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

ABSTRACT: Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment.

SUBMITTER: Duan Y 

PROVIDER: S-EPMC8902595 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature