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

Jörn Anemüller; Jeng Ren Duann; Terrence J. Sejnowski; Scott Makeig

doi:10.1016/j.neucom.2005.12.029

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

Jörn Anemüller^*, Jeng Ren Duann, Terrence J. Sejnowski, Scott Makeig

^*Corresponding author for this work

Institute of Education

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

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.

Original language	English
Pages (from-to)	1502-1512
Number of pages	11
Journal	Neurocomputing
Volume	69
Issue number	13-15
DOIs	https://doi.org/10.1016/j.neucom.2005.12.029
State	Published - Aug 2006

Keywords

Biomedical signal analysis
Complex independent component analysis (complex ICA)
Convolution model
Functional magnetic resonance imaging (fMRI)
Hemodynamic response
Primary visual cortex (VI)
Spatio-temporal dynamics
Statistical signal processing

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10.1016/j.neucom.2005.12.029

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title = "Spatio-temporal dynamics in fMRI recordings revealed with complex independent component analysis",

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.",

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year = "2006",

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language = "English",

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AU - Anemüller, Jörn

AU - Duann, Jeng Ren

AU - Sejnowski, Terrence J.

AU - Makeig, Scott

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AB - 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.

KW - Biomedical signal analysis

KW - Complex independent component analysis (complex ICA)

KW - Convolution model

KW - Functional magnetic resonance imaging (fMRI)

KW - Hemodynamic response

KW - Primary visual cortex (VI)

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