标题： 通过物理信息三维神经网络增强脑源重建 显示英文标题

标题： Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks

摘要： 在神经科学中，重建脑源是一项基础性挑战，这对于理解大脑功能和功能障碍至关重要。脑电图（EEG）信号具有高时间分辨率。然而，由于该问题的不适定结构，从这些信号中识别出脑源的确切空间位置仍然很困难。传统方法主要依赖于人工设计的先验知识，而忽略了数据驱动学习的灵活性，而最近的深度学习方法则专注于端到端学习，通常仅使用前向模型的物理信息来生成训练数据。 我们提出了一个新颖的混合方法——3D-PIUNet，用于EEG源定位，有效结合了传统技术和深度学习技术的优点。3D-PIUNet通过伪逆映射从测量值到源空间获得初始物理信息估计。其次，将大脑视为三维体积，我们使用三维卷积U-Net捕获空间依赖性，并根据学习的数据先验细化解决方案。训练模型依赖于模拟的伪真实脑源数据，涵盖不同的源分布。在该数据上训练后，我们的模型显著提高了空间准确性，在传统方法和端到端数据驱动方法中表现出优越性能。 此外，我们用来自视觉任务的真实EEG数据验证了我们的发现，其中3D-PIUNet成功识别了视觉皮层并重建了预期的时间行为，从而展示了其实际应用价值。 显示英文摘要

摘要： Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains difficult due to the ill-posed structure of the problem. Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data. We propose the novel hybrid method 3D-PIUNet for EEG source localization that effectively integrates the strengths of traditional and deep learning techniques. 3D-PIUNet starts from an initial physics-informed estimate by using the pseudo inverse to map from measurements to source space. Secondly, by viewing the brain as a 3D volume, we use a 3D convolutional U-Net to capture spatial dependencies and refine the solution according to the learned data prior. Training the model relies on simulated pseudo-realistic brain source data, covering different source distributions. Trained on this data, our model significantly improves spatial accuracy, demonstrating superior performance over both traditional and end-to-end data-driven methods. Additionally, we validate our findings with real EEG data from a visual task, where 3D-PIUNet successfully identifies the visual cortex and reconstructs the expected temporal behavior, thereby showcasing its practical applicability.