Wirelessly transmitted subthalamic nucleus signals predict endogenous pain levels in Parkinson's disease patients
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908425018933248 |
|---|---|
| author | Reza, Abdi Yanagisawa, Takufumi Tani, Naoki Fukuma, Ryohei Emura, Takuto Oshino, Satoru Seymour, Ben Kishima, Haruhiko |
| author_facet | Reza, Abdi Yanagisawa, Takufumi Tani, Naoki Fukuma, Ryohei Emura, Takuto Oshino, Satoru Seymour, Ben Kishima, Haruhiko |
| contents | Parkinson disease (PD) patients experience pain fluctuations that significantly reduce their quality of life. Despite the vast knowledge of the subthalamic nucleus (STN) role in PD, the STN biomarkers for pain fluctuations and the relationship between bilateral subthalamic nucleus (STN) activities and pain occurrence are still less understood. This observational study used data-driven methods by collecting annotated pain followed by a series of corresponding binary pain ratings and wirelessly transmitted STN signals, then leveraging the explainable machine learning algorithm to predict binary pain levels and sort the feature influence. The binary pain levels could be predicted among annotated pain reports corresponding to PD-related pain characteristics. The STN activity from both sides could impact pain prediction, with gamma and beta bands in the contralateral STN and delta and theta bands in the ipsilateral STN showing a prominent role. This study emphasizes the role of bilateral STN biomarkers on endogenous pain fluctuations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21439 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wirelessly transmitted subthalamic nucleus signals predict endogenous pain levels in Parkinson's disease patients Reza, Abdi Yanagisawa, Takufumi Tani, Naoki Fukuma, Ryohei Emura, Takuto Oshino, Satoru Seymour, Ben Kishima, Haruhiko Neurons and Cognition Parkinson disease (PD) patients experience pain fluctuations that significantly reduce their quality of life. Despite the vast knowledge of the subthalamic nucleus (STN) role in PD, the STN biomarkers for pain fluctuations and the relationship between bilateral subthalamic nucleus (STN) activities and pain occurrence are still less understood. This observational study used data-driven methods by collecting annotated pain followed by a series of corresponding binary pain ratings and wirelessly transmitted STN signals, then leveraging the explainable machine learning algorithm to predict binary pain levels and sort the feature influence. The binary pain levels could be predicted among annotated pain reports corresponding to PD-related pain characteristics. The STN activity from both sides could impact pain prediction, with gamma and beta bands in the contralateral STN and delta and theta bands in the ipsilateral STN showing a prominent role. This study emphasizes the role of bilateral STN biomarkers on endogenous pain fluctuations. |
| title | Wirelessly transmitted subthalamic nucleus signals predict endogenous pain levels in Parkinson's disease patients |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2506.21439 |