Mattout J, Pélégrini-Issac M, Garnero L, Benali H
Wellcome Department of Imaging Neuroscience, London, UK.
Neuroimage. 2005 Jun;26(2):356-73. doi: 10.1016/j.neuroimage.2005.01.026. Epub 2005 Mar 16.
Spatially characterizing and quantifying the brain electromagnetic response using MEG/EEG data still remains a critical issue since it requires solving an ill-posed inverse problem that does not admit a unique solution. To overcome this lack of uniqueness, inverse methods have to introduce prior information about the solution. Most existing approaches are directly based upon extrinsic anatomical and functional priors and usually attempt at simultaneously localizing and quantifying brain activity. By contrast, this paper deals with a preprocessing tool which aims at better conditioning the source reconstruction process, by relying only upon intrinsic knowledge (a forward model and the MEG/EEG data itself) and focusing on the key issue of localization. Based on a discrete and realistic anatomical description of the cortex, we first define functionally Informed Basis Functions (fIBF) that are subject specific. We then propose a multivariate method which exploits these fIBF to calculate a probability-like coefficient of activation associated with each dipolar source of the model. This estimated distribution of activation coefficients may then be used as an intrinsic functional prior, either by taking these quantities into account in a subsequent inverse method, or by thresholding the set of probabilities in order to reduce the dimension of the solution space. These two ways of constraining the source reconstruction process may naturally be coupled. We successively describe the proposed Multivariate Source Prelocalization (MSP) approach and illustrate its performance on both simulated and real MEG data. Finally, the better conditioning induced by the MSP process in a classical regularization scheme is extensively and quantitatively evaluated.
利用脑磁图/脑电图（MEG/EEG）数据对大脑电磁响应进行空间特征描述和量化仍然是一个关键问题，因为这需要解决一个不适定的逆问题，该问题不存在唯一解。为了克服这种缺乏唯一性的问题，逆方法必须引入关于解的先验信息。大多数现有方法直接基于外在的解剖学和功能先验信息，通常试图同时定位和量化大脑活动。相比之下，本文介绍了一种预处理工具，该工具旨在仅依靠内在知识（一个正向模型和MEG/EEG数据本身）并聚焦于定位的关键问题，从而更好地调节源重建过程。基于对皮质的离散且现实的解剖学描述，我们首先定义特定于个体的功能知情基函数（fIBF）。然后，我们提出一种多变量方法，该方法利用这些fIBF来计算与模型的每个偶极源相关的类似概率的激活系数。然后，这种估计的激活系数分布可以用作内在功能先验，既可以在后续的逆方法中考虑这些量，也可以对概率集进行阈值处理以减小解空间的维度。约束源重建过程的这两种方式可以自然地结合起来。我们依次描述所提出的多变量源预定位（MSP）方法，并在模拟和真实MEG数据上说明其性能。最后，对MSP过程在经典正则化方案中所带来的更好的调节效果进行了广泛而定量的评估。
