Predicting acupuncture efficacy for neck pain based on functional connectivity features: a machine learning study.

Objective: To explore the mechanisms of acupuncture-induced neck pain relief and identify appropriate candidates using neuroimaging and machine learning techniques.

Methods: Eighty neck pain patients were included, with clinical data and functional magnetic resonance imaging scans collected pre- and post-treatment. A support vector machine (SVM) model was built using pre-treatment brain functional connectivity to predict acupuncture responsiveness, identifying key features as potential biomarkers for effectiveness. Longitudinal analysis of these features was conducted in responders and non-responders.

Results: This study enrolled 80 neck pain patients (48 acupuncture responders and 32 non-responders) for SVM model construction and longitudinal analysis of predictive features pre-/post-treatment. The SVM model achieved an accuracy of 0.85 in distinguishing the two groups. A total of 117 functional connectivity edges were identified as predictive features, potential biomarkers for acupuncture responses. Longitudinal analysis showed 6 predictive features altered post-treatment in responders versus 44 in non-responders. After FDR correction, only 3 functional connectivity features in responders negatively correlated with pain VAS scores ( < 0.05). These findings indicate more targeted changes in predictive features among responders compared to non-responders.

Conclusion: Using pre-treatment neuroimaging features to predict acupuncture effectiveness for neck pain shows promise. This approach could aid in developing personalized acupuncture strategies by identifying likely beneficiaries, guiding alternative interventions for non-responders.

Trial Registration: International Traditional Medicine Clinical Trial Registry (registration number: ITMCTR2023000001, protocol version number: V1.0).