A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models

Knowledge of motor cortex connectivity is of great value in cognitive neuroscience, in order to provide a better understanding of motor organization and its alterations in pathological conditions. Traditional methods provide connectivity estimations which may vary depending on the task. This work ai...

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Autores principales:	Mauro Ursino, Giulia Ricci, Laura Astolfi, Floriana Pichiorri, Manuela Petti, Elisa Magosso
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	EEG motor cortex after stroke network model model–based connectivity non–linear coupling excitatory/inhibitory synaptic connections Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/2a7910b62658414ea51ca19d57ea0ab0
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id	oai:doaj.org-article:2a7910b62658414ea51ca19d57ea0ab0
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spelling	oai:doaj.org-article:2a7910b62658414ea51ca19d57ea0ab02021-11-25T16:58:10ZA Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models10.3390/brainsci111114792076-3425https://doaj.org/article/2a7910b62658414ea51ca19d57ea0ab02021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3425/11/11/1479https://doaj.org/toc/2076-3425Knowledge of motor cortex connectivity is of great value in cognitive neuroscience, in order to provide a better understanding of motor organization and its alterations in pathological conditions. Traditional methods provide connectivity estimations which may vary depending on the task. This work aims to propose a new method for motor connectivity assessment based on the hypothesis of a task-independent connectivity network, assuming nonlinear behavior. The model considers six cortical regions of interest (ROIs) involved in hand movement. The dynamics of each region is simulated using a neural mass model, which reproduces the oscillatory activity through the interaction among four neural populations. Parameters of the model have been assigned to simulate both power spectral densities and coherences of a patient with left-hemisphere stroke during resting condition, movement of the affected, and movement of the unaffected hand. The presented model can simulate the three conditions using a single set of connectivity parameters, assuming that only inputs to the ROIs change from one condition to the other. The proposed procedure represents an innovative method to assess a brain circuit, which does not rely on a task-dependent connectivity network and allows brain rhythms and desynchronization to be assessed on a quantitative basis.Mauro UrsinoGiulia RicciLaura AstolfiFloriana PichiorriManuela PettiElisa MagossoMDPI AGarticleEEGmotor cortex after strokenetwork modelmodel–based connectivitynon–linear couplingexcitatory/inhibitory synaptic connectionsNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENBrain Sciences, Vol 11, Iss 1479, p 1479 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EEG motor cortex after stroke network model model–based connectivity non–linear coupling excitatory/inhibitory synaptic connections Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	EEG motor cortex after stroke network model model–based connectivity non–linear coupling excitatory/inhibitory synaptic connections Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Mauro Ursino Giulia Ricci Laura Astolfi Floriana Pichiorri Manuela Petti Elisa Magosso A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models
description	Knowledge of motor cortex connectivity is of great value in cognitive neuroscience, in order to provide a better understanding of motor organization and its alterations in pathological conditions. Traditional methods provide connectivity estimations which may vary depending on the task. This work aims to propose a new method for motor connectivity assessment based on the hypothesis of a task-independent connectivity network, assuming nonlinear behavior. The model considers six cortical regions of interest (ROIs) involved in hand movement. The dynamics of each region is simulated using a neural mass model, which reproduces the oscillatory activity through the interaction among four neural populations. Parameters of the model have been assigned to simulate both power spectral densities and coherences of a patient with left-hemisphere stroke during resting condition, movement of the affected, and movement of the unaffected hand. The presented model can simulate the three conditions using a single set of connectivity parameters, assuming that only inputs to the ROIs change from one condition to the other. The proposed procedure represents an innovative method to assess a brain circuit, which does not rely on a task-dependent connectivity network and allows brain rhythms and desynchronization to be assessed on a quantitative basis.
format	article
author	Mauro Ursino Giulia Ricci Laura Astolfi Floriana Pichiorri Manuela Petti Elisa Magosso
author_facet	Mauro Ursino Giulia Ricci Laura Astolfi Floriana Pichiorri Manuela Petti Elisa Magosso
author_sort	Mauro Ursino
title	A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models
title_short	A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models
title_full	A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models
title_fullStr	A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models
title_full_unstemmed	A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models
title_sort	novel method to assess motor cortex connectivity and event related desynchronization based on mass models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2a7910b62658414ea51ca19d57ea0ab0
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A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models

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