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An ICA Approach to Detect Functionally Different Intra-regional Neuronal Signals in MEG Data

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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Abstract

Cerebral processing mainly relies on functional connectivity among involved regions. Neuro-imaging techniques able to assess these links with suitable time resolution are electro- and magneto-encephalography (EEG and MEG), even if it is difficult to localize recorded extra-cranial information, particularly within restricted areas, due to complexity of the ‘inverse problem’. By means of Independent Component Analysis (ICA) a procedure ‘blind’ to position and biophysical properties of the generators, our aim in this work was to identify cerebral functionally different sources in a restricted area. MEG data of 5 subjects were collected performing a relax-movement motor task in 5 different days. ICA reliably extracted neural networks differently modulated during the task in the frequency range of interest. In conclusion, a procedure solely based on statistical properties of the signals, disregarding their spatial positions, was demonstrated able to discriminate functionally different neuronal pools activities in a very restricted cortical area.

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Authors and Affiliations

  1. AFaR – Center of Medical Statistics and Information Technology, Fatebenefratelli Hospital, Rome, Italy

    Giulia Barbati, Camillo Porcaro, Filippo Zappasodi & Franca Tecchio

  2. ISTC – Institute of Science and Technologies of Cognition, CNR, Rome, Italy

    Filippo Zappasodi & Franca Tecchio

Authors
  1. Giulia Barbati
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  2. Camillo Porcaro
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  3. Filippo Zappasodi
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  4. Franca Tecchio
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Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada,  

    Alberto Prieto

  3. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

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© 2005 Springer-Verlag Berlin Heidelberg

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Barbati, G., Porcaro, C., Zappasodi, F., Tecchio, F. (2005). An ICA Approach to Detect Functionally Different Intra-regional Neuronal Signals in MEG Data. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_133

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  • DOI: https://doi.org/10.1007/11494669_133

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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