Adolescent ADHD and electrophysiological reward responsiveness: A machine learning approach to evaluate classification accuracy and prognosis

• Published in: Psychiatry research Vol. 323; p. 115139
• Main Authors: Hámori, György, File, Bálint, Fiáth, Richárd, Pászthy, Bea, Réthelyi, János M., Ulbert, István, Bunford, Nóra
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.05.2023
• Subjects: ADHD; Adolescent; Attention Deficit Disorder with Hyperactivity - diagnosis; Biomarker; Event-related potential (ERP); Evoked Potentials - physiology; Humans; Machine Learning; Prognosis; Reinforcement sensitivity; Reward; ADHD; Reinforcement sensitivity; Biomarker; Event-related potential (ERP); Adolescent
• ISSN: 0165-1781; 1872-7123
• DOI: 10.1016/j.psychres.2023.115139

We evaluated event-related potential (ERP) indices of reinforcement sensitivity as ADHD biomarkers by examining, in N=306 adolescents (Mage=15.78, SD=1.08), the extent to which ERP amplitude and latency variables measuring reward anticipation and response (1) differentiate, in age- and sex-matched subsamples, (i) youth with vs. without ADHD, (ii) youth at-risk for vs. not at-risk for ADHD, and, in the with ADHD subsample, (iii) youth with the inattentive vs. the hyperactive/impulsive (H/I) and combined presentations. We further examined the extent to which ERP variables (2) predict, in the ADHD subsample, substance use (i) concurrently and (ii) prospectively at 18-month follow-up. Linear support vector machine analyses indicated ERPs weakly differentiate youth with/without (65%) – and at-risk for/not at-risk for (63%) – ADHD but better differentiate ADHD presentations (78%). Regression analyses showed in adolescents with ADHD, ERPs explain a considerable proportion of variance (50%) in concurrent alcohol use and, controlling for concurrent marijuana and tobacco use, explain a considerable proportion of variance (87 and 87%) in, and predict later marijuana and tobacco use. Findings are consistent with the dual-pathway model of ADHD. Results also highlight limitations of a dichotomous, syndromic classification and indicate differences in neural reinforcement sensitivity are a promising ADHD prognostic biomarker.