Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use

Brain : a Journal of Neurology
Katia M HarléMartin P Paulus

Abstract

Bayesian ideal observer models quantify individuals' context- and experience-dependent beliefs and expectations about their environment, which provides a powerful approach (i) to link basic behavioural mechanisms to neural processing; and (ii) to generate clinical predictors for patient populations. Here, we focus on (ii) and determine whether individual differences in the neural representation of the need to stop in an inhibitory task can predict the development of problem use (i.e. abuse or dependence) in individuals experimenting with stimulants. One hundred and fifty-seven non-dependent occasional stimulant users, aged 18-24, completed a stop-signal task while undergoing functional magnetic resonance imaging. These individuals were prospectively followed for 3 years and evaluated for stimulant use and abuse/dependence symptoms. At follow-up, 38 occasional stimulant users met criteria for a stimulant use disorder (problem stimulant users), while 50 had discontinued use (desisted stimulant users). We found that those individuals who showed greater neural responses associated with Bayesian prediction errors, i.e. the difference between actual and expected need to stop on a given trial, in right medial prefrontal cortex/anterio...Continue Reading

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Citations

Jan 21, 2018·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Anna S HuangRita Z Goldstein
Jan 26, 2018·Wiley Interdisciplinary Reviews. Cognitive Science·Stefan FrässleKlaas E Stephan
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Jan 14, 2021·The European Journal of Neuroscience·Ju-Chi YuXiaosi Gu
Jul 6, 2021·Drug and Alcohol Dependence·Jonathon R HowlettMartin P Paulus
Jul 21, 2021·NeuroImage. Clinical·Matthew LehetKatharine N Thakkar

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