Hybrid ICA-seed-based methods for fMRI functional connectivity assessment: A feasibility study

Robert E. Kelly, Zhishun Wang, George S. Alexopoulos, Faith M. Gunning, Christopher F. Murphy, Sarah Shizuko Morimoto, Dora Kanellopoulos, Zhiru Jia, Kelvin O. Lim, Matthew J. Hoptman

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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.

Original languageEnglish (US)
Article number868976
JournalInternational Journal of Biomedical Imaging
StatePublished - 2010


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