A neural mass model of interconnected regions simulates rhythm propagation observed via TMS-EEG.
Standard
A neural mass model of interconnected regions simulates rhythm propagation observed via TMS-EEG. / Cona, F; Zavaglia, Melissa; Massimini, M; Rosanova, M; Ursino, M.
In: NEUROIMAGE, Vol. 57, No. 3, 3, 2011, p. 1045-1058.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - A neural mass model of interconnected regions simulates rhythm propagation observed via TMS-EEG.
AU - Cona, F
AU - Zavaglia, Melissa
AU - Massimini, M
AU - Rosanova, M
AU - Ursino, M
PY - 2011
Y1 - 2011
N2 - Knowledge of cortical rhythms represents an important aspect of modern neuroscience, to understand how the brain realizes its functions. Recent data suggest that different regions in the brain may exhibit distinct electroencephalogram (EEG) rhythms when perturbed by Transcranial Magnetic Stimulation (TMS) and that these rhythms can change due to the connectivity among regions. In this context, in silico simulations may help the validation of these hypotheses that would be difficult to be verified in vivo. Neural mass models can be very useful to simulate specific aspects of electrical brain activity and, above all, to analyze and identify the overall frequency content of EEG in a cortical region of interest (ROI). In this work we implemented a model of connectivity among cortical regions to fit the impulse responses in three ROIs recorded during a series of TMS/EEG experiments performed in five subjects and using three different impulse intensities. In particular we investigated Brodmann Area (BA) 19 (occipital lobe), BA 7 (parietal lobe) and BA 6 (frontal lobe). Results show that the model can reproduce the natural rhythms of the three regions quite well, acting on a few internal parameters. Moreover, the model can explain most rhythm changes induced by stimulation of another region, and inter-subject variability, by estimating just a few long-range connectivity parameters among ROIs.
AB - Knowledge of cortical rhythms represents an important aspect of modern neuroscience, to understand how the brain realizes its functions. Recent data suggest that different regions in the brain may exhibit distinct electroencephalogram (EEG) rhythms when perturbed by Transcranial Magnetic Stimulation (TMS) and that these rhythms can change due to the connectivity among regions. In this context, in silico simulations may help the validation of these hypotheses that would be difficult to be verified in vivo. Neural mass models can be very useful to simulate specific aspects of electrical brain activity and, above all, to analyze and identify the overall frequency content of EEG in a cortical region of interest (ROI). In this work we implemented a model of connectivity among cortical regions to fit the impulse responses in three ROIs recorded during a series of TMS/EEG experiments performed in five subjects and using three different impulse intensities. In particular we investigated Brodmann Area (BA) 19 (occipital lobe), BA 7 (parietal lobe) and BA 6 (frontal lobe). Results show that the model can reproduce the natural rhythms of the three regions quite well, acting on a few internal parameters. Moreover, the model can explain most rhythm changes induced by stimulation of another region, and inter-subject variability, by estimating just a few long-range connectivity parameters among ROIs.
KW - Adult
KW - Humans
KW - Brain/physiology
KW - Models, Neurological
KW - Transcranial Magnetic Stimulation
KW - Algorithms
KW - Electroencephalography
KW - Neural Pathways/physiology
KW - Adult
KW - Humans
KW - Brain/physiology
KW - Models, Neurological
KW - Transcranial Magnetic Stimulation
KW - Algorithms
KW - Electroencephalography
KW - Neural Pathways/physiology
M3 - SCORING: Journal article
VL - 57
SP - 1045
EP - 1058
JO - NEUROIMAGE
JF - NEUROIMAGE
SN - 1053-8119
IS - 3
M1 - 3
ER -
