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Monitoring Acupuncture Effects on Human Brain by fMRI
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Predicting acupuncture efficacy for neck pain based on functional connectivity features: a machine learning study.

Zhen Gao1, Mengjie Cui2, Cheng Xu3

  • 1Shanxi University of Traditional Chinese Medicine Experimental Management Center, Taiyuan, China.

Annals of Medicine
|August 23, 2025
PubMed
Summary
This summary is machine-generated.

Neuroimaging and machine learning predict acupuncture effectiveness for neck pain. This identifies personalized treatment strategies and potential biomarkers for acupuncture response.

Keywords:
Acupuncturemachine learningneck painresting-state functional connectivity

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Neck pain is a prevalent condition with various treatment options.
  • Acupuncture is a common therapy for neck pain, but predicting its effectiveness can be challenging.
  • Understanding the mechanisms of acupuncture-induced pain relief is crucial for optimizing treatment.

Purpose of the Study:

  • To explore the mechanisms of acupuncture-induced neck pain relief.
  • To identify potential biomarkers for predicting acupuncture responsiveness using neuroimaging.
  • To develop a machine learning model for patient stratification.

Main Methods:

  • Eighty neck pain patients underwent functional magnetic resonance imaging (fMRI) and clinical assessments.
  • A support vector machine (SVM) model was trained on pre-treatment brain functional connectivity data.
  • Longitudinal analysis compared functional connectivity changes between acupuncture responders and non-responders.

Main Results:

  • The SVM model accurately predicted acupuncture responsiveness with 85% accuracy.
  • 117 functional connectivity edges were identified as predictive biomarkers.
  • Responders showed more targeted alterations in predictive features post-treatment compared to non-responders.

Conclusions:

  • Pre-treatment neuroimaging features can predict acupuncture effectiveness for neck pain.
  • This approach facilitates personalized acupuncture strategies.
  • It aids in identifying suitable candidates for acupuncture and guiding alternative treatments for non-responders.