Monti Martin M
Department of Psychology, University of California Los Angeles, CA, USA.
Front Hum Neurosci. 2011 Mar 18;5:28. doi: 10.3389/fnhum.2011.00028. eCollection 2011.
Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making.
功能磁共振成像（fMRI）是研究人类认知神经基础最广泛使用的工具之一。fMRI数据的标准分析依赖于一般线性模型（GLM）方法，以将刺激诱发信号与噪声分离。至关重要的是，这种方法依赖于关于数据的一些假设，为了使推断有效，这些假设必须得到满足。本文回顾了用于分析fMRI时间序列的GLM方法，特别关注此类数据符合GLM框架假设的程度，以及为纠正任何违反这些假设而开发的方法。不符合假设的主要后果不是使效应大小估计产生偏差，而是将偏差引入方差估计中，从而影响检验统计量、功效和假阳性率。此外，这种偏差可能对个体受试者和组水平统计都产生普遍影响，可能在重复研究中产生定性不同的结果，尤其是在常用于推断的阈值处理之后。
