Comparing functional connectivity via thresholding correlations and singular value decomposition.
We compare two common methods for detecting functional connectivity: thresholding correlations and singular value decomposition (SVD). We find that thresholding correlations are better at detecting focal regions of correlated voxels, whereas SVD is better at detecting extensive regions of correlated voxels. We apply these results to resting state networks in an fMRI dataset to look for connectivity in cortical thickness.
Worsley
Keith J
KJ
Department of Mathematics and Statistics, McGill University, Montreal, Canada. keith.worsley@mcgill.ca
Chen
Jen-I
JI
Lerch
Jason
J
Evans
Alan C
AC
D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological