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Biostatistics Advance Access published online on April 20, 2005
Biostatistics, doi:10.1093/biostatistics/kxi027
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© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org.
Received January 1, 2004
Revised March 9, 2005
Accepted April 12, 2005

Article

Spatio-temporal Modeling of Localized Brain Activity

F. DuBois Bowman *

* To whom correspondence should be addressed.
F. DuBois Bowman, E-mail: dbowma3{at}sph.emory.edu


  Abstract

Functional neuroimaging, including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), plays an important role in identifying specific brain regions associated with experimental stimuli or psychiatric disorders such as schizophrenia. PET and fMRI produce massive data sets that contain both temporal correlations from repeated scans and complex spatial correlations. Several methods exist for handling temporal correlations, some of which rely on transforming the response data to induce either a known or independence covariance structure. Despite the presence of spatial correlations between the volume elements (voxels) comprising a brain scan, conventional methods perform voxel-by-voxel analyses of measured brain activity. We propose a two-stage spatio-temporal model for estimation and testing of localized activity. Our second-stage model specifies a spatial autoregression, capturing correlations within neural processing clusters defined by a data-driven cluster analysis. We use maximum likelihood methods to estimate parameters from our spatial autoregressive model. Our model protects against type-I errors, enables the detection of both localized and regional activations (including volume of interest effects), provides information on functional connectivity in the brain, and establishes a framework to produce spatially smoothed maps of distributed brain activity for each individual. We illustrate the application of our model using PET data from a study of working memory in individuals with schizophrenia.

Keywords: Neuroimaging; spatial autoregressive model; hierarchical model; random effects; cluster analysis; PET; fMRI.
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