FENG Jie, ZHANG Han, XIA Yie, WANG Ziwei, YANG Junkang, WU Siyan, YANG
Yuan
2026, 21(1): 13-19.
Major Depressive Disorder (MDD) is a globally prevalent mental health condition with high
disability rates. Its diagnosis currently relies primarily on clinical interviews and scale assessments, and there
remains a lack of reliable objective biomarkers. Event-related potential (ERP), as a non-invasive, real-time brain
function detection technology with millisecond-level temporal resolution, can directly reflect neural activities
during cognitive and emotional tasks, providing a new perspective for the diagnosis and treatment of MDD. This
review systematically summarizes key findings from ERP studies on cognitive and emotional processing
abnormalities in MDD, indicating that multiple ERP components exhibit characteristic alterations in MDD
patients, reflecting dysfunctions in cognitive control, emotion regulation, and reward processing across multiple
stages. Although these indicators commonly demonstrate transdiagnostic heterogeneity and state-dependent
variability, their dynamic patterns and multi-component integrated analyses show potential for assisting in
diagnosis, predicting treatment efficacy, and monitoring interventions including pharmacotherapy,
neuromodulation, and psychological therapy. Current research still faces challenges such as insufficient paradigm
standardization, clinical heterogeneity, and reproducibility issues. Future efforts should focus on promoting
multi-center collaboration, integrating multimodal data, and employing machine learning modeling to advance
the translation of ERPs from experimental markers to clinically practical tools, ultimately contributing to
personalized diagnosis and treatment of MDD.
