How to use fMRI functional localizers to improve EEG/MEG source estimation.

How to use fMRI functional localizers to improve EEG/MEG source estimation.
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DOI:
10.1016/j.jneumeth.2014.07.015
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发表时间:
2015-07-30
影响因子:
3
通讯作者:
Norcia, Anthony M.
Norcia, Anthony M.
中科院分区:
医学4区
文献类型:
--
作者:
Cottereau, Benoit R.;Ales, Justin M.;Norcia, Anthony M.

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脑电图和脑磁图具有很好的时间分辨率,但是对产生传感器记录的信号的神经源的估计是一个困难的不适定问题。功能核磁共振成像的高空间分辨率使其成为利用数据融合改进脑磁图源定位的理想工具。然而,这两种技术的结合仍然具有挑战性,因为EEG/MEG和BOLD信号的神经发生器在某些情况下可能非常不同。在这里,我们描述了一种由我们的团队在过去十年中开发的数据融合方法,其中fMRI用于提供基于每个受试者单独定义的功能区的源约束。这个小型回顾描述了使用这种方法执行源估计所需的不同步骤。它还提供了在进行fmri信息的EEG/MEG源成像时应避免的陷阱列表。最后,它描述了使用基于roi的方法进行群体级分析和感官系统研究的优势。
EEG and MEG have excellent temporal resolution, but the estimation of the neural sources that generate the signals recorded by the sensors is a difficult, ill-posed problem. The high spatial resolution of functional MRI makes it an ideal tool to improve the localization of the EEG/MEG sources using data fusion. However, the combination of the two techniques remains challenging, as the neural generators of the EEG/MEG and BOLD signals might in some cases be very different. Here we describe a data fusion approach that was developed by our team over the last decade in which fMRI is used to provide source constraints that are based on functional areas defined individually for each subject. This mini-review describes the different steps that are necessary to perform source estimation using this approach. It also provides a list of pitfalls that should be avoided when doing fMRI-informed EEG/MEG source imaging. Finally, it describes the advantages of using a ROI-based approach for group-level analysis and for the study of sensory systems.
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