Friston K J, Holmes A P, Poline J B, Grasby P J, Williams S C, Frackowiak R S, Turner R
Wellcome Department of Cognitive Neurology, Institute of Neurology, United Kingdom.
Neuroimage. 1995 Mar;2(1):45-53. doi: 10.1006/nimg.1995.1007.
This paper presents a general approach to the analysis of functional MRI time-series from one or more subjects. The approach is predicated on an extension of the general linear model that allows for correlations between error terms due to physiological noise or correlations that ensue after temporal smoothing. This extension uses the effective degrees of freedom associated with the error term. The effective degrees of freedom are a simple function of the number of scans and the temporal auto correlation function. A specific form for the latter can be assumed if the data are smoothed, in time, to accentuate hemodynamic responses with a neural basis. This assumption leads to an expedient implementation of a flexible statistical framework. The importance of this small extension is that, in contradistinction to our previous approach, any parametric statistical analysis can be implemented. We demonstrate this point using a multiple regression analysis that tests for effects of interest (activations due to word generation), while taking explicit account of some obvious confounds.
本文提出了一种分析来自一个或多个受试者的功能磁共振成像时间序列的通用方法。该方法基于通用线性模型的扩展，该扩展允许由于生理噪声导致的误差项之间存在相关性，或者在时间平滑后产生的相关性。此扩展使用与误差项相关的有效自由度。有效自由度是扫描次数和时间自相关函数的简单函数。如果对数据进行时间平滑以突出具有神经基础的血液动力学反应，则可以假设后者的特定形式。这一假设导致了一个灵活统计框架的便捷实现。这一微小扩展的重要性在于，与我们之前的方法不同，任何参数统计分析都可以实施。我们通过多元回归分析来证明这一点，该分析在明确考虑一些明显混杂因素的同时，测试感兴趣的效应（因单词生成而产生的激活）。
