Exploratory fMRI analysis by autocorrelation maximization.
A novel and computationally efficient method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data.
Friman
Ola
O
Department of Biomedical Engineering, Linköping University, University Hospital, Linköping, Sweden.
Borga
Magnus
M
Lundberg
Peter
P
Knutsson
Hans
H
D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging; D008433:Mathematics; D008606:Mental Processes / Q000502:physiology; D008962:Models, Theoretical; D025341:Principal Component Analysis; D013223:Statistics as Topic / Q000379:methods*; D013997:Time Factors