• DocumentCode
    1567021
  • Title

    Detecting granger causality in the corticostriatal learning and rewards network using MEG

  • Author

    Kanal, Eliezer ; Ozkurt, Tolga ; Sclabassi, Robert J. ; Sun, Mingui

  • Author_Institution
    Dept. of Bioeng., Univ. of Pittsburgh, Pittsburgh, PA
  • fYear
    2009
  • Firstpage
    1
  • Lastpage
    2
  • Abstract
    Much of the neural activity at the network scale occurs in both telencephalic and mes- and diencephalic tissue. Non-invasive functional imaging such activity has historically been limited to functional magnetic resonance imaging (fMRI) experiments, which has a minimum temporal resolution of four seconds. In this paper, we describe the use of the recently developed exSSS signal processing method to extract the functional activity of the striatum and orbitofrontal cortex (OFC) from functional magnetoencephalography (MEG) signal. The activation was achieved via the replication of a gambling paradigm found in the literature, and the observed neural activation is consistent with previously reported results.
  • Keywords
    feature extraction; image resolution; magnetoencephalography; medical signal detection; medical signal processing; neurophysiology; MEG signal; corticostriatal learning; diencephalic tissue; exSSS signal processing method; functional activity extraction; functional magnetic resonance imaging; granger causality detection; magnetoencephalography; minimum temporal resolution; neural activity; orbitofrontal cortex; telencephalic tissue; Biomedical engineering; Brain modeling; Cognition; Computer networks; Humans; Imaging phantoms; Magnetic fields; Magnetic heads; Reactive power; Signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bioengineering Conference, 2009 IEEE 35th Annual Northeast
  • Conference_Location
    Boston, MA
  • Print_ISBN
    978-1-4244-4362-8
  • Electronic_ISBN
    978-1-4244-4364-2
  • Type

    conf

  • DOI
    10.1109/NEBC.2009.4967664
  • Filename
    4967664