Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea

Abstract

Primary dysmenorrhea (PDM) is a common complaint in women throughout the menstrual years. Acupuncture has been shown to be effective in dysmenorrhea; however, there are large interindividual differences in patients' responses to acupuncture treatment. Fifty-four patients with PDM were recruited and randomized into real or sham acupuncture treatment groups (over the course of three menstrual cycles). Pain-related functional connectivity (FC) matrices were constructed at baseline and post-treatment period. The different neural mechanisms altered by real and sham acupuncture were detected with multivariate analysis of variance. Multivariate pattern analysis (MVPA) based on a machine learning approach was used to explore whether the different FC patterns predicted the acupuncture treatment response in the PDM patients. The results showed that real but not sham acupuncture significantly relieved pain severity in PDM patients. Real and sham acupuncture displayed differences in FC alterations between the descending pain modulatory system (DPMS) and sensorimotor network (SMN), the salience network (SN) and SMN, and the SN and default mode network (DMN). Furthermore, MVPA found that these FC patterns at baseline could predict the acupuncture treatment response in PDM patients. The present study verified differentially altered brain mechanisms underlying real and sham acupuncture in PDM patients and supported the use of neuroimaging biomarkers for individual-based precise acupuncture treatment in patients with PDM.

Keywords: acupuncture; functional connectivity; machine learning; multivariate pattern analyses; primary dysmenorrhea.

FIGURE 1

Procedures and data used for the study.

FIGURE 2

The brain networks selected in the present study.

FIGURE 3

FC matrix pattern in each group. (A,D) The FC matrix pattern at baseline. (B,E) The FC matrix pattern after treatment. The color bar indicates the correlation coefficient between two regions. (C,F) FC alterations after acupuncture treatment; the color bar indicates the T value. (A,B,C) Real acupuncture group. (D,E,F) Sham acupuncture group. Abbreviations: FC, functional connectivity; L, left side; R, right side; S1, primary somatosensory cortex; Tha, thalamus; mPFC, medial prefrontal cortex; PCC, posterior cingulate cortex; IPC, inferior parietal cortex; aINS, anterior insula; dlPFC, dorsolateral prefrontal cortex; RVM, rostroventral medulla; PAG, periaqueductal gray.

FIGURE 4

Changes in the different brain connections following real and sham acupuncture for PDM. The line charts display the different alterations between real and sham acupuncture for PDM in these FCs after treatment. Abbreviations: FC, functional connectivity; L, left side; R, right side; S1, primary somatosensory cortex; aINS, anterior insula; dlPFC, dorsolateral prefrontal cortex; RVM, rostroventral medulla; Tha, thalamus; PAG, periaqueductal gray; IPC, inferior parietal cortex.

FIGURE 5

MVPA-based clinical prediction results. (A) Baseline FC patterns predict VAS change scores after treatment. (B) Baseline FC patterns predict VAS change rates after treatment. Abbreviations: MVPA, multivariate pattern analysis; MAE, mean absolute error; VAS, visual analog scale.