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Association between negative cognitive bias and depression: A symptom-level approach.

ABSTRACT: Cognitive models of depression posit that negatively biased self-referent processing and attention have important roles in the disorder. However, depression is a heterogeneous collection of symptoms and all symptoms are unlikely to be associated with these negative cognitive biases. The current study involved 218 community adults whose depression ranged from no symptoms to clinical levels of depression. Random forest machine learning was used to identify the most important depression symptom predictors of each negative cognitive bias. Depression symptoms were measured with the Beck Depression Inventory-II. Model performance was evaluated using predictive R-squared (Rpred2), the expected variance explained in data not used to train the algorithm, estimated by 10 repetitions of 10-fold cross-validation. Using the self-referent encoding task (SRET), depression symptoms explained 34% to 45% of the variance in negative self-referent processing. The symptoms of sadness, self-dislike, pessimism, feelings of punishment, and indecision were most important. Notably, many depression symptoms made virtually no contribution to this prediction. In contrast, for attention bias for sad stimuli, measured with the dot-probe task using behavioral reaction time (RT) and eye gaze metrics, no reliable symptom predictors were identified. Findings indicate that a symptom-level approach may provide new insights into which symptoms, if any, are associated with negative cognitive biases in depression. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

SUBMITTER: Beevers CG

PROVIDER: S-EPMC6449499 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Association between negative cognitive bias and depression: A symptom-level approach.

Beevers Christopher G CG Mullarkey Michael C MC Dainer-Best Justin J Stewart Rochelle A RA Labrada Jocelyn J Allen John J B JJB McGeary John E JE Shumake Jason J

Journal of abnormal psychology 20190117 3

Cognitive models of depression posit that negatively biased self-referent processing and attention have important roles in the disorder. However, depression is a heterogeneous collection of symptoms and all symptoms are unlikely to be associated with these negative cognitive biases. The current study involved 218 community adults whose depression ranged from no symptoms to clinical levels of depression. Random forest machine learning was used to identify the most important depression symptom pre ...[more]

PMID: 30652884

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Project description:BackgroundAnxiety and depression often co-occur during adolescence, but the associations between symptoms of these two disorders in this developmental period are not yet fully understood. Network analysis provides a valuable approach to uncover meaningful associations among symptoms and offers insights for prevention and intervention strategies. This study aimed to investigate symptom-level associations between anxiety and depression using network analysis and to identify core symptoms, bridge symptoms, and differences in network structure across different stages of adolescence.MethodsThe cross-sectional study was conducted in March 2022 in Shenzhen, China. Participants completed the Generalized Anxiety Disorder Scale-7 and Patient Health Questionnaire Depression Scale, along with demographic questionnaires assessing age and gender. Chinese adolescents aged 10 to 17 who were in Grades 5 or 6 of elementary school, Grades 1 or 2 of middle school, or Grades 1 or 2 of high school, and who could comprehensively understand and read Chinese were recruited as participants. Students in Grade 3 of middle and high schools were excluded due to their upcoming high school or college entrance examinations. Based on age, participants were categorized into early, middle, and late developmental stages of adolescence.Results"Loss of control" was among the most central symptoms in the comorbidity network throughout all three developmental stages; "excessive worry" and "anhedonia" emerged as the core symptoms in early adolescence, and "restlessness" as the core symptom in late adolescence. "Anhedonia," "sad mood," and "fatigue" were identified as bridge symptoms between anxiety and depression across all three developmental stages of adolescence. The global strength of the network in middle adolescence was significantly higher compared to the other two stages.ConclusionThese findings highlight the core and bridge symptoms that require special attention and intervention at each stage of adolescence. Moreover, significantly higher network connectivity in middle adolescence suggests this is a critical period for intervention to prevent the development of comorbid mental disorders.

Project description:Many individuals with major depressive disorder (MDD) experience cognitive dysfunction including impaired cognitive control and negative cognitive styles. Functional connectivity magnetic resonance imaging studies of individuals with current MDD have documented altered resting-state connectivity within the default-mode network and across networks. However, no studies to date have evaluated the extent to which impaired connectivity within the cognitive control network (CCN) may be present in remitted MDD (rMDD), nor have studies examined the temporal stability of such attenuation over time. This represents a major gap in understanding stable, trait-like depression risk phenotypes. In this study, resting-state functional connectivity data were collected from 52 unmedicated young adults with rMDD and 47 demographically matched healthy controls, using three bilateral seeds in the CCN (dorsolateral prefrontal cortex, inferior parietal lobule, and dorsal anterior cingulate cortex). Mean connectivity within the entire CCN was attenuated among individuals with rMDD, was stable and reliable over time, and was most pronounced with the right dorsolateral prefrontal cortex and right inferior parietal lobule, results that were corroborated by supplemental independent component analysis. Attenuated connectivity in rMDD appeared to be specific to the CCN as opposed to representing attenuated within-network coherence in other networks (e.g., default-mode, salience). In addition, attenuated connectivity within the CCN mediated relationships between rMDD status and cognitive risk factors for depression, including ruminative brooding, pessimistic attributional style, and negative automatic thoughts. Given that these cognitive markers are known predictors of relapse, these results suggest that attenuated connectivity within the CCN could represent a biomarker for trait phenotypes of depression risk. Hum Brain Mapp 38:2939-2954, 2017. © 2017 Wiley Periodicals, Inc.

Dataset Information

Association between negative cognitive bias and depression: A symptom-level approach.

Publications

Association between negative cognitive bias and depression: A symptom-level approach.

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