Likelihood-Based Hypothesis Tests for Brain Activation Detection From MRI Data Disturbed by Colored Noise: A Simulation Study
Likelihood-Based Hypothesis Tests for Brain Activation Detection From MRI Data Disturbed by Colored Noise: A Simulation Study
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DOI：
10.1109/tmi.2008.2004427
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发表时间：
2009-02-01
影响因子：
10.6
通讯作者：
Sijbers, J.
中科院分区：
文献类型：
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作者：
den Dekker, A. J.;Poot, D. H. J.;Sijbers, J.
被时间色噪声破坏的功能磁共振成像(FMRI)数据通常在功能激活检测之前被预处理(即，预白化或预着色)。在这篇文章中，我们提出了基于似然的假设检验，它直接解释了功能激活检测框架内的有色噪声。提出了三种基于似然比的检验方法：广义似然比检验、Wald检验和Rao检验。FMRI时间序列被建模为线性回归模型，其中一个回归变量描述与任务相关的血流动力学响应，一个回归变量描述恒定的基线，一个回归变量描述潜在的漂移。噪声的时间相关结构被建模为自回归(AR)模型。AR模型的阶数由实际零数据集确定，采用Akaike的信息准则(惩罚因子为3)作为阶数选择准则。所提出的检验是基于数据的似然函数的精确表达式。利用蒙特卡罗模拟实验，从检测率和虚警率两个方面对提出的测试的性能进行了评估，并与当前的一般线性模型(GLM)测试进行了比较，后者在单独的步骤中估计了噪声的颜色。结果表明，GLM、GLR和Wald检验统计量的理论渐近分布不能可靠地用于计算有限长度时间序列的激活检测阈值。此外，在固定虚警率的情况下，提出的GLR检验统计量的检测率略有提高，但在统计上比普通的基于GLM的检验方法有显著的改善。最后，仿真结果表明，如果AR模型的阶数没有选择得足够高来充分描述噪声的相关结构，则所考虑的所有测试都表现出严重的劣化，而(轻微)过度建模的影响被观察到的危害较小。
Functional magnetic resonance imaging (fMRI) data that are corrupted by temporally colored noise are generally preprocessed (i.e., prewhitened or precolored) prior to functional activation detection. In this paper, we propose likelihood-based hypothesis tests that account for colored noise directly within the framework of functional activation detection. Three likelihood-based tests are proposed: the generalized likelihood ratio (GLR) test, the Wald test, and the Rao test. The fMRI time series Is modeled as a linear regression model, where one regressor describes the task-related hemodynamic response, one regressor accounts for a constant baseline and one regressor describes potential drift. The temporal correlation structure of the noise is modeled as an autoregressive (AR) model. The order of the AR model is determined from practical null data sets using Akaike's information criterion (with penalty factor 3) as order selection criterion. The tests proposed are based on exact expressions for the likelihood function of the data. Using Monte Carlo simulation experiments, the performance of the proposed tests is evaluated in terms of detection rate and false alarm rate properties and compared to the current general linear model (GLM) test, which estimates the coloring of the noise in a separate step. Results show that theoretical asymptotic distributions of the GLM, GLR, and Wald test statistics cannot be reliably used for computing thresholds for activation detection from finite length time series. Furthermore, it is shown that, for a fixed false alarm rate, the detection rate of the proposed GLR test statistic is slightly, but (statistically) significantly improved compared to that of the common GLM-based tests. Finally, simulations results reveal that all tests considered show seriously inferior performance if the order of the AR model is not chosen sufficiently high to give an adequate description of the correlation structure of the noise, whereas the effects of (slightly) overmodeling are observed to be less harmful.