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Short Term Evaluation of Brain Activities in fMRI Data by Spatiotemporal Independent Component Analysis

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Medical Data Analysis (ISMDA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2526))

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Abstract

At present, Independent Components Analysis (ICA) represents the most important and efficient approach for extraction of independent non- Gaussian linearly mixed signals. This statistic-informative technique has been successfully applied to fMRI temporal data, which can be considered as an overlapped mixture of hemodynamic signals, physiological perturbations and noise. In this paper an extension to spatial application of ICA (sICA) was performed. The results confirmed that the spatial approach permits to obtain improved identification of brain activities, even when the temporal length of data is reduced.

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, “La Sapienza” University of Rome, via Eudossiana, 18, I-00184, Rome, Italy

    M. Balsi, V. Cimagalli, A. Londei & P.L. Romanelli

  2. ECONA - Interuniversity Center for Research on Cognitive Processing in Natural and Artificial Systems, Rome, Italy

    M. Balsi, V. Cimagalli & A. Londei

  3. Department of Neurological Sciences, “La Sapienza” University of Rome, Viale dell.Università, 30, Rome, Italy

    G. Cruccu, G.D. Iannetti & P.L. Romanelli

Authors
  1. M. Balsi
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  2. V. Cimagalli
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  3. G. Cruccu
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  4. G.D. Iannetti
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  5. A. Londei
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  6. P.L. Romanelli
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Editor information

Editors and Affiliations

  1. Dept. of Human Physiology and Pharmacology, University of Rome La Sapienza, P. le A. Moro, 5, 00185, Rome, Italy

    Alfredo Colosimo  & Paolo Sirabella  & 

  2. TCE Laboratory, Istituto Superiore di Sanità, V. le Regina Elena, 299, 00161, Rome, Italy

    Alessandro Giuliani

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© 2002 Springer-Verlag Berlin Heidelberg

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Balsi, M., Cimagalli, V., Cruccu, G., Iannetti, G., Londei, A., Romanelli, P. (2002). Short Term Evaluation of Brain Activities in fMRI Data by Spatiotemporal Independent Component Analysis. In: Colosimo, A., Sirabella, P., Giuliani, A. (eds) Medical Data Analysis. ISMDA 2002. Lecture Notes in Computer Science, vol 2526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36104-9_19

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  • DOI: https://doi.org/10.1007/3-540-36104-9_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00044-0

  • Online ISBN: 978-3-540-36104-6

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