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dc.contributor.authorWang, John M.
dc.contributor.authorZhu, Lusha
dc.contributor.authorBrown, Vanessa M.
dc.contributor.authorDe La Garza, Richard II
dc.contributor.authorNewton, Thomas
dc.contributor.authorKing-Casas, Brooks
dc.contributor.authorChiu, Pearl H.
dc.date.accessioned2019-04-02T12:54:38Z
dc.date.available2019-04-02T12:54:38Z
dc.date.issued2018-08-03
dc.identifier.issn24519022
dc.identifier.urihttp://hdl.handle.net/10919/88800
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_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherElsevier
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAddiction
dc.subjectCocaine
dc.subjectComputational psychiatry
dc.subjectDopamine
dc.subjectfMRI
dc.subjectPrediction error
dc.subjectReinforcement learning
dc.titleIn Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Useen_US
dc.typeArticle - Refereed
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.
dc.title.serialBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
dc.identifier.doihttps://doi.org/10.1016/j.bpsc.2018.07.009
dc.identifier.volume4
dc.identifier.issue3
dc.type.dcmitypeText
dc.identifier.pmid30297162


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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International