Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.
Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.
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DOI：
10.1155/2012/574971
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发表时间：
2012
影响因子：
7.6
通讯作者：
Cordes D
中科院分区：
文献类型：
--
作者：
Jin M;Nandy R;Curran T;Cordes D
局部典型相关分析(CCA)是一种用来更准确地确定fMRI数据中激活模式的多变量方法。在其传统的公式中，CCA有几个缺陷限制了它在功能磁共振中的应用。一个主要的缺陷是，与一般线性模型(GLM)不同，时间回归变量的一般线性对比度的测试没有被纳入CCA形式。为了克服这一缺陷，利用多元多元回归(MVMR)和CCA的等价性，导出了一种新的方向检验统计量。这一扩展将允许在更复杂的fMRI设计中使用CCA来推断一般的线性对比度，而不需要重新参数化设计矩阵，也不需要重新估计每个感兴趣的特定对比度的CCA解决方案。在对CCA的空间系数进行适当约束的情况下，该检验统计量对于从噪声fMRI数据中推断诱发的大脑区域激活的能力比传统的GLM中的t检验更强。使用模拟数据和伪真数据的定量结果以及来自fMRI数据的激活图来验证这种新的检验统计量的优势。
Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.