Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning - Duan, YuMei; Zhao, WeiDong; Luo, Cheng; Liu, XiaoJu; Jiang, Hong; Tang, YiQian; Liu, Chang; Yao, DeZhong (Frontiers in Human Neuroscience, 2022)
Although emerging evidence has implicated structural/functional abnormalities of ASD patients, 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 ASD patients 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. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in ADOS_G and regional gray matter volume in the gyrus rectus, left middle temporal gyrus and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the grey matter alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but also can be extended to other diseases.