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Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning.

YuMei Duan1, WeiDong Zhao2, Cheng Luo3

  • 1Department of Computer and Software, Chengdu Jincheng College, Chengdu, China.

Frontiers in Human Neuroscience
|March 11, 2022
PubMed
Summary

This study used machine learning to find brain imaging markers for Autism Spectrum Disorder (ASD), identifying specific regions like the inferior frontal gyrus and temporal gyrus. These markers correlate with ASD symptom severity, aiding in individual prediction.

Keywords:
autism spectrum disordermachine learningmulti-site datasearchlight techniquestructural MRI

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Autism Spectrum Disorder Research

Background:

  • Autism Spectrum Disorder (ASD) diagnosis lacks definitive neuroimaging markers due to inconsistent findings, hindering individual prediction.
  • Existing statistical analyses of brain differences in ASD are insufficient for personalized diagnostic or prognostic applications.

Purpose of the Study:

  • To identify reliable neuroimaging markers for Autism Spectrum Disorder (ASD) using machine learning.
  • To distinguish individuals with ASD from typically developing controls (TDC) based on neuroimaging data.
  • To enhance the interpretability of machine learning models in neuroimaging for understanding ASD pathophysiology.

Main Methods:

  • Employed machine learning techniques within a unified neuroimaging framework.
  • Utilized three levels of assessment: model-level, feature-level, and biology-level for enhanced interpretability.
  • Analyzed regional gray matter (GM) volume and its correlation with clinical severity scores from the Autism Diagnostic Observation Schedule (ADOS_G).

Main Results:

  • Identified specific neuroimaging markers for ASD, including regions in the inferior frontal gyrus, temporal gyrus, parietal gyrus, and insula.
  • Found significant negative correlations between communication skill scores (ADOS_G) and GM volume in several brain regions, including the gyrus rectus and middle/inferior temporal gyri.
  • Detected negative correlations between verbal and non-verbal communication skills and the right angular gyrus volume.

Conclusions:

  • The identified neuroimaging markers and their correlations with clinical severity suggest gray matter alterations in ASD.
  • The interpretable machine learning framework provides insights into the pathophysiological mechanisms of ASD.
  • This approach can potentially be extended to identify neuroimaging markers and understand mechanisms in other neurological disorders.