针刺治疗颈痛的适宜人群筛选:一项机器学习研究  

Population screening for acupuncture treatment of neck pain:a machine learning study

作　　者：高珍 崔梦洁 王海军 徐成[3] 顾倪瑄 冀来喜 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;Department of Radiology,Shanxi Provincial People's Hospital)

机构地区：[1]山西中医药大学实验管理中心,太原030024 [2]山西中医药大学第二临床学院,太原030024 [3]山西省人民医院影像科

出　　处：《中国针灸》2025年第4期405-412,共8页Chinese Acupuncture & Moxibustion

基　　金：山西省基础研究计划(面上项目):202203021221195;山西中医药大学优秀博士毕业生来晋工作奖励经费科研启动基金项目:2023BKS12;山西中医药大学博士科研启动基金项目:2023BK17。

摘　　要：目的:基于机器学习算法,采用功能性磁共振成像(fMRI)技术筛选针刺治疗颈痛的适宜人群。方法:招募80例颈痛患者,采用FPX25手持式压力测痛仪在颈痛频繁发作和穴位敏化程度较高的区域探查压痛点,选取压痛阈值最低的4个点进行针刺,隔日1次,每周3次,共治疗2周。以治疗前大脑低频振幅(ALFF)作为预测特征,分别构建支持向量机(SVM)、逻辑回归(LR)以及K近邻算法(KNN)模型,用以预测颈痛患者对针刺治疗的响应性;对治疗前后ALFF特征进行纵向分析,以揭示针灸治疗反应性的潜在生物学标志。结果:SVM模型可成功区分针刺高响应者(针刺高响应组,48例)和针刺低响应者(针刺低响应者,32例),模型准确率达82.5%。基于SVM模型,4个脑区的ALFF值被识别为一致性预测特征,包括右侧颞中回、右侧枕上回以及双侧后扣带回。针刺高响应组患者治疗后左侧后扣带回ALFF值降低(P<0.05),而针刺低响应组患者治疗后右侧枕上回ALFF值增高(P<0.01)。纵向的功能连接(FC)分析发现,相较于治疗前,针刺高响应组患者治疗后左侧后扣带回与双侧小脑Crus1、右侧脑岛、双侧角回、左侧内侧额上回和左侧中扣带回FC增强[高斯随机场(GRF)校正,体素水平P<0.05,团块水平P<0.05];针刺低响应组患者治疗后左侧后扣带回与左侧小脑Crus2、左侧颞中回、右侧后扣带回和左侧角回FC增强,左侧后扣带回与右侧缘上回FC降低(GRF校正,体素水平P<0.05,团块水平P<0.05)。结论:本研究证实了基于治疗前ALFF特征预测颈痛针刺疗效的可行性,针刺治疗颈痛的疗效可能与影响默认模式网络,进而改变疼痛感知和情绪调节功能有关。Objective To screen the population for acupuncture treatment of neck pain,using functional magnetic resonance imaging(fMRI)technology and based on machine learning algorithms.MethodsEighty 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.Results 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(P<0.05),whereas in the patients with low acupuncture response,the ALFF value in the right superior occipital gyrus increased after treatment(P<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 Crusl of the cerebellum,the right insula,the bilateral angular gyrus,the left medial superior frontal gyrus,

关 键 词：颈痛 针灸 针刺适宜人群 功能性磁共振成像 机器学习 

分 类 号：R245[医药卫生—针灸推拿学]