Literature DB >> 32074255

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

Isabel M Berwian1,2, Julia G Wenzel3, Anne G E Collins4, Erich Seifritz2, Klaas E Stephan1,5,6, Henrik Walter3, Quentin J M Huys1,2,7,8.   

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.

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Year:  2020        PMID: 32074255      PMCID: PMC7042923          DOI: 10.1001/jamapsychiatry.2019.4971

Source DB:  PubMed          Journal:  JAMA Psychiatry        ISSN: 2168-622X            Impact factor:   21.596


  33 in total

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Authors:  Gabriel S Dichter; Moria J Smoski; Alexey B Kampov-Polevoy; Robert Gallop; James C Garbutt
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Authors:  Joseph B McGlinchey; Mark Zimmerman; Diane Young; Iwona Chelminski
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3.  Anhedonia in depression: biological mechanisms and computational models.

Authors:  Jessica A Cooper; Amanda R Arulpragasam; Michael T Treadway
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Review 4.  Computational psychiatry.

Authors:  Xiao-Jing Wang; John H Krystal
Journal:  Neuron       Date:  2014-11-05       Impact factor: 17.173

Review 5.  Relapse prevention with antidepressant drug treatment in depressive disorders: a systematic review.

Authors:  John R Geddes; Stuart M Carney; Christina Davies; Toshiaki A Furukawa; David J Kupfer; Ellen Frank; Guy M Goodwin
Journal:  Lancet       Date:  2003-02-22       Impact factor: 79.321

6.  Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence.

Authors:  E Frank; R F Prien; R B Jarrett; M B Keller; D J Kupfer; P W Lavori; A J Rush; M M Weissman
Journal:  Arch Gen Psychiatry       Date:  1991-09

7.  When does depression become a disorder? Using recurrence rates to evaluate the validity of proposed changes in major depression diagnostic thresholds.

Authors:  Jerome C Wakefield; Mark F Schmitz
Journal:  World Psychiatry       Date:  2013-02       Impact factor: 49.548

8.  Relapse in major depressive disorder: analysis with the life table.

Authors:  M B Keller; R W Shapiro; P W Lavori; N Wolfe
Journal:  Arch Gen Psychiatry       Date:  1982-08

9.  Negative symptoms of schizophrenia are associated with abnormal effort-cost computations.

Authors:  James M Gold; Gregory P Strauss; James A Waltz; Benjamin M Robinson; Jamie K Brown; Michael J Frank
Journal:  Biol Psychiatry       Date:  2013-02-07       Impact factor: 13.382

10.  Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis.

Authors:  Quentin Jm Huys; Diego A Pizzagalli; Ryan Bogdan; Peter Dayan
Journal:  Biol Mood Anxiety Disord       Date:  2013-06-19
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  10 in total

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Journal:  Curr Psychiatry Rep       Date:  2022-01-25       Impact factor: 5.285

2.  Low predictive power of clinical features for relapse prediction after antidepressant discontinuation in a naturalistic setting.

Authors:  Henrik Walter; Quentin J M Huys; Isabel M Berwian; Julia G Wenzel; Leonie Kuehn; Inga Schnuerer; Erich Seifritz; Klaas E Stephan
Journal:  Sci Rep       Date:  2022-07-01       Impact factor: 4.996

3.  Choices favoring cognitive effort in a foraging environment decrease when multiple forms of effort and delay are interleaved.

Authors:  Claudio Toro-Serey; Gary A Kane; Joseph T McGuire
Journal:  Cogn Affect Behav Neurosci       Date:  2021-11-30       Impact factor: 3.526

4.  Prognostic models for predicting relapse or recurrence of major depressive disorder in adults.

Authors:  Andrew S Moriarty; Nicholas Meader; Kym Ie Snell; Richard D Riley; Lewis W Paton; Carolyn A Chew-Graham; Simon Gilbody; Rachel Churchill; Robert S Phillips; Shehzad Ali; Dean McMillan
Journal:  Cochrane Database Syst Rev       Date:  2021-05-06

5.  Apathy in small vessel cerebrovascular disease is associated with deficits in effort-based decision making.

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Journal:  Brain       Date:  2021-05-07       Impact factor: 15.255

Review 6.  Brain-based mechanisms of late-life depression: Implications for novel interventions.

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7.  Effect and neural mechanisms of the transcutaneous vagus nerve stimulation for relapse prevention in patients with remitted major depressive disorder: protocol for a longitudinal study.

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Journal:  BMJ Open       Date:  2022-02-22       Impact factor: 2.692

Review 8.  On what motivates us: a detailed review of intrinsic v. extrinsic motivation.

Authors:  Laurel S Morris; Mora M Grehl; Sarah B Rutter; Marishka Mehta; Margaret L Westwater
Journal:  Psychol Med       Date:  2022-07-07       Impact factor: 10.592

9.  Anticipatory energization revealed by pupil and brain activity guides human effort-based decision making.

Authors:  Irma T Kurniawan; Marcus Grueschow; Christian C Ruff
Journal:  J Neurosci       Date:  2021-06-07       Impact factor: 6.167

Review 10.  [Positive cognitive neuroscience : Positive valence systems of the Research Domain Criteria initiative].

Authors:  Henrik Walter; Anna Daniels; Sarah A Wellan
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  10 in total

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