Noise reduction in BOLD-based fMRI using component analysis. Export

@article{citeulike:833288, abstract = {Principle Component Analysis (PCA) and Independent Component Analysis (ICA) were used to decompose the fMRI time series signal and separate the BOLD signal change from the structured and random noise. Rather than using component analysis to identify spatial patterns of activation and noise, the approach we took was to identify PCA or ICA components contributing primarily to the noise. These noise components were identified using an unsupervised algorithm that examines the Fourier decomposition of each component time series. Noise components were then removed before subsequent reconstruction of the time series data. The BOLD contrast sensitivity (CS(BOLD)), defined as the ability to detect a BOLD signal change in the presence of physiological and scanner noise, was then calculated for all voxels. There was an increase in CS(BOLD) values of activated voxels after noise reduction as a result of decreased image-to-image variability in the time series of each voxel. A comparison of PCA and ICA revealed significant differences in their treatment of both structured and random noise. ICA proved better for isolation and removal of structured noise, while PCA was superior for isolation and removal of random noise. This provides a framework for using and evaluating component analysis techniques for noise reduction in fMRI.}, address = {Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.}, author = {Thomas, C. G. and Harshman, R. A. and Menon, R. S.}, citeulike-article-id = {833288}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/12414291}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=12414291}, comment = {good state-of-the-art. They present a comparative method based on PCA and ICA for denoising structural AND random noise.}, issn = {1053-8119}, journal = {Neuroimage}, keywords = {fmri, ica, noise}, month = {November}, number = {3}, pages = {1521--1537}, posted-at = {2006-09-18 19:38:01}, priority = {2}, title = {Noise reduction in BOLD-based fMRI using component analysis.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/12414291}, volume = {17}, year = {2002} }

TY - JOUR ID - citeulike:833288 L3 - citeulike-article-id:833288 N2 - Principle Component Analysis (PCA) and Independent Component Analysis (ICA) were used to decompose the fMRI time series signal and separate the BOLD signal change from the structured and random noise. Rather than using component analysis to identify spatial patterns of activation and noise, the approach we took was to identify PCA or ICA components contributing primarily to the noise. These noise components were identified using an unsupervised algorithm that examines the Fourier decomposition of each component time series. Noise components were then removed before subsequent reconstruction of the time series data. The BOLD contrast sensitivity (CS(BOLD)), defined as the ability to detect a BOLD signal change in the presence of physiological and scanner noise, was then calculated for all voxels. There was an increase in CS(BOLD) values of activated voxels after noise reduction as a result of decreased image-to-image variability in the time series of each voxel. A comparison of PCA and ICA revealed significant differences in their treatment of both structured and random noise. ICA proved better for isolation and removal of structured noise, while PCA was superior for isolation and removal of random noise. This provides a framework for using and evaluating component analysis techniques for noise reduction in fMRI. IS - 3 JF - Neuroimage SN - 1053-8119 AD - Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada. EP - 1537 TI - Noise reduction in BOLD-based fMRI using component analysis. VL - 17 SP - 1521 KW - fmri KW - ica KW - noise N1 - good state-of-the-art. They present a comparative method based on PCA and ICA for denoising structural AND random noise. AU - Thomas, CG AU - Harshman, RA AU - Menon, RS PY - 2002/11// UR - http://view.ncbi.nlm.nih.gov/pubmed/12414291 ER -