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Optimal Spline Smoothing of fMRI Time Series by Generalized Cross-Validation (2002)
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| Venue: | NeuroImage |
| Citations: | 4 - 1 self |
BibTeX
@ARTICLE{Carew02optimalspline,
author = {John D. Carew and Grace Wahba and Xianhong Xie and Erik V. Nordheim and M. Elizabeth Meyerand},
title = {Optimal Spline Smoothing of fMRI Time Series by Generalized Cross-Validation},
journal = {NeuroImage},
year = {2002},
volume = {18},
pages = {950--961}
}
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Abstract
Linear parametric regression models of fMRI time series have correlated residuals. One approach to address this problem is to condition the autocorrelation structure by temporal smoothing. Smoothing splines with the degree of smoothing selected by generalized crossvalidation (GCV-Spline) provide a method to find an optimal smoother for an fMRI time series. The purpose of this study was to determine if GCV-Spline of fMRI time series yields unbiased variance estimates of linear regression model parameters. GCV-Spline was evaluated with a real fMRI data set and bias of the variance estimator was computed for simulated time series with autocorrelation structures derived from fMRI data. The results from the real data suggest that GCV-Spline determines appropriate amounts of smoothing. The simulations show that the variance estimates are, on average, unbiased. This study demonstrates that GCV-Spline is an appropriate method for smoothing fMRI time series.
Keyphrases
fmri time series optimal spline smoothing generalized cross-validation autocorrelation structure variance estimate linear parametric regression model gcv-spline determines appropriate amount temporal smoothing optimal smoother linear regression model parameter appropriate method real data variance estimator generalized crossvalidation real fmri data set fmri data simulated time series
