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Time-resolved parameterization of aperiodic and periodic brain activity

Macroscopic neural dynamics comprise both aperiodic and periodic signal components. Recent advances in parameterizing neural power spectra offer practical tools for evaluating these features separately. Although neural signals vary dynamically and express non-stationarity in relation to ongoing beha...

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Detalles Bibliográficos
Autores principales: Wilson, Luc Edward, da Silva Castanheira, Jason, Baillet, Sylvain
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467511/
https://www.ncbi.nlm.nih.gov/pubmed/36094163
http://dx.doi.org/10.7554/eLife.77348
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author Wilson, Luc Edward
da Silva Castanheira, Jason
Baillet, Sylvain
author_facet Wilson, Luc Edward
da Silva Castanheira, Jason
Baillet, Sylvain
author_sort Wilson, Luc Edward
collection PubMed
description Macroscopic neural dynamics comprise both aperiodic and periodic signal components. Recent advances in parameterizing neural power spectra offer practical tools for evaluating these features separately. Although neural signals vary dynamically and express non-stationarity in relation to ongoing behaviour and perception, current methods yield static spectral decompositions. Here, we introduce Spectral Parameterization Resolved in Time (SPRiNT) as a novel method for decomposing complex neural dynamics into periodic and aperiodic spectral elements in a time-resolved manner. First, we demonstrate, with naturalistic synthetic data, SPRiNT’s capacity to reliably recover time-varying spectral features. We emphasize SPRiNT’s specific strengths compared to other time-frequency parameterization approaches based on wavelets. Second, we use SPRiNT to illustrate how aperiodic spectral features fluctuate across time in empirical resting-state EEG data (n=178) and relate the observed changes in aperiodic parameters over time to participants’ demographics and behaviour. Lastly, we use SPRiNT to demonstrate how aperiodic dynamics relate to movement behaviour in intracranial recordings in rodents. We foresee SPRiNT responding to growing neuroscientific interests in the parameterization of time-varying neural power spectra and advancing the quantitation of complex neural dynamics at the natural time scales of behaviour.
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spelling pubmed-94675112022-09-13 Time-resolved parameterization of aperiodic and periodic brain activity Wilson, Luc Edward da Silva Castanheira, Jason Baillet, Sylvain eLife Neuroscience Macroscopic neural dynamics comprise both aperiodic and periodic signal components. Recent advances in parameterizing neural power spectra offer practical tools for evaluating these features separately. Although neural signals vary dynamically and express non-stationarity in relation to ongoing behaviour and perception, current methods yield static spectral decompositions. Here, we introduce Spectral Parameterization Resolved in Time (SPRiNT) as a novel method for decomposing complex neural dynamics into periodic and aperiodic spectral elements in a time-resolved manner. First, we demonstrate, with naturalistic synthetic data, SPRiNT’s capacity to reliably recover time-varying spectral features. We emphasize SPRiNT’s specific strengths compared to other time-frequency parameterization approaches based on wavelets. Second, we use SPRiNT to illustrate how aperiodic spectral features fluctuate across time in empirical resting-state EEG data (n=178) and relate the observed changes in aperiodic parameters over time to participants’ demographics and behaviour. Lastly, we use SPRiNT to demonstrate how aperiodic dynamics relate to movement behaviour in intracranial recordings in rodents. We foresee SPRiNT responding to growing neuroscientific interests in the parameterization of time-varying neural power spectra and advancing the quantitation of complex neural dynamics at the natural time scales of behaviour. eLife Sciences Publications, Ltd 2022-09-12 /pmc/articles/PMC9467511/ /pubmed/36094163 http://dx.doi.org/10.7554/eLife.77348 Text en © 2022, Wilson et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Wilson, Luc Edward
da Silva Castanheira, Jason
Baillet, Sylvain
Time-resolved parameterization of aperiodic and periodic brain activity
title Time-resolved parameterization of aperiodic and periodic brain activity
title_full Time-resolved parameterization of aperiodic and periodic brain activity
title_fullStr Time-resolved parameterization of aperiodic and periodic brain activity
title_full_unstemmed Time-resolved parameterization of aperiodic and periodic brain activity
title_short Time-resolved parameterization of aperiodic and periodic brain activity
title_sort time-resolved parameterization of aperiodic and periodic brain activity
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467511/
https://www.ncbi.nlm.nih.gov/pubmed/36094163
http://dx.doi.org/10.7554/eLife.77348
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