Paper No: 78
Subspaces of Spatially Varying Independent Components in fMRI
Author(s): Jarkko Ylipaavalniemi, Ricardo Vigário
Abstract
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. Particularly,
in studies using 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 incorporates the temporal dynamics of the
stimulation, and finds particular projections that maximally correlate
with the stimuli. |
