학술논문
Association Between a Directly Translated Cognitive Measure of Negative Bias and Self-reported Psychiatric Symptoms.
Document Type
Academic Journal
Author
Daniel-Watanabe L; Institute of Cognitive Neuroscience, University College London, London, United Kingdom; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.; McLaughlin M; Institute of Cognitive Neuroscience, University College London, London, United Kingdom.; Gormley S; Institute of Cognitive Neuroscience, University College London, London, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.; Robinson OJ; Institute of Cognitive Neuroscience, University College London, London, United Kingdom. Electronic address: o.robinson@ucl.ac.uk.
Source
Publisher: Elsevier, Inc Country of Publication: United States NLM ID: 101671285 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2451-9030 (Electronic) Linking ISSN: 24519022 NLM ISO Abbreviation: Biol Psychiatry Cogn Neurosci Neuroimaging Subsets: MEDLINE
Subject
Language
English
Abstract
Background: Negative interpretation biases are thought to be core symptoms of mood and anxiety disorders. However, prior work using cognitive tasks to measure such biases is largely restricted to case-control group studies, which cannot be used for inference about individuals without considerable additional validation. Moreover, very few measures are fully translational (i.e., can be used across animals and humans in treatment-development pipelines). This investigation aimed to produce the first measure of negative cognitive biases that is both translational and sensitive to individual differences, and then to determine which specific self-reported psychiatric symptoms are related to bias.
Methods: A total of 1060 (n = 990 complete) participants performed a cognitive task of negative bias along with psychiatric symptom questionnaires. We tested the hypothesis that individual levels of mood and anxiety disorder symptomatology would covary positively with negative bias on the cognitive task using a combination of computational modeling of behavior, confirmatory factor analysis, exploratory factor analysis, and structural equation modeling.
Results: Participants with higher depression symptoms (β = -0.16, p = .017) who were older (β = -0.11, p = .001) and had lower IQ (β = 0.14, p < .001) showed greater negative bias. Confirmatory factor analysis and structural equation modeling suggested that no other psychiatric symptom (or transdiagnostic latent factor) covaried with task performance over and above the effect of depression, while exploratory factor analysis suggested combining depression/anxiety symptoms in a single latent factor. Generating groups using symptom cutoffs or latent mixture modeling recapitulated our prior case-control findings.
Conclusions: This measure, which uniquely spans both the clinical group-to-individual and preclinical animal-to-human generalizability gaps, can be used to measure individual differences in depression vulnerability for translational treatment-development pipelines.
(Copyright © 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
Methods: A total of 1060 (n = 990 complete) participants performed a cognitive task of negative bias along with psychiatric symptom questionnaires. We tested the hypothesis that individual levels of mood and anxiety disorder symptomatology would covary positively with negative bias on the cognitive task using a combination of computational modeling of behavior, confirmatory factor analysis, exploratory factor analysis, and structural equation modeling.
Results: Participants with higher depression symptoms (β = -0.16, p = .017) who were older (β = -0.11, p = .001) and had lower IQ (β = 0.14, p < .001) showed greater negative bias. Confirmatory factor analysis and structural equation modeling suggested that no other psychiatric symptom (or transdiagnostic latent factor) covaried with task performance over and above the effect of depression, while exploratory factor analysis suggested combining depression/anxiety symptoms in a single latent factor. Generating groups using symptom cutoffs or latent mixture modeling recapitulated our prior case-control findings.
Conclusions: This measure, which uniquely spans both the clinical group-to-individual and preclinical animal-to-human generalizability gaps, can be used to measure individual differences in depression vulnerability for translational treatment-development pipelines.
(Copyright © 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)