Transdiagnostic behavioral and genetic contributors to repetitive negative thinking: A machine learning approach.

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DOI: 10.1016/j.jpsychires.2023.05.039


Background: Repetitive negative thinking (RNT) is a symptom that can negatively impact the treatment and course of common psychiatric disorders such as depression and anxiety. We aimed to characterize behavioral and genetic correlates of RNT to infer potential contributors to its genesis and maintenance.

Methods: We applied a machine learning (ML) ensemble method to define the contribution of fear, interoceptive, reward, and cognitive variables to RNT, along with polygenic risk scores (PRS) for neuroticism, obsessive compulsive disorder (OCD), worry, insomnia, and headaches. We used the PRS and 20 principal components of the behavioral and cognitive variables to predict intensity of RNT. We employed the Tulsa-1000 study, a large database of deeply phenotyped individuals recruited between 2015 and 2018.

Results: PRS for neuroticism was the main predictor of RNT intensity (R2=0.027,p

Limitations: This study is an exploratory approach that must be validated with a second, independent cohort. Furthermore, this is an association study, limiting causal inference.

Conclusions: RNT is highly determined by genetic risk for neuroticism, a behavioral construct that confers risk to a variety of internalizing disorders, and by emotional processing and learning features, including interoceptive aversiveness. These results suggest that targeting emotional and interoceptive processing areas, which involve central autonomic network structures, could be useful in the modulation of RNT intensity.

Journal Title

Journal of psychiatric research



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MeSH Keywords

Humans; Pessimism; Thinking; Surveys and Questionnaires; Anxiety Disorders; Anxiety


Machine learning ensemble method; Neuroticism; Polygenic risk score; Principal component analysis; Repetitive negative thinking

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