Mechanism-Driven Translation of Electroacupuncture for Depression: Bridging the Gap Between Preclinical and Clinical Research

Electroacupuncture (EA) has demonstrated multi‑level antidepressant effects, yet the strength of clinical evidence remains insufficient and its efficacy consistency is highly contested. The key bottlenecks include heterogeneous research designs, a lack of standardized stimulation parameters and acupoint selection, insufficient attention to disease biological subtyping, and a disconnect between mechanistic studies and clinical outcomes. To address these gaps, this review proposes a mechanism‑driven precision translation pathway centered on patient stratification. By integrating neural circuitry, immune‑inflammatory pathways, biomarkers, and multimodal assessment tools, we argue that the primary translational bottleneck is not a lack of mechanisms but the failure to systematically integrate them into a clinical decision‑oriented paradigm. Enhancing interpretability and clinical scalability through parameter optimization, targeted neural network modulation, and objective indicator integration may transform EA from a complementary therapy into a precision intervention with clear mechanisms and reliable evidence.