Format

Send to

Choose Destination
IEEE Trans Med Imaging. 2003 Apr;22(4):504-14.

Mixtures of general linear models for functional neuroimaging.

Author information

1
Wellcome Department of Imaging Neuroscience, University College, 12 Queen Square, London WC1N 3BG, UK. wpenny@fil.ion.ucl.ac.uk

Abstract

We set out a new general framework for making inferences from neuroimaging data, which includes a standard approach to neuroimaging analysis, statistical parametric mapping (SPM), as a special case. The model offers numerous conceptual and statistical advantages that derive from analyzing data at the "cluster level" rather than the "voxel level" and from explicit modeling of the shape and position of clusters of activation. This provides a natural and principled way to pool data from nearby voxels for parameter and variance-component estimation. The model can also be viewed as performing a spatio-temporal cluster analysis. The parameters of the model are estimated using an expectation maximization (EM) algorithm.

PMID:
12774896
DOI:
10.1109/TMI.2003.809140
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for IEEE Engineering in Medicine and Biology Society
Loading ...
Support Center