Unraveling spatio-temporal dynamics in fMRI recordings using complex ICA

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

doi:10.1007/978-3-540-30110-3_139

Unraveling spatio-temporal dynamics in fMRI recordings using complex ICA

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

Graduate Institute of Cognitive Neuroscience

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 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 spatiotemporal 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 V1¹.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Carlos G. Puntonet, Alberto Prieto
Publisher	Springer Verlag
Pages	1103-1110
Number of pages	8
ISBN (Electronic)	3540230564, 9783540230564
DOIs	https://doi.org/10.1007/978-3-540-30110-3_139
State	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3195
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-30110-3_139

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Anemüller, J., Duann, J. R., Sejnowski, T. J., & Makeig, S. (2004). Unraveling spatio-temporal dynamics in fMRI recordings using complex ICA. In C. G. Puntonet, & A. Prieto (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 1103-1110). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3195). Springer Verlag. https://doi.org/10.1007/978-3-540-30110-3_139

Anemüller, Jörn ; Duann, Jeng Ren ; Sejnowski, Terrence J. et al. / Unraveling spatio-temporal dynamics in fMRI recordings using complex ICA. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Carlos G. Puntonet ; Alberto Prieto. Springer Verlag, 2004. pp. 1103-1110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Anemüller, J, Duann, JR, Sejnowski, TJ & Makeig, S 2004, Unraveling spatio-temporal dynamics in fMRI recordings using complex ICA. in CG Puntonet & A Prieto (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3195, Springer Verlag, pp. 1103-1110. https://doi.org/10.1007/978-3-540-30110-3_139

Unraveling spatio-temporal dynamics in fMRI recordings using complex ICA. / Anemüller, Jörn; Duann, Jeng Ren; Sejnowski, Terrence J. et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Carlos G. Puntonet; Alberto Prieto. Springer Verlag, 2004. p. 1103-1110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3195).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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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 spatiotemporal 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 V11.

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Anemüller J, Duann JR, Sejnowski TJ, Makeig S. Unraveling spatio-temporal dynamics in fMRI recordings using complex ICA. In Puntonet CG, Prieto A, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 1103-1110. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-30110-3_139

Unraveling spatio-temporal dynamics in fMRI recordings using complex ICA

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