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

Siyi Yu,Fang Zeng,Qi Zhang,Jie Yang,Mingguo Xie,Jin Tian,Xiaoli Guo,Fanrong Liang,Wei Wei,Shuqin Liu

doi:10.3389/fnins.2020.559191

Siyi Yu, Fang Zeng + Show 8 more

Open Access

https://doi.org/10.3389/fnins.2020.559191

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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.

Highlights

Primary dysmenorrhea (PDM), cyclic menstrual pain in the absence of pelvic anomalies, is a common, and often debilitating, gynecological condition that affects between 45 and 95% of menstruating women (Coco, 1999)
For the sham acupuncture treatment group, increased functional connectivity (FC) were found in the right S1-right anterior insula (aINS), left inferior parietal cortices (IPC)-bilateral aINS, right dorsolateral prefrontal cortices (dlPFC)-left S1, right dlPFC-posterior cingulate cortex (PCC) and right dlPFC-right IPC, while FC between the periaqueductal gray (PAG)-rostroventral medulla (RVM) was decreased after sham treatment (Figures 3D– F)
As we found significant group differences in FC alterations between the real and sham groups, we used the baseline FCs as predictors of the treatment response and controlled for the effect of age, duration and treatment method

Summary

Introduction

Primary dysmenorrhea (PDM), cyclic menstrual pain in the absence of pelvic anomalies, is a common, and often debilitating, gynecological condition that affects between 45 and 95% of menstruating women (Coco, 1999). Previous research has found that baseline clinical and demographic factors influence treatment response, but these characteristics have not achieved the accuracy required for prediction (Underwood et al, 2007; Azevedo et al, 2019; Witt et al, 2019). Some studies have focused on quantitative sensory testing (QST) in the prediction of analgesic effects, but with contradictory results (Grosen et al, 2013). In light of these studies showing limited individual predictive value for clinical measures, brain-based biomarkers have recently shown promise at predicting response to treatment (Chen et al, 2018; Reggente et al, 2018)

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