Utilizing Hierarchical Efficacy Regions of the Human Brain for Treatment Prediction Through Information Fusion

Brain network dysfunction is an important feature of the disease. Acupuncture’s therapeutic effects are achieved through dynamic interregional brain interactions and the fusion of multisource neural information. In this study, we enrolled 177 patients with Functional Dyspepsia (FD) who received 20 sessions of acupuncture treatment and underwent functional magnetic resonance imaging before and after treatment. Subsequently, we constructed the Hierarchical Efficacy Network (HEffNet) to predict the improvement value of the Nepean Dyspepsia Symptom Index by iteratively identifying the core trunk (efficacy region) from the Brain Tree to form a hierarchical information integration path. The model uses a Kalman filter and an improved Gated Graph Neural Network (GGNN) to capture the topological connectivity of brain regions. Experimental results showed that the features of hierarchical efficacy regions were significantly related to therapeutic efficacy. Patients who experienced significant symptom improvement formed a multi-level stable network across the frontal lobe, limbic lobe, insula, and subcortical nuclei, confirming that acupuncture works by optimizing the information fusion between brain regions. The HEffNet model outperformed baseline models in the prediction task. Ablation experiments verified the effectiveness of the GGNN and residual modules. The therapeutic efficacy of acupuncture for FD stems from the hierarchical information fusion of multiple brain regions. HEffNet quantifies this mechanism to achieve precise efficacy prediction and provides a new paradigm for studying acupuncture’s central mechanisms.