Evidence map›Paper›PMID 38774780›Full record

ArticleComputational psychiatry (Cambridge, Mass.)2022

Components of Behavioral Activation Therapy for Depression Engage Specific Reinforcement Learning Mechanisms in a Pilot Study.

Quentin J M Huys, Evan M Russek, George Abitante, Thorsten Kahnt, Jacqueline K Gollan

Registry-linked trialAbstract read
In one paragraph

Article in Computational psychiatry (Cambridge, Mass.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03538535 (Computational Modeling of Reinforcement Learning in Depression), which is not on this map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03538535 nacompletednot on this map

Computational Modeling of Reinforcement Learning in Depression

TypeinterventionalSponsorNorthwestern UniversityRan2018 to 2019Enrolled13ConditionsDepression, AnxietyArmsGo/No-Go Active Learning (GOAL)
3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Depression Severity Moderates Reward Learning Among Smokers With Current or Past Major Depressive Disorder in a Smoking Cessation Randomized Clinical Trial.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024
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  2. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Quentin J M HuysDivision of Psychiatry, University College London, London, UK.ORCID https://orcid.org/0000-0002-8999-574X
Evan M RussekMax Planck UCL Centre for Computational Psychiatry and Ageing Research and Wellcome Trust Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK.ORCID https://orcid.org/0000-0002-2929-6912
George AbitanteDepartment of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.ORCID https://orcid.org/0000-0002-6422-8974
Thorsten KahntNational Institute on Drug Abuse Intramural Research Program, Baltimore, MD, USA.ORCID https://orcid.org/0000-0002-3575-2670
Jacqueline K GollanDepartment of Psychiatry and Behavioral Science, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.ORCID https://orcid.org/0000-0003-2315-4347

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Behavioral activation is an evidence-based treatment for depression. Theoretical considerations suggest that treatment response depends on reinforcement learning mechanisms. However, which reinforcement learning mechanisms are engaged by and mediate the therapeutic effect of behavioral activation remains only partially understood, and there are no procedures to measure such mechanisms. Objective: To perform a pilot study to examine whether reinforcement learning processes measured through tasks or self-report are related to treatment response to behavioral activation. Method: The pilot study enrolled 13 outpatients (12 completers) with major depressive disorder, from July of 2018 through February of 2019, into a nine-week trial with BA. Psychiatric evaluations, decision-making tests and self-reported reward experience and anticipations were acquired before, during and after the treatment. Task and self-report data were analysed by using reinforcement-learning models. Inferred parameters were related to measures of depression severity through linear mixed effects models. Results: Treatment effects during different phases of the therapy were captured by specific decision-making processes in the task. During the weeks focusing on the active pursuit of reward, treatment effects were more pronounced amongst those individuals who showed an increase in Pavlovian appetitive influence. During the weeks focusing on the avoidance of punishments, treatment responses were more pronounced in those individuals who showed an increase in Pavlovian avoidance. Self-reported anticipation of reinforcement changed according to formal RL rules. Individual differences in the extent to which learning followed RL rules related to changes in anhedonia. Conclusions: In this pilot study both task- and self-report-derived measures of reinforcement learning captured individual differences in treatment response to behavioral activation. Appetitive and aversive Pavlovian reflexive processes appeared to be modulated by separate psychotherapeutic interventions, and the modulation strength covaried with response to specific interventions. Self-reported changes in reinforcement expectations are also related to treatment response. Trial Registry Name: Set Your Goal: Engaging in GO/No-Go Active Learning, #NCT03538535, http://www.clinicaltrials.gov.

Indexed as

Behavioral Activation treatmentdecision-makinginstrumental learninglearningOrthogonalized Go/No learningPavlovian reflexesreward learning

Identifiers

PMID38774780
PMCPMC11104310

What Socratic holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.