ADHD Classification Within and Cross Cohort Using an Ensembled Feature Selection Framework

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder that often persists into adulthood. However, as lacking objective measures, several studies have questioned the stability in diagnosing of ADHD from childhood to adulthood. In this study, we propose a novel feature selection framework based on functional connectivity (FCs) pattern, the so-called ‘FS_RIWEL,’ which could classify ADHD from age-matched healthy controls (HCs) with $\sim 80$% accuracy (both for children and adults). More importantly, the feature space learned from child ADHD dataset can discriminate adult ADHD from HCs at $\sim 70$% accuracy. To the best of our knowledge, this is the first attempt to perform a cross-cohort prediction between the adult and child ADHD using FC features. In addition, the most frequently selected FCs indicate that ADHD exhibit widely-impaired FC patterns in frontoparietal, basal ganglia, cerebellum network and so on suggesting that FCs may serve as potential biomarkers for ADHD diagnosis.