Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation
Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation
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DOI：
10.1016/j.neuroimage.2010.03.001
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发表时间：
2010-08-01
期刊：
影响因子：
5.7
通讯作者：
Golland, Polina
中科院分区：
文献类型：
--
作者：
Ou, Wanmei;Nummenmaa, Aapo;Golland, Polina
我们提出了一种新的方法，fMRI知情的区域估计（FIRE），它利用从fMRI的信息在E/MEG源重建FIRE利用神经和血管活动之间的空间对齐。同时允许它们的动态特性的实质差异。此外，利用基于区域的方法，FIRE独立地估计每个区域的模型参数。因此，它可以有效地应用于源位置的密集网格。FIRE核心的优化过程与重新加权的最小范数算法有关。所提出的方法中的权重从当前源估计和fMRI数据两者计算，在fMRI或E/MEG测量中，我们采用Monte Carlo评估程序将所提出的方法与其他几种E/MEG-fMRI联合算法进行比较。我们的结果表明，FIRE在空间和时间精度之间提供了最佳的估计精度。fMRI数据显示，FIRE显著减少了最小范数估计中存在的源定位的模糊性，并且它准确地捕获了相邻功能区域中的激活时间（C）2010 Elsevier Inc.版权所有
We propose a novel method, fMRI-Informed Regional Estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction FIRE takes advantage of the spatial alignment between the neural and the vascular activities. while allowing for substantial differences in their dynamics Furthermore, with a region-based approach, FIRE estimates the model parameters for each region independently Hence, it can be efficiently applied on a dense grid of source locations The optimization procedure at the core of FIRE is related to the re-weighted minimum-norm algorithms The weights in the proposed approach are computed from both the current source estimates and fMRI data, leading to robust estimates in the presence of silent sources in either fMRI or E/MEG measurements We employ a Monte Carlo evaluation procedure to compare the proposed method to several other joint E/MEG-fMRI algorithms Our results show that FIRE provides the best trade-off in estimation accuracy between the spatial and the temporal accuracy Analysis using human E/MEG-fMRI data reveals that FIRE significantly reduces the ambiguities in source localization present in the minimum-norm estimates, and that it accurately captures activation timing in adjacent functional regions (C) 2010 Elsevier Inc. All rights reserved