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

• URL: http://arxiv.org/abs/2510.25023v1
• Date: Tue, 28 Oct 2025 22:45:52 GMT
• Title: Disentangling Shared and Private Neural Dynamics with SPIRE: A Latent Modeling Framework for Deep Brain Stimulation
• Authors: Rahil Soroushmojdehi, Sina Javadzadeh, Mehrnaz Asadi, Terence D. Sanger,
• Abstract summary: SPIRE is a deep multi-encoder autoencoder that factorizes recordings into shared and private latent subspaces.<n>It robustly recovers cross-regional structure and reveals how externals reorganize it.<n>It is applied to intracranial deep brain stimulation (DBS) recordings.
• Score: 0.1259953341639576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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