Journal
NEUROIMAGE
Volume 15, Issue 1, Pages 1-15Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1006/nimg.2001.0933
Keywords
fMRI; hemodynamic response function; linear regression; random effects; EM algorithm; bias reduction
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We propose a method for the statistical analysis of fMRI data that seeks a compromise between efficiency, generality, validity, simplicity, and execution speed. The main differences between this analysis and previous ones are: a simple bias reduction and regularization for voxel-wise autoregressive model parameters; the combination of effects and their estimated standard deviations across different runs/sessions/subjects via a hierarchical random effects analysis using the EM algorithm; overcoming the problem of a small number of runs/session/subjects using a regularized variance ratio to increase the degrees of freedom. (C) 2002 Elsevier Science.
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