Reward and punishment learning deficits among bipolar disorder subtypes.
Bipolar disorder(1)
Computational biology(6)
learning(3)
punishment(5)
reinforcement(2)
reward(4)
Journal
Journal of affective disorders
ISSN: 1573-2517
Titre abrégé: J Affect Disord
Pays: Netherlands
ID NLM: 7906073
Informations de publication
Date de publication:
01 11 2023
01 11 2023
Historique:
received:
24
01
2023
revised:
24
07
2023
accepted:
14
08
2023
medline:
11
9
2023
pubmed:
18
8
2023
entrez:
17
8
2023
Statut:
ppublish
Résumé
Reward sensitivity is an essential dimension related to mood fluctuations in bipolar disorder (BD), but there is currently a debate around hypersensitivity or hyposensitivity hypotheses to reward in BD during remission, probably related to a heterogeneous population within the BD spectrum and a lack of reward bias evaluation. Here, we examine reward maximization vs. punishment avoidance learning within the BD spectrum during remission. Patients with BD-I (n = 45), BD-II (n = 34) and matched (n = 30) healthy controls (HC) were included. They performed an instrumental learning task designed to dissociate reward-based from punishment-based reinforcement learning. Computational modeling was used to identify the mechanisms underlying reinforcement learning performances. Behavioral results showed a significant reward learning deficit across BD subtypes compared to HC, captured at the computational level by a lower sensitivity to rewards compared to punishments in both BD subtypes. Computational modeling also revealed a higher choice randomness in BD-II compared to BD-I that reflected a tendency of BD-I to perform better during punishment avoidance learning than BD-II. Our patients were not naive to antipsychotic treatment and were not euthymic (but in syndromic remission) according to the International Society for Bipolar Disorder definition. Our results are consistent with the reward hyposensitivity theory in BD. Computational modeling suggests distinct underlying mechanisms that produce similar observable behaviors, making it a useful tool for distinguishing how symptoms interact in BD versus other disorders. In the long run, a better understanding of these processes could contribute to better prevention and management of BD.
Sections du résumé
BACKGROUND
Reward sensitivity is an essential dimension related to mood fluctuations in bipolar disorder (BD), but there is currently a debate around hypersensitivity or hyposensitivity hypotheses to reward in BD during remission, probably related to a heterogeneous population within the BD spectrum and a lack of reward bias evaluation. Here, we examine reward maximization vs. punishment avoidance learning within the BD spectrum during remission.
METHODS
Patients with BD-I (n = 45), BD-II (n = 34) and matched (n = 30) healthy controls (HC) were included. They performed an instrumental learning task designed to dissociate reward-based from punishment-based reinforcement learning. Computational modeling was used to identify the mechanisms underlying reinforcement learning performances.
RESULTS
Behavioral results showed a significant reward learning deficit across BD subtypes compared to HC, captured at the computational level by a lower sensitivity to rewards compared to punishments in both BD subtypes. Computational modeling also revealed a higher choice randomness in BD-II compared to BD-I that reflected a tendency of BD-I to perform better during punishment avoidance learning than BD-II.
LIMITATIONS
Our patients were not naive to antipsychotic treatment and were not euthymic (but in syndromic remission) according to the International Society for Bipolar Disorder definition.
CONCLUSIONS
Our results are consistent with the reward hyposensitivity theory in BD. Computational modeling suggests distinct underlying mechanisms that produce similar observable behaviors, making it a useful tool for distinguishing how symptoms interact in BD versus other disorders. In the long run, a better understanding of these processes could contribute to better prevention and management of BD.
Identifiants
pubmed: 37591352
pii: S0165-0327(23)01053-4
doi: 10.1016/j.jad.2023.08.075
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
694-702Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest FV has been invited to scientific meetings, consulted and/or served as speaker and received compensation by Lundbeck, Servier, Recordati, Janssen, Otsuka, LivaNova, and Chiesi. He has received research support by Lundbeck and LivaNova. None of these links of interest are related to this work. AP, CD, MG, MP, JB declare that no competing interests exist.