ArticleScientific reports2024
Machine learning reveals differential effects of depression and anxiety on reward and punishment processing.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Brain rhythms of depression: A predictive processing perspective.Trends in neurosciences · 2026Review
- Predicting comorbid anxiety in adolescents with major depressive disorder: an EEG-based machine learning approach with SHAP interpretability.Translational psychiatry · 2026Article
- Predicting Major Depressive Disorder Using Neural Networks from Spectral Measures of EEG Data.Bioengineering (Basel, Switzerland) · 2025Article
- Neurobehavioral mechanisms of fear and anxiety in multiple sclerosis.Communications medicine · 2025Article
- A network analysis of affective and motivational individual differences and error monitoring in a non-clinical sample.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Risk avoidance and social anxiety in adolescence: Examination of event-related potentials and theta-dynamics on the Balloon Risk Avoidance Task.Brain and cognition · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Recent studies suggest that depression and anxiety are associated with unique aspects of EEG responses to reward and punishment, respectively; also, abnormal responses to punishment in depressed individuals are related to anxiety, the symptoms of which are comorbid with depression. In a non-clinical sample, we aimed to investigate the relationships between reward processing and anxiety, between punishment processing and anxiety, between reward processing and depression, and between punishment processing and depression. Towards this aim, we separated feedback-related brain activity into delta and theta bands to isolate activity that indexes functionally distinct processes. Based on the delta/theta frequency and feedback valence, we then used machine learning (ML) to classify individuals with high severity of depressive symptoms and individuals with high severity of anxiety symptoms versus controls. The significant difference between the depression and control groups was driven mainly by delta activity; there were no differences between reward- and punishment-theta activities. The high severity of anxiety symptoms was marginally more strongly associated with the punishment- than the reward-theta feedback processing. The findings provide new insights into the differences in the impacts of anxiety and depression on reward and punishment processing; our study shows the utility of ML in testing brain-behavior hypotheses and emphasizes the joint effect of theta-RewP/FRN and delta frequency on feedback-related brain activity.
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