Estimating the global order of the fMRI noise model
Estimating the global order of the fMRI noise model
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DOI：
10.1016/j.neuroimage.2005.03.015
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发表时间：
2005-07-15
期刊：
影响因子：
5.7
通讯作者：
Van Hulle, MM
中科院分区：
文献类型：
--
作者：
Gautama, T;Van Hulle, MM
基于GLM的fMRI分析技术的主要问题之一是在回归后的残差信号中存在时间自相关性。一种可能的校正方法是预白化方法，它将自回归(或其他)模型与残差匹配，并使用模型的预期时间自相关来变换数据和设计矩阵，使得残差成为白噪声。在这篇文章中。介绍了一种基于回归后的残差估计数据集全局自回归模型阶数的方法。所提出的全局标准化部分自相关(SPAC)方法测试在一定滞后处的部分自相关的空间轮廓是否是随机的，并使用随机场理论来解释fMRI数据中典型的空间相关性。在合成数据和fMRI数据上进行了测试，并与两种传统的模型阶数估计方法进行了比较。(C)2005 Elsevier Inc.保留所有权利。
One of the major issues in GLM-based fMRI analysis techniques is the presence of temporal autocorrelations in the residual signal after regression. A possible correction method is that of prewhitening, which fits an autoregressive (or other) model to the residual and uses the expected temporal autocorrelations of the model to transform the data and design matrix such that the residual becomes white noise. In this article. a method is introduced to estimate the global autoregressive model order of a data set, based on the residuals after regression. The proposed global standardized partial autocorrelation (SPAC) method tests whether the spatial profile of partial autocorrelations at a certain lag is random, and uses random field theory to account for the spatial correlations typical for fMRI data. It is tested both on synthetic and fMRI data, and is compared to two traditional techniques for model order estimation. (c) 2005 Elsevier Inc. All rights reserved.