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

To explore the mechanisms of acupuncture-induced neck pain relief and identify appropriate candidates using neuroimaging and machine learning techniques. 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. 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 (<i>p</i> &lt; 0.05). These findings indicate more targeted changes in predictive features among responders compared to non-responders. 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. International Traditional Medicine Clinical Trial Registry (registration number: ITMCTR2023000001, protocol version number: V1.0)