Neurocomputational reinforcement learning models of reward learning and cognition in childhood ADHD: effects of methylphenidate treatment and relation to treatment response

• Applicant: Professor Dr. Lorenz Deserno
• Subject Area: Biological Psychiatry
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 533682086

Methylphenidate (MPH) is the first-line pharmacological treatment in childhood Attention-Deficit Hyperactivity Disorder (ADHD). However, 20-30% of the children do not respond to MPH treatment and 30-50% experience only partial remission. Despite considerable knowledge on the pharmacological mechanisms of MPH, the neurobiological underpinnings of differences in treatment response remain poorly understood. Delineating the neurobehavioral correlates of MPH treatment, and their relation to the variability of treatment response, may both further our understanding of the broad neurobehavioral aberrations present in ADHD and facilitate treatment outcome prediction. The neurocomputational framework of reinforcement learning advanced insight to how monoaminergic drugs, including MPH, shape neurocognitive functioning in healthy individuals. Specifically, MPH may promote the interplay of reward learning and cognition by enhancing sensitivity to rewards when exerting effortful cognitive performance. Despite the availability of appropriate computational neuroscience probes, investigations on MPH treatment effects on neurocognition in childhood ADHD have not yet made use of these methodological advancements. Further, although reward processing is disrupted in childhood ADHD, research on its modulation by MPH treatment is scarce compared to frequently investigated cognitive performance domains. Hence, the key question, how MPH treatment in patients with childhood ADHD impacts the interplay of reward learning and cognitive performance, and whether this relates to differences in MPH treatment response, remains open. In more detail, the current proposal aims to reveal the aberrant interplay of reward learning and cognition in childhood ADHD within the neurocomputational reinforcement learning framework. By drawing upon work in healthy individuals, these neurocomputational methods are yet unexploited to delineate the effects of MPH and to investigate differences in treatment response. Therefore, the interplay of reward learning and cognitive performance will be tackled in two experiments capturing reward-based cognitive flexibility and the role of reward learning in inhibitory cognitive control. The experiments will be conducted during functional Magnetic Resonance Imaging and Electroencephalography and will be analyzed with computational modeling. Critically, childhood ADHD patients naïve to MPH will be studied before and after individual titration of their first-time MPH treatment. Two comparison groups will be included, a patient group receiving MPH treatment continuously and a healthy control group, to adequately detect effects of MPH treatment and diagnostic status. This will generate novel insights to the effects of MPH treatment on the interplay of reward learning and cognitive performance and their relation to differences in MPH treatment response.

• DFG Programme: Research Grants
• International Connection: Netherlands, United Kingdom
• Cooperation Partners: Professor Dr. Tobias U. Hauser; Professorin Hanneke E. M. den Ouden