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Stimulus-Independent Data Analysis for fMRI

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Emergent Neural Computational Architectures Based on Neuroscience

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2036))

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Abstract

We discuss methods for analyzing fMRI data, stimulus-based such as baseline substraction and correlation analysis versus stimulus- independent methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) with respect to their capabil- ities of separating noise sources from functional activity. The methods are applied to a finger tapping fMRI experiment and it is shown that the stimulus-independent methods in addition to the extraction of the stimulus can reveal several non-stimulus related influences such as head movements or breathing.

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References

  1. J.F. Cardoso, A. Souloumiac, 1993, Blind Beamforming for Non Gaussian Signals, IEE-Proceedings-F, 140,No 6, 362–370

    Google Scholar 

  2. S. Dodel, J.M. Herrmann, T. Geisel, 2000, Comparison of Temporal and Spatial ICA in fMRI Data Analysis, Proc. ICA 2000, Helsinki, Finland, 543–547

    Google Scholar 

  3. J. Frahm, 1999, Magnetic Resonance Functional Neuroimaging: New Insights into the Human Brain, Current Science 76, 735–743

    Google Scholar 

  4. McKeown MJ. Sejnowski TJ, 1998, Independent Component Analysis of fMRI Data-Examining the Assumptions, Human Brain Mapping 6(5-6), 368–372

    Google Scholar 

  5. P. McCullagh, 1987, Tensor Methods in Statistics, Chapman and Hall, New York

    Google Scholar 

  6. W.H. Press, 1994, Numerical recipes in C, Cambridge Univ. Press

    Google Scholar 

  7. A. Stuart, K. Ord, 1994, Kendall’s Advanced Theory of Statistics, Vol. 1 Distribution theory, Halsted Press

    Google Scholar 

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Author information

Authors and Affiliations

  1. Max-Planck-Institut für Strömungsforschung, Bunsenstraße 10, 37073, Göttingen, Germany

    Silke Dodel, J. Michael Herrmann & Theo Geisel

Authors
  1. Silke Dodel
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  2. J. Michael Herrmann
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  3. Theo Geisel
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Editor information

Editors and Affiliations

  1. Centre of Informatics, SCET, University of Sunderland, St PetersWay, Sunderland SR6 0DD, UK

    Stefan Wermter

  2. Department of Computer Science, University of York, York, Y010 5DD, UK

    Jim Austin

  3. Institute for Adaptive and Neural Computation, University of Edinburgh, 5 Forrest Hill, Edinburgh, UK

    David Willshaw

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© 2001 Springer-Verlag Berlin Heidelberg

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Dodel, S., Herrmann, J.M., Geisel, T. (2001). Stimulus-Independent Data Analysis for fMRI. In: Wermter, S., Austin, J., Willshaw, D. (eds) Emergent Neural Computational Architectures Based on Neuroscience. Lecture Notes in Computer Science(), vol 2036. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44597-8_3

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  • DOI: https://doi.org/10.1007/3-540-44597-8_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42363-8

  • Online ISBN: 978-3-540-44597-5

  • eBook Packages: Springer Book Archive

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