Spectral graph theory of brain oscillations—-Revisited and improved

Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multis...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Verma, Parul [verfasserIn] Nagarajan, Srikantan [verfasserIn] Raj, Ashish [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Brain activity Connectomes Magnetoencephalography Spectral graph theory

Übergeordnetes Werk:	Enthalten in: NeuroImage - Orlando, Fla. : Academic Press, 1992, 249
Übergeordnetes Werk:	volume:249

DOI / URN:	10.1016/j.neuroimage.2022.118919

Katalog-ID:	ELV007407270

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520			\|a Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso-(regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters and without multistable dynamics can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately.
650		4	\|a Brain activity
650		4	\|a Connectomes
650		4	\|a Magnetoencephalography
650		4	\|a Spectral graph theory
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topic_facet	Brain activity Connectomes Magnetoencephalography Spectral graph theory
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authorswithroles_txt_mv	Verma, Parul @@aut@@ Nagarajan, Srikantan @@aut@@ Raj, Ashish @@aut@@
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author	Verma, Parul
spellingShingle	Verma, Parul ddc 610 fid LING bkl 44.64 bkl 44.90 misc Brain activity misc Connectomes misc Magnetoencephalography misc Spectral graph theory Spectral graph theory of brain oscillations—-Revisited and improved
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title	Spectral graph theory of brain oscillations—-Revisited and improved
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title_full	Spectral graph theory of brain oscillations—-Revisited and improved
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title_sort	spectral graph theory of brain oscillations—-revisited and improved
title_auth	Spectral graph theory of brain oscillations—-Revisited and improved
abstract	Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso-(regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters and without multistable dynamics can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately.
abstractGer	Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso-(regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters and without multistable dynamics can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately.
abstract_unstemmed	Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso-(regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters and without multistable dynamics can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately.
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title_short	Spectral graph theory of brain oscillations—-Revisited and improved
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up_date	2024-07-07T00:38:42.583Z
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Spectral graph theory of brain oscillations—-Revisited and improved

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