Distinct Neuroimaging Subtypes within ADHD Population Based on Semi-supervised Learning

Motivation: Attention deficit hyperactivity disorder (ADHD) is a childhood-onset disease whose diagnosis and subtyping methods are primarily based on clinical traits, which is prone to subjectivity and instability. Also, the patient outcome and neuroimaging signatures of these subtypes are not clear. Goal(s): We aimed to use a data-driven approach for subtyping. Approach: We used a semi-supervised learning method based on 929 ADHD patients selected from ABCD study. Results: We identified three distinct subtypes in ADHD based on cortical thickness (under-developed, over-developed, and mixed subtypes). Follow-up analysis found significant differences in cognitive and behavior outcomes, disease progression, and response to medication among the subtypes. Impact: We identified three distinct subtypes in ADHD based on cortical thickness (under-developed, over-developed, and mixed subtypes), with unique cognitive, behavioral, progression profiles, and treatment responses. These findings may shed insights into personalized treatment in ADHD.