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

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.

Figure 1

Study design and research flow chart.

Figure 2

Regions of interest. Sensorimotor network (SMN): bilateral post-central (S1), and bilateral insula. Salience network (SN): the dorsolateral prefrontal cortex (dlPFC), and bilateral dorsal anterior cingulate cortex (dACC). Default mode network (DMN): bilateral inferior parietal cortex (IPC), precuneus, isthmus cingulate cortex (ICC), and posterior cingulate cortex (PCC).

Figure 3

Flowchart of the MVPA procedure. (A) Obtaining quantitative information from preprocessed PDG-PET scans. (B) Extracting metabolism data across all voxels in all ROIs. (C) Constructing feature matrixes of the SUVR. (D) Building the SVR model with LOOCV to predict each participant's response to acupuncture. FDG-PET, fluorodeoxyglucose positron emission tomography; LOOCV, leave-one-out cross-validation; ROIs, regions of interest; sMRI, structural magnetic resonance imaging; SUVR, standardized uptake value ratio; SVR, support vector regression.

Figure 4

Predicting treatment effects using baseline SUVR patterns in special networks. (A) SUVR pattern in the SMN and DMN as a predictor for the pooled group. (B) SUVR pattern in the DMN as a predictor for the real acupuncture treatment group. DMN, default mode network; SMN, sensorimotor network; SN, salience network; SUVR, standardized uptake value ratio.