Attention-deficit hyperactivity disorder (ADHD), one of the most common psychiatric disorders, is characterised by unstable response patterns across multiple cognitive domains. However, the neural mechanisms that explain these characteristic features remain unclear. Using a computational multilevel approach, we propose that ADHD is caused by impaired gain modulation in systems that generate this phenotypic increased behavioural variability. Using Marr's three levels of analysis as a heuristic framework, we focus on this variable behaviour, detail how it can be explained algorithmically, and how it might be implemented at a neural level through catecholamine influences on corticostriatal loops. This computational, multilevel, approach to ADHD provides a framework for bridging gaps between descriptions of neuronal activity and behaviour, and provides testable predictions about impaired mechanisms. The Need for a Better Neurocomputational Understanding of ADHD Maintaining one's mental focus is hard, especially when reading a dry and complicated paper. Suddenly you would rather clean the kitchen or surf the Internet. Nevertheless, most people maintain focus and persist with the task at hand. Neurobiologically, we propose that the catecholaminergic brain systems (see Glossary) modulate attention [1] by increasing the neural gain and, thus, suppressing cognitive switching [2] (Box 1). For 5% of the population, the ability to focus is disturbed to an extent that strongly affects their daily functioning. Many are diagnosed with ADHD [3], a developmental psychiatric disorder thought to arise, in part, out of a genetic vulnerability [4] . ADHD is characterised by inattention, hyperactivity, and/or impulsivity [5] and its negative effects on a person's occupational success, wellbeing, and health risks (e.g., for substance abuse [6]) make it important to understand this disorder. Research on ADHD has intensified since the early 1990s [6] without clear candidate genes or brain response patterns predicting the disorder having been identified. There is no unifying theory explaining the pathophysiology of ADHD. Indeed, current classification criteria are likely to subsume multiple brain disorders with a similar behavioural expression within the label 'ADHD'. Here, we use a multilevel approach to propose that ADHD crucially involves an impairment of neural gain modulation leading to inappropriately variable behaviour. By using Marr's three levels of analysis [7] (Box 2), we show how it is possible to translate behavioural findings into Trends ADHD is one of the most common psychiatric disorders during childhood, but the neurocognitive mechanisms behind it remain elusive. Behaviourally, ADHD is best characterized by increased variability across multiple cognitive domains and timescales. By using Marr's three levels of analysis, we show how impairments in neural gain can explain ADHD abnormalities, spanning from behaviour to neural activity. On an algorithmic and implementation level, we show how increased variability can be caused by neural gain impairments, and how it can be modelled using reinforcement learning and corticostriatal network models. We furthermore show how these levels can be linked to impairments in catecholamine systems (dopamine and noradrenaline).
Using a computational multilevel approach, a study presented in REF suggests that ADHD is associated with impaired gain modulation in systems that generate increased behavioral variability.
Computational Psychiatry of ADHD: Neural Gain Impairments across Marrian Levels of Analysis
Trends in Neurosciences
Trends in Neurosciences
Psychology Medicine