Abstract
Purpose: Cancer pain management remains a significant clinical challenge. While acupuncture has shown potential in alleviating cancer pain, its underlying mechanisms are not yet fully understood. This study investigates the neurophysiological mechanisms underlying acupuncture’s analgesic effects using multimodal bioelectrical signal analysis.
Patients and Methods: Fifteen cancer pain patients underwent acupuncture while wearing portable, multi-sensor devices to capture bioelectrical signals. Pain levels were assessed using the Numerical Rating Scale (NRS) before and during needle retention. Neurophysiological changes were evaluated using Principal Component Analysis, Joint Time-Frequency Analysis, power spectrum analysis, spectral analysis, and dynamic functional network analysis.
Results: There was a significant reduction in NRS scores from pre-treatment to the retention period, indicating pain relief. Principal component analysis showed significant differences in bioelectrical signals between these periods. Power spectrum analysis revealed decreased signal power during retention. Functional network analysis demonstrated a reduction in connectivity strength between electroencephalography and electromyography signals. Spectral analysis identified distinct real-time and staged characteristics of bioelec-trical signals, with correlation analysis confirming a positive relationship between NRS score changes and bioelectrical signal alterations.
Conclusion: Acupuncture alleviates cancer pain by reducing functional connectivity between injured tissues and the brain, with immediate effects. Prolonging needle retention may enhance therapeutic outcomes. These findings provide new insights into the neurophysiological basis of acupuncture’s analgesic effects, supporting its role in cancer pain management.
| Original language | English |
|---|---|
| Pages (from-to) | 1435-1450 |
| Number of pages | 16 |
| Journal | Journal of Pain Research |
| Volume | 18 |
| DOIs | |
| Publication status | Published - 20 Mar 2025 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2025 Huang et al.
Funding
This work was supported by the Shenzhen Science and Technology Program Fund (JCYJ20220531091407016); and Futian Healthcare Research Project (FTWS069; FTWS055).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- acupuncture
- analgesic mechanism
- cancer pain
- deep learning
- multimodal bioelectrical signals
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