The repository contains data and supporting materials necessary to reproduce the analyses and figures reported in Grabowska, A., Sondej, F., & Senderecka, M. (2024). A Machine Learning Study of Anxiety-related Symptoms and Error-related Brain Activity. Journal of Cognitive Neuroscience, 36(5).
Source of funding: Sonata Bis grant 2020/38/E/HS6/00490 from the National Science Centre of Poland.
The project aims to explain associations between biomarkers of error-related brain activity (ERN, Pe ERP components) and 15 different phenomena related to anxiety:
- rumination;
- overestimation of threat;
- prospective and inhibitory intolerance of uncertainty;
- behavioral inhibition;
- mixed anxiety measured with STAI-T;
- mixed anxiety measured with DASS-21;
- OCD checking, ordering, obsessing, washing, and neutralizing symptoms;
- OCD;
- thought suppression;
- self-esteem.
The project relies on PCA for spatial feature extraction and linear/nonlinear regression.
**Abstract**
Changes in error processing are observable in a range of anxiety-related disorders. Numerous studies, however, have reported contradictory and nonreplicating findings, thus the exact mapping of brain response to errors (i.e., error-related negativity [ERN]; error-related positivity, Pe) onto specific anxiety symptoms remains unclear. In this study, we collected 16 self-reported scores of anxiety dimensions and obtained spatial features of EEG recordings from 171 individuals. We then used machine learning to (1) identify symptoms that are central for elevated ERN/Pe and (2) estimate the generalizability of traditional statistical approaches. ERN was associated with rumination, threat overestimation, and inhibitory intolerance of uncertainty. Pe was associated with rumination, prospective intolerance of uncertainty, and behavioral inhibition. Our findings emphasize that not only the amplitude of ERN but also other sources of brain signal variance encode information relevant to individual differences in error processing. The results of the generalizability check reveal the need for a change in result-validation methods to move toward robust findings that reflect stable individual differences and clinically useful biomarkers. Our study benefits from the use of machine learning to improve the generalizability of results.
This project is part of the Error Monitoring Project, which aims to clarify the relationship and dynamics between error processing, individual differences and symptoms of psychopathology.
