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MEG/EEG 源估计的空间保真度:一种通用评估方法。

Spatial fidelity of MEG/EEG source estimates: A general evaluation approach.

机构信息

Harvard-MIT Division of Health Sciences and Technology (HST), Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA.

Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Massachusetts General Hospital (MGH), Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA.

出版信息

Neuroimage. 2021 Jan 1;224:117430. doi: 10.1016/j.neuroimage.2020.117430. Epub 2020 Oct 7.

Abstract

Low spatial resolution is often cited as the most critical limitation of magneto- and electroencephalography (MEG and EEG), but a unifying framework for quantifying the spatial fidelity of M/EEG source estimates has yet to be established; previous studies have focused on linear estimation methods under ideal scenarios without noise. Here we present an approach that quantifies the spatial fidelity of M/EEG estimates from simulated patch activations over the entire neocortex superposed on measured resting-state data. This approach grants more generalizability in the evaluation process that allows for, e.g., comparing linear and non-linear estimates in the whole brain for different signal-to-noise ratios (SNR), number of active sources and activation waveforms. Using this framework, we evaluated the MNE, dSPM, sLORETA, eLORETA, and MxNE methods and found that the spatial fidelity varies significantly with SNR, following a largely sigmoidal curve whose shape varies depending on which aspect of spatial fidelity that is being quantified and the source estimation method. We believe that these methods and results will be useful when interpreting M/EEG source estimates as well as in methods development.

摘要

空间分辨率低通常被认为是磁共振和脑电图(MEG 和 EEG)最关键的限制,但尚未建立用于量化 M/EEG 源估计空间保真度的统一框架;以前的研究主要集中在没有噪声的理想情况下的线性估计方法。在这里,我们提出了一种方法,该方法通过对叠加在测量静息状态数据上的整个新皮层上的模拟贴片激活进行模拟,来量化 M/EEG 估计的空间保真度。这种方法在评估过程中具有更强的通用性,例如,可以在不同的信噪比 (SNR)、活动源数量和激活波形下比较整个大脑中的线性和非线性估计。使用此框架,我们评估了 MNE、dSPM、sLORETA、eLORETA 和 MxNE 方法,发现空间保真度随 SNR 显著变化,呈现出大致的 S 形曲线,其形状取决于正在量化的空间保真度的哪个方面以及源估计方法。我们相信,这些方法和结果在解释 M/EEG 源估计以及在方法开发方面将非常有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5dd2/7793168/5eec7bb385fd/nihms-1658689-f0001.jpg

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