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INDEPENDENT COMPONENT ANALYSIS APPLIED TO fMRI DATA: A GENERATIVE MODEL FOR
 

Summary: INDEPENDENT COMPONENT ANALYSIS APPLIED
TO fMRI DATA: A GENERATIVE MODEL FOR
VALIDATING RESULTS
V. Calhoun1,2
, T. Adali,2
and G. Pearlson1
1
Johns Hopkins University Division of Psychiatric Neuro-Imaging,
600 N. Wolfe St., Baltimore, MD 21205
2
University of Maryland Baltimore County, Dept. of CSEE, Baltimore, MD 21250
ABSTRACT
We introduce and apply a synthesis/analysis model for analyzing functional
Magnetic Resonance Imaging (fMRI) data using independent component
analysis (ICA). Our model assumes statistically independent spatial sources in
the brain. We also assume that the fMRI scanner acquires overdetermined
data such that there are more time points than brain sources. We discuss the
properties of each of the signals present in the model. The analysis portion of
the model includes several candidates for spatial smoothing, ICA algorithm,
and data reduction. We use the Kullback-Leibler divergence between the

  

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

 

Collections: Engineering