DocumentCode
773338
Title
Dimension reduction and spatiotemporal regression: applications to neuroimaging
Author
Shedden, Kerby ; Li, Ker-Chau
Author_Institution
Dept. of Stat., Michigan Univ., Ann Arbor, MI, USA
Volume
5
Issue
5
fYear
2003
Firstpage
30
Lastpage
36
Abstract
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.
Keywords
biology computing; brain; data analysis; data reduction; spatiotemporal phenomena; statistical analysis; brain activity levels; neuroimaging; spatiotemporal regression; spatiotemporal variation; statistical dimension reduction; temporal components; Brain; Data analysis; Fluid flow measurement; Independent component analysis; Magnetic resonance imaging; Neuroimaging; Positron emission tomography; Principal component analysis; Probability; Spatiotemporal phenomena;
fLanguage
English
Journal_Title
Computing in Science & Engineering
Publisher
ieee
ISSN
1521-9615
Type
jour
DOI
10.1109/MCISE.2003.1225858
Filename
1225858
Link To Document