The effect of connectivity on EEG rhythms, power spectral density and coherence among coupled neural populations: analysis with a neural mass model.
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The effect of connectivity on EEG rhythms, power spectral density and coherence among coupled neural populations: analysis with a neural mass model. / Zavaglia, Melissa; Astolfi, Laura; Babiloni, Fabio; Ursino, Mauro.
In: IEEE T BIO-MED ENG, Vol. 55, No. 1, 1, 2008, p. 69-77.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - The effect of connectivity on EEG rhythms, power spectral density and coherence among coupled neural populations: analysis with a neural mass model.
AU - Zavaglia, Melissa
AU - Astolfi, Laura
AU - Babiloni, Fabio
AU - Ursino, Mauro
PY - 2008
Y1 - 2008
N2 - In the present work, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. In particular, we analyze a circuit, composed of three interconnected populations, each with a different synaptic kinetics (hence, with a different intrinsic rhythm). Results demonstrate that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). Analysis of coherence, and of the position of peaks in power spectral density, reveals important information on the possible connections among populations, especially useful to follow temporal changes in connectivity. Subsequently, the model is validated by comparing the power spectral density simulated in one population with that computed in the controlateral cingulated cortex (a region involved in motion preparation) during a right foot movement task in four normal subjects. The model is able to simulate real spectra quite well with only moderate parameter changes within the subject. In perspective, the results may be of value for a deeper comprehension of mechanism causing EEGs rhythms, for the study of brain connectivity and for the test of neurophysiological hypotheses.
AB - In the present work, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. In particular, we analyze a circuit, composed of three interconnected populations, each with a different synaptic kinetics (hence, with a different intrinsic rhythm). Results demonstrate that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). Analysis of coherence, and of the position of peaks in power spectral density, reveals important information on the possible connections among populations, especially useful to follow temporal changes in connectivity. Subsequently, the model is validated by comparing the power spectral density simulated in one population with that computed in the controlateral cingulated cortex (a region involved in motion preparation) during a right foot movement task in four normal subjects. The model is able to simulate real spectra quite well with only moderate parameter changes within the subject. In perspective, the results may be of value for a deeper comprehension of mechanism causing EEGs rhythms, for the study of brain connectivity and for the test of neurophysiological hypotheses.
KW - Humans
KW - Computer Simulation
KW - Action Potentials/physiology
KW - Brain/physiology
KW - Models, Neurological
KW - Electroencephalography/methods
KW - Nerve Net/physiology
KW - Synaptic Transmission/physiology
KW - Neural Pathways/physiology
KW - Biological Clocks/physiology
KW - Humans
KW - Computer Simulation
KW - Action Potentials/physiology
KW - Brain/physiology
KW - Models, Neurological
KW - Electroencephalography/methods
KW - Nerve Net/physiology
KW - Synaptic Transmission/physiology
KW - Neural Pathways/physiology
KW - Biological Clocks/physiology
M3 - SCORING: Journal article
VL - 55
SP - 69
EP - 77
JO - IEEE T BIO-MED ENG
JF - IEEE T BIO-MED ENG
SN - 0018-9294
IS - 1
M1 - 1
ER -
