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Spatial and Temporal Independent Component Analysis of Functional MRI Data Containing a
 

Summary: Spatial and Temporal Independent Component
Analysis of Functional MRI Data Containing a
Pair of Task-Related Waveforms
V.D. Calhoun,1,4* T. Adali,4
G.D. Pearlson,1
and J.J. Pekar2,3*
1
Division of Psychiatric Neuro-Imaging, Johns Hopkins University, Baltimore, Maryland
2
Department of Radiology, Johns Hopkins University, Baltimore, Maryland
3
FM Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute,
Baltimore, Maryland
4
Department of CSEE, University of Maryland, Baltimore, Maryland
Abstract: Independent component analysis (ICA) is a technique that attempts to separate data into maximally
independent groups. Achieving maximal independence in space or time yields two varieties of ICA mean-
ingful for functional MRI (fMRI) applications: spatial ICA (SICA) and temporal ICA (TICA). SICA has so far
dominated the application of ICA to fMRI. The objective of these experiments was to study ICA with two
predictable components present and evaluate the importance of the underlying independence assumption in

  

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

 

Collections: Engineering