ArticleHuman brain mapping2023
Clinical-functional brain connectivity signature predicts longitudinal symptom improvement after acupuncture treatment in patients with functional dyspepsia.
Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating machine learning into acupuncture research: a scoping review.Frontiers in neurology · 2025Pooled it
- Cerebral responses to acupuncture for uremic pruritus: study protocol for a randomized controlled trial based on fNIRS.Frontiers in human neuroscience · 2026Article
- Predicting acupuncture efficacy for neck pain based on functional connectivity features: a machine learning study.Annals of medicine · 2025Article
- Acupuncture's Multisystem Neuroimmunomodulation: Central-Peripheral Interactions in Gastroenteric, Psychiatric, and Chronic Pain Disorders.CNS neuroscience & therapeutics · 2025Review
- Effects of acupuncture versus moxibustion on functional dyspepsia: a randomized clinical trial.Chinese medicine · 2025Article
- Altered gray matter morphometry in psychogenic erectile dysfunction patients: A Surface-based morphometry study.Scientific reports · 2025Article
- The efficacy and safety of auricular acupoint therapy on treating functional dyspepsia with insomnia: study protocol for a randomized controlled trial.Frontiers in medicine · 2025Article
- Clinical-functional brain connectivity signature predicts longitudinal symptom improvement after acupuncture treatment in patients with functional dyspepsia.Human brain mapping · 2023Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whilst acupuncture has been shown to be an effective treatment for functional dyspepsia (FD), its efficacy varies significantly among patients. Knowing beforehand how each patient responds to acupuncture treatment will facilitate the ability to produce personalized prescriptions, therefore, improving acupuncture efficacy. The objective of this study was to construct the prediction model, based on the clinical-neuroimaging signature, to forecast the individual symptom improvement of FD patients following a 4-week acupuncture treatment and to identify the critical predictive features that could potentially serve as biomarkers for predicting the efficacy of acupuncture for FD. Clinical-functional brain connectivity signatures were extracted from samples in the training-test set (100 FD patients) and independent validation set (60 FD patients). Based on these signatures and support vector machine algorithms, prediction models were developed in the training test set, followed by model performance evaluation and predictive features extraction. Subsequently, the external robustness of the extracted predictive features in predicting acupuncture efficacy was evaluated by the independent validation set. The developed prediction models possessed an accuracy of 88% in predicting acupuncture responders, as well as an R
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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.