Literature DB >> 20689619

Spatio-temporal dynamics in fMRI recordings revealed with complex independent component analysis.

Jörn Anemüller1, Jeng-Ren Duann, Terrence J Sejnowski, Scott Makeig.   

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.

Entities:  

Year:  2006        PMID: 20689619      PMCID: PMC2916201          DOI: 10.1016/j.neucom.2005.12.029

Source DB:  PubMed          Journal:  Neurocomputing        ISSN: 0925-2312            Impact factor:   5.719


  9 in total

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3.  Single-trial variability in event-related BOLD signals.

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4.  Independent component analysis of fMRI data in the complex domain.

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Journal:  Magn Reson Med       Date:  2002-07       Impact factor: 4.668

Review 5.  Nonlinear complex-valued extensions of Hebbian learning: an essay.

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Journal:  Neural Comput       Date:  2005-04       Impact factor: 2.026

6.  Analysis of fMRI data by blind separation into independent spatial components.

Authors:  M J McKeown; S Makeig; G G Brown; T P Jung; S S Kindermann; A J Bell; T J Sejnowski
Journal:  Hum Brain Mapp       Date:  1998       Impact factor: 5.038

7.  Spatially independent activity patterns in functional MRI data during the stroop color-naming task.

Authors:  M J McKeown; T P Jung; S Makeig; G Brown; S S Kindermann; T W Lee; T J Sejnowski
Journal:  Proc Natl Acad Sci U S A       Date:  1998-02-03       Impact factor: 11.205

8.  An information-maximization approach to blind separation and blind deconvolution.

Authors:  A J Bell; T J Sejnowski
Journal:  Neural Comput       Date:  1995-11       Impact factor: 2.026

9.  Complex independent component analysis of frequency-domain electroencephalographic data.

Authors:  Jörn Anemüller; Terrence J Sejnowski; Scott Makeig
Journal:  Neural Netw       Date:  2003-11
  9 in total
  4 in total

1.  Independent component analysis applied to the removal of motion artifacts from electrocardiographic signals.

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2.  Utility of independent component analysis for interpretation of intracranial EEG.

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Journal:  Front Hum Neurosci       Date:  2010-11-02       Impact factor: 3.169

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4.  Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: a concurrent EEG-fMRI study.

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  4 in total

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