Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use.
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/anterior cingulate cortex, caudate, anterior insula, and thalamus were more likely to exhibit problem use 3 years later. Importantly, these computationally based neural predictors outperformed clinical measures and non-model based neural variables in predicting clinical status. In conclusion, young adults who show exaggerated brain processing underlying whether to 'stop' or to 'go' are more likely to develop stimulant abuse. Thus, Bayesian cognitive models provide both a computational explanation and potential predictive biomarkers of belief processing deficits in individuals at risk for stimulant addiction.
1 Department of Psychiatry, University of California San Diego, La Jolla, CA, USA kharle@ucsd.edu.
Katia M
KM
Harlé
2 Department of Psychology, CUNY Queens College, Flushing, NY, USA.
Jennifer L
JL
Stewart
3 Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
Shunan
S
Zhang
1 Department of Psychiatry, University of California San Diego, La Jolla, CA, USA 4 Mental Health, VA San Diego Healthcare System, La Jolla, CA, USA.
Susan F
SF
Tapert
3 Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
Angela J
AJ
Yu
1 Department of Psychiatry, University of California San Diego, La Jolla, CA, USA 4 Mental Health, VA San Diego Healthcare System, La Jolla, CA, USA 5 Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.
Martin P
MP
Paulus
D000293:Adolescent; D000328:Adult; D019969:Amphetamine-Related Disorders / Q000503:physiopathology*; D001499:Bayes Theorem; D001921:Brain / Q000503:physiopathology*; D002421:Caudate Nucleus / Q000503:physiopathology; D000697:Central Nervous System Stimulants; D002540:Cerebral Cortex / Q000503:physiopathology; D019970:Cocaine-Related Disorders / Q000503:physiopathology*; D015331:Cohort Studies; D005260:Female; D059907:Functional Neuroimaging; D006179:Gyrus Cinguli / Q000503:physiopathology; D006801:Humans; D007266:Inhibition, Psychological; D016015:Logistic Models; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D008297:Male; D008385:Marijuana Smoking; D009433:Neural Inhibition; D017397:Prefrontal Cortex / Q000503:physiopathology; D011446:Prospective Studies; D019966:Substance-Related Disorders / Q000503:physiopathology; D013788:Thalamus / Q000503:physiopathology; D055815:Young Adult
D000697:Central Nervous System Stimulants
Bayesian model; addiction; inhibitory control; stimulant