Treatment of baseline drifts in fMRI time series analysis
Treatment of baseline drifts in fMRI time series analysis
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DOI：
10.1097/00004728-199905000-00025
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发表时间：
1999-05-01
影响因子：
1.3
通讯作者：
Russell, DP
中科院分区：
文献类型：
--
作者：
Lowe, MJ;Russell, DP
目的：在功能磁共振成像（fMRI）时程数据中，基线信号强度的逐渐漂移是常见的。方法：利用仿真和fMRI数据研究了三种解释基线漂移的统计模型。提出了一种方法，其中的时间过程数据是线性最小二乘拟合到一个参考函数，其中包括基线漂移的斜率作为一个自由parameter.Results：它表明，最小二乘法是等效的互相关与Gram-Schmidt正交化。此外，它表明，某些范例设计提高了灵敏度的统计测试时，使用任何常用的漂移校正方法。最小二乘法的结果在各种有用的参数，如激活幅度，具有良好的特征Error.Conclusion：非常简单的技术可以有效地解释所观察到的漂移。设计关于时间序列中点对称的范例是很重要的。在计算置信水平时，需要考虑基线漂移的适当统计模型，以确保准确的置信水平评估。
Purpose: Gradual drifting of baseline signal intensity is common in functional MRI (fMRI) time course data. Methods for dealing with this effect are studied.Method: Simulations and fMRI data are used to study three statistical models that account for baseline drift. A method is proposed in which the time course data are linear least-squares fit to a reference function that includes the slope of the baseline drift as a free parameter.Results: It is shown that the least-squares method is equivalent to cross-correlation with Gram-Schmidt orthogonalization. Additionally, it is shown that certain paradigm designs improve the sensitivity of statistical tests when using any of the drift correction methods commonly employed. The least-squares method results in a variety of useful parameters such as activation amplitude, with a well characterized error.Conclusion: Very simple techniques can effectively account for observed drifts. It is important to design paradigms that are symmetric about the midpoint of the time series. In calculating confidence levels, a proper statistical model that accounts for baseline drifts is necessary to ensure accurate confidence level assessment.