title: Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study.
abstract: Brain functional connectivity (FC) is often assessed from fMRI data using seed-based methods, such as those of detecting temporal correlation between a predefined region (seed) and all other regions in the brain; or using multivariate methods, such as independent component analysis (ICA). ICA is a useful data-driven tool, but reproducibility issues complicate group inferences based on FC maps derived with ICA. These reproducibility issues can be circumvented with hybrid methods that use information from ICA-derived spatial maps as seeds to produce seed-based FC maps. We report results from five experiments to demonstrate the potential advantages of hybrid ICA-seed-based FC methods, comparing results from regressing fMRI data against task-related a priori time courses, with "back-reconstruction" from a group ICA, and with five hybrid ICA-seed-based FC methods: ROI-based with (1) single-voxel, (2) few-voxel, and (3) many-voxel seed; and dual-regression-based with (4) single ICA map and (5) multiple ICA map seed.
authors.affiliation: Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medical College, 21 Bloomingdale Road, White Plains, NY 10605, USA.
authors.forename: Robert E
authors.initials: RE
authors.lastname: Kelly
authors.forename: Zhishun
authors.initials: Z
authors.lastname: Wang
authors.forename: George S
authors.initials: GS
authors.lastname: Alexopoulos
authors.forename: Faith M
authors.initials: FM
authors.lastname: Gunning
authors.forename: Christopher F
authors.initials: CF
authors.lastname: Murphy
authors.forename: Sarah Shizuko
authors.initials: SS
authors.lastname: Morimoto
authors.forename: Dora
authors.initials: D
authors.lastname: Kanellopoulos
authors.forename: Zhiru
authors.initials: Z
authors.lastname: Jia
authors.forename: Kelvin O
authors.initials: KO
authors.lastname: Lim
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Hoptman