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On fractality of functional near-infrared spectroscopy signals: analysis and applications

Significance: The human brain is a highly complex system with nonlinear, dynamic behavior. A majority of brain imaging studies employing functional near-infrared spectroscopy (fNIRS), however, have considered only the spatial domain and have ignored the temporal properties of fNIRS recordings. Metho...

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Autores principales: Zhu, Li, Haghani, Sasan, Najafizadeh, Laleh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7189210/
https://www.ncbi.nlm.nih.gov/pubmed/32377544
http://dx.doi.org/10.1117/1.NPh.7.2.025001
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author Zhu, Li
Haghani, Sasan
Najafizadeh, Laleh
author_facet Zhu, Li
Haghani, Sasan
Najafizadeh, Laleh
author_sort Zhu, Li
collection PubMed
description Significance: The human brain is a highly complex system with nonlinear, dynamic behavior. A majority of brain imaging studies employing functional near-infrared spectroscopy (fNIRS), however, have considered only the spatial domain and have ignored the temporal properties of fNIRS recordings. Methods capable of revealing nonlinearities in fNIRS recordings can provide new insights about how the brain functions. Aim: The temporal characteristics of fNIRS signals are explored by comprehensively investigating their fractal properties. Approach: Fractality of fNIRS signals is analyzed using scaled windowed variance (SWV), as well as using visibility graph (VG), a method which converts a given time series into a graph. Additionally, the fractality of fNIRS signals obtained under resting-state and task-based conditions is compared, and the application of fractality in differentiating brain states is demonstrated for the first time via various classification approaches. Results: Results from SWV analysis show the existence of high fractality in fNIRS recordings. It is shown that differences in the temporal characteristics of fNIRS signals related to task-based and resting-state conditions can be revealed via the VGs constructed for each case. Conclusions: fNIRS recordings, regardless of the experimental conditions, exhibit high fractality. Furthermore, VG-based metrics can be employed to differentiate rest and task-execution brain states.
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spelling pubmed-71892102020-05-06 On fractality of functional near-infrared spectroscopy signals: analysis and applications Zhu, Li Haghani, Sasan Najafizadeh, Laleh Neurophotonics Research Papers Significance: The human brain is a highly complex system with nonlinear, dynamic behavior. A majority of brain imaging studies employing functional near-infrared spectroscopy (fNIRS), however, have considered only the spatial domain and have ignored the temporal properties of fNIRS recordings. Methods capable of revealing nonlinearities in fNIRS recordings can provide new insights about how the brain functions. Aim: The temporal characteristics of fNIRS signals are explored by comprehensively investigating their fractal properties. Approach: Fractality of fNIRS signals is analyzed using scaled windowed variance (SWV), as well as using visibility graph (VG), a method which converts a given time series into a graph. Additionally, the fractality of fNIRS signals obtained under resting-state and task-based conditions is compared, and the application of fractality in differentiating brain states is demonstrated for the first time via various classification approaches. Results: Results from SWV analysis show the existence of high fractality in fNIRS recordings. It is shown that differences in the temporal characteristics of fNIRS signals related to task-based and resting-state conditions can be revealed via the VGs constructed for each case. Conclusions: fNIRS recordings, regardless of the experimental conditions, exhibit high fractality. Furthermore, VG-based metrics can be employed to differentiate rest and task-execution brain states. Society of Photo-Optical Instrumentation Engineers 2020-04-29 2020-04 /pmc/articles/PMC7189210/ /pubmed/32377544 http://dx.doi.org/10.1117/1.NPh.7.2.025001 Text en © 2020 The Authors https://creativecommons.org/licenses/by/4.0/ Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
spellingShingle Research Papers
Zhu, Li
Haghani, Sasan
Najafizadeh, Laleh
On fractality of functional near-infrared spectroscopy signals: analysis and applications
title On fractality of functional near-infrared spectroscopy signals: analysis and applications
title_full On fractality of functional near-infrared spectroscopy signals: analysis and applications
title_fullStr On fractality of functional near-infrared spectroscopy signals: analysis and applications
title_full_unstemmed On fractality of functional near-infrared spectroscopy signals: analysis and applications
title_short On fractality of functional near-infrared spectroscopy signals: analysis and applications
title_sort on fractality of functional near-infrared spectroscopy signals: analysis and applications
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7189210/
https://www.ncbi.nlm.nih.gov/pubmed/32377544
http://dx.doi.org/10.1117/1.NPh.7.2.025001
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