In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use

dc.contributor.authorWang, John M.en
dc.contributor.authorZhu, Lushaen
dc.contributor.authorBrown, Vanessa M.en
dc.contributor.authorDe La Garza, Richard IIen
dc.contributor.authorNewton, Thomas F.en
dc.contributor.authorCasas, Brooksen
dc.contributor.authorChiu, Pearl H.en
dc.contributor.departmentFralin Biomedical Research Instituteen
dc.contributor.departmentBiomedical Engineering and Sciencesen
dc.description.abstractBackground: In substance-dependent individuals, drug deprivation and drug use trigger divergent behavioral responses to environmental cues. These divergent responses are consonant with data showing that short- and long-term adaptations in dopamine signaling are similarly sensitive to state of drug use. The literature suggests a drug state–dependent role of learning in maintaining substance use; evidence linking dopamine to both reinforcement learning and addiction provides a framework to test this possibility. Methods: In a randomized crossover design, 22 participants with current cocaine use disorder completed a probabilistic loss-learning task during functional magnetic resonance imaging while on and off cocaine (44 sessions). Another 54 participants without Axis I psychopathology served as a secondary reference group. Within-drug state and paired-subjects’ learning effects were assessed with computational model–derived individual learning parameters. Model-based neuroimaging analyses evaluated effects of drug use state on neural learning signals. Relationships among model-derived behavioral learning rates (α+, α−), neural prediction error signals (δ+, δ−), cocaine use, and desire to use were assessed. Results: During cocaine deprivation, cocaine-dependent individuals exhibited heightened positive learning rates (α+), heightened neural positive prediction error (δ+) responses, and heightened association of α+ with neural δ+ responses. The deprivation-enhanced neural learning signals were specific to successful loss avoidance, comparable to participants without psychiatric conditions, and mediated a relationship between chronicity of drug use and desire to use cocaine. Conclusions: Neurocomputational learning signals are sensitive to drug use status and suggest that heightened reinforcement by successful avoidance of negative outcomes may contribute to drug seeking during deprivation. More generally, attention to drug use state is important for delineating substrates of addiction. © 2018en
dc.description.notesThis work was supported in part by the National Institutes of Health (Grant Nos. R01MH091872 and R21DA042274 [to PHC], Grant No. R01DA036017 to [BK-C], and Grant Nos. RC1DA028387 and R01DA023624 [to RDLG]). PHC, BK-C, RDLG, and TN designed the experiments. JMW analyzed the data with input from LZ, VMB, PHC, and BK-C. PHC, BK-C, RDLG, and TN supervised this work. JMW and PHC drafted the manuscript with input from all authors. All authors edited and approved the final version. We acknowledge the technical assistance of George Christopoulos, Dongil Chung, Jacob Lee, James Mahoney, Dharol Tankersley, Katherine McCurry, Nina Lauharatanahirun, and members of the Chiu, De La Garza, King-Casas, and Newton Labs. The authors report no biomedical financial interests or potential conflicts of interest.en
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.subjectComputational psychiatryen
dc.subjectPrediction erroren
dc.subjectReinforcement learningen
dc.titleIn Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Useen
dc.title.serialBiological Psychiatry: Cognitive Neuroscience and Neuroimagingen
dc.typeArticle - Refereeden


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