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

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.

Diagnostic Classification of ASD Using Fractal Functional Connectivity of fMRI and Logistic Regression.

Our study used functional magnetic resonance imaging and fractal functional connectivity (FC) methods to analyze the brain networks of Autism Spectrum Disorder (ASD) and typically developing participants using data available on ABIDE databases. Blood-Oxygen-Level-Dependent time series were extracted from 236 regions of interest of cortical, subcortical, and cerebellar regions using Gordon's, Harvard Oxford, and Diedrichsen atlases respectively. We computed the fractal FC matrices which resulted in 27,730 features, ranked using XGBoost feature ranking.