Naturalistic acute pain states decoded from neural and facial dynamics
Abstract Pain remains poorly understood in task-free contexts, limiting our understanding of its neurobehavioral basis in naturalistic settings. Here, we use a multimodal, data-driven approach with intracranial electroencephalography, pain self-reports, and facial expression analysis to study acute...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59756-5 |
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| author | Yuhao Huang Jay Gopal Bina Kakusa Alice H. Li Weichen Huang Jeffrey B. Wang Amit Persad Ashwin Ramayya Josef Parvizi Vivek P. Buch Corey J. Keller |
| author_facet | Yuhao Huang Jay Gopal Bina Kakusa Alice H. Li Weichen Huang Jeffrey B. Wang Amit Persad Ashwin Ramayya Josef Parvizi Vivek P. Buch Corey J. Keller |
| author_sort | Yuhao Huang |
| collection | DOAJ |
| description | Abstract Pain remains poorly understood in task-free contexts, limiting our understanding of its neurobehavioral basis in naturalistic settings. Here, we use a multimodal, data-driven approach with intracranial electroencephalography, pain self-reports, and facial expression analysis to study acute pain in twelve epilepsy patients under continuous neural and audiovisual monitoring. Using machine learning, we successfully decode individual participants’ high versus low pain states from distributed neural activity, involving mesolimbic regions, striatum, and temporoparietal cortex. Neural representation of pain remains stable for hours and is modulated by pain onset and relief. Objective facial expressions also classify pain states, concordant with neural findings. Importantly, we identify transient periods of momentary pain as a distinct naturalistic acute pain measure, which can be reliably discriminated from affect-neutral periods using neural and facial features. These findings reveal reliable neurobehavioral markers of acute pain across naturalistic contexts, underscoring the potential for monitoring and personalizing pain interventions in real-world settings. |
| format | Article |
| id | doaj-art-609bc89788e84c15b4eaa097bbd64b42 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-609bc89788e84c15b4eaa097bbd64b422025-08-20T02:25:17ZengNature PortfolioNature Communications2041-17232025-05-0116111310.1038/s41467-025-59756-5Naturalistic acute pain states decoded from neural and facial dynamicsYuhao Huang0Jay Gopal1Bina Kakusa2Alice H. Li3Weichen Huang4Jeffrey B. Wang5Amit Persad6Ashwin Ramayya7Josef Parvizi8Vivek P. Buch9Corey J. Keller10Department of Neurosurgery, Stanford University School of MedicineBrown UniversityDepartment of Neurosurgery, Stanford University School of MedicineDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of MedicineDepartment of Neurology, Stanford University School of MedicineDepartment of Anesthesia and Critical Care Medicine, The Johns Hopkins University School of MedicineDepartment of Neurosurgery, Stanford University School of MedicineDepartment of Neurosurgery, Stanford University School of MedicineDepartment of Neurology, Stanford University School of MedicineDepartment of Neurosurgery, Stanford University School of MedicineDepartment of Psychiatry and Behavioral Sciences, Stanford University School of MedicineAbstract Pain remains poorly understood in task-free contexts, limiting our understanding of its neurobehavioral basis in naturalistic settings. Here, we use a multimodal, data-driven approach with intracranial electroencephalography, pain self-reports, and facial expression analysis to study acute pain in twelve epilepsy patients under continuous neural and audiovisual monitoring. Using machine learning, we successfully decode individual participants’ high versus low pain states from distributed neural activity, involving mesolimbic regions, striatum, and temporoparietal cortex. Neural representation of pain remains stable for hours and is modulated by pain onset and relief. Objective facial expressions also classify pain states, concordant with neural findings. Importantly, we identify transient periods of momentary pain as a distinct naturalistic acute pain measure, which can be reliably discriminated from affect-neutral periods using neural and facial features. These findings reveal reliable neurobehavioral markers of acute pain across naturalistic contexts, underscoring the potential for monitoring and personalizing pain interventions in real-world settings.https://doi.org/10.1038/s41467-025-59756-5 |
| spellingShingle | Yuhao Huang Jay Gopal Bina Kakusa Alice H. Li Weichen Huang Jeffrey B. Wang Amit Persad Ashwin Ramayya Josef Parvizi Vivek P. Buch Corey J. Keller Naturalistic acute pain states decoded from neural and facial dynamics Nature Communications |
| title | Naturalistic acute pain states decoded from neural and facial dynamics |
| title_full | Naturalistic acute pain states decoded from neural and facial dynamics |
| title_fullStr | Naturalistic acute pain states decoded from neural and facial dynamics |
| title_full_unstemmed | Naturalistic acute pain states decoded from neural and facial dynamics |
| title_short | Naturalistic acute pain states decoded from neural and facial dynamics |
| title_sort | naturalistic acute pain states decoded from neural and facial dynamics |
| url | https://doi.org/10.1038/s41467-025-59756-5 |
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