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
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;
Journal_Title :
Computing in Science & Engineering
DOI :
10.1109/MCISE.2003.1225858
