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Related Experiment Video

Updated: Sep 1, 2025

Monitoring Acupuncture Effects on Human Brain by fMRI

Monitoring Acupuncture Effects on Human Brain by fMRI

Published on: April 8, 2010

Predicting acupuncture efficacy for functional dyspepsia based on functional brain network features: a machine

Tao Yin¹, Zhaoxuan He^1,2, Yuan Chen³

¹Acupuncture and Tuina School, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China.

Cerebral Cortex (New York, N.Y. : 1991)

|August 14, 2022

Summary

Predicting acupuncture effectiveness for functional dyspepsia (FD) is now possible using brain imaging biomarkers. This approach helps identify patients likely to benefit from acupuncture, personalizing treatment for better outcomes.

Keywords:

acupuncture functional dyspepsia machine learning magnetic resonance imaging resting-state functional connectivity

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Neuroscience
Gastroenterology
Medical Imaging

Background:

Acupuncture shows efficacy in treating functional dyspepsia (FD), but patient response varies.
Objective biomarkers are needed to predict individual responses to acupuncture for FD.

Purpose of the Study:

To develop a predictive model for acupuncture responsiveness in FD patients using pre-treatment functional brain network data.
To identify key brain network features that predict treatment outcomes in FD patients undergoing acupuncture.

Main Methods:

Support vector machine models were constructed using functional brain MRI data from 100 FD patients.
Pre-treatment functional brain networks were analyzed as predictive features.
Longitudinal analysis compared brain network changes between responders and non-responders.

Main Results:

The prediction model achieved 76% accuracy in distinguishing acupuncture responders from non-responders.
The model explained 24% of the variance in dyspeptic symptom relief.
Thirty-eight critical predictive features involving the orbitofrontal cortex, caudate, hippocampus, and anterior insula were identified.

Conclusions:

Functional brain network analysis offers a promising method for predicting acupuncture efficacy in FD patients.
Identifying predictive biomarkers can guide personalized acupuncture treatment strategies for FD.
Brain network alterations are more significant in acupuncture responders, highlighting their role in treatment success.