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
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;
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
DOI :
10.1109/NEBC.2009.4967664