Applied multivariate analysis
Brains and phantoms: an ICA study of fMRI
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Arabica: Robust ICA in a Pipeline
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Distributional convergence of subspace estimates in FastICA: a bootstrap study
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Uniqueness of linear factorizations into independent subspaces
Journal of Multivariate Analysis
Hi-index | 0.00 |
In contrast to the traditional hypothesis-driven methods, independent component analysis (ICA) is commonly used in functional magnetic resonance imaging (fMRI) studies to identify, in a blind manner, spatially independent elements of functional brain activity. ICA is particularly useful in studies with multi-modal stimuli or natural environments, where the brain responses are poorly predictable, and their individual elements may not be directly relatable to the given stimuli. This paper extends earlier work on analyzing the consistency of ICA estimates, by focusing on the spatial variability of the components, and presents a novel method for reliably identifying subspaces of functionally related independent components. Furthermore, two approaches are considered for refining the decomposition within the subspaces. Blind refinement is based on clustering all estimates in the subspace to reveal its internal structure. Guided refinement, incorporating the temporal dynamics of the stimulation, finds particular projections that maximally correlate with the stimuli.