Discriminating ADHD From Healthy Controls Using a Novel Feature Selection Method Based on Relative Importance and Ensemble Learning.

Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder that often persists into adulthood, resulting in adverse effects on work performance and social function. The current diagnosis of ADHD primarily depends on the judgment of clinical symptoms, which highlights the need for objective imaging biomarkers. In this study, we aim to classify ADHD (both children and adults [34/112]) from age-matched healthy controls (HCs [28/77]) with functional connectivity (FCs) pattern derived from resting-state functional magnetic resonance imaging (rs-fMRI) data. However, the neuroimaging classification of brain disorders often meets a situation of high dimensional features were presented with limited sample size. Thus an efficient method that is able to reduce original feature dimension into a much more refined subspace is highly desired. Here we proposed a novel Feature Selection method based on Relative Importance and Ensemble Learning (FS_RIEL). Compared with traditional feature selection methods, FS_RIEL algorithm improved the ADHD classification by about 15% in both child and adult ADHD classification, achieving 80-86% accuracy. Moreover, we found the most frequently selected FCs were mainly involved in frontoparietal network, default network, salience network, basal ganglia network and cerebellum network in both child and adult ADHD cohorts, which indicates that ADHD is characterized by a widely-impaired brain connectivity profile that may serve as potential biomarkers for its early diagnosis.