Population screening for acupuncture treatment of neck pain: a machine learning study.
10.13703/j.0255-2930.20240731-k0006
- Author:
Zhen GAO
1
;
Mengjie CUI
2
;
Haijun WANG
2
;
Cheng XU
3
;
Nixuan GU
2
;
Laixi JI
2
Author Information
1. Experimental Management Center, Shanxi University of CM, Taiyuan 030024, China.
2. Second Clinical College, Shanxi University of CM, Taiyuan
3. Department of Radiology, Shanxi Provincial People's Hospital.
- Publication Type:Journal Article
- Keywords:
acupuncture;
functional magnetic resonance imaging;
machine learning;
neck pain;
population for acupuncture
- MeSH:
Humans;
Neck Pain/physiopathology*;
Acupuncture Therapy;
Female;
Male;
Adult;
Middle Aged;
Machine Learning;
Magnetic Resonance Imaging;
Young Adult;
Brain/physiopathology*;
Acupuncture Points;
Aged
- From:
Chinese Acupuncture & Moxibustion
2025;45(4):405-412
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To screen the population for acupuncture treatment of neck pain, using functional magnetic resonance imaging (fMRI) technology and based on machine learning algorithms.
METHODS:Eighty patients with neck pain were recruited. Using FPX25 handheld pressure algometer, the tender points were detected in the areas with high-frequent onset of neck pain and high degree of acupoint sensitization. Acupuncture was delivered at 4 tender points with the lowest pain threshold, once every two days; and the treatment was given 3 times a week and for 2 consecutive weeks. The amplitude of low-frequency fluctuation (ALFF) of the brain before treatment was taken as a predictive feature to construct support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN) models to predict the responses of neck pain patients to acupuncture treatment. A longitudinal analysis of the ALFF features was performed before and after treatment to reveal the potential biological markers of the reactivity to the acupuncture therapy.
RESULTS:The SVM model could successfully distinguish high responders (48 cases) and low responders (32 cases) to acupuncture treatment, and its accuracy rate reached 82.5%. Based on the SVM model, the ALFF values of 4 brain regions were identified as the consistent predictive features, including the right middle temporal gyrus, the right superior occipital gyrus, and the bilateral posterior cingulate gyrus. In the patients with high acupuncture response, the ALFF value in the left posterior cingulate gyrus decreased after treatment (P<0.05), whereas in the patients with low acupuncture response, the ALFF value in the right superior occipital gyrus increased after treatment (P<0.01). The longitudinal functional connectivity (FC) analysis found that compared with those before treatment, the high responders showed the enhanced FC after treatment between the left posterior cingulate gyrus and various regions, including the bilateral Crus1 of the cerebellum, the right insula, the bilateral angular gyrus, the left medial superior frontal gyrus, and the left middle cingulate gyrus (GRF: corrected, voxel level: P<0.05, mass level: P<0.05). In contrast, the low responders exhibited the enhanced FC between the left posterior cingulate gyrus and the left Crus2 of the cerebellum, the left middle temporal gyrus, the right posterior cingulate gyrus, and the left angular gyrus; besides, FC was reduced in low responders between the left posterior cingulate gyrus and the right supramarginal gyrus (GRF: corrected, voxel level: P<0.05, mass level: P<0.05).
CONCLUSION:This study validates the practicality of pre-treatment ALFF feature prediction for acupuncture efficacy on neck pain. The therapeutic effect of acupuncture on neck pain is potentially associated with its impact on the default mode network, and then, alter the pain perception and emotional regulation.
