Chapter

Independent Component Analysis and Blind Signal Separation

Volume 3195 of the series Lecture Notes in Computer Science pp 1103-1110

Unraveling Spatio-temporal Dynamics in fMRI Recordings Using Complex ICA

  • Jörn AnemüllerAffiliated withSwartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San DiegoComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, Howard Hughes Medical Institute
  • , Jeng-Ren DuannAffiliated withSwartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San DiegoComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, Howard Hughes Medical Institute
  • , Terrence J. SejnowskiAffiliated withSwartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San DiegoComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, Howard Hughes Medical Institute
  • , Scott MakeigAffiliated withSwartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San DiegoComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, Howard Hughes Medical Institute

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Abstract

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented as a spatially fixed pattern of activation, which leads to the instantaneous mixing model. To allow modeling patterns of spatio-temporal dynamics, in particular, the flow of oxygenated blood, we have developed a convolutive ICA approach: spatial complex ICA applied to frequency-domain fMRI data. In several frequency-bands, we identify components pertaining to activity in primary visual cortex (V1) and blood supply vessels. One such component, obtained in the 0.10-Hz band, is analyzed in detail and found to likely reflect flow of oxygenated blood in V1.