Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards

Neuroimage Clin. 2014 Dec 24;7:359-66. doi: 10.1016/j.nicl.2014.12.013. eCollection 2015.

Abstract

Objectives: Autism spectrum disorders (ASD) are diagnosed based on early-manifesting clinical symptoms, including markedly impaired social communication. We assessed the viability of resting-state functional MRI (rs-fMRI) connectivity measures as diagnostic biomarkers for ASD and investigated which connectivity features are predictive of a diagnosis.

Methods: Rs-fMRI scans from 59 high functioning males with ASD and 59 age- and IQ-matched typically developing (TD) males were used to build a series of machine learning classifiers. Classification features were obtained using 3 sets of brain regions. Another set of classifiers was built from participants' scores on behavioral metrics. An additional age and IQ-matched cohort of 178 individuals (89 ASD; 89 TD) from the Autism Brain Imaging Data Exchange (ABIDE) open-access dataset (http://fcon_1000.projects.nitrc.org/indi/abide/) were included for replication.

Results: High classification accuracy was achieved through several rs-fMRI methods (peak accuracy 76.67%). However, classification via behavioral measures consistently surpassed rs-fMRI classifiers (peak accuracy 95.19%). The class probability estimates, P(ASD|fMRI data), from brain-based classifiers significantly correlated with scores on a measure of social functioning, the Social Responsiveness Scale (SRS), as did the most informative features from 2 of the 3 sets of brain-based features. The most informative connections predominantly originated from regions strongly associated with social functioning.

Conclusions: While individuals can be classified as having ASD with statistically significant accuracy from their rs-fMRI scans alone, this method falls short of biomarker standards. Classification methods provided further evidence that ASD functional connectivity is characterized by dysfunction of large-scale functional networks, particularly those involved in social information processing.

Trial registration: ClinicalTrials.gov NCT01031407.

Keywords: Autism; Biomarkers; Machine learning classification; Social brain.

Publication types

  • Clinical Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Brain / physiopathology*
  • Child Development Disorders, Pervasive / diagnosis*
  • Child Development Disorders, Pervasive / physiopathology*
  • Humans
  • Image Interpretation, Computer-Assisted
  • Magnetic Resonance Imaging
  • Male
  • Neural Pathways / physiopathology*
  • Young Adult

Associated data

  • ClinicalTrials.gov/NCT01031407