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Independent Component Analysis of fMRI Data in the Complex Domain
 

Summary: Independent Component Analysis of fMRI Data in the
Complex Domain
V.D. Calhoun,1,4* T. Adali,4
G.D. Pearlson,1
P.C.M. van Zijl,2,3
and J.J. Pekar2,3*
In BOLD fMRI a series of MR images is acquired and examined
for task-related amplitude changes. These functional changes
are small, so it is important to maximize detection efficiency.
Virtually all fMRI processing strategies utilize magnitude infor-
mation and ignore the phase, resulting in an unnecessary loss
of efficiency. As the optimum way to model the phase informa-
tion is not clear, a flexible modeling technique is useful. To
analyze complex data sets, independent component analysis
(ICA), a data-driven approach, is proposed. In ICA, the data are
modeled as spatially independent components multiplied by
their respective time-courses. There are thus three possible
approaches: 1) the time-courses can be complex-valued, 2) the
images can be complex-valued, or 3) both the time-courses and
the images can be complex-valued. These analytic approaches

  

Source: Adali, Tulay - Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County

 

Collections: Engineering