Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task

Twenty-three young adults (4 Females, 25.13 ± 3.72 years) performed an intermittent maximal handgrip force task using their dominant hand for 20 min (3.5 s squeeze/6.5 s release, 120 blocks) with concurrent cortical activity imaging by functional Near-Infrared Spectroscopy (fNRIS; OMM-3000, Shimadzu...

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Main Authors: Elizabeth L. Urquhart, Xinlong Wang, Hanli Liu, Paul J. Fadel, George Alexandrakis
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-03-01
Series:Frontiers in Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2020.00167/full
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author Elizabeth L. Urquhart
Xinlong Wang
Hanli Liu
Paul J. Fadel
George Alexandrakis
author_facet Elizabeth L. Urquhart
Xinlong Wang
Hanli Liu
Paul J. Fadel
George Alexandrakis
author_sort Elizabeth L. Urquhart
collection DOAJ
description Twenty-three young adults (4 Females, 25.13 ± 3.72 years) performed an intermittent maximal handgrip force task using their dominant hand for 20 min (3.5 s squeeze/6.5 s release, 120 blocks) with concurrent cortical activity imaging by functional Near-Infrared Spectroscopy (fNRIS; OMM-3000, Shimadzu Corp., 111 channels). Subjects were grouped as physically active (n = 10) or inactive (n = 12) based on a questionnaire (active-exercise at least four times a week, inactive- exercise less than two times a week). We explored how motor task fatigue affected the vasomotion-induced oscillations in ΔHbO as measured by fNIRS at each hemodynamic frequency band: endothelial component (0.003–0.02 Hz) associated to microvascular activity, neurogenic component (0.02–0.04 Hz) related to intrinsic neuronal activity, and myogenic component (0.04–0.15 Hz) linked to activity of smooth muscles of arterioles. To help understand how these three neurovascular regulatory mechanisms relate to handgrip task performance we quantified several dynamic fNIRS metrics, including directional phase transfer entropy (dPTE), a computationally efficient and data-driven method used as a marker of information flow between cortical regions, and directional connectivity (DC), a means to compute directionality of information flow between two cortical regions. The relationship between static functional connectivity (SFC) and functional connectivity variability (FCV) was also explored to understand their mutual dependence for each frequency band in the context of handgrip performance as fatigued increased. Our findings ultimately showed differences between subject groups across all fNIRS metrics and hemodynamic frequency bands. These findings imply that physical activity modulates neurovascular control mechanisms at the endogenic, neurogenic, and myogenic frequency bands resulting in delayed fatigue onset and enhanced performance. The dynamic cortical network metrics quantified in this work for young, healthy subjects provides baseline measurements to guide future work on older individuals and persons with impaired cardiovascular health.
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spelling doaj.art-e0bbdde79f2b4814a0aed665fd4461b92022-12-21T18:39:50ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2020-03-011410.3389/fnins.2020.00167499029Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip TaskElizabeth L. Urquhart0Xinlong Wang1Hanli Liu2Paul J. Fadel3George Alexandrakis4Bioengineering Department, University of Texas at Arlington, Arlington, TX, United StatesBioengineering Department, University of Texas at Arlington, Arlington, TX, United StatesBioengineering Department, University of Texas at Arlington, Arlington, TX, United StatesDepartment of Kinesiology, University of Texas at Arlington, Arlington, TX, United StatesBioengineering Department, University of Texas at Arlington, Arlington, TX, United StatesTwenty-three young adults (4 Females, 25.13 ± 3.72 years) performed an intermittent maximal handgrip force task using their dominant hand for 20 min (3.5 s squeeze/6.5 s release, 120 blocks) with concurrent cortical activity imaging by functional Near-Infrared Spectroscopy (fNRIS; OMM-3000, Shimadzu Corp., 111 channels). Subjects were grouped as physically active (n = 10) or inactive (n = 12) based on a questionnaire (active-exercise at least four times a week, inactive- exercise less than two times a week). We explored how motor task fatigue affected the vasomotion-induced oscillations in ΔHbO as measured by fNIRS at each hemodynamic frequency band: endothelial component (0.003–0.02 Hz) associated to microvascular activity, neurogenic component (0.02–0.04 Hz) related to intrinsic neuronal activity, and myogenic component (0.04–0.15 Hz) linked to activity of smooth muscles of arterioles. To help understand how these three neurovascular regulatory mechanisms relate to handgrip task performance we quantified several dynamic fNIRS metrics, including directional phase transfer entropy (dPTE), a computationally efficient and data-driven method used as a marker of information flow between cortical regions, and directional connectivity (DC), a means to compute directionality of information flow between two cortical regions. The relationship between static functional connectivity (SFC) and functional connectivity variability (FCV) was also explored to understand their mutual dependence for each frequency band in the context of handgrip performance as fatigued increased. Our findings ultimately showed differences between subject groups across all fNIRS metrics and hemodynamic frequency bands. These findings imply that physical activity modulates neurovascular control mechanisms at the endogenic, neurogenic, and myogenic frequency bands resulting in delayed fatigue onset and enhanced performance. The dynamic cortical network metrics quantified in this work for young, healthy subjects provides baseline measurements to guide future work on older individuals and persons with impaired cardiovascular health.https://www.frontiersin.org/article/10.3389/fnins.2020.00167/fullfatiguesensory-motor cortexdirectional phase transfer entropydirectional connectivityfunctional connectivity variability
spellingShingle Elizabeth L. Urquhart
Xinlong Wang
Hanli Liu
Paul J. Fadel
George Alexandrakis
Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task
Frontiers in Neuroscience
fatigue
sensory-motor cortex
directional phase transfer entropy
directional connectivity
functional connectivity variability
title Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task
title_full Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task
title_fullStr Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task
title_full_unstemmed Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task
title_short Differences in Net Information Flow and Dynamic Connectivity Metrics Between Physically Active and Inactive Subjects Measured by Functional Near-Infrared Spectroscopy (fNIRS) During a Fatiguing Handgrip Task
title_sort differences in net information flow and dynamic connectivity metrics between physically active and inactive subjects measured by functional near infrared spectroscopy fnirs during a fatiguing handgrip task
topic fatigue
sensory-motor cortex
directional phase transfer entropy
directional connectivity
functional connectivity variability
url https://www.frontiersin.org/article/10.3389/fnins.2020.00167/full
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