Yin Tao, Sun Guojuan, Tian Zilei, Liu Mailan, Gao Yujie, Dong Mingkai, Wu Feng, Li Zhengjie, Liang Fanrong, Zeng Fang, Lan Lei
Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Front Neurol. 2020 Nov 9;11:588207. doi: 10.3389/fneur.2020.588207. eCollection 2020.
The purpose of the present study was to explore whether and to what extent the neuroimaging markers could predict the relief of the symptoms of patients with migraine without aura (MWoA) following a 4-week acupuncture treatment period. In study 1, the advanced multivariate pattern analysis was applied to perform a classification analysis between 40 patients with MWoA and 40 healthy subjects (HS) based on the z-transformed amplitude of low-frequency fluctuation (zALFF) maps. In study 2, the meaningful classifying features were selected as predicting features and the support vector regression models were constructed to predict the clinical efficacy of acupuncture in reducing the frequency of migraine attacks and headache intensity in 40 patients with MWoA. In study 3, a region of interest-based comparison between the pre- and post-treatment zALFF maps was conducted in 33 patients with MwoA to assess the changes in predicting features after acupuncture intervention. The zALFF value of the foci in the bilateral middle occipital gyrus, right fusiform gyrus, left insula, and left superior cerebellum could discriminate patients with MWoA from HS with higher than 70% accuracy. The zALFF value of the clusters in the right and left middle occipital gyrus could effectively predict the relief of headache intensity ( = 0.38 ± 0.059, mean squared error = 2.626 ± 0.325) and frequency of migraine attacks ( = 0.284 ± 0.072, mean squared error = 20.535 ± 2.701) after the 4-week acupuncture treatment period. Moreover, the zALFF values of these two clusters were both significantly reduced after treatment. The present study demonstrated the feasibility and validity of applying machine learning technologies and individual cerebral spontaneous activity patterns to predict acupuncture treatment outcomes in patients with MWoA. The data provided a quantitative benchmark for selecting acupuncture for MWoA.
本研究的目的是探讨神经影像标志物能否以及在何种程度上预测无先兆偏头痛（MWoA）患者在为期4周的针灸治疗期后症状的缓解情况。在研究1中，应用先进的多变量模式分析，基于低频波动的z变换幅度（zALFF）图，对40例MWoA患者和40例健康受试者（HS）进行分类分析。在研究2中，选择有意义的分类特征作为预测特征，并构建支持向量回归模型，以预测针灸对40例MWoA患者降低偏头痛发作频率和头痛强度的临床疗效。在研究3中，对33例MWoA患者治疗前后的zALFF图进行基于感兴趣区域的比较，以评估针灸干预后预测特征的变化。双侧枕中回、右侧梭状回、左侧岛叶和左侧小脑上部病灶的zALFF值能够以高于70%的准确率区分MWoA患者和HS。左右枕中回簇的zALFF值能够有效预测为期4周的针灸治疗期后头痛强度（ = 0.38 ± 0.059，均方误差 = 2.626 ± 0.325）和偏头痛发作频率（ = 0.284 ± 0.072，均方误差 = 20.535 ± 2.701）的缓解情况。此外，治疗后这两个簇的zALFF值均显著降低。本研究证明了应用机器学习技术和个体脑自发活动模式预测MWoA患者针灸治疗效果的可行性和有效性。这些数据为MWoA的针灸选择提供了定量基准。
