Diagnostic Classification of ASD Improves with Structural Connectivity of DTI and Logistic Regression

Abstract

In this study, we examined the structural connectivity (SC) of autism spectrum disorder (ASD) and typical development using the distance correlation and machine learning algorithm. We preprocessed diffusion tensor images using a standard pipeline and parcellated the brain into 48 regions using atlas. We derived diffusion measures in white matter tracts, such as fractional anisotropy, radial diffusivity, axial diffusivity, mean diffusivity, and mode of anisotropy. Additionally, SC is determined by the Euclidean distance between these features. The SC were ranked using XGBoost and significant features were fed as the input to the logistic regression classifier. We obtained an average 10-fold cross-validation classification accuracy of 81% for the top 20 features. The SC computed from the anterior limb of internal capsule L to superior corona radiata R regions significantly contributed to the classification models. Our study shows the potential utility of adopting SC changes as the biomarker for the diagnosis of ASD.