TY - JOUR
T1 - Spatial and temporal reproducibility-based ranking of the independent components of BOLD fMRI data
AU - Zeng, Weiming
AU - Qiu, Anqi
AU - Chodkowski, Betty Ann
AU - Pekar, James J.
PY - 2009/7/15
Y1 - 2009/7/15
N2 - Independent component analysis (ICA) decomposes fMRI data into spatially independent maps and their corresponding time courses. However, distinguishing the neurobiologically and biophysically reasonable components from those representing noise and artifacts is not trivial. We present a simple method for the ranking of independent components, by assessing the resemblance between components estimated from all the data, and components estimated from only the odd- (or even-) numbered time points. We show that the meaningful independent components of fMRI data resemble independent components estimated from downsampled data, and thus tend to be highly ranked by the method.
AB - Independent component analysis (ICA) decomposes fMRI data into spatially independent maps and their corresponding time courses. However, distinguishing the neurobiologically and biophysically reasonable components from those representing noise and artifacts is not trivial. We present a simple method for the ranking of independent components, by assessing the resemblance between components estimated from all the data, and components estimated from only the odd- (or even-) numbered time points. We show that the meaningful independent components of fMRI data resemble independent components estimated from downsampled data, and thus tend to be highly ranked by the method.
KW - Independent component analysis
KW - Maximum mean correlation
KW - fMRI
UR - http://www.scopus.com/inward/record.url?scp=67349150665&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67349150665&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2009.02.048
DO - 10.1016/j.neuroimage.2009.02.048
M3 - Article
C2 - 19286465
AN - SCOPUS:67349150665
SN - 1053-8119
VL - 46
SP - 1041
EP - 1054
JO - NeuroImage
JF - NeuroImage
IS - 4
ER -