Brain Mechanism of Acupuncture Treatment of Chronic Pain: An Individual-Level Positron Emission Tomography Study

Jin Xu; Hongjun Xie; Liying Liu; Zhifu Shen; Lu Yang; Wei Wei; Xiaoli Guo; Fanrong Liang; Siyi Yu; Jie Yang

doi:10.3389/fneur.2022.884770

Brain Mechanism of Acupuncture Treatment of Chronic Pain: An Individual-Level Positron Emission Tomography Study

Front Neurol. 2022 May 2:13:884770. doi: 10.3389/fneur.2022.884770. eCollection 2022.

Authors

Jin Xu¹, Hongjun Xie², Liying Liu¹, Zhifu Shen³, Lu Yang¹, Wei Wei¹, Xiaoli Guo¹, Fanrong Liang¹, Siyi Yu¹, Jie Yang¹

Affiliations

¹ Department of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
² Department of Nuclear Medicine, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
³ Department of Traditional Chinese and Western Medicine, North Sichuan Medical College, Nanchong, China.

Abstract

Objective: Acupuncture has been shown to be effective in the treatment of chronic pain. However, their neural mechanism underlying the effective acupuncture response to chronic pain is still unclear. We investigated whether metabolic patterns in the pain matrix network might predict acupuncture therapy responses in patients with primary dysmenorrhea (PDM) using a machine-learning-based multivariate pattern analysis (MVPA) on positron emission tomography data (PET).

Methods: Forty-two patients with PDM were selected and randomized into two groups: real acupuncture and sham acupuncture (three menstrual cycles). Brain metabolic data from the three special brain networks (the sensorimotor network (SMN), default mode network (DMN), and salience network (SN)) were extracted at the individual level by using PETSurfer in fluorine-18 fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) data. MVPA analysis based on metabolic network features was employed to predict the pain relief after treatment in the pooled group and real acupuncture treatment, separately.

Results: Paired t-tests revealed significant alterations in pain intensity after real but not sham acupuncture treatment. Traditional mass-univariate correlations between brain metabolic and alterations in pain intensity were not significant. The MVPA results showed that the brain metabolic pattern in the DMN and SMN did predict the pain relief in the pooled group of patients with PDM (R ² = 0.25, p = 0.005). In addition, the metabolic pattern in the DMN could predict the pain relief after treatment in the real acupuncture treatment group (R ² = 0.40, p = 0.01).

Conclusion: This study indicates that the individual-level metabolic patterns in DMN is associated with real acupuncture treatment response in chronic pain. The present findings advanced the knowledge of the brain mechanism of the acupuncture treatment in chronic pain.

Keywords: acupuncture; biomarker; machine learning; metabolic; primary dysmenorrhea.