分离脑区共性和特异性神经活动，提升深部脑刺激分析精度
Disentangling Shared and Private Neural Dynamics with SPIRE: A Latent Modeling Framework for Deep Brain Stimulation
- 设计多编码器自编码框架，通过新损失函数解耦共享与私有隐空间
- 在合成数据上优于传统模型，对非线性畸变和时间错位具有鲁棒性
- 适用于深部脑刺激数据，可跨部位频率识别刺激特异性信号
分离多脑区神经数据中的共享网络动态与区域特异性活动是建模的核心挑战。我们提出SPIRE（Shared-Private Inter-Regional Encoder），一种深度多编码器自编码器，通过新型对齐与解耦损失将记录分解为共享和私有隐空间。仅基于基线数据训练的SPIRE能稳健恢复跨脑区结构，并揭示外部扰动如何重构该结构。在具有真实隐变量的合成基准测试中，当存在非线性畸变和时间错位时，SPIRE优于经典概率模型。应用于颅内深部脑刺激（DBS）记录时，发现共享隐变量可可靠编码刺激特异性特征，且在不同部位和频率间具有泛化能力。这些结果确立了SPIRE作为刺激条件下多脑区神经动力学分析的实用、可复现工具。
原文摘要 · Abstract (English)
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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