AI 中文总结
本文提出DeepOp-Informed框架，将生物物理原理嵌入深度算子学习模型，在脑磁图模拟和青少年听觉诱发电记录中提升了电生理源重建的性能与泛化能力。
AI 中文摘要
电生理脑信号通常通过间接且含噪的测量方式获取，这些测量是对潜在神经活动的变换表示。源重建是从这些测量中解析出潜在神经信号的逆问题，对于绘制脑功能图谱至关重要，但由于该问题不适定且对噪声敏感，其解决仍具挑战性。深度学习方法已在一系列逆问题中展现出应用前景，但许多方法未明确纳入支配数据生成的生物物理原理，限制了数据效率和跨被试的适应性。本文提出DeepOp-Informed，这是一种生物物理信息几何深度算子学习框架，通过自定义可微层将感知过程的生物物理原理嵌入模型，实现更高效的学习和更优的重建性能。该层使神经网络能够适应因脑解剖结构和传感器定位差异导致的信号生成物理过程的被试特异性变化。在真实的脑磁图模拟中，DeepOp-Informed对留存被试的正向模型具有泛化能力，相较于多个神经网络和经典基线方法降低了重建误差；应用于青少年听觉诱发电记录时，其生成的重建结果在解剖结构上合理，定位于听觉皮层。尽管本文的应用聚焦于脑磁图，但该框架具有通用性，可适配其他成像模态。
英文摘要
Electrophysiological brain signals are typically acquired through indirect and noisy measurements, providing transformed representations of the underlying neural activity. Source reconstruction---the inverse problem of resolving underlying neural signals from these measurements---is essential for mapping brain function but remains challenging because it is ill-posed and sensitive to noise. Deep learning methods have shown promise across a range of inverse problems, yet many do not explicitly incorporate the biophysical principles governing data generation, limiting data efficiency and adaptation across subjects. Here, we introduce DeepOp-Informed, a biophysics-informed geometric deep operator learning framework that embeds the biophysics of the sensing process into the model through a custom differentiable layer, enabling more efficient learning and improved reconstruction performance. This layer enables the neural network to adapt to subject-specific variations in the physics of signal generation, resulting from differences in brain anatomy and sensor positioning. In realistic magnetoencephalography simulations, DeepOp-Informed generalizes to forward models from held-out subjects, reducing reconstruction error relative to several neural-network and classical baselines. Applied to adolescent auditory-evoked recordings, it produces anatomically plausible reconstructions localized to the auditory cortex. While our application focuses on magnetoencephalography, the framework is general and may be adaptable to other imaging modalities.
