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In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use.
Wang, John M; Zhu, Lusha; Brown, Vanessa M; De La Garza, Richard; Newton, Thomas; King-Casas, Brooks; Chiu, Pearl H.
Afiliação
  • Wang JM; Virginia Tech Carilion Research Institute, Roanoke, Virginia; Department of Psychology, Virginia Tech, Virginia.
  • Zhu L; Virginia Tech Carilion Research Institute, Roanoke, Virginia.
  • Brown VM; Virginia Tech Carilion Research Institute, Roanoke, Virginia; Department of Psychology, Virginia Tech, Virginia.
  • De La Garza R; Baylor College of Medicine, Houston, Texas.
  • Newton T; Baylor College of Medicine, Houston, Texas.
  • King-Casas B; Virginia Tech Carilion Research Institute, Roanoke, Virginia; Department of Psychology, Virginia Tech, Virginia; Virginia Tech-Wake Forest University School of Biomedical Engineering and Science, Blacksburg, Virginia. Electronic address: bkcasas@vtc.vt.edu.
  • Chiu PH; Virginia Tech Carilion Research Institute, Roanoke, Virginia; Department of Psychology, Virginia Tech, Virginia. Electronic address: chiup@vtc.vt.edu.
Article em En | MEDLINE | ID: mdl-30297162
ABSTRACT

BACKGROUND:

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizagem da Esquiva / Encéfalo / Transtornos Relacionados ao Uso de Cocaína / Aprendizagem Tipo de estudo: Clinical_trials / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans / Male / Middle aged Idioma: En Revista: Biol Psychiatry Cogn Neurosci Neuroimaging Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizagem da Esquiva / Encéfalo / Transtornos Relacionados ao Uso de Cocaína / Aprendizagem Tipo de estudo: Clinical_trials / Prognostic_studies / Risk_factors_studies Limite: Adult / Humans / Male / Middle aged Idioma: En Revista: Biol Psychiatry Cogn Neurosci Neuroimaging Ano de publicação: 2019 Tipo de documento: Article