TITLE:
Deep Learning-Based Neuroimaging Biomarkers for Antidepressant Decision-Making in Bipolar Depression: A Multimodal fMRI and EEG Study
AUTHORS:
Rocco de Filippis, Abdullah Al Foysal
KEYWORDS:
Bipolar Disorder, Antidepressants, Treatment-Emergent Switching, Deep Learning, 3D CNN, LSTM, fMRI, EEG, Neuroimaging Biomarkers, Precision Psychiatry
JOURNAL NAME:
Open Access Library Journal,
Vol.13 No.5,
May
29,
2026
ABSTRACT: The use of antidepressants in bipolar depression remains one of the most controversial decisions in psychiatric practice, with significant risks of treatment-emergent affective switching. Current clinical guidelines rely primarily on symptom history and clinical intuition, lacking objective biomarkers to predict individual treatment response. We introduce a deep learning framework that encodes multimodal neuroimaging data functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to predict antidepressant treatment outcomes in bipolar depression. Our approach employs a 3D Convolutional Neural Network (CNN) for volumetric fMRI analysis to identify neural signatures of treatment responders versus switchers, alongside 1D CNN and Long Short-Term Memory (LSTM) architectures for EEG-based classification of bipolar versus unipolar depression. To enable controlled benchmarking, we generate physiologically plausible synthetic neuroimaging datasets with affect-specific parameterization reflecting established neurobiological findings. The 3D fMRI CNN achieves perfect discrimination between responders and switchers (accuracy = 1.000, AUC = 1.000, F1 = 1.000), while EEG models demonstrate robust classification of bipolar versus unipolar depression (accuracy > 0.980 for both CNN and LSTM). Beyond prediction, we provide interpretable pathway analyses through attention visualization and regional activation mapping, facilitating inspection of candidate neural circuits underlying treatment response. Finally, we outline key barriers to clinical translation including synthetic-only validation, the need for real-world multi-site validation, and potential generalization challenges, proposing methodological steps required for robust deployment in clinical decision support systems.