Mixtures of general linear models for functional neuroimaging

28 May 2003

journal article
research article
Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Medical Imaging

Vol. 22 (4) , 504-514
https://doi.org/10.1109/tmi.2003.809140

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.

Keywords

This publication has 19 references indexed in Scilit:

Face Repetition Effects in Implicit and Explicit Memory Tests as Measured by fMRI
Cerebral Cortex, 2002
SMEM Algorithm for Mixture Models
Neural Computation, 2000
Anatomically Informed Basis Functions
NeuroImage, 2000
On Clustering fMRI Time Series
NeuroImage, 1999
Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI
Cerebral Cortex, 1997
Event-related f MRI
Human Brain Mapping, 1997
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
Biometrika, 1995
Statistical parametric maps in functional imaging: A general linear approach
Human Brain Mapping, 1994
A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors
IEEE Transactions on Medical Imaging, 1989
Generalized Linear Models
Published by Springer Nature ,1983