Abstract
Highlights EEG-based multi-scale deep learning predicts individual iTBS response in major depressive disorder. • Two neurophysiological subtypes are identified with distinct baseline symptoms and clinical outcomes. • DCM-PEB analysis reveals subtype-specific frontal–temporal–parietal–motor connectivity patterns. • Connectivity patterns correlate with treatment efficacy, suggesting candidate mechanistic biomarkers. Abstract Background Response to transcranial magnetic stimulation (TMS) in major depressive disorder (MDD) is highly variable, underscoring the need for biomarkers that both predict treatment efficacy and elucidate underlying neural mechanisms. Methods We integrated deep learning and computational modeling to identify subtype-specific responses to intermittent theta-burst stimulation (iTBS) in MDD. Resting-state EEG and event-related potentials were collected from 198 patients across two independent cohorts (training: N = 125; validation: N = 73). A total of 55,476 EEG epochs were analyzed using a multi-scale convolutional recurrent neural network (MCRNN). To probe circuit-level mechanisms, Dynamic Causal Modeling with Parametric Empirical Bayes (DCM-PEB) was applied to assess subtype-specific effective connectivity. Results The MCRNN achieved robust predictive performance (accuracy = 0.91; 95% CI: 0.85–0.97 in the training cohort; 0.86; 95% CI: 0.76–0.96in the validation cohort), reliably stratifying patients into two neurophysiological subtypes. These subtypes differed in baseline symptom severity and clinical response trajectories. DCM-PEB revealed distinct effective connectivity signatures within frontal–temporal–parietal–motor circuits, with posterior probability exceeding 0.99, linking subtype-specific neural dynamics to treatment outcomes. Conclusion EEG-based deep learning, combined with biophysically informed connectivity modeling, enables reliable prediction of iTBS outcomes in MDD. Subtype-specific disruptions in frontal–temporal coupling emerge as candidate biomarkers, offering mechanistic insight into neuromodulation response and a framework for personalized TMS interventions. Keywords Depression deep learning EEG intermittent theta-burst stimulation DCM PEB
Authors
Zheng, L., Fu, J., Sun, Z., Han, Y., Shao, T., Wang, Q. & Lin, Z.
10.1016/j.neuroimage.2026.121697