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Gueguen MCM, Anlló H, Bonagura D, Kong J, Hafezi S, Palminteri S, Konova AB. Recent Opioid Use Impedes Range Adaptation in Reinforcement Learning in Human Addiction. Biol Psychiatry 2024; 95:974-984. [PMID: 38101503 PMCID: PMC11065633 DOI: 10.1016/j.biopsych.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 11/22/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND Drugs like opioids are potent reinforcers thought to co-opt value-based decisions by overshadowing other rewarding outcomes, but how this happens at a neurocomputational level remains elusive. Range adaptation is a canonical process of fine-tuning representations of value based on reward context. Here, we tested whether recent opioid exposure impacts range adaptation in opioid use disorder, potentially explaining why shifting decision making away from drug taking during this vulnerable period is so difficult. METHODS Participants who had recently (<90 days) used opioids (n = 34) or who had abstained from opioid use for ≥ 90 days (n = 20) and comparison control participants (n = 44) completed a reinforcement learning task designed to induce robust contextual modulation of value. Two models were used to assess the latent process that participants engaged while making their decisions: 1) a Range model that dynamically tracks context and 2) a standard Absolute model that assumes stationary, objective encoding of value. RESULTS Control participants and ≥90-days-abstinent participants with opioid use disorder exhibited choice patterns consistent with range-adapted valuation. In contrast, participants with recent opioid use were more prone to learn and encode value on an absolute scale. Computational modeling confirmed the behavior of most control participants and ≥90-days-abstinent participants with opioid use disorder (75%), but a minority in the recent use group (38%), was better fit by the Range model than the Absolute model. Furthermore, the degree to which participants relied on range adaptation correlated with duration of continuous abstinence and subjective craving/withdrawal. CONCLUSIONS Reduced context adaptation to available rewards could explain difficulty deciding about smaller (typically nondrug) rewards in the aftermath of drug exposure.
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Affiliation(s)
- Maëlle C M Gueguen
- Department of Psychiatry, Brain Health Institute and University Behavioral Health Care, Rutgers University-New Brunswick, Piscataway, New Jersey; Intercultural Cognitive Network, Tokyo, Japan
| | - Hernán Anlló
- Intercultural Cognitive Network, Tokyo, Japan; Watanabe Laboratory, School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan; Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale U960, École Normale Supérieure-Université de Recherche Paris Science et Lettres, Paris, France
| | - Darla Bonagura
- Department of Psychiatry, Brain Health Institute and University Behavioral Health Care, Rutgers University-New Brunswick, Piscataway, New Jersey; Intercultural Cognitive Network, Tokyo, Japan
| | - Julia Kong
- Department of Psychiatry, Brain Health Institute and University Behavioral Health Care, Rutgers University-New Brunswick, Piscataway, New Jersey
| | - Sahar Hafezi
- Department of Psychiatry, Brain Health Institute and University Behavioral Health Care, Rutgers University-New Brunswick, Piscataway, New Jersey
| | - Stefano Palminteri
- Intercultural Cognitive Network, Tokyo, Japan; Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale U960, École Normale Supérieure-Université de Recherche Paris Science et Lettres, Paris, France
| | - Anna B Konova
- Department of Psychiatry, Brain Health Institute and University Behavioral Health Care, Rutgers University-New Brunswick, Piscataway, New Jersey; Intercultural Cognitive Network, Tokyo, Japan.
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Lowet AS, Zheng Q, Meng M, Matias S, Drugowitsch J, Uchida N. An opponent striatal circuit for distributional reinforcement learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.02.573966. [PMID: 38260354 PMCID: PMC10802299 DOI: 10.1101/2024.01.02.573966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards - an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2,3, but little is known about whether, where, and how neurons in this circuit encode information about higher-order moments of reward distributions4. To fill this gap, we used high-density probes (Neuropixels) to acutely record striatal activity from well-trained, water-restricted mice performing a classical conditioning task in which reward mean, reward variance, and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Remarkably, chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons - D1 and D2 MSNs - contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 MSNs5-15 to reap the computational benefits of distributional RL.
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Affiliation(s)
- Adam S Lowet
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Program in Neuroscience, Harvard University, Boston, MA, USA
| | - Qiao Zheng
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Melissa Meng
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Sara Matias
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Jan Drugowitsch
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Naoshige Uchida
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
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