MEG source imaging method using fast L1 minimum-norm and its applications to signals with brain noise and human resting-state source amplitude images.
MEG source imaging method using fast L1 minimum-norm and its applications to signals with brain noise and human resting-state source amplitude images.
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DOI：
10.1016/j.neuroimage.2013.09.022
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发表时间：
2014-01-01
期刊：
影响因子：
5.7
通讯作者：
Lee RR
中科院分区：
文献类型：
--
作者：
Huang MX;Huang CW;Robb A;Angeles A;Nichols SL;Baker DG;Song T;Harrington DL;Theilmann RJ;Srinivasan R;Heister D;Diwakar M;Canive JM;Edgar JC;Chen YH;Ji Z;Shen M;El-Gabalawy F;Levy M;McLay R;Webb-Murphy J;Liu TT;Drake A;Lee RR
本研究开发了一种基于快速矢量时空分析的快速 MEG 源成像技术，使用 L1 最小范数（Fast-VESTAL），然后使用该方法获得不同频段的静息态脑磁图（MEG）信号的源幅度图像。 Fast-VESTAL 技术由两个步骤组成。首先，获得传感器波形协方差矩阵的主要空间模式的 L1 最小范数 MEG 源图像。接下来，使用从步骤 1 的空间源图像构建的逆算子获得具有毫秒时间分辨率的准确源时间进程。通过模拟，评估了 Fast-VESTAL 的性能：1）定位多个相关源的能力； 2）忠实恢复源时间进程的能力； 3) 对不同 SNR 条件（包括负 dB 级别的 SNR）的鲁棒性； 4）处理相关大脑噪音的能力； 5）MEG源图像的统计图。还开发了一种客观的预白化方法，并将其与 Fast-VESTAL 集成，以消除相关的大脑噪声。然后通过对人类正中神经 MEG 反应的分析来检查 Fast-VESTAL 的性能。结果表明，该方法可以轻松地区分整个体感网络中的来源。接下来，应用 Fast-VESTAL 从 41 名健康对照受试者的静息态信号中获取第一张全头 MEG 源振幅图像，适用于所有标准频段。提供了静息态 MEG 源图像与已知神经生理学之间的比较。此外，在模拟和 MEG 人体反应案例中，使用传统波束形成器技术获得的结果与 Fast-VESTAL 的结果进行了比较，这突出了波束形成器的信号泄漏和源时程失真问题。
The present study developed a fast MEG source imaging technique based on Fast Vector-based Spatio-Temporal Analysis using a L1-minimum-norm (Fast-VESTAL) and then used the method to obtain the source amplitude images of resting-state magnetoencephalography (MEG) signals for different frequency bands. The Fast-VESTAL technique consists of two steps. First, L1-minimum-norm MEG source images were obtained for the dominant spatial modes of sensor-waveform covariance matrix. Next, accurate source time-courses with millisecond temporal resolution were obtained using an inverse operator constructed from the spatial source images of Step 1. Using simulations, Fast-VESTAL’s performance of was assessed for its 1) ability to localize multiple correlated sources; 2) ability to faithfully recover source time-courses; 3) robustness to different SNR conditions including SNR with negative dB levels; 4) capability to handle correlated brain noise; and 5) statistical maps of MEG source images. An objective pre-whitening method was also developed and integrated with Fast-VESTAL to remove correlated brain noise. Fast-VESTAL’s performance was then examined in the analysis of human mediannerve MEG responses. The results demonstrated that this method easily distinguished sources in the entire somatosensory network. Next, Fast-VESTAL was applied to obtain the first whole-head MEG source-amplitude images from resting-state signals in 41 healthy control subjects, for all standard frequency bands. Comparisons between resting-state MEG sources images and known neurophysiology were provided. Additionally, in simulations and cases with MEG human responses, the results obtained from using conventional beamformer technique were compared with those from Fast-VESTAL, which highlighted the beamformer’s problems of signal leaking and distorted source time-courses.
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