Cortical Resonance Frequencies Emerge from Network Size and Connectivity.
Neural oscillations occur within a wide frequency range with different brain regions exhibiting resonance-like characteristics at specific points in the spectrum. At the microscopic scale, single neurons possess intrinsic oscillatory properties, such that is not yet known whether cortical resonance...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1004740 |
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| author | Caroline A Lea-Carnall Marcelo A Montemurro Nelson J Trujillo-Barreto Laura M Parkes Wael El-Deredy |
| author_facet | Caroline A Lea-Carnall Marcelo A Montemurro Nelson J Trujillo-Barreto Laura M Parkes Wael El-Deredy |
| author_sort | Caroline A Lea-Carnall |
| collection | DOAJ |
| description | Neural oscillations occur within a wide frequency range with different brain regions exhibiting resonance-like characteristics at specific points in the spectrum. At the microscopic scale, single neurons possess intrinsic oscillatory properties, such that is not yet known whether cortical resonance is consequential to neural oscillations or an emergent property of the networks that interconnect them. Using a network model of loosely-coupled Wilson-Cowan oscillators to simulate a patch of cortical sheet, we demonstrate that the size of the activated network is inversely related to its resonance frequency. Further analysis of the parameter space indicated that the number of excitatory and inhibitory connections, as well as the average transmission delay between units, determined the resonance frequency. The model predicted that if an activated network within the visual cortex increased in size, the resonance frequency of the network would decrease. We tested this prediction experimentally using the steady-state visual evoked potential where we stimulated the visual cortex with different size stimuli at a range of driving frequencies. We demonstrate that the frequency corresponding to peak steady-state response inversely correlated with the size of the network. We conclude that although individual neurons possess resonance properties, oscillatory activity at the macroscopic level is strongly influenced by network interactions, and that the steady-state response can be used to investigate functional networks. |
| format | Article |
| id | doaj-art-d86bde09d5d94aa3b9ee8371cf5c92a9 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2016-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-d86bde09d5d94aa3b9ee8371cf5c92a92025-08-20T02:22:38ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582016-02-01122e100474010.1371/journal.pcbi.1004740Cortical Resonance Frequencies Emerge from Network Size and Connectivity.Caroline A Lea-CarnallMarcelo A MontemurroNelson J Trujillo-BarretoLaura M ParkesWael El-DeredyNeural oscillations occur within a wide frequency range with different brain regions exhibiting resonance-like characteristics at specific points in the spectrum. At the microscopic scale, single neurons possess intrinsic oscillatory properties, such that is not yet known whether cortical resonance is consequential to neural oscillations or an emergent property of the networks that interconnect them. Using a network model of loosely-coupled Wilson-Cowan oscillators to simulate a patch of cortical sheet, we demonstrate that the size of the activated network is inversely related to its resonance frequency. Further analysis of the parameter space indicated that the number of excitatory and inhibitory connections, as well as the average transmission delay between units, determined the resonance frequency. The model predicted that if an activated network within the visual cortex increased in size, the resonance frequency of the network would decrease. We tested this prediction experimentally using the steady-state visual evoked potential where we stimulated the visual cortex with different size stimuli at a range of driving frequencies. We demonstrate that the frequency corresponding to peak steady-state response inversely correlated with the size of the network. We conclude that although individual neurons possess resonance properties, oscillatory activity at the macroscopic level is strongly influenced by network interactions, and that the steady-state response can be used to investigate functional networks.https://doi.org/10.1371/journal.pcbi.1004740 |
| spellingShingle | Caroline A Lea-Carnall Marcelo A Montemurro Nelson J Trujillo-Barreto Laura M Parkes Wael El-Deredy Cortical Resonance Frequencies Emerge from Network Size and Connectivity. PLoS Computational Biology |
| title | Cortical Resonance Frequencies Emerge from Network Size and Connectivity. |
| title_full | Cortical Resonance Frequencies Emerge from Network Size and Connectivity. |
| title_fullStr | Cortical Resonance Frequencies Emerge from Network Size and Connectivity. |
| title_full_unstemmed | Cortical Resonance Frequencies Emerge from Network Size and Connectivity. |
| title_short | Cortical Resonance Frequencies Emerge from Network Size and Connectivity. |
| title_sort | cortical resonance frequencies emerge from network size and connectivity |
| url | https://doi.org/10.1371/journal.pcbi.1004740 |
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