In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use.
In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use.
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DOI：
10.1016/j.bpsc.2018.07.009
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发表时间：
2019-03
期刊：
影响因子：
--
通讯作者：
Chiu PH
中科院分区：
文献类型：
--
作者：
Wang JM;Zhu L;Brown VM;De La Garza R 2nd;Newton T;King-Casas B;Chiu PH
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. These literatures suggest 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. In a randomized crossover design, 22 participants with current cocaine use disorder completed a probabilistic loss-learning task during functional MRI while on- and off-cocaine (44 sessions). 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. 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 non-psychiatric participants, and mediated a relationship between chronicity of drug use and desire to use cocaine. 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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