The Mind Research Network, Albuquerque, NM, USA.
Neuroimage. 2012 Feb 15;59(4):4141-59. doi: 10.1016/j.neuroimage.2011.10.010. Epub 2011 Oct 14.
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 intersubject 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.
功能神经影像学的一个关键挑战是跨受试者的有意义的结果组合。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可变性，以至于没有两个个体在对相同刺激的相同位置具有相同的神经激活。这种受试者间的可变性限制了组水平的推断，因为平均激活模式可能无法代表个体中的模式。一种有前途的多受试者分析方法是组独立成分分析（GICA），它可以识别组成分并在个体水平上重建激活。GICA 已经得到了相当大的普及，特别是在无法指定时间响应模型的研究中。然而，对于 GICA 在受试者间可变性的实际条件下的性能，缺乏全面的理解。在这项研究中，我们使用模拟功能磁共振成像（fMRI）数据来确定 GICA 在空间、时间和幅度可变性条件下的能力和局限性。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效果如何，何时空间可变性会阻止估计？（2）为什么会发生组件分裂，它如何受到模型阶数的影响？（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？总体而言，我们的结果表明 GICA 具有很好的能力来捕捉受试者间的差异，并且我们针对功能成像数据的应用提出了一些关于分析选择的建议。
