Franceschini Stefano, Ambrosanio Michele, Autorino Maria Maddalena, Maqsood Sohail, Baselice Fabio
Department of Engineering, University of Naples Parthenope, Naples, Italy.
Department of Economics, Law, Cybersecurity and Sports Sciences (DiSEGIM), University of Naples Parthenope, Naples, Italy.
Front Neurosci. 2025 Jul 21;19:1578473. doi: 10.3389/fnins.2025.1578473. eCollection 2025.
Within this manuscript a deep learning algorithm designed to achieve both spatial and temporal source reconstruction based on signals captured by MEG devices is introduced. Brain signal estimation at source level is a significant challenge in magnetoencephalographic (MEG) data processing. Traditional algorithms offer excellent temporal resolution but are limited in spatial resolution due to the inherent ill-posed nature of the problem. Nevertheless, many applications require precise localization of pathological tissues to provide reliable information for clinicians. In this context, deep learning solutions emerge as promising candidates for high resolution signals estimations. The proposed approach, termed "Deep-MEG," employs a hybrid neural network architecture capable of extracting both temporal and spatial information from signals captured by MEG sensors. The algorithm is capable to handling the entire brain and, therefore, is not limited to cortical sources imaging. To validate its efficacy, the Authors conducted simulations involving multiple active sources using a realistic forward model, and subsequently compared the results with those obtained using various state-of-the-art reconstruction algorithms. Finally Deep-MEG has been tested also with real MEG data.
在本手稿中，介绍了一种深度学习算法，该算法旨在基于MEG设备捕获的信号实现空间和时间源重建。源水平的脑信号估计是脑磁图（MEG）数据处理中的一项重大挑战。传统算法具有出色的时间分辨率，但由于问题固有的不适定性，其空间分辨率有限。然而，许多应用需要对病理组织进行精确定位，以便为临床医生提供可靠信息。在这种情况下，深度学习解决方案成为高分辨率信号估计的有希望的候选方案。所提出的方法称为“深度MEG”，采用了一种混合神经网络架构，能够从MEG传感器捕获的信号中提取时间和空间信息。该算法能够处理整个大脑，因此不限于皮质源成像。为了验证其有效性，作者使用逼真的正向模型进行了涉及多个活动源的模拟，随后将结果与使用各种最新重建算法获得的结果进行了比较。最后，深度MEG也使用真实的MEG数据进行了测试。
