Modeling and inference of multisubject fMRI data - Using mixed-effects models for joint analysis
Modeling and inference of multisubject fMRI data - Using mixed-effects models for joint analysis
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DOI：
10.1109/memb.2006.1607668
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发表时间：
2006-03-01
影响因子：
--
通讯作者：
Nichols, T
中科院分区：
文献类型：
--
作者：
Mumford, JA;Nichols, T
This article reviews four commonly used approaches to group modeling in fMRI. The methods differ in their computational intensity (FSL with its two-level estimation including MCM being the most intense) and assumptions (SPM2 with its assumption of spatially homogeneous covariance V/sub g/ being most restrictive). This study also distinguishes fixed-effects models from mixed-effects models and motivates the importance of a mixed-effects model for group fMRI analysis. The sections following that describe single-subject modeling and show a general method for estimating the group model.
