General multilevel linear modeling for group analysis in FMRI
General multilevel linear modeling for group analysis in FMRI
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DOI：
10.1016/s1053-8119(03)00435-x
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发表时间：
2003-10-01
期刊：
影响因子：
5.7
通讯作者：
Smith, SM
中科院分区：
文献类型：
--
作者：
Beckmann, CF;Jenkinson, M;Smith, SM
本文讨论了多被试和/或多场次功能磁共振成像（FMRI）数据的一般建模。特别地，我们表明，如果在第二层的（协）方差被设定为等于单一层面形式的（协）方差之和（使用具有已知协方差的最佳线性无偏估计，即BLUE），那么一个两级混合效应模型（其中群体层面的感兴趣参数是从单场次层面的参数和方差估计值中估计出来的）可以等同于一个单一的完全混合效应模型（其中群体层面的感兴趣参数是直接从所有原始单场次的时间序列数据中估计出来的）。这一结果对FMRI中的群体研究具有重要意义，因为它表明群体分析仅需要第一层的参数估计值及其（协）方差，推广了FMRI中已确立的“汇总统计量”方法。这个简单且通用的框架允许对每个被试使用不同的预白化和不同的第一层回归变量。该框架包含多个层面以及诸如群体层面的重复测量、配对或非配对t检验和F检验等情况；文中给出了此类模型的明确示例。通过基于真实FMRI数据的典型第一层协方差结构进行数值模拟，我们证明了考虑较低层面的协方差和异质性可以使较高层面的Z分数大幅提高。© 2003爱思唯尔公司。保留所有权利。
This article discusses general modeling of multisubject and/or multisession FMRI data. In particular, we show that a two-level mixed-effects model (where parameters of interest at the group level are estimated from parameter and variance estimates from the single-session level) can be made equivalent to a single complete mixed-effects model (where parameters of interest at the group level are estimated directly from all of the original single sessions' time series data) if the (co-)variance at the second level is set equal to the sum of the (co-)variances in the single-level form, using the BLUE with known covariances. This result has significant implications for group studies in FMRI, since it shows that the group analysis requires only values of the parameter estimates and their (co-)variance from the first level, generalizing the well-established "summary statistics" approach in FMRI. The simple and generalized framework allows different prewhitening and different first-level regressors to be used for each subject. The framework incorporates multiple levels and cases such as repeated measures, paired or unpaired t tests and F tests at the group level; explicit examples of such models are given in the article. Using numerical simulations based on typical first-level covariance structures from real FMRI data we demonstrate that by taking into account lower-level covariances and heterogeneity a substantial increase in higher-level Z score is possible. (C) 2003 Elsevier Inc. All rights reserved.