Disentangling Shared and Private Neural Dynamics with SPIRE: A Latent Modeling Framework for Deep Brain Stimulation

Disentangling shared network-level dynamics from region-specific activity is a central challenge in modeling multi-region neural data. We introduce SPIRE (Shared-Private Inter-Regional Encoder), a deep multi-encoder autoencoder that factorizes recordings into shared and private latent subspaces with novel alignment and disentanglement losses. Trained solely on baseline data, SPIRE robustly recovers cross-regional structure and reveals how external perturbations reorganize it. On synthetic benchmarks with ground-truth latents, SPIRE outperforms classical probabilistic models under nonlinear distortions and temporal misalignments. Applied to intracranial deep brain stimulation (DBS) recordings, SPIRE shows that shared latents reliably encode stimulation-specific signatures that generalize across sites and frequencies. These results establish SPIRE as a practical, reproducible tool for analyzing multi-region neural dynamics under stimulation. Understanding how distributed brain regions coordinate--and how this coordination is reorganized by interventions such as deep brain stimulation (DBS)--remains a major challenge. Disorders like dystonia and Parkinson's involve dysfunction in basal ganglia-thalamo-cortical circuits (Galvan et al., 2015; Jinnah & Hess, 2006; Obeso et al., 2008; Zhuang et al., 2004), and while DBS of targets such as globus pallidus internus (GPi) and subthalamic nucleus (STN) is clinically effective (Ben-abid, 2003; Lozano et al., 2019; Larsh et al., 2021) its network-level mechanisms remain poorly understood. Latent variable models can capture such effects by reducing neural activity to low-dimensional subspaces, but existing methods have key limitations. Classical models such as Gaussian Process Factor Analysis (GPFA) (Y u et al., 2008) and Canonical Correlation Analysis (CCA) (Bach & Jordan, 2005) assume linearity. DLAG (Delayed Latents Across Groups) (Gokcen et al., 2022) disentangles shared vs. private dynamics but is restricted to linear-Gaussian structure and spiking data. Multimodal models (SharedAE (Yi et al.), MMV AE (Shi et al., 2019)) align shared spaces but are not designed for intracranial recordings under stimulation. Critically, none of these frameworks provide a nonlinear, disentangling model that can separate shared versus private dynamics in human local field potential (LFP) data under external perturbation. Addressing this gap is essential: understanding how stimulation reorganizes intrinsic cross-regional coordination could reveal circuit-level mechanisms of DBS that remain invisible to local analyses.