Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study.
Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study.
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DOI：
10.1016/j.neuroimage.2011.10.010
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发表时间：
2012-02-15
期刊：
影响因子：
5.7
通讯作者：
Calhoun, Vince D.
中科院分区：
文献类型：
--
作者：
Allen, Elena A.;Erhardt, Erik B.;Wei, Yonghua;Eichele, Tom;Calhoun, Vince D.
功能神经影像学的一个关键挑战是跨学科结果的有意义的组合。即使在健康参与者的样本中，大脑形态和功能组织也表现出相当大的变异性，因此没有两个人在同一位置对相同的刺激做出相同的神经激活。这种受试者间的变异性限制了在组水平上的推断，因为平均激活模式可能无法代表在个体中看到的模式。一个有前途的多学科分析方法是组独立成分分析（GICA），它确定组成分和重建激活在个人水平。GICA已经获得了相当大的普及，特别是在时间响应模型不能指定的研究。然而，缺乏对GICA在受试者间差异的现实条件下的性能的全面了解。在这项研究中，我们使用模拟功能性磁共振成像（fMRI）数据，以确定GICA的能力和局限性的条件下的空间，时间和幅度的变化。使用SimTB工具箱生成的模拟解决了GICA研究中常见的问题，例如：（1）个体受试者激活的估计程度如何以及空间变异性何时会妨碍估计？(2)为什么会发生组件拆分，模型顺序如何影响组件拆分？(3)我们应该如何分析组件特征，以最大限度地提高对受试者间差异的敏感性？总的来说，我们的研究结果表明，GICA捕捉受试者之间的差异，我们提出了一些建议，应用功能成像数据的分析选择。
A key challenge in functional neuroimaging is the meaningful combination of results across subjects. Even in a sample of healthy participants, brain morphology and functional organization exhibit considerable variability, such that no two individuals have the same neural activation at the same location in response to the same stimulus. This inter-subject variability limits inferences at the group-level as average activation patterns may fail to represent the patterns seen in individuals. A promising approach to multi-subject analysis is group independent component analysis (GICA), which identifies group components and reconstructs activations at the individual level. GICA has gained considerable popularity, particularly in studies where temporal response models cannot be specified. However, a comprehensive understanding of the performance of GICA under realistic conditions of inter-subject variability is lacking. In this study we use simulated functional magnetic resonance imaging (fMRI) data to determine the capabilities and limitations of GICA under conditions of spatial, temporal, and amplitude variability. Simulations, generated with the SimTB toolbox, address questions that commonly arise in GICA studies, such as: (1) How well can individual subject activations be estimated and when will spatial variability preclude estimation? (2) Why does component splitting occur and how is it affected by model order? (3) How should we analyze component features to maximize sensitivity to inter-subject differences? Overall, our results indicate an excellent capability of GICA to capture between-subject differences and we make a number of recommendations regarding analytic choices for application to functional imaging data.
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