Multiresolution imaging of MEG cortical sources using an explicit piecewise model
Multiresolution imaging of MEG cortical sources using an explicit piecewise model
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DOI：
10.1016/j.neuroimage.2007.07.046
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发表时间：
2007-11-15
期刊：
影响因子：
5.7
通讯作者：
Baillet, Sylvain
中科院分区：
文献类型：
--
作者：
Cottereau, Benoit;Jerbi, Karim;Baillet, Sylvain
MEG磁场成像神经发生器通常被认为是计算合理的方法（一方面通常产生较差的空间分辨率）和更复杂的方法（另一方面可能导致难以处理的计算成本）之间的折衷。我们采用多分辨率图像模型选择（MiMS）技术来解决在不增加计算负荷的情况下获得高分辨率源图像的问题。MiMS源模型的构建块是皮层表面的包块，可以结合解剖和功能先验在多个空间分辨率下进行设计。该方法采用电流多极展开法对扩展脑包激活的参数化模型进行了简化，并优化了图像模型的广义交叉验证误差，使得广义交叉验证误差对于广义神经电流的线性估计是封闭的。模型选择可以通过任何传统的神经电流成像方法进行补充，这些方法仅限于从MiMS获得的最佳图像支持。对大脑激活的位置和空间范围的估计进行了讨论，并使用广泛的蒙特卡罗模拟进行了评估。实验评估采用体位范式下的脑磁图数据。结果表明，MiMS是一种有效的图像模型选择技术，在真实噪声水平下具有鲁棒性。(c) 2007爱思唯尔公司版权所有。
Imaging neural generators from MEG magnetic fields is often considered as a compromise between computationally-reasonable methodology that usually yields poor spatial resolution on the one hand, and more sophisticated approaches on the other hand, potentially leading to intractable computational costs.We approach the problem of obtaining well-resolved source images with unexcessive computation load with a multiresolution image model selection (MiMS) technique. The building blocks of the MiMS source model are parcels of the cortical surface which can be designed at multiple spatial resolutions with the combination of anatomical and functional priors. Computation charge is reduced owing to 1) compact parametric models of the activation of extended brain parcels using current multipole expansions and 2) the optimization of the generalized cross-validation error on image models, which is closed-form for the broad class of linear estimators of neural currents. Model selection can be complemented by any conventional imaging approach of neural currents restricted to the optimal image support obtained from MiMS.The estimation of the location and spatial extent of brain activations is discussed and evaluated using extensive Monte-Carlo simulations. An experimental evaluation was conducted with MEG data, from a somatotopic paradigm. Results show that MiMS is an efficient image model selection technique with robust performances at realistic noise levels. (c) 2007 Elsevier Inc. All rights reserved.