Gao Zhen, Cui Mengjie, Wang Haijun, Xu Cheng, Gu Nixuan, Ji Laixi
Experimental Management Center, Shanxi University of CM, Taiyuan 030024, China.
Second Clinical College, Shanxi University of CM, Taiyuan 030024.
Zhongguo Zhen Jiu. 2025 Apr 12;45(4):405-412. doi: 10.13703/j.0255-2930.20240731-k0006. Epub 2025 Jan 6.
To screen the population for acupuncture treatment of neck pain, using functional magnetic resonance imaging (fMRI) technology and based on machine learning algorithms.
Eighty patients with neck pain were recruited. Using FPX25 handheld pressure algometer, the tender points were detected in the areas with high-frequent onset of neck pain and high degree of acupoint sensitization. Acupuncture was delivered at 4 tender points with the lowest pain threshold, once every two days; and the treatment was given 3 times a week and for 2 consecutive weeks. The amplitude of low-frequency fluctuation (ALFF) of the brain before treatment was taken as a predictive feature to construct support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN) models to predict the responses of neck pain patients to acupuncture treatment. A longitudinal analysis of the ALFF features was performed before and after treatment to reveal the potential biological markers of the reactivity to the acupuncture therapy.
The SVM model could successfully distinguish high responders (48 cases) and low responders (32 cases) to acupuncture treatment, and its accuracy rate reached 82.5%. Based on the SVM model, the ALFF values of 4 brain regions were identified as the consistent predictive features, including the right middle temporal gyrus, the right superior occipital gyrus, and the bilateral posterior cingulate gyrus. In the patients with high acupuncture response, the ALFF value in the left posterior cingulate gyrus decreased after treatment (<0.05), whereas in the patients with low acupuncture response, the ALFF value in the right superior occipital gyrus increased after treatment (<0.01). The longitudinal functional connectivity (FC) analysis found that compared with those before treatment, the high responders showed the enhanced FC after treatment between the left posterior cingulate gyrus and various regions, including the bilateral Crus1 of the cerebellum, the right insula, the bilateral angular gyrus, the left medial superior frontal gyrus, and the left middle cingulate gyrus (GRF: corrected, voxel level: <0.05, mass level: <0.05). In contrast, the low responders exhibited the enhanced FC between the left posterior cingulate gyrus and the left Crus2 of the cerebellum, the left middle temporal gyrus, the right posterior cingulate gyrus, and the left angular gyrus; besides, FC was reduced in low responders between the left posterior cingulate gyrus and the right supramarginal gyrus (GRF: corrected, voxel level: <0.05, mass level: <0.05).
This study validates the practicality of pre-treatment ALFF feature prediction for acupuncture efficacy on neck pain. The therapeutic effect of acupuncture on neck pain is potentially associated with its impact on the default mode network, and then, alter the pain perception and emotional regulation.
采用功能磁共振成像（fMRI）技术并基于机器学习算法，对人群进行颈部疼痛针灸治疗的筛选。
招募80例颈部疼痛患者。使用FPX25手持式压力痛觉计，在颈部疼痛高发且穴位敏化程度高的区域检测压痛点。在4个疼痛阈值最低的压痛点进行针灸，每两天1次；每周治疗3次，连续治疗2周。将治疗前大脑的低频振幅（ALFF）作为预测特征，构建支持向量机（SVM）、逻辑回归（LR）和K近邻（KNN）模型，以预测颈部疼痛患者对针灸治疗的反应。对治疗前后的ALFF特征进行纵向分析，以揭示对针灸治疗反应性的潜在生物学标志物。
SVM模型能够成功区分针灸治疗的高反应者（48例）和低反应者（32例），准确率达到82.5%。基于SVM模型，确定4个脑区的ALFF值为一致的预测特征，包括右侧颞中回、右侧枕上回和双侧扣带回后部。在针灸高反应患者中，治疗后左侧扣带回后部的ALFF值降低（<0.05），而在针灸低反应患者中，治疗后右侧枕上回的ALFF值升高（<0.01）。纵向功能连接（FC）分析发现，与治疗前相比，高反应者治疗后左侧扣带回后部与多个区域之间的FC增强，包括双侧小脑脚1、右侧岛叶、双侧角回、左侧额上内侧回和左侧扣带中回（GRF：校正后，体素水平：<0.05，团块水平：<0.05）。相比之下，低反应者左侧扣带回后部与左侧小脑脚2、左侧颞中回、右侧扣带回后部和左侧角回之间的FC增强；此外，低反应者左侧扣带回后部与右侧缘上回之间的FC降低（GRF：校正后，体素水平：<0.05，团块水平：<0.05）。
本研究验证了治疗前ALFF特征预测针灸治疗颈部疼痛疗效的实用性。针灸对颈部疼痛的治疗效果可能与其对默认模式网络的影响有关，进而改变疼痛感知和情绪调节。
