Details

Autor(en) / Beteiligte

• Forthman, Katherine L.
• Kuplicki, Rayus
• Yeh, Hung-wen
• Khalsa, Sahib S.
• Paulus, Martin P.
• Guinjoan, Salvador M.

Titel

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

Ist Teil von

• Journal of psychiatric research, 2023-06, Vol.162, p.207-213

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. 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. PRS for neuroticism was the main predictor of RNT intensity (R2=0.027,p<0.001). Behavioral variables indicative of faulty fear learning and processing, as well as aberrant interoceptive aversiveness, were significant contributors to RNT severity. Unexpectedly, we observed no contribution of reward behavior and diverse cognitive function variables. This study is an exploratory approach that must be validated with a second, independent cohort. Furthermore, this is an association study, limiting causal inference. 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. •A machine learning ensemble method identified behavioral and genetic predictors of repetitive negative thinking (RNT).•Heightened response to aversive and fear inducing stimuli are associated with RNT.•The only genetic trait associated with RNT was polygenic risk score for neuroticism.•Our results warrant the exploration of interventions that target fear learning and interoceptive aversiveness.•Our results indicate that RNT is either a component of neuroticism or a contributor to the development and stability of it.