Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG

Abstract Time-varying neurophysiological activity has been classically explored using correlation based sliding window analysis. However, this method employs only lower order statistics to track dynamic functional connectivity of the brain. We introduce recursive dynamic functional connectivity (rdF...

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Autores principales:	Siddharth Panwar, Shiv Dutt Joshi, Anubha Gupta, Sandhya Kunnatur, Puneet Agarwal
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/918a195f3c7b4919a03320b284376b9a
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spelling	oai:doaj.org-article:918a195f3c7b4919a03320b284376b9a2021-12-02T14:06:25ZRecursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG10.1038/s41598-021-81884-32045-2322https://doaj.org/article/918a195f3c7b4919a03320b284376b9a2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81884-3https://doaj.org/toc/2045-2322Abstract Time-varying neurophysiological activity has been classically explored using correlation based sliding window analysis. However, this method employs only lower order statistics to track dynamic functional connectivity of the brain. We introduce recursive dynamic functional connectivity (rdFC) that incorporates higher order statistics to generate a multi-order connectivity pattern by analyzing neurophysiological data at multiple time scales. The technique builds a hierarchical graph between various temporal scales as opposed to traditional approaches that analyze each scale independently. We examined more than a million rdFC patterns obtained from morphologically diverse EEGs of 2378 subjects of varied age and neurological health. Spatiotemporal evaluation of these patterns revealed three dominant connectivity patterns that represent a universal underlying correlation structure seen across subjects and scalp locations. The three patterns are both mathematically equivalent and observed with equal prevalence in the data. The patterns were observed across a range of distances on the scalp indicating that they represent a spatially scale-invariant correlation structure. Moreover, the number of patterns representing the correlation structure has been shown to be linked with the number of nodes used to generate them. We also show evidence that temporal changes in the rdFC patterns are linked with seizure dynamics.Siddharth PanwarShiv Dutt JoshiAnubha GuptaSandhya KunnaturPuneet AgarwalNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Siddharth Panwar Shiv Dutt Joshi Anubha Gupta Sandhya Kunnatur Puneet Agarwal Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG
description	Abstract Time-varying neurophysiological activity has been classically explored using correlation based sliding window analysis. However, this method employs only lower order statistics to track dynamic functional connectivity of the brain. We introduce recursive dynamic functional connectivity (rdFC) that incorporates higher order statistics to generate a multi-order connectivity pattern by analyzing neurophysiological data at multiple time scales. The technique builds a hierarchical graph between various temporal scales as opposed to traditional approaches that analyze each scale independently. We examined more than a million rdFC patterns obtained from morphologically diverse EEGs of 2378 subjects of varied age and neurological health. Spatiotemporal evaluation of these patterns revealed three dominant connectivity patterns that represent a universal underlying correlation structure seen across subjects and scalp locations. The three patterns are both mathematically equivalent and observed with equal prevalence in the data. The patterns were observed across a range of distances on the scalp indicating that they represent a spatially scale-invariant correlation structure. Moreover, the number of patterns representing the correlation structure has been shown to be linked with the number of nodes used to generate them. We also show evidence that temporal changes in the rdFC patterns are linked with seizure dynamics.
format	article
author	Siddharth Panwar Shiv Dutt Joshi Anubha Gupta Sandhya Kunnatur Puneet Agarwal
author_facet	Siddharth Panwar Shiv Dutt Joshi Anubha Gupta Sandhya Kunnatur Puneet Agarwal
author_sort	Siddharth Panwar
title	Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG
title_short	Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG
title_full	Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG
title_fullStr	Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG
title_full_unstemmed	Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG
title_sort	recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp eeg
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/918a195f3c7b4919a03320b284376b9a
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_version_	1718392034932293632

Recursive dynamic functional connectivity reveals a characteristic correlation structure in human scalp EEG

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