The Relationship between Oscillations in Brain Regions and Functional Connectivity: A Critical Analysis with the Aid of Neural Mass Models
Propagation of brain rhythms among cortical regions is a relevant aspect of cognitive neuroscience, which is often investigated using functional connectivity (FC) estimation techniques. The aim of this work is to assess the relationship between rhythm propagation, FC and brain functioning using data...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
|
| Series: | Brain Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3425/11/4/487 |
| _version_ | 1797538010082836480 |
|---|---|
| author | Giulia Ricci Elisa Magosso Mauro Ursino |
| author_facet | Giulia Ricci Elisa Magosso Mauro Ursino |
| author_sort | Giulia Ricci |
| collection | DOAJ |
| description | Propagation of brain rhythms among cortical regions is a relevant aspect of cognitive neuroscience, which is often investigated using functional connectivity (FC) estimation techniques. The aim of this work is to assess the relationship between rhythm propagation, FC and brain functioning using data generated from neural mass models of connected Regions of Interest (ROIs). We simulated networks of four interconnected ROIs, each with a different intrinsic rhythm (in θ, α, β and γ ranges). Connectivity was estimated using eight estimators and the relationship between structural connectivity and FC was assessed as a function of the connectivity strength and of the inputs to the ROIs. Results show that the Granger estimation provides the best accuracy, with a good capacity to evaluate the connectivity strength. However, the estimated values strongly depend on the input to the ROIs and hence on nonlinear phenomena. When a population works in the linear region, its capacity to transmit a rhythm increases drastically. Conversely, when it saturates, oscillatory activity becomes strongly affected by rhythms incoming from other regions. Changes in functional connectivity do not always reflect a physical change in the synapses. A unique connectivity network can propagate rhythms in very different ways depending on the specific working conditions. |
| first_indexed | 2024-03-10T12:24:22Z |
| format | Article |
| id | doaj.art-b96f42d1f2fb4c0fb39b8a417a5f98ec |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T12:24:22Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-b96f42d1f2fb4c0fb39b8a417a5f98ec2023-11-21T15:12:56ZengMDPI AGBrain Sciences2076-34252021-04-0111448710.3390/brainsci11040487The Relationship between Oscillations in Brain Regions and Functional Connectivity: A Critical Analysis with the Aid of Neural Mass ModelsGiulia Ricci0Elisa Magosso1Mauro Ursino2Department of Electrical, Electronic and Information Engineering, Campus of Cesena, University of Bologna, 47521 Cesena, ItalyDepartment of Electrical, Electronic and Information Engineering, Campus of Cesena, University of Bologna, 47521 Cesena, ItalyDepartment of Electrical, Electronic and Information Engineering, Campus of Cesena, University of Bologna, 47521 Cesena, ItalyPropagation of brain rhythms among cortical regions is a relevant aspect of cognitive neuroscience, which is often investigated using functional connectivity (FC) estimation techniques. The aim of this work is to assess the relationship between rhythm propagation, FC and brain functioning using data generated from neural mass models of connected Regions of Interest (ROIs). We simulated networks of four interconnected ROIs, each with a different intrinsic rhythm (in θ, α, β and γ ranges). Connectivity was estimated using eight estimators and the relationship between structural connectivity and FC was assessed as a function of the connectivity strength and of the inputs to the ROIs. Results show that the Granger estimation provides the best accuracy, with a good capacity to evaluate the connectivity strength. However, the estimated values strongly depend on the input to the ROIs and hence on nonlinear phenomena. When a population works in the linear region, its capacity to transmit a rhythm increases drastically. Conversely, when it saturates, oscillatory activity becomes strongly affected by rhythms incoming from other regions. Changes in functional connectivity do not always reflect a physical change in the synapses. A unique connectivity network can propagate rhythms in very different ways depending on the specific working conditions.https://www.mdpi.com/2076-3425/11/4/487cortical rhythmsconnectivityneural mass modelsexcitatory and inhibitory synapsesGranger causalitynonlinear neural phenomena |
| spellingShingle | Giulia Ricci Elisa Magosso Mauro Ursino The Relationship between Oscillations in Brain Regions and Functional Connectivity: A Critical Analysis with the Aid of Neural Mass Models Brain Sciences cortical rhythms connectivity neural mass models excitatory and inhibitory synapses Granger causality nonlinear neural phenomena |
| title | The Relationship between Oscillations in Brain Regions and Functional Connectivity: A Critical Analysis with the Aid of Neural Mass Models |
| title_full | The Relationship between Oscillations in Brain Regions and Functional Connectivity: A Critical Analysis with the Aid of Neural Mass Models |
| title_fullStr | The Relationship between Oscillations in Brain Regions and Functional Connectivity: A Critical Analysis with the Aid of Neural Mass Models |
| title_full_unstemmed | The Relationship between Oscillations in Brain Regions and Functional Connectivity: A Critical Analysis with the Aid of Neural Mass Models |
| title_short | The Relationship between Oscillations in Brain Regions and Functional Connectivity: A Critical Analysis with the Aid of Neural Mass Models |
| title_sort | relationship between oscillations in brain regions and functional connectivity a critical analysis with the aid of neural mass models |
| topic | cortical rhythms connectivity neural mass models excitatory and inhibitory synapses Granger causality nonlinear neural phenomena |
| url | https://www.mdpi.com/2076-3425/11/4/487 |
| work_keys_str_mv | AT giuliaricci therelationshipbetweenoscillationsinbrainregionsandfunctionalconnectivityacriticalanalysiswiththeaidofneuralmassmodels AT elisamagosso therelationshipbetweenoscillationsinbrainregionsandfunctionalconnectivityacriticalanalysiswiththeaidofneuralmassmodels AT mauroursino therelationshipbetweenoscillationsinbrainregionsandfunctionalconnectivityacriticalanalysiswiththeaidofneuralmassmodels AT giuliaricci relationshipbetweenoscillationsinbrainregionsandfunctionalconnectivityacriticalanalysiswiththeaidofneuralmassmodels AT elisamagosso relationshipbetweenoscillationsinbrainregionsandfunctionalconnectivityacriticalanalysiswiththeaidofneuralmassmodels AT mauroursino relationshipbetweenoscillationsinbrainregionsandfunctionalconnectivityacriticalanalysiswiththeaidofneuralmassmodels |