Abstract

Motivation: The diagnosis of Autism Spectrum Disorder (ASD) poses a substantial challenge, primarily due to the lack of a definitive biomarker. Goal(s): Our primary objective is to uncover white matter irregularities prevalent in pediatric autism. Approach: Our study leveraged Tract-Based Spatial Statistics (TBSS) analysis to scrutinize deviations in the white matter microstructure in ASD, and we implemented an eXtreme Gradient Boosting (XGBoost) model to effectively differentiate between individuals with ASD and healthy controls. Results: Through the TBSS analysis, we identified notable disparities between groups. Moreover, the XGBoost model demonstrated exceptional proficiency in accurately classifying individuals with ASD and healthy controls. Impact: This study delved into the white matter microstructural alterations in individuals with ASD by examining DKI data and its associated white matter tract integrity (WMTI) metrics. Additionally, our machine learning findings offered fresh perspectives toward objectively diagnosing ASD.