An ICA approach to detect functionally different intra-regional neuronal signals in MEG data

  • Authors:

  • Giulia Barbati;Camillo Porcaro;Filippo Zappasodi;Franca Tecchio

  • Affiliations:

  • AFaR – Center of Medical Statistics and Information Technology, Fatebenefratelli Hospital, Rome, Italy;AFaR – Center of Medical Statistics and Information Technology, Fatebenefratelli Hospital, Rome, Italy;AFaR – Center of Medical Statistics and Information Technology, Fatebenefratelli Hospital, Rome, Italy;AFaR – Center of Medical Statistics and Information Technology, Fatebenefratelli Hospital, Rome, Italy

  • Venue:
  • IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
  • Year:
  • 2005

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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.