Optimal Spline Smoothing of FMRI Time Series
نویسندگان
چکیده
Smoothing splines with generalized cross-validation parameter selection (GCV-spline) provide a method to find an optimal smoother for an fMRI time series. The purpose of this study was to compare the variance of parameter estimates and the bias of the variance estimator for a linear regression model smoothed with GCV-spline and the low-pass filter in SPM99 (SPM-HRF). The mean bias with the SPM-HRF method was 22.2% greater than the GCV-spline method. The variance was not significantly different between the two methods. This study demonstrates that GCV-spline is an appropriate method for smoothing fMRI time
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Optimal spline smoothing of fMRI time series by generalized cross-validation.
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 cross-validation (GCV-spline) provide a method to find an optimal smoother for an fMRI time series. The purpose of this study was to dete...
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