Topological signatures of brain dynamics: persistent homology reveals individuality and brain–behavior links
IntroductionUnderstanding individual differences in brain dynamics is a central goal in neuroscience. While conventional time series features capture signal properties of local brain regions, they often fail to reveal the deeper structure embedded in the brain's complex activity patterns.Method...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1607941/full |
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| author | Yue Wang Yue Wang Junxing Xian Yuanyuan Chen Yuanyuan Chen Yan Yan Yan Yan |
| author_facet | Yue Wang Yue Wang Junxing Xian Yuanyuan Chen Yuanyuan Chen Yan Yan Yan Yan |
| author_sort | Yue Wang |
| collection | DOAJ |
| description | IntroductionUnderstanding individual differences in brain dynamics is a central goal in neuroscience. While conventional time series features capture signal properties of local brain regions, they often fail to reveal the deeper structure embedded in the brain's complex activity patterns.MethodsResting-state fMRI data from approximately 1,000 subjects in the Human Connectome Project were analyzed. A TDA-based framework integrating time-delay embeddings and persistent homology was employed to extract global dynamic features from resting-state fMRI data. Classification models and canonical correlation analysis (CCA) were employed to examine the associations between brain topological features and individual characteristics, including gender and behavioral traits.ResultsTopological features exhibited high test-retest reliability and enabled accurate individual identification across sessions. In classification tasks, these features outperformed commonly used temporal features in predicting gender. Canonical correlation analysis identified a significant brain-behavior mode that links topological brain patterns to cognitive measures and psychopathological risks. Regression analyses across behavioral domains showed that persistent homology features matched or exceeded the predictive performance of traditional features in higher-order domains such as cognition, emotion, and personality, while traditional features performed slightly better in sensory-related domains.DiscussionThese findings highlight persistent homology as a robust and informative framework for modeling individual differences in brain function, offering promising avenues for personalized neuroimaging analysis. |
| format | Article |
| id | doaj-art-bef656688a5249a58af5869abc24fb06 |
| institution | DOAJ |
| issn | 1662-5161 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj-art-bef656688a5249a58af5869abc24fb062025-08-20T03:21:46ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612025-05-011910.3389/fnhum.2025.16079411607941Topological signatures of brain dynamics: persistent homology reveals individuality and brain–behavior linksYue Wang0Yue Wang1Junxing Xian2Yuanyuan Chen3Yuanyuan Chen4Yan Yan5Yan Yan6Academy of Medical Engineering and Translational Medicine, Medical School of Tianjin University, Tianjin University, Tianjin, ChinaCollege of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaAcademy of Medical Engineering and Translational Medicine, Medical School of Tianjin University, Tianjin University, Tianjin, ChinaCollege of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, ChinaState Key Laboratory of Biomedical Imaging Science and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, ChinaCenter of Medical Information, Wenzhou Institute of Technology, Wenzhou, ChinaIntroductionUnderstanding individual differences in brain dynamics is a central goal in neuroscience. While conventional time series features capture signal properties of local brain regions, they often fail to reveal the deeper structure embedded in the brain's complex activity patterns.MethodsResting-state fMRI data from approximately 1,000 subjects in the Human Connectome Project were analyzed. A TDA-based framework integrating time-delay embeddings and persistent homology was employed to extract global dynamic features from resting-state fMRI data. Classification models and canonical correlation analysis (CCA) were employed to examine the associations between brain topological features and individual characteristics, including gender and behavioral traits.ResultsTopological features exhibited high test-retest reliability and enabled accurate individual identification across sessions. In classification tasks, these features outperformed commonly used temporal features in predicting gender. Canonical correlation analysis identified a significant brain-behavior mode that links topological brain patterns to cognitive measures and psychopathological risks. Regression analyses across behavioral domains showed that persistent homology features matched or exceeded the predictive performance of traditional features in higher-order domains such as cognition, emotion, and personality, while traditional features performed slightly better in sensory-related domains.DiscussionThese findings highlight persistent homology as a robust and informative framework for modeling individual differences in brain function, offering promising avenues for personalized neuroimaging analysis.https://www.frontiersin.org/articles/10.3389/fnhum.2025.1607941/fullfunctional magnetic resonance imagingtopological data analysispersistent homologyindividual differencesbrain-behavior relationships |
| spellingShingle | Yue Wang Yue Wang Junxing Xian Yuanyuan Chen Yuanyuan Chen Yan Yan Yan Yan Topological signatures of brain dynamics: persistent homology reveals individuality and brain–behavior links Frontiers in Human Neuroscience functional magnetic resonance imaging topological data analysis persistent homology individual differences brain-behavior relationships |
| title | Topological signatures of brain dynamics: persistent homology reveals individuality and brain–behavior links |
| title_full | Topological signatures of brain dynamics: persistent homology reveals individuality and brain–behavior links |
| title_fullStr | Topological signatures of brain dynamics: persistent homology reveals individuality and brain–behavior links |
| title_full_unstemmed | Topological signatures of brain dynamics: persistent homology reveals individuality and brain–behavior links |
| title_short | Topological signatures of brain dynamics: persistent homology reveals individuality and brain–behavior links |
| title_sort | topological signatures of brain dynamics persistent homology reveals individuality and brain behavior links |
| topic | functional magnetic resonance imaging topological data analysis persistent homology individual differences brain-behavior relationships |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1607941/full |
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