期刊
NEUROIMAGE
卷 29, 期 1, 页码 145-154出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2005.07.054
关键词
dimensionality estimation; data reduction; PCA; principal component analysis; fMRI; functional magnetic resonance imaging
资金
- PHS HHS [P5033812] Funding Source: Medline
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.
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