An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data
An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data
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DOI：
10.1006/nimg.2002.1113
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发表时间：
2002-09-01
期刊：
影响因子：
5.7
通讯作者：
McIntosh, AR
中科院分区：
文献类型：
--
作者：
Della-Maggiore, V;Chan, W;McIntosh, AR
我们提出了两套Monte Carlo模拟的结果，旨在评估的一些预处理参数的SPM 99功能磁共振成像（fMRI）的分析的鲁棒性。统计鲁棒性估计通过实施参数和非参数模拟方法的基础上获得的图像从事件相关的功能磁共振成像实验。模拟数据集进行了测试，以下参数的组合：基函数，全局缩放，低通滤波器，高通滤波器和自回归建模的串行自相关。基于单受试者SPM分析，我们得出了以下结论：（1）经典血流动力学响应函数是一种比具有时间导数的HRF更可靠的fMRI时间序列建模基函数。(2)应避免全局缩放，因为它可能会显着降低功率取决于实验设计。(3)高通滤波器的使用对于具有固定刺激间隔的事件相关设计可能是有益的。(4)在处理短刺激间隔（< 8 s）的fMRI时间序列时，推荐使用一阶自回归模型，而不是低通滤波器（HRF），因为它在提供相对好的功效的同时降低了推断偏差的风险。对于刺激间隔大于8秒的数据集，不建议使用时间平滑，因为它会降低功率。虽然我们的结果的普遍性可能是有限的，我们采用的方法可以很容易地由其他科学家来确定最佳的参数组合来分析他们的数据。(C)2002 Elsevier Science（美国）。
We present the results from two sets of Monte Carlo simulations aimed at evaluating the robustness of some preprocessing parameters of SPM99 for the analysis of functional magnetic resonance imaging (fMRI). Statistical robustness was estimated by implementing parametric and nonparametric simulation approaches based on the images obtained from an event-related fMRI experiment. Simulated datasets were tested for combinations of the following parameters: basis function, global scaling, low-pass filter, high-pass filter and autoregressive modeling of serial autocorrelation. Based on single-subject SPM analysis, we derived the following conclusions that may serve as a guide for initial analysis of fMRI data using SPM99: (1) The canonical hemodynamic response function is a more reliable basis function to model the fMRI time series than HRF with time derivative. (2) Global scaling should be avoided since it may significantly decrease the power depending on the experimental design. (3) The use of a high-pass filter may be beneficial for event-related designs with fixed interstimulus intervals. (4) When dealing with fMRI time series with short interstimulus intervals (< 8 s), the use of first-order autoregressive model is recommended over a low-pass filter (HRF) because it reduces the risk of inferential bias while providing a relatively good power. For datasets with interstimulus intervals longer than 8 seconds, temporal smoothing is not recommended since it decreases power. While the generalizability of our results may be limited, the methods we employed can be easily implemented by other scientists to determine the best parameter combination to analyze their data. (C) 2002 Elsevier Science (USA).