Generative Deep Learning Reconstructs Subcortical Neural Signals from Cortical Recordings for Closed-Loop Brain Stimulation

Closed-loop deep brain stimulation (DBS) relies on continuous neural biomarker sensing, yet clinical utility is often limited by signal dropout, stimulation artifacts, and hardware constraints in subcortical recordings. Here, we develop a deep learning framework combining spectral processing with generative diffusion models to digitally reconstruct deep brain signals from cortical electrocorticography (ECoG), enabling continuous subcortical biomarker inference without direct deep brain sensing. We validate this approach across 723 h of simultaneous cortico-subcortical recordings from 49 patients with movement disorders (Parkinson’s disease, dystonia, Tourette syndrome) across three international centers. The framework decodes subcortical activity across multiple deep brain targets (subthalamic nucleus, globus pallidus internus, thalamus), behavioral states (rest, movement, sleep), and therapeutic conditions (medication and stimulation ON and OFF), with performance remaining above chance in every condition tested. Using generative diffusion models, we achieve raw signal reconstruction that preserves clinically relevant neural features, including beta burst dynamics that correlate with motor symptom severity (UPDRS-III R² = 0.70). We demonstrate clinical utility by showing that cortically-derived signals can rescue state detection during DBS recording failures and augment limited sensing configurations. This digital approach to deep brain inference could expand the applicability of adaptive neuromodulation therapies and enable closed-loop control for emerging non-invasive stimulation techniques.