Dataset Information

Challenging the negative learning bias hypothesis of depression: reversal learning in a naturalistic psychiatric sample.

ABSTRACT:

Background

Classic theories posit that depression is driven by a negative learning bias. Most studies supporting this proposition used small and selected samples, excluding patients with comorbidities. However, comorbidity between psychiatric disorders occurs in up to 70% of the population. Therefore, the generalizability of the negative bias hypothesis to a naturalistic psychiatric sample as well as the specificity of the bias to depression, remain unclear. In the present study, we tested the negative learning bias hypothesis in a large naturalistic sample of psychiatric patients, including depression, anxiety, addiction, attention-deficit/hyperactivity disorder, and/or autism. First, we assessed whether the negative bias hypothesis of depression generalized to a heterogeneous (and hence more naturalistic) depression sample compared with controls. Second, we assessed whether negative bias extends to other psychiatric disorders. Third, we adopted a dimensional approach, by using symptom severity as a way to assess associations across the sample.

Methods

We administered a probabilistic reversal learning task to 217 patients and 81 healthy controls. According to the negative bias hypothesis, participants with depression should exhibit enhanced learning and flexibility based on punishment v. reward. We combined analyses of traditional measures with more sensitive computational modeling.

Results

In contrast to previous findings, this sample of depressed patients with psychiatric comorbidities did not show a negative learning bias.

Conclusions

These results speak against the generalizability of the negative learning bias hypothesis to depressed patients with comorbidities. This study highlights the importance of investigating unselected samples of psychiatric patients, which represent the vast majority of the psychiatric population.

SUBMITTER: Brolsma SCA

PROVIDER: S-EPMC8842187 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Challenging the negative learning bias hypothesis of depression: reversal learning in a naturalistic psychiatric sample.

Brolsma Sophie C A SCA Vrijsen Janna N JN Vassena Eliana E Rostami Kandroodi Mojtaba M Bergman M Annemiek MA van Eijndhoven Philip F PF Collard Rose M RM den Ouden Hanneke E M HEM Schene Aart H AH Cools Roshan R

Psychological medicine 20200615 2

<h4>Background</h4>Classic theories posit that depression is driven by a negative learning bias. Most studies supporting this proposition used small and selected samples, excluding patients with comorbidities. However, comorbidity between psychiatric disorders occurs in up to 70% of the population. Therefore, the generalizability of the negative bias hypothesis to a naturalistic psychiatric sample as well as the specificity of the bias to depression, remain unclear. In the present study, we test ...[more]

PMID: 32538342

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Project description:INTRODUCTION:Although neuroscience has made tremendous progress towards understanding the basic neural circuitry underlying important processes such as attention, memory and emotion, little progress has been made in applying these insights to psychiatric populations to make clinically meaningful treatment predictions. The overall aim of the Tulsa 1000 (T-1000) study is to use the NIMH Research Domain Criteria framework in order to establish a robust and reliable dimensional set of variables that quantifies the positive and negative valence, cognition and arousal domains, including interoception, to generate clinically useful treatment predictions. METHODS AND ANALYSIS:The T-1000 is a naturalistic study that will recruit, assess and longitudinally follow 1000 participants, including healthy controls and treatment-seeking individuals with mood, anxiety, substance use and eating disorders. Each participant will undergo interview, behavioural, biomarker and neuroimaging assessments over the course of 1?year. The study goal is to determine how disorders of affect, substance use and eating behaviour organise across different levels of analysis (molecules, genes, cells, neural circuits, physiology, behaviour and self-report) to predict symptom severity, treatment outcome and long-term prognosis. The data will be used to generate computational models based on Bayesian statistics. The final end point of this multilevel latent variable analysis will be standardised assessments that can be developed into clinical tools to help clinicians predict outcomes and select the best intervention for each individual, thereby reducing the burden of mental disorders, and taking psychiatry a step closer towards personalised medicine. ETHICS AND DISSEMINATION:Ethical approval was obtained from Western Institutional Review Board screening protocol #20101611. The dissemination plan includes informing health professionals of results for clinical practice, submitting results to journals for peer-reviewed publication, presenting results at national and international conferences and making the dataset available to researchers and mental health professionals. TRIAL REGISTRATION NUMBER:NCT02450240; Pre-results.

Dataset Information

Challenging the negative learning bias hypothesis of depression: reversal learning in a naturalistic psychiatric sample.

Background

Methods

Results

Conclusions

Publications

Challenging the negative learning bias hypothesis of depression: reversal learning in a naturalistic psychiatric sample.

Similar Datasets

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