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.

Early Fractional Amplitude of Low Frequency Fluctuation Can Predict the Efficacy of Transcutaneous Auricular Vagus Nerve Stimulation Treatment for Migraine Without Aura

“…Under the limited translational applicability of standard mass-univariate analytical methods that are typically used in neuroimaging, a great hope is given to a data-driven multivariate machine learning technique—multivariate pattern analysis (MVPA), which is sensitive to the fine-grained spatial discriminative patterns and exploration of inherent multivariate nature from high-dimensional neuroimaging data. Previous studies have widely applied MVPA in the classification or prediction of individual treatment response ( Redlich et al, 2016 ; Cash et al, 2019 ; Tu et al, 2019 ; Messina and Filippi, 2020 ; Yin et al, 2020 ; Yu et al, 2020 ). For example, one recent study applied MVPA to identify the useful biomarkers of the FC between the medial prefrontal cortex (mPFC) and specific subcortical regions, which could significantly predict the changes in symptoms in patients with chronic low back pain receiving 4-week acupuncture treatment ( Tu et al, 2019 ).…”

The altered intrinsic functional connectivity after acupuncture at shenmen (HT7) in acute sleep deprivation

“…In short, we speculated that the abnormal FC within and between networks after acute SD might explain SD-induced impairments in cognitions and emotional discrimination, and be interpreted as a possible compensatory adaptation of the human brain that could enable partial recovery of certain behaviors. Acupuncture, as the key component of Traditional Chinese Medicine, has been reported to modulate FC across the brain networks (8,39,40). Previous neuroimaging studies have also illustrated remarkable changes in brain activities responsive to acupuncture at the "Shenmen" point intervening on SD (10,41).…”

Artificial intelligence-directed acupuncture: a review

“…Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. The imaging method provides almost all the data for the development of the efficacy prediction model of acupuncture therapy, which primarily includes amplitude of low frequency fluctuation (ALFF), functional connectivity (FC), and regional homogeneity (ReHo) [ 55 , 56 ]. SVM the most widely used prediction algorithm for acupuncture efficacy.…”

“…The results of MVPA show that the FC patterns of descending pain modulatory system (DPMS), SMN, SN and SMN, and the SN and DMN are features for the construction of the SVM model. This model can obtain a squared correlation of 0.27 ( P = 0.002) and an MAE of 0.36 for the prediction in the visual analogue scale (VAS) change scores after treatment, while can obtain a squared correlation of 0.30 ( P = 0.0009) and an MAE of 2.26 for the prediction in VAS change rate [ 55 ].…”