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オリジナルソース

タイトル: A computational approach to understanding effort-based decision-making in depression

概要: Importance: Motivational dysfunction is a core feature of depression, and can have debilitating effects on everyday function. However, it is unclear which disrupted cognitive processes underlie impaired motivation, and whether impairments persist following remission. Decision-making concerning exerting effort to obtain rewards offers a promising framework for understanding motivation, especially when examined with computational tools which can offer precise quantification of latent processes. Objective: To understand the computational mechanisms driving motivational dysfunction in depression. Design, Setting, and Participants: We conducted two studies: a Pilot study in healthy volunteers (N=67, 66% female, mean[SD] age=28.45[9.88]) to validate our computational model, before applying it in a Case-control study including current (N=41, 71% female, mean[SD] age=30.24[11.57]) and remitted (N=46, 63% female, mean[SD] age=26.91[7.06]) unmedicated depressed individuals, and healthy volunteers with (N=36, 64% female, mean[SD] age=26.06[8.19]) and without (N=57, 68% female, mean[SD] age=26.70[8.14]) a family history of depression. The Pilot study data was collected during 2015 and the Case-control study data was collected between 2015 and 2019. Exposures: Effort-based decision-making was assessed using the Apple Gathering Task, in which participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. Main Outcome and Measures: The probability to accept offers as a function of reward and effort levels was examined. A comprehensive Bayesian computational analysis was implemented to examine the precise computational mechanisms influencing decision-making. Results: Four fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups had a lower willingness to exert effort than control participants. In contrast with previous findings, computational analysis revealed that this difference was primarily driven by lower effort acceptance bias, but not altered effort or reward sensitivity. Conclusion and Relevance: This work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to both symptoms and risk of relapse, and might represent a fruitful target for treatment and prevention.