Gao Zhen, Cui Mengjie, Xu Cheng, Wang Haijun, Zhang Yanlin, Ji Laixi
Shanxi University of Traditional Chinese Medicine Experimental Management Center, Taiyuan, China.
Shanxi University of Traditional Chinese Medicine Second Clinical College, Taiyuan, China.
Ann Med. 2025 Dec;57(1):2548388. doi: 10.1080/07853890.2025.2548388. Epub 2025 Aug 22.
To explore the mechanisms of acupuncture-induced neck pain relief and identify appropriate candidates using neuroimaging and machine learning techniques.
Eighty neck pain patients were included, with clinical data and functional magnetic resonance imaging scans collected pre- and post-treatment. A support vector machine (SVM) model was built using pre-treatment brain functional connectivity to predict acupuncture responsiveness, identifying key features as potential biomarkers for effectiveness. Longitudinal analysis of these features was conducted in responders and non-responders.
This study enrolled 80 neck pain patients (48 acupuncture responders and 32 non-responders) for SVM model construction and longitudinal analysis of predictive features pre-/post-treatment. The SVM model achieved an accuracy of 0.85 in distinguishing the two groups. A total of 117 functional connectivity edges were identified as predictive features, potential biomarkers for acupuncture responses. Longitudinal analysis showed 6 predictive features altered post-treatment in responders versus 44 in non-responders. After FDR correction, only 3 functional connectivity features in responders negatively correlated with pain VAS scores ( < 0.05). These findings indicate more targeted changes in predictive features among responders compared to non-responders.
Using pre-treatment neuroimaging features to predict acupuncture effectiveness for neck pain shows promise. This approach could aid in developing personalized acupuncture strategies by identifying likely beneficiaries, guiding alternative interventions for non-responders.
International Traditional Medicine Clinical Trial Registry (registration number: ITMCTR2023000001, protocol version number: V1.0).
运用神经影像学和机器学习技术，探索针刺缓解颈部疼痛的机制，并确定合适的受试对象。
纳入80例颈部疼痛患者，收集其治疗前后的临床数据和功能磁共振成像扫描结果。利用治疗前的脑功能连接构建支持向量机（SVM）模型，以预测针刺反应性，确定关键特征作为疗效的潜在生物标志物。对反应者和无反应者的这些特征进行纵向分析。
本研究纳入80例颈部疼痛患者（48例针刺反应者和32例无反应者）进行SVM模型构建及治疗前后预测特征的纵向分析。SVM模型区分两组的准确率为0.85。共确定117条功能连接边为预测特征，即针刺反应的潜在生物标志物。纵向分析显示，反应者治疗后有6个预测特征发生改变，无反应者有44个。经FDR校正后,反应者中只有3个功能连接特征与疼痛VAS评分呈负相关（<0.05）。这些结果表明，与无反应者相比，反应者的预测特征变化更具针对性。
利用治疗前神经影像学特征预测针刺治疗颈部疼痛的疗效具有一定前景。这种方法有助于制定个性化针刺策略，通过识别可能的受益者，为无反应者指导替代干预措施。
国际传统医学临床试验注册中心（注册号：ITMCTR2023000001，方案版本号：V1.0）
