Predicting the longitudinal efficacy of medication for depression using electroencephalography and machine learning

These findings highlight the potential of using non-invasive EEG recordings and data-driven methods to optimize clinical decision-making. Functional connectivity metrics could be used by clinicians to identify responders early in the treatment process, thereby reducing the burden of lengthy trial-and-error methods. Future research should focus on larger datasets, alternative feature extraction techniques, and the integration of multimodal biomarkers. These advancements could pave the way for personalized treatment strategies to enhance patient outcomes and mitigate the socio-economic impact of MDD. The institutional review board of Taipei Veterans General Hospital (2021-07-01B) approved this study.