning approaches can identify patterns in imaging data that predict diagnostic status, but most studies using functional connectivity MRI (fcMRI) data achieved only modest accuracies of 60-80%. We used conditional random forest (CRF), an ensemble learning technique protected against bias from feature correlation (which exists in fcMRI matrices). We selected 252 low-motion resting-state functional MRI scans from the Autism Brain Imaging Data Exchange, including 126 typically developing (TD) and 126 ASD participants, matched for age, nonverbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was us

SUBMITTER: Jahedi A 

PROVIDER: S-EPMC5653144 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature