Computational Mechanisms of Effort and Reward Decisions in Patients With Depression and Their Association With Relapse After Antidepressant Discontinuation

Abstract

Importance: Nearly 1 in 3 patients with major depressive disorder who respond to antidepressants relapse within 6 months of treatment discontinuation. No predictors of relapse exist to guide clinical decision-making in this scenario.

Objectives: To establish whether the decision to invest effort for rewards represents a persistent depression process after remission, predicts relapse after remission, and is affected by antidepressant discontinuation.

Design, setting, and participants: This longitudinal randomized observational prognostic study in a Swiss and German university setting collected data from July 1, 2015, to January 31, 2019, from 66 healthy controls and 123 patients in remission from major depressive disorder in response to antidepressants prior to and after discontinuation. Study recruitment took place until January 2018.

Exposure: Discontinuation of antidepressants.

Main outcomes and measures: Relapse during the 6 months after discontinuation. Choice and decision times on a task requiring participants to choose how much effort to exert for various amounts of reward and the mechanisms identified through parameters of a computational model.

Results: A total of 123 patients (mean [SD] age, 34.5 [11.2] years; 94 women [76%]) and 66 healthy controls (mean [SD] age, 34.6 [11.0] years; 49 women [74%]) were recruited. In the main subsample, mean (SD) decision times were slower for patients (n = 74) compared with controls (n = 34) (1.77 [0.38] seconds vs 1.61 [0.37] seconds; Cohen d = 0.52; P = .02), particularly for those who later relapsed after discontinuation of antidepressants (n = 21) compared with those who did not relapse (n = 39) (1.95 [0.40] seconds vs 1.67 [0.34] seconds; Cohen d = 0.77; P < .001). This slower decision time predicted relapse (accuracy = 0.66; P = .007). Patients invested less effort than healthy controls for rewards (F1,98 = 33.970; P < .001). Computational modeling identified a mean (SD) deviation from standard drift-diffusion models that was more prominent for patients than controls (patients, 0.67 [1.56]; controls, -0.71 [1.93]; Cohen d = 0.82; P < .001). Patients also showed higher mean (SD) effort sensitivity than controls (patients, 0.31 [0.92]; controls, -0.08 [1.03]; Cohen d = 0.51; P = .05). Relapsers differed from nonrelapsers in terms of the evidence required to make a decision for the low-effort choice (mean [SD]: relapsers, 1.36 [0.35]; nonrelapsers, 1.17 [0.26]; Cohen d = 0.65; P = .02). Group differences generally did not reach significance in the smaller replication sample (27 patients and 21 controls), but decision time prediction models from the main sample generalized to the replication sample (validation accuracy = 0.71; P = .03).

Conclusions and relevance: This study found that the decision to invest effort was associated with prospective relapse risk after antidepressant discontinuation and may represent a persistent disease process in asymptomatic remitted major depressive disorder. Markers based on effort-related decision-making could potentially inform clinical decisions associated with antidepressant discontinuation.

Figure 1.. Study Design, Task, and Computational Model

Study design. Patients with depression in remission taking antidepressant medication (ADM) and matched healthy controls were included in the study and assessed during main assessment 1 (MA1). Group differences between patients and healthy controls at MA1 indicate an association of disease persisting into medicated remission. Patients were randomized to either discontinue their medication prior to the second main assessment (MA2; group 1W2 [where W represents withdrawal]) or wait for a period of similar length and discontinue after MA2 (group 12W). Differences in changes between MA1 and MA2 in the 2 separate groups were investigated to gain an understanding of the consequences of discontinuation. After discontinuation, all patients entered the follow-up period of 6 months; some patients had a relapse during this period, while other patients finished this period without relapse. Comparing performance at MA1 between patients who relapsed and patients who did not relapse during follow-up provides information on differences between these patient subgroups and allowed for the identification of predictors of relapse after antidepressant discontinuation. The numbers indicate how many participants in the main sample were assessed at each stage. B, Computational model. The drift rate (ν_t) of the drift-diffusion model (DDM) depended on a weighted sum of effort (number of button presses) and reward for each of the 2 presented options. Parameters were individually fitted to provide measures of individual reward sensitivity (β_rew), effort sensitivity (β_eff), and a deviation from standard DDMs, implemented as a probability (pswitch) that allowed participants to choose the low-effort choice for small high-reward option (ie, 3 and 4) through an additional process. Further individually adjusted parameters were starting point (S₀), nondecision time (τ_nd), starting boundary (b), and linear boundary scaling over trials (β_scale).

Figure 2.. Raw Behavioral Data and Model Fits in the Main Sample

A, Fraction of high-effort choices as a function of reward offered for the high-effort choice comparing patients vs controls. B, Time to first button press as a function of reward offered for the high-effort choice comparing patients vs controls. C, Fraction of high-effort choices as a function of reward offered for the high-effort choice comparing relapsers vs nonrelapsers. D, Time to first button press as a function of reward offered for the high-effort choice comparing relapsers vs nonrelapsers. Solid lines indicate group mean values in the raw data and the surrounding shaded areas indicate the SDs of the raw data. The blue diamonds indicate significant post hoc tests corrected for the false-discovery rate for the individual reward levels. Dotted and dashed lines indicate the mean values of the surrogate data generated from models in all panels. The standard drift-diffusion model (DDM; dotted lines) forces fast decisions to accompany deterministic behavior and hence a prominent inverted U-shape dependence of decision times on reward levels (panels B and D). Inclusion of the deviation process allows the deterministic decisions to be accompanied by longer decision times (dashed lines).

Figure 3.. Mean Lower Boundary and Relapse Prediction

A, Mean lower boundary for relapse and no-relapse groups in the main sample. B, Mean lower boundary for relapse and no-relapse groups in the replication sample. Error bars indicate SDs. C, Balanced accuracy of relapse prediction using decision times for the main sample. D, Balanced accuracy of relapse prediction using decision times for the replication sample. Error bars indicate 95% bayesian credible intervals. Dashed blue lines indicate the chance level. LOOCV indicates leave-one-out cross-validation. ^aP < .05 from chance level.