Dimension reduction and spatiotemporal regression: applications to neuroimaging

One method for characterizing spatiotemporal variation in brain activity levels is based on the use of statistical dimension reduction. This reduction finds temporal components in data that best preserve the spatiotemporal regression structure. The method does this by suppressing more prominent wave...

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Bibliographic Details
Published in:Computing in science & engineering 2003-09, Vol.5 (5), p.30-36
Main Authors: Sheddeni, K., Ker-Chau Li
Format: Article
Language:English
Subjects:
Brain
Data analysis
Fluid flow measurement
Independent component analysis
Magnetic resonance imaging
Neuroimaging
Positron emission tomography
Principal component analysis
Probability
Spatiotemporal phenomena
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Summary:One method for characterizing spatiotemporal variation in brain activity levels is based on the use of statistical dimension reduction. This reduction finds temporal components in data that best preserve the spatiotemporal regression structure. The method does this by suppressing more prominent waveforms that do not vary in a spatially predictable pattern.
ISSN:1521-9615
1558-366X
DOI:10.1109/MCISE.2003.1225858