Automatic fMRI-Guided MEG Multidipole Localization for Visual Responses
Automatic fMRI-Guided MEG Multidipole Localization for Visual Responses
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DOI：
10.1002/hbm.20570
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发表时间：
2009-04-01
影响因子：
4.8
通讯作者：
Jaaskelainen, Iiro P.
中科院分区：
文献类型：
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作者：
Auranen, Toni;Nummenmaa, Aapo;Jaaskelainen, Iiro P.
在此之前，我们介绍了在Jun等人（[2005]，Neuroimage 28：84-98）提出的时空贝叶斯偶极子分析设置中使用个体皮层位置和方向约束。然而，该模型的性能是有限的收敛速度慢，多峰的数值估计后验分布。在本文中，我们提出了一个直观的方式来利用功能磁共振成像（fMRI）数据的马尔可夫链蒙特卡罗抽样为基础的逆估计脑磁图（MEG）数据。我们使用模拟MEG和fMRI数据表明，该方法的收敛性和定位精度显着提高的帮助下，fMRI引导的建议分布。我们进一步证明，使用相同的视觉刺激范式在功能磁共振成像和脑磁图，这种类型的自动化方法的有用性时，调查激活模式与几个空间上接近和时间上重叠的来源。从理论上讲，MEG逆估计是没有偏见的，即使没有fMRI信息，也应该产生相同的结果，然而，在实践中，由于采样器的混合特性有限，后验分布的多峰性会导致问题。在这个帐户上，该算法的行为可能更像一个随机优化器，而不是使一个完整的贝叶斯后验分析。《脑图谱》30：1087-1099，2009年。(C)2008 Wiley-Liss，Inc.
Previously, we introduced the use of individual cortical location and orientation constraints in the spatiotemporal Bayesian dipole analysis setting proposed by Jun et al. ([2005], Neuroimage 28:84-98). However, the model's performance was limited by slow convergence and multimodality of the numerically estimated posterior distribution. In this paper, we present an intuitive way to exploit functional magnetic resonance imaging (fMRI) data in the Markov chain Monte Carlo sampling -based inverse estimation of magnetoencephalographic (MEG) data. We used Simulated MEG and fMRI data to show that the convergence and localization accuracy of the method is significantly improved with the help of fMRI-guided proposal distributions. We further demonstrate, using an identical visual stimulation paradigm in both fMRI and MEG, the usefulness of this type of automated approach when investigating activation patterns with several spatially close and temporally overlapping Sources. Theoretically, the MEG inverse estimates are not biased and should yield the same results even without fMRI information, however, in practice the multimodality of the posterior distribution causes problems due to the limited mixing properties of the sampler. On this account, the algorithm acts perhaps more as a stochastic optimizer than enables a full Bayesian posterior analysis. Hum Brain Mapp 30:1087-1099, 2009. (C) 2008 Wiley-Liss, Inc.