Joint independent component analysis for simultaneous EEG-fMRI: Principle and simulation
Joint independent component analysis for simultaneous EEG-fMRI: Principle and simulation
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DOI：
10.1016/j.ijpsycho.2007.05.016
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发表时间：
2008-03-01
影响因子：
3
通讯作者：
Calhoun, Vince D.
中科院分区：
文献类型：
--
作者：
Moosmann, Matthias;Eichele, Tom;Calhoun, Vince D.
一个优化的方案融合脑电图和事件相关电位与功能磁共振成像（BOLD-MRI）数据应同时评估所有可用的电生理和血液动力学信息在一个共同的数据空间。在这样做时，应该可以识别潜在神经源的特征，其试验到试验的动态在两种模式中共同反映。我们提出了一个联合独立成分分析（jICA）模型，同时从多个科目的单次试验脑电功能磁共振成像测量分析。我们概述了JICA方法的基本思想，并在现实的噪声条件下模拟数据的结果。我们的研究结果表明，这种方法是一种可行的和生理上合理的数据驱动的方式来实现时空映射的事件相关的反应在人脑中。(C)2007 Elsevier B. V.保留所有权利。
An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-MRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain. (C) 2007 Elsevier B.V. All rights reserved.