Classifiers for ADHD Based on Gray-White Matter Structural Connectivity Couplings and Corresponding Transcriptional Signatures

Motivation: The research aims to uncover intricate gray-white matter structural connectivity (GWSC) patterns and associated gene expression profiles in ADHD. Goal(s): Develop machine-learning classifiers based on GWSC to distinguish ADHD from controls, bridging its gap with gene expression to unveil neurobiological mechanisms. Approach: Utilize T1-weighted and diffusion-weighted MRI data to construct GWSC networks. Employed four machine-learning classifiers for classification. Analyzed transcriptomes from the Allen Human Brain Atlas to link with gene expression. Results: Classifiers achieved over 75% accuracy, with Gaussian-kernel SVM leading at 82.6%. Ventromedial prefrontal cortex emerged as a key contributor. Transcriptome analysis identified enrichment in "neuron projection development."Impact: These findings empower clinicians with accurate ADHD classifiers and pinpoint the ventromedial prefrontal cortex as a hub. The revelation of gene expression nuances in neuron projection development advances targeted interventions, fostering a shift towards more personalized and effective ADHD treatments.