A slice-wise latent structure regression method for the analysis of functional magnetic resonance imaging data

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We introduce a novel slice-wise latent structure regression (LSR) method for the analysis of functional magnetic resonance imaging (fMRI) data instead of the conventional voxel-wise generalized least squares (GLS) method. LSR method is designed for application to data sets from slices where fMRI responses (voxels Y-* of a slice) are highly correlated with the design matrix X-*. Also, we compared the performances of LSR, principal component regression (PCR), and GLS methods in terms of model parameters using experimental fMRI data. The LSR method exhibits an enhanced predictive ability and model coefficients as compared to the PCR and GLS methods. (c) 2013 Wiley Periodicals, Inc. Concepts Magn Reson Part A 42A: 130-139, 2013.
Publisher
WILEY-BLACKWELL
Issue Date
2013-07
Language
English
Article Type
Article
Keywords

FMRI DATA; RIDGE-REGRESSION; PREDICTION; PROJECTION

Citation

CONCEPTS IN MAGNETIC RESONANCE PART A, v.42, no.4, pp.130 - 139

ISSN
1546-6086
DOI
10.1002/cmr.a.21270
URI
http://hdl.handle.net/10203/192854
Appears in Collection
MA-Journal Papers(저널논문)EE-Journal Papers(저널논문)
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