Functional Connectome-Based Predictive Modeling in Autism

Biol Psychiatry. 2022 Oct 15;92(8):626-642. doi: 10.1016/j.biopsych.2022.04.008. Epub 2022 Apr 25.


Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

Keywords: Clinical translation; Development; Fingerprinting; Individual differences; Machine learning; Resting-state fMRI.

Publication types

  • Review
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Autism Spectrum Disorder* / diagnostic imaging
  • Autistic Disorder* / diagnostic imaging
  • Brain / diagnostic imaging
  • Connectome*
  • Forecasting
  • Humans
  • Magnetic Resonance Imaging