Computational Psychiatry of ADHD: Neural Gain Impairments across Marrian Levels of Analysis

Trends Neurosci. 2016 Feb;39(2):63-73. doi: 10.1016/j.tins.2015.12.009. Epub 2016 Jan 17.


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.

Keywords: Attention-deficit hyperactivity disorder (ADHD); behavioural variability; computational psychiatry; dopamine; neural gain; noradrenaline; norepinephrine.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Attention Deficit Disorder with Hyperactivity / diagnosis
  • Attention Deficit Disorder with Hyperactivity / metabolism*
  • Attention Deficit Disorder with Hyperactivity / psychology*
  • Brain / metabolism*
  • Computational Biology / methods*
  • Humans
  • Nerve Net / metabolism*
  • Neural Pathways / metabolism
  • Psychiatry / methods*