Changes in EEG power spectral density and cortical connectivity in healthy and tetraplegic patients during amotor imagery task

  • Authors:

  • Filippo Cona;Melissa Zavaglia;Laura Astolfi;Fabio Babiloni;Mauro Ursino

  • Affiliations:

  • Department of Electronics, Computer Science and Systems, University of Bologna, Cesena, Italy;Department of Electronics, Computer Science and Systems, University of Bologna, Cesena, Italy;Department of Human Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy and Istituti di ricovero e cura a carattere scientifico Fondazione Santa Lucia, Rome, Italy;Department of Human Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy and Istituti di ricovero e cura a carattere scientifico Fondazione Santa Lucia, Rome, Italy;Department of Electronics, Computer Science and Systems, University of Bologna, Cesena, Italy

  • Venue:
  • Computational Intelligence and Neuroscience - Neuromath: advanced methods for the estimation of human brain activity and connectivity
  • Year:
  • 2009

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Abstract

Knowledge of brain connectivity is an important aspect of modern neuroscience, to understand how the brain realizes its functions. In this work, neural mass models including four groups of excitatory and inhibitory neurons are used to estimate the connectivity among three cortical regions of interests (ROIs) during a foot-movement task. Real data were obtained via high-resolution scalp EEGs on two populations: healthy volunteers and tetraplegic patients. A 3-shell Boundary Element Model of the head was used to estimate the cortical current density and to derive cortical EEGs in the three ROIs. The model assumes that each ROI can generate an intrinsic rhythm in the beta range, and receives rhythms in the alpha and gamma ranges from other two regions. Connectivity strengths among the ROIs were estimated by means of an original genetic algorithm that tries to minimize several cost functions of the difference between real and model power spectral densities. Results show that the stronger connections are those from the cingulate cortex to the primary and supplementary motor areas, thus emphasizing the pivotal role played by the CMA_L during the task. Tetraplegic patients exhibit higher connectivity strength on average, with significant statistical differences in some connections. The results are commented and virtues and limitations of the proposed method discussed.