title: Comparing functional connectivity via thresholding correlations and singular value decomposition.
abstract: 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.
authors.affiliation: Department of Mathematics and Statistics, McGill University, Montreal, Canada. keith.worsley@mcgill.ca
authors.forename: Keith J
authors.initials: KJ
authors.lastname: Worsley
authors.forename: Jen-I
authors.initials: JI
authors.lastname: Chen
authors.forename: Jason
authors.initials: J
authors.lastname: Lerch
authors.forename: Alan C
authors.initials: AC
authors.lastname: Evans
mesh_terms: 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