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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MDPI AG
2021-11-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/11/11/1479 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:39:06Z |
| format | Article |
| id | doaj.art-2a7910b62658414ea51ca19d57ea0ab0 |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T05:39:06Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-2a7910b62658414ea51ca19d57ea0ab02023-11-22T22:38:17ZengMDPI AGBrain Sciences2076-34252021-11-011111147910.3390/brainsci11111479A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass ModelsMauro Ursino0Giulia Ricci1Laura Astolfi2Floriana Pichiorri3Manuela Petti4Elisa Magosso5Department of Electrical, Electronic and Information Engineering Guglielmo Marconi, Campus of Cesena, University of Bologna, Via Dell’Università 50, 47521 Cesena, ItalyDepartment of Electrical, Electronic and Information Engineering Guglielmo Marconi, Campus of Cesena, University of Bologna, Via Dell’Università 50, 47521 Cesena, ItalyDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, 00185 Roma, ItalyFondazione Santa Lucia, IRCCS Via Ardeatina 306/354, 00179 Roma, ItalyDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, 00185 Roma, ItalyDepartment of Electrical, Electronic and Information Engineering Guglielmo Marconi, Campus of Cesena, University of Bologna, Via Dell’Università 50, 47521 Cesena, ItalyKnowledge 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.https://www.mdpi.com/2076-3425/11/11/1479EEGmotor cortex after strokenetwork modelmodel–based connectivitynon–linear couplingexcitatory/inhibitory synaptic connections |
| spellingShingle | 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 Brain Sciences EEG motor cortex after stroke network model model–based connectivity non–linear coupling excitatory/inhibitory synaptic connections |
| title | 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_short | 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 |
| topic | EEG motor cortex after stroke network model model–based connectivity non–linear coupling excitatory/inhibitory synaptic connections |
| url | https://www.mdpi.com/2076-3425/11/11/1479 |
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