Wavelet-based estimation of a semiparametric generalized linear model of fMRI time-series. Export

@article{citeulike:797359, abstract = {This paper addresses the problem of detecting significant changes in fMRI time series that are correlated to a stimulus time course. This paper provides a new approach to estimate the parameters of a semiparametric generalized linear model of fMRI time series. The fMRI signal is described as the sum of two effects: a smooth trend and the response to the stimulus. The trend belongs to a subspace spanned by large scale wavelets. The wavelet transform provides an approximation to the Karhunen-Lo\`{e}ve transform for the long memory noise and we have developed a scale space regression that permits to carry out the regression in the wavelet domain while omitting the scales that are contaminated by the trend. In order to demonstrate that our approach outperforms the state-of-the art detrending technique, we evaluated our method against a smoothing spline approach. Experiments with simulated data and experimental fMRI data, demonstrate that our approach can infer and remove drifts that cannot be adequately represented with splines.}, address = {Department of Electrical Engineering, University of Colorado, Boulder, CO 80302, USA. francois.meyer@colorado.edu}, author = {Meyer, F. G.}, citeulike-article-id = {797359}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/12760549}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=12760549}, comment = {use wavelet to model drifts}, issn = {0278-0062}, journal = {IEEE Trans Med Imaging}, keywords = {drift, fmri, glm}, month = {March}, number = {3}, pages = {315--322}, posted-at = {2006-08-11 19:05:25}, priority = {2}, title = {Wavelet-based estimation of a semiparametric generalized linear model of fMRI time-series.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/12760549}, volume = {22}, year = {2003} }

TY - JOUR ID - citeulike:797359 L3 - citeulike-article-id:797359 N2 - This paper addresses the problem of detecting significant changes in fMRI time series that are correlated to a stimulus time course. This paper provides a new approach to estimate the parameters of a semiparametric generalized linear model of fMRI time series. The fMRI signal is described as the sum of two effects: a smooth trend and the response to the stimulus. The trend belongs to a subspace spanned by large scale wavelets. The wavelet transform provides an approximation to the Karhunen-Loève transform for the long memory noise and we have developed a scale space regression that permits to carry out the regression in the wavelet domain while omitting the scales that are contaminated by the trend. In order to demonstrate that our approach outperforms the state-of-the art detrending technique, we evaluated our method against a smoothing spline approach. Experiments with simulated data and experimental fMRI data, demonstrate that our approach can infer and remove drifts that cannot be adequately represented with splines. IS - 3 JF - IEEE Trans Med Imaging SN - 0278-0062 AD - Department of Electrical Engineering, University of Colorado, Boulder, CO 80302, USA. francois.meyer@colorado.edu EP - 322 TI - Wavelet-based estimation of a semiparametric generalized linear model of fMRI time-series. VL - 22 SP - 315 KW - drift KW - fmri KW - glm N1 - use wavelet to model drifts AU - Meyer, FG PY - 2003/03// UR - http://view.ncbi.nlm.nih.gov/pubmed/12760549 ER -