Machine learning reveals differential effects of depression and anxiety on reward and punishment processing
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-58031-9 |
| _version_ | 1797209413862293504 |
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| author | Anna Grabowska Jakub Zabielski Magdalena Senderecka |
| author_facet | Anna Grabowska Jakub Zabielski Magdalena Senderecka |
| author_sort | Anna Grabowska |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T09:54:19Z |
| format | Article |
| id | doaj.art-68728399cc1144f3802557e40b3893b5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T09:54:19Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-68728399cc1144f3802557e40b3893b52024-04-14T11:14:59ZengNature PortfolioScientific Reports2045-23222024-04-0114111310.1038/s41598-024-58031-9Machine learning reveals differential effects of depression and anxiety on reward and punishment processingAnna Grabowska0Jakub Zabielski1Magdalena Senderecka2Doctoral School in the Social Sciences, Jagiellonian UniversityInstitute of Philosophy, Jagiellonian UniversityInstitute of Philosophy, Jagiellonian UniversityAbstract 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.https://doi.org/10.1038/s41598-024-58031-9DepressionAnxietyFeedback processingEEGMachine learning |
| spellingShingle | Anna Grabowska Jakub Zabielski Magdalena Senderecka Machine learning reveals differential effects of depression and anxiety on reward and punishment processing Scientific Reports Depression Anxiety Feedback processing EEG Machine learning |
| title | Machine learning reveals differential effects of depression and anxiety on reward and punishment processing |
| title_full | Machine learning reveals differential effects of depression and anxiety on reward and punishment processing |
| title_fullStr | Machine learning reveals differential effects of depression and anxiety on reward and punishment processing |
| title_full_unstemmed | Machine learning reveals differential effects of depression and anxiety on reward and punishment processing |
| title_short | Machine learning reveals differential effects of depression and anxiety on reward and punishment processing |
| title_sort | machine learning reveals differential effects of depression and anxiety on reward and punishment processing |
| topic | Depression Anxiety Feedback processing EEG Machine learning |
| url | https://doi.org/10.1038/s41598-024-58031-9 |
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