Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms
Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms
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DOI：
10.1002/hbm.1024
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发表时间：
2001-05-01
影响因子：
4.8
通讯作者：
Pekar, JJ
中科院分区：
文献类型：
--
作者：
Calhoun, VD;Adali, T;Pekar, JJ
独立成分分析（伊卡）是一种尝试将数据分离成最大独立组的技术。在空间或时间上实现最大的独立性产生了两种对功能磁共振成像（fMRI）应用有意义的伊卡：空间伊卡（SICA）和时间伊卡（TICA）。迄今为止，SICA在功能磁共振成像中的应用一直占据主导地位。这些实验的目的是研究伊卡与两个可预测的组件，目前和评估的重要性，在伊卡的应用程序中的基本独立性假设。设计了四种新颖的视觉激活范式，每种范式由两个时空成分组成，它们分别是空间依赖的、时间依赖的、空间和时间依赖的、或空间和时间不相关的。模拟数据的产生和fMRI数据从六个主题，获得使用这些范例。用回归分析分析来自每个范例的数据，以确定信号是否如预期那样发生。然后将空间和时间伊卡应用于这些数据，一般的结果是伊卡只在预期的地方找到成分，例如，S（T）伊卡“失败”（即，产生与“不言自明”的成分无关的独立成分）对于空间（时间）依赖的范例，否则“起作用”。回归分析被证明是对这些数据的一种有用的“检查”，然而强有力的假设并不总是可用的，伊卡的优点是它可以在不进行特定建模假设的情况下表征数据。我们报告了伊卡方法背后的一些假设仔细检查，提供应用伊卡时将提供难以解释的结果的例子，并提供应用伊卡的fMRI数据，特别是当一个以上的任务相关的组件是存在于数据的建议。Hum.脑图谱13：43-53，2001年。(C)2001 Wiley-Liss，Inc.
Independent component analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial ICA (SICA) and temporal ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study ICA with two predictable components present and evaluate the importance of the underlying independence assumption in the application of ICA. Four novel visual activation paradigms were designed, each consisting of two spatiotemporal components that were either spatially dependent, temporally dependent, both spatially and temporally dependent, or spatially and temporally uncorrelated, respectively. Simulated data were generated and fMRI data from six subjects were acquired using these paradigms. Data from each paradigm were analyzed with regression analysis in order to determine if the signal was occurring as expected. Spatial and temporal ICA were then applied to these data, with the general result that ICA found components only where expected, e.g., S(T)ICA "failed" (i.e., yielded independent components unrelated to the "self-evident" components) for paradigms that were spatially (temporally) dependent, and "worked" otherwise. Regression analysis proved a useful "check" for these data, however strong hypotheses will not always be available, and a strength of ICA is that it can characterize data without making specific modeling assumptions. We report a careful examination of some of the assumptions behind ICA methodologies, provide examples of when applying ICA would provide difficult-to-interpret results, and offer suggestions for applying ICA to fMRI data especially when more than one task-related component is present in the data. Hum. Brain Mapping 13:43-53, 2001. (C) 2001 Wiley-Liss, Inc.