Brain Topography

, Volume 19, Issue 3, pp 107–123

Estimate of Causality Between Independent Cortical Spatial Patterns During Movement Volition in Spinal Cord Injured Patients

  • Laura Astolfi
  • Hovagim Bakardjian
  • Febo Cincotti
  • Donatella Mattia
  • Maria Grazia Marciani
  • Fabrizio De Vico Fallani
  • Alfredo Colosimo
  • Serenella Salinari
  • Fumikazu Miwakeichi
  • Yoko Yamaguchi
  • Pablo Martinez
  • Andrzej Cichocki
  • Andrea Tocci
  • Fabio Babiloni
Original Paper

Abstract

Static hemodynamic or neuroelectric images of brain regions activated during particular tasks do not convey the information of how these regions communicate to each other. Cortical connectivity estimation aims at describing these interactions as connectivity patterns which hold the direction and strength of the information flow between cortical areas. In this study, we attempted to estimate the causality between distributed cortical systems during a movement volition task in preparation for execution of simple movements by a group of normal healthy subjects and by a group of Spinal Cord Injured (SCI) patients. To estimate the causality between the spatial distributed patterns of cortical activity in the frequency domain, we applied a series of processing steps on the recorded EEG data. From the high-resolution EEG recordings we estimated the cortical waveforms for the regions of interest (ROIs), each representing a selected sensor group population. The solutions of the linear inverse problem returned a series of cortical waveforms for each ROI considered and for each trial analyzed. For each subject, the cortical waveforms were then subjected to Independent Component Analysis (ICA) pre-processing. The independent components obtained by the application of the ThinICA algorithm were further processed by a Partial Directed Coherence algorithm, in order to extract the causality between spatial cortical patterns of the estimated data. The source-target cortical dependencies found in the group of normal subjects were relatively similar in all frequency bands analyzed. For the normal subjects we observed a common source pattern in an ensemble of cortical areas including the right parietal and right lip primary motor areas and bilaterally the primary foot and posterior SMA areas. The target of this cortical network, in the Granger-sense of causality, was shown to be a smaller network composed mostly by the primary foot motor areas and the posterior SMA bilaterally. In the case of the SCI population, both the source and the target cortical patterns had larger sizes than in the normal population. The source cortical areas included always the primary foot and lip motor areas, often bilaterally. In addition, the right parietal area and the bilateral premotor area 6 were also involved. Again, the patterns remained substantially stable across the different frequency bands analyzed. The target cortical patterns observed in the SCI population had larger extensions when compared to the normal ones, since in most cases they involved the bilateral activation of the primary foot movement areas as well as the SMA, the primary lip areas and the parietal cortical areas.

Keywords

ThinICA Distributed current density estimates Brodmann areas Inverse problem High-resolution EEG Functional connectivity Partial Directed Coherence 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Laura Astolfi
    • 2
    • 7
  • Hovagim Bakardjian
    • 1
  • Febo Cincotti
    • 2
  • Donatella Mattia
    • 2
  • Maria Grazia Marciani
    • 2
  • Fabrizio De Vico Fallani
    • 2
    • 4
  • Alfredo Colosimo
    • 4
  • Serenella Salinari
    • 7
  • Fumikazu Miwakeichi
    • 5
  • Yoko Yamaguchi
    • 6
  • Pablo Martinez
    • 1
  • Andrzej Cichocki
    • 1
  • Andrea Tocci
    • 7
  • Fabio Babiloni
    • 2
    • 3
    • 8
  1. 1.Laboratory for Advanced Brain Signal ProcessingRiken Brain Science InstituteHirosawa, Wako, SaitamaJapan
  2. 2.IRCCS “Fondazione Santa Lucia”RomeItaly
  3. 3.Dipartimento di Fisiologia umana e FarmacologiaUniversity of Rome “La Sapienza”RomeItaly
  4. 4.Centro di Ricerca de “La Sapienza” per l’Analisi dei Modelli e dell’Informazione nei Sistemi BiomediciRomeItaly
  5. 5.Laboratory for Dynamics of Emergent IntelligenceRiken Brain Science InstituteHirosawa, Waka-shi, SaitamaJapan
  6. 6.Department of Medical System Engineering, Faculty of EngineeringChiba UniversityChibaJapan
  7. 7.Department of Informatica e SistemisticaUniversity of Rome “La Sapienza”RomeItaly
  8. 8.Department of Human Physiology and PharmacologyUniversity of Rome “La Sapienza”RomeItaly

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