Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters.

In invasive electrophysiological recordings, a variety of neural oscillations can be detected across the cortex, with overlap in space and time. This overlap complicates measurement of neural oscillations using standard referencing schemes, like common average or bipolar referencing. Here, we illust...

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Autores principales:	Natalie Schaworonkow, Bradley Voytek
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/fc28bffa1a8c427289535c19e7d50c73
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id	oai:doaj.org-article:fc28bffa1a8c427289535c19e7d50c73
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spelling	oai:doaj.org-article:fc28bffa1a8c427289535c19e7d50c732021-12-02T19:58:03ZEnhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters.1553-734X1553-735810.1371/journal.pcbi.1009298https://doaj.org/article/fc28bffa1a8c427289535c19e7d50c732021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009298https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358In invasive electrophysiological recordings, a variety of neural oscillations can be detected across the cortex, with overlap in space and time. This overlap complicates measurement of neural oscillations using standard referencing schemes, like common average or bipolar referencing. Here, we illustrate the effects of spatial mixing on measuring neural oscillations in invasive electrophysiological recordings and demonstrate the benefits of using data-driven referencing schemes in order to improve measurement of neural oscillations. We discuss referencing as the application of a spatial filter. Spatio-spectral decomposition is used to estimate data-driven spatial filters, a computationally fast method which specifically enhances signal-to-noise ratio for oscillations in a frequency band of interest. We show that application of these data-driven spatial filters has benefits for data exploration, investigation of temporal dynamics and assessment of peak frequencies of neural oscillations. We demonstrate multiple use cases, exploring between-participant variability in presence of oscillations, spatial spread and waveform shape of different rhythms as well as narrowband noise removal with the aid of spatial filters. We find high between-participant variability in the presence of neural oscillations, a large variation in spatial spread of individual rhythms and many non-sinusoidal rhythms across the cortex. Improved measurement of cortical rhythms will yield better conditions for establishing links between cortical activity and behavior, as well as bridging scales between the invasive intracranial measurements and noninvasive macroscale scalp measurements.Natalie SchaworonkowBradley VoytekPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009298 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Natalie Schaworonkow Bradley Voytek Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters.
description	In invasive electrophysiological recordings, a variety of neural oscillations can be detected across the cortex, with overlap in space and time. This overlap complicates measurement of neural oscillations using standard referencing schemes, like common average or bipolar referencing. Here, we illustrate the effects of spatial mixing on measuring neural oscillations in invasive electrophysiological recordings and demonstrate the benefits of using data-driven referencing schemes in order to improve measurement of neural oscillations. We discuss referencing as the application of a spatial filter. Spatio-spectral decomposition is used to estimate data-driven spatial filters, a computationally fast method which specifically enhances signal-to-noise ratio for oscillations in a frequency band of interest. We show that application of these data-driven spatial filters has benefits for data exploration, investigation of temporal dynamics and assessment of peak frequencies of neural oscillations. We demonstrate multiple use cases, exploring between-participant variability in presence of oscillations, spatial spread and waveform shape of different rhythms as well as narrowband noise removal with the aid of spatial filters. We find high between-participant variability in the presence of neural oscillations, a large variation in spatial spread of individual rhythms and many non-sinusoidal rhythms across the cortex. Improved measurement of cortical rhythms will yield better conditions for establishing links between cortical activity and behavior, as well as bridging scales between the invasive intracranial measurements and noninvasive macroscale scalp measurements.
format	article
author	Natalie Schaworonkow Bradley Voytek
author_facet	Natalie Schaworonkow Bradley Voytek
author_sort	Natalie Schaworonkow
title	Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters.
title_short	Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters.
title_full	Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters.
title_fullStr	Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters.
title_full_unstemmed	Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters.
title_sort	enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/fc28bffa1a8c427289535c19e7d50c73
work_keys_str_mv	AT natalieschaworonkow enhancingoscillationsinintracranialelectrophysiologicalrecordingswithdatadrivenspatialfilters AT bradleyvoytek enhancingoscillationsinintracranialelectrophysiologicalrecordingswithdatadrivenspatialfilters
_version_	1718375818276634624

Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters.

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