The patient–acupuncturist interaction was a critical influencing factor for acupuncture effects but its mechanism remains unclear. This study aimed to examine the inter‐brain mechanism of patient–acupuncturist dyad during acupuncture stimulation in a naturalistic clinical setting. Seventy healthy subjects (simulated “patients”) were randomly assigned to two groups and received verum acupuncture group or sham acupuncture by one acupuncturist. Functional near‐infrared spectroscopy hyperscanning was used to simultaneously record the neural responses of “patient”–acupuncturist dyad during acupuncture stimulation in each group. The results showed that inter‐brain neural synchronization (INS) in the prefrontal cortex (PFC) of “patient”–acupuncturist dyad was significantly increased during verum but not sham acupuncture stimuli, and positively correlated with the needling sensations of “patients.” Granger causality analysis demonstrated that there were no significant differences in INS direction between the “patient” and the acupuncturist. This study identified the increase of INS between “patient” and acupuncturist, and suggested that PFC was important to the interaction of “patient”–acupuncturist dyad.

Objectives. The aim of the study was to predict the effect of acupuncture for treating functional dyspepsia (FD) using the support vector machine (SVM) techniques based on initial deqi sensations of patients. Methods. This retrospective study involved 90 FD patients who had received four weeks of acupuncture treatment. The support vector classification model was used to distinguish higher responders (patients with Symptom Index of Dyspepsia improvement score ≥ 2) from lower responders (patients with Symptom Index of Dyspepsia improvement score < 2). A support vector regression model was used to predict the change in the Symptom Index of Dyspepsia at the end of acupuncture treatment. Deqi sensations of patients in the first acupuncture treatment of a 20-session acupuncture intervention were defined as features and used to train models. Models were validated by 10-fold cross-validation and evaluated by accuracy, specificity, sensitivity, the area under the receive-operating curve, the coefficient of determination (R2), and the mean squared error. Results. The two models could predict the efficacy of acupuncture successfully. These models had an accuracy of 0.84 in predicting acupuncture response, and an R2 of 0.16 in the prediction of symptom improvements, respectively. The presence or absence of deqi sensation, the duration of deqi sensation, distention, and pain were finally selected as significant predicting features. Conclusion. Based on the SVM algorithms and deqi sensation, the current study successfully predicted the acupuncture response as well as clinical symptom improvement in FD patients at the end of treatment. Our prediction models are expected to promote the clinical efficacy of acupuncture treatment for FD, reduce medical expenditures, and optimize the allocation of medical resources.