Multi-scale EEG analysis identifies neural circuit signatures of iTBS responsiveness in major depressive disorder.

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.