title: Neural specificity of acupuncture stimulation from support vector machine classification analysis.
abstract: Acupoint specificity, as a crucial issue in acupuncture neuroimaging studies, is still a controversial topic. Previous studies have generally adopted a block-based general linear model (GLM) approach, which predicts the temporal changes in the blood oxygenation level-dependent signal conforming to the "on-off" specifications. However, this method might become impractical since the precise timing and duration of acupuncture actions cannot be specified a priori. In the current study, we applied a data-driven multivariate classification approach, namely, support vector machine (SVM), to explore the neural specificity of acupuncture at gall bladder 40 (GB40) using kidney 3 (KI3) as a control condition (belonging to different meridians but the same nerve segment). In addition, to verify whether the typical GLM approach is sensitive enough in exploring the neural response patterns evoked by acupuncture, we also employed the GLM method to the same data sets. The SVM analysis detected distinct neural response patterns between GB40 and KI3--positive predominantly for the GB40, while negative following the KI3. By contrast, group analysis from the GLM showed that acupuncture at these different acupoints can both evoke similar widespread signal decreases in multiple brain regions, and most of these regions were spatially overlapped, mainly distributing in the limbic and subcortical structures. Our findings may provide additional evidence to support the specificity of acupuncture, relevant to its clinical efficacy. Moreover, we also proved that GLM analysis is prone to be susceptible to errors and is not appropriate for detecting neural response patterns evoked by acupuncture stimulation.
authors.affiliation: Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, China.
authors.forename: Ting
authors.initials: T
authors.lastname: Xue
authors.forename: Lijun
authors.initials: L
authors.lastname: Bai
authors.forename: Shangjie
authors.initials: S
authors.lastname: Chen
authors.forename: Chongguang
authors.initials: C
authors.lastname: Zhong
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Feng
authors.forename: Hu
authors.initials: H
authors.lastname: Wang
authors.forename: Zhenyu
authors.initials: Z
authors.lastname: Liu
authors.forename: Youbo
authors.initials: Y
authors.lastname: You
authors.forename: Fangyuan
authors.initials: F
authors.lastname: Cui
authors.forename: Yanshuang
authors.initials: Y
authors.lastname: Ren
authors.forename: Jie
authors.initials: J
authors.lastname: Tian
authors.forename: Yijun
authors.initials: Y
authors.lastname: Liu
mesh_terms: D026881:Acupuncture / Q000379:methods*; D015669:Acupuncture Points; D015670:Acupuncture Therapy / Q000379:methods; D000328:Adult; D001921:Brain / Q000473:pathology; D005260:Female; D006801:Humans; D007668:Kidney / Q000473:pathology; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015999:Multivariate Analysis; D009474:Neurons / Q000473:pathology*; D010100:Oxygen / Q000097:blood; D060388:Support Vector Machine; D013997:Time Factors
chemical_list: D010100:Oxygen