Journal
NEUROIMAGE
Volume 29, Issue 1, Pages 145-154Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2005.07.054
Keywords
dimensionality estimation; data reduction; PCA; principal component analysis; fMRI; functional magnetic resonance imaging
Funding
- PHS HHS [P5033812] Funding Source: Medline
Ask authors/readers for more resources
A new method based on an autoregressive noise model of order I is introduced to the problem of detecting the number of components in fMRI data. Unlike current information-theoretic criteria like AIC, MDL, and related PPCA, which do not incorporate autocorrelations in the noise, the new method leads to more consistent estimates of the model order, as illustrated in simulated and real fMRI resting-state data. (c) 2005 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available
Share Paper
