Trends in Neurosciences
Volume 39, Issue 2, February 2016, Pages 63-73
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Computational Psychiatry of ADHD: Neural Gain Impairments across Marrian Levels of Analysis

https://doi.org/10.1016/j.tins.2015.12.009Get rights and content
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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).

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)
neural gain
dopamine
noradrenaline
norepinephrine
computational psychiatry
behavioural variability

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