arXiv 1909.026362019-09-05q-bio.QMcs.LGstat.ML

上下文最小范数估计（CMNE）：神经网络中源估计的深度学习方法

Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks

Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Harvard-MIT Division of Health Sciences and Technology（哈佛-麻省理工卫生科学与技术部）；Institute for Medical Engineering, Research Campus STIMULATE, Otto-von-Guericke University（奥托·冯·格里克大学医学工程研究所（STIMULATE研究校区））；Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau（伊尔默瑙工业大学生物医学工程与信息学研究所）

Christoph Dinh, John GW Samuelsson, Alexander Hunold, Matti S Hämäläinen, Sheraz Khan

备注：: 14 pages, 9 figures

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摘要：

脑磁图（MEG）和脑电图（EEG）源估计到目前为止主要是逐个样本地导出的，即，在时间上彼此独立。然而，神经元组装是高度互连的，限制了空间中神经活动的时间演变，如通过MEG和EEG检测的。因此，所观察到的神经电流是高度依赖于上下文的。在这里，提出了一种新方法，将预测深度学习网络与最小范数估计（MNE）方法集成在一起。具体来说，我们使用长短期记忆（LSTM）网络，一种递归神经网络，来预测大脑活动。因为我们在估计中使用过去的活动（上下文），所以我们称我们的方法为上下文MNE（CMNE）。我们证明，这些上下文算法可以用于预测活动的基础上以前的大脑状态，当与MNE结合使用时，它们会导致更准确的源估计。为了评估CMNE的性能，它进行了测试，从人类听觉诱发反应实验的模拟和实验数据。

英文摘要：

Magnetoencephalography (MEG) and Electroencephalography (EEG) source estimates have thus far mostly been derived sample by sample, i.e., independent of each other in time. However, neuronal assemblies are heavily interconnected, constraining the temporal evolution of neural activity in space as detected by MEG and EEG. The observed neural currents are thus highly context dependent. Here, a new method is presented which integrates predictive deep learning networks with the Minimum-Norm Estimates (MNE) approach. Specifically, we employ Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, for predicting brain activity. Because we use past activity (context) in the estimation, we call our method Contextual MNE (CMNE). We demonstrate that these contextual algorithms can be used for predicting activity based on previous brain states and when used in conjunction with MNE, they lead to more accurate source estimation. To evaluate the performance of CMNE, it was tested on simulated and experimental data from human auditory evoked response experiments.

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