Authors:
Filippo Cona
1
;
Melissa Zavaglia
1
;
Mauro Ursino
1
and
Mario Rosanova
2
Affiliations:
1
University of Bologna, Italy
;
2
“Luigi Sacco” and University of Milan, Italy
Keyword(s):
Transcranial magnetic stimulation, Neural mass models, Cortical rhythms, Effective connectivity.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
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 rhythms when perturbed by Transcranial Magnetic Stimulation (TMS) (Rosanova et al., 2009) and that these rhythms can change due to the connectivity among regions. In this context, 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 (Ursino, Cona and Zavaglia, 2010) to fit the impulse responses in three ROIs during an experiment of TMS stimulation. 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, model can explain most rhythm changes induced by stimulation of another region, by using just a few long-range connectivity parameters among ROIs.
(More)