Ho, Chao-ChungChao-ChungHoPeng, Syu-JyunSyu-JyunPengYu, Yu-HsiangYu-HsiangYuChu, Yeong-RueyYeong-RueyChuHuang, Shiau-ShianShiau-ShianHuangPO-HSIU KUO2025-03-032025-03-032024-12-15https://scholars.lib.ntu.edu.tw/handle/123456789/725419Background: The symptom variability in major depressive disorder (MDD) complicates treatment assessment, necessitating a thorough understanding of MDD symptoms and potential biomarkers. Methods: In this prospective study, we enrolled 54 MDD patients and 39 controls. Over the course of weeks 1, 2, and 4 participants underwent evaluations, with electroencephalograms (EEG) recorded at baseline and week 1. Our investigation considered five previously identified syndromal factors derived from the 17-item Hamilton Depression Rating Scale (17-item HAMD) for assessing depression: core, insomnia, somatic anxiety, psychomotor-insight, and anorexia. We assessed treatment response and EEG characteristics across all syndromal factors and total scores, all of which are based on the 17-item HAMD. To analyze the topology of brain networks, we employed functional connectivity (FC) and a graph theory-based method across various frequency bands. Results: The healthy control group had notably higher values in delta band EEG FC compared to the MDD patient group. Similar distinctions were observed between the responder and non-responder patient groups. Further exploration of baseline FC values across distinct syndromal factors revealed significant variations among the core, psychomotor-insight, and anorexia subgroups when using a specific graph theory-based approach, focusing on global efficiency and average clustering coefficient. Limitations: Different antidepressants were included in this study. Therefore, the results should be interpreted with caution. Conclusions: Our findings suggest that delta band EEG FC holds promise as a valuable predictor of antidepressant efficacy. It demonstrates an ability to adapt to individual variations in depressive symptomatology, offering insights into personalized treatment for patients with depression.enDepressionEarly predictionElectroencephalographyFunctional connectivitySymptom clusters[SDGs]SDG3journal article10.1016/j.jad.2024.08.13939187193