ArticleFrontiers in neuroscience2020
Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating machine learning into acupuncture research: a scoping review.Frontiers in neurology · 2025Pooled it
- Artificial intelligence guided acupuncture decision making and treatment: a review of research.Frontiers in medicine · 2026Review
- Decoding the mechanisms of acupuncture by neuroimaging: an integrated review from networks to molecules.Frontiers in human neuroscience · 2026Review
- A Clinical Study on the Efficacy of Acupuncture Treatment in Essential Hypertension: Protocol for Randomized Controlled Trial.JMIR research protocols · 2025Article
- The neurobiology of plant-based therapeutics in women's reproductive health: mechanisms, efficacy, and clinical translation.Frontiers in nutrition · 2025Review
- Gut microbiota and metabolic dysregulation in polycystic ovary syndrome: effects of acupuncture as an adjunct toFrontiers in microbiology · 2025Article
- Acupuncture-Neuroimaging Research Trends over Past Two Decades: A Bibliometric Analysis.Chinese journal of integrative medicine · 2023Review
- Insight into Dysmenorrhea Research from 1992 to 2022: A Bibliometric Analysis.Journal of pain research · 2023Review
- Bibliometric Analysis of Global Research Landscape and Hotspots on Dysmenorrhea: Where are We Now?Journal of pain research · 2023Article
- Revealing the Central Mechanism of Acupuncture for Primary Dysmenorrhea Based on Neuroimaging: A Narrative Review.Pain research & management · 2023Review
- Article
- Artificial intelligence-directed acupuncture: a review.Chinese medicine · 2022Review
- The altered intrinsic functional connectivity after acupuncture at shenmen (HT7) in acute sleep deprivation.Frontiers in neurology · 2022Article
- The Effect of Long-Term Menstrual Pain on Large-Scale Brain Network in Primary Dysmenorrhea Patients.Journal of pain research · 2022Article
- Orbitofrontal Cortex Functional Connectivity-Based Classification for Chronic Insomnia Disorder Patients With Depression Symptoms.Frontiers in psychiatry · 2022Article
- Early Fractional Amplitude of Low Frequency Fluctuation Can Predict the Efficacy of Transcutaneous Auricular Vagus Nerve Stimulation Treatment for Migraine Without Aura.Frontiers in molecular neuroscience · 2022Article
- Article
- The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review.Frontiers in pharmacology · 2022Article
- Analgesia Effect of Verum and Sham Acupuncture Treatments in Primary Dysmenorrhea: A MRI Pilot Study.Journal of personalized medicine · 2021Article
- Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.