The natural frequencies of the resting human brain: an MEG-based atlas

Brain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natura...

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Detalles Bibliográficos
Autores: Capilla González, Almudena, Arana, Lydia, García Huéscar, Marta, Melcón Martín, María, Gross, Joachim, Campo Martínez-Lage, Pablo
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/708602
Acceso en línea:http://hdl.handle.net/10486/708602
https://dx.doi.org/10.1016/j.neuroimage.2022.119373
Access Level:acceso abierto
Palabra clave:Brain oscillations
Clustering
Human
Magnetoencephalography
Resting state
Spectral analysis
Psicología
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spelling The natural frequencies of the resting human brain: an MEG-based atlasCapilla González, AlmudenaArana, LydiaGarcía Huéscar, MartaMelcón Martín, MaríaGross, JoachimCampo Martínez-Lage, PabloBrain oscillationsClusteringHumanMagnetoencephalographyResting stateSpectral analysisPsicologíaBrain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natural frequencies of the resting human brain on a voxel-by-voxel basis. We analysed resting-state magnetoencephalography (MEG) data from 128 healthy adult volunteers obtained from the Open MEG Archive (OMEGA). Spectral power was computed in source space in 500 ms steps for 82 frequency bins logarithmically spaced from 1.7 to 99.5 Hz. We then applied k-means clustering to detect characteristic spectral profiles and to eventually identify the natural frequency of each voxel. Our results provided empirical confirmation of the canonical frequency bands and revealed a region-specific organisation of intrinsic oscillatory activity, following both a medial-to-lateral and a posterior-to-anterior gradient of increasing frequency. In particular, medial fronto-temporal regions were characterised by slow rhythms (delta/theta). Posterior regions presented natural frequencies in the alpha band, although with differentiated generators in the precuneus and in sensory-specific cortices (i.e., visual and auditory). Somatomotor regions were distinguished by the mu rhythm, while the lateral prefrontal cortex was characterised by oscillations in the high beta range (>20 Hz). Importantly, the brain map of natural frequencies was highly replicable in two independent subsamples of individuals. To the best of our knowledge, this is the most comprehensive atlas of ongoing oscillatory activity performed to date. Critically, the identification of natural frequencies is a fundamental step towards a better understanding of the functional architecture of the human brainThis work was supported by FEDER/Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación, Spain (grant PGC2018-100682-B-I00 to AC and PC) and the Comunidad de Madrid POEJ/FSE (grant PEJD-2017-PRE/SOC-3859 to AC). MM was supported by the Universidad Autónoma de Madrid (FPI-UAM-2017 fellowship). JG was supported by Deutsche Forschungsgemeinschaft (GR 2024/5-1 and GR 2024/8-1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscriptElsevierDepartamento de Psicología BásicaDepartamento de Psicología Biológica y de la SaludFacultad de Psicología20222022-06-11research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/708602https://dx.doi.org/10.1016/j.neuroimage.2022.119373reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7086022026-06-23T12:46:27Z
dc.title.none.fl_str_mv The natural frequencies of the resting human brain: an MEG-based atlas
title The natural frequencies of the resting human brain: an MEG-based atlas
spellingShingle The natural frequencies of the resting human brain: an MEG-based atlas
Capilla González, Almudena
Brain oscillations
Clustering
Human
Magnetoencephalography
Resting state
Spectral analysis
Psicología
title_short The natural frequencies of the resting human brain: an MEG-based atlas
title_full The natural frequencies of the resting human brain: an MEG-based atlas
title_fullStr The natural frequencies of the resting human brain: an MEG-based atlas
title_full_unstemmed The natural frequencies of the resting human brain: an MEG-based atlas
title_sort The natural frequencies of the resting human brain: an MEG-based atlas
dc.creator.none.fl_str_mv Capilla González, Almudena
Arana, Lydia
García Huéscar, Marta
Melcón Martín, María
Gross, Joachim
Campo Martínez-Lage, Pablo
author Capilla González, Almudena
author_facet Capilla González, Almudena
Arana, Lydia
García Huéscar, Marta
Melcón Martín, María
Gross, Joachim
Campo Martínez-Lage, Pablo
author_role author
author2 Arana, Lydia
García Huéscar, Marta
Melcón Martín, María
Gross, Joachim
Campo Martínez-Lage, Pablo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Psicología Básica
Departamento de Psicología Biológica y de la Salud
Facultad de Psicología
dc.subject.none.fl_str_mv Brain oscillations
Clustering
Human
Magnetoencephalography
Resting state
Spectral analysis
Psicología
topic Brain oscillations
Clustering
Human
Magnetoencephalography
Resting state
Spectral analysis
Psicología
description Brain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natural frequencies of the resting human brain on a voxel-by-voxel basis. We analysed resting-state magnetoencephalography (MEG) data from 128 healthy adult volunteers obtained from the Open MEG Archive (OMEGA). Spectral power was computed in source space in 500 ms steps for 82 frequency bins logarithmically spaced from 1.7 to 99.5 Hz. We then applied k-means clustering to detect characteristic spectral profiles and to eventually identify the natural frequency of each voxel. Our results provided empirical confirmation of the canonical frequency bands and revealed a region-specific organisation of intrinsic oscillatory activity, following both a medial-to-lateral and a posterior-to-anterior gradient of increasing frequency. In particular, medial fronto-temporal regions were characterised by slow rhythms (delta/theta). Posterior regions presented natural frequencies in the alpha band, although with differentiated generators in the precuneus and in sensory-specific cortices (i.e., visual and auditory). Somatomotor regions were distinguished by the mu rhythm, while the lateral prefrontal cortex was characterised by oscillations in the high beta range (>20 Hz). Importantly, the brain map of natural frequencies was highly replicable in two independent subsamples of individuals. To the best of our knowledge, this is the most comprehensive atlas of ongoing oscillatory activity performed to date. Critically, the identification of natural frequencies is a fundamental step towards a better understanding of the functional architecture of the human brain
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-06-11
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/708602
https://dx.doi.org/10.1016/j.neuroimage.2022.119373
url http://hdl.handle.net/10486/708602
https://dx.doi.org/10.1016/j.neuroimage.2022.119373
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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