Baseline Brain Gray Matter Volume as a Predictor of Acupuncture Outcome in Treating Migraine

Frontiers in Neurology

Ya-Jie Zhang¹  Da-Peng Liu¹  Lin-Peng Wang¹  Marc Fishers²  Xiao Zeng³  Lan Zhang³  Zi-Liang Xu³  Peng Liu³  Jin-Bo Sun³  Xue-Juan Yang³  Wei Qin³  Lu Liu⁴ 

[1] Beijing Key Laboratory of Acupuncture Neuromodulation, Acupuncture and Moxibustion Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China;Department of Neurology, Beth Israel Deaconess Medical Centre and Harvard Medical School, Boston, MA, United States;Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China;Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing, China;

关键词: migraine;    acupuncture;    prediction;    gray matter;    machine learning;   

DOI  :  10.3389/fneur.2020.00111

来源: DOAJ

【 摘 要 】

Background: The present study aimed to investigate the use of imaging biomarkers to predict the outcome of acupuncture in patients with migraine without aura (MwoA).Methods: Forty-one patients with MwoA received 4 weeks of acupuncture treatment and two brain imaging sessions at the Beijing Traditional Chinese Medicine Hospital affiliated with Capital Medical University. Patients kept a headache diary for 4 weeks before treatment and during acupuncture treatment. Responders were defined as those with at least a 50% reduction in the number of migraine days. The machine learning method was used to distinguish responders from non-responders based on pre-treatment brain gray matter (GM) volume. Longitudinal changes in GM predictive regions were also analyzed.Results: After 4 weeks of acupuncture, 19 patients were classified as responders. Based on 10-fold cross-validation for the selection of GM features, the linear support vector machine produced a classification model with 73% sensitivity, 85% specificity, and 83% accuracy. The area under the receiver operating characteristic curve was 0.7871. This classification model included 10 GM areas that were mainly distributed in the frontal, temporal, parietal, precuneus, and cuneus gyri. The reduction in the number of migraine days was correlated with baseline GM volume in the cuneus, parietal, and frontal gyri in all patients. Moreover, the left cuneus showed a longitudinal increase in GM volume in responders.Conclusion: The results suggest that pre-treatment brain structure could be a novel predictor of the outcome of acupuncture in the treatment of MwoA. Imaging features could be a useful tool for the prediction of acupuncture efficacy, which would enable the development of a personalized medicine strategy.

【 授权许可】

Unknown