High-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)

Background: Transcutaneous auricular Vagus Nerve Stimulation (taVNS) applies low-intensity electrical current to the ear with the intention of activating the auricular branch of the Vagus nerve. The sensitivity and selectivity of stimulation applied to the ear depends on current flow pattern produce...

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Autores principales:	Erica Kreisberg, Zeinab Esmaeilpour, Devin Adair, Niranjan Khadka, Abhishek Datta, Bashar W. Badran, J. Douglas Bremner, Marom Bikson
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Non-invasive brain stimulation Transcutaneous auricular vagus nerve stimulation Auricular branch of the vagus nerve Computational modelling Finite element method models Current flow models Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/7d2c4e3d19f24e35b8b02c778cfef700
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spelling	oai:doaj.org-article:7d2c4e3d19f24e35b8b02c778cfef7002021-11-20T04:58:23ZHigh-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)1935-861X10.1016/j.brs.2021.09.001https://doaj.org/article/7d2c4e3d19f24e35b8b02c778cfef7002021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1935861X21002278https://doaj.org/toc/1935-861XBackground: Transcutaneous auricular Vagus Nerve Stimulation (taVNS) applies low-intensity electrical current to the ear with the intention of activating the auricular branch of the Vagus nerve. The sensitivity and selectivity of stimulation applied to the ear depends on current flow pattern produced by a given electrode montage (size and placement). Objective: We compare different electrodes designs for taVNS considering both the predicted peak electric fields (sensitivity) and their spatial distribution (selectivity). Methods: Based on optimized high-resolution (0.47 mm) T1 and T2 weighted MRI, we developed an anatomical model of the left ear and the surrounding head tissues including brain, CSF/meninges, skull, muscle, blood vessels, fat, cartilage, and skin. The ear was further segmented into 6 regions of interest (ROI) based on various nerve densities: cavum concha, cymba concha, crus of helix, tragus, antitragus, and earlobe. A range of taVNS electrode montages were reproduced spanning varied electrodes sizes and placements over the tragus, cymba concha, earlobe, cavum concha, and crus of helix. Electric field across the ear (from superficial skin to cartilage) for each montage at 1 mA or 2 mA taVNS, assuming an activation threshold of 6.15 V/m, 12.3 V/m or 24.6 V/m was predicted using a Finite element method (FEM). Finally, considering every ROI, we calculated the sensitivity and selectivity of each montage. Results: Current flow patterns through the ear were highly specific to the electrode montage. Electric field was maximal at the ear regions directly under the electrodes, and for a given total current, increases with decreasing electrode size. Depending on the applied current and nerves threshold, activation may also occur in the regions between multiple anterior surface electrodes. Each considered montage was selective for one or two regions of interest. For example, electrodes across the tragus restricted significant electric field to the tragus. Stimulation across the earlobe restricted significant electric field to the earlobe and the antitragus. Because of this relative selectivity, use of control ear montages in experimental studies, support testing of targeting. Relative targeting was robust across assumptions of activation threshold and tissue properties. Discussion: Computational models provide additional insight on how details in electrode shape and placement impact sensitivity (how much current is needed) and selectivity (spatial distribution), thereby supporting analysis of existing approaches and optimization of new devices. Our result suggest taVNS current patterns and relative target are robust across individuals, though (variance in) axon morphology was not represented.Erica KreisbergZeinab EsmaeilpourDevin AdairNiranjan KhadkaAbhishek DattaBashar W. BadranJ. Douglas BremnerMarom BiksonElsevierarticleNon-invasive brain stimulationTranscutaneous auricular vagus nerve stimulationAuricular branch of the vagus nerveComputational modellingFinite element method modelsCurrent flow modelsNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENBrain Stimulation, Vol 14, Iss 6, Pp 1419-1430 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Non-invasive brain stimulation Transcutaneous auricular vagus nerve stimulation Auricular branch of the vagus nerve Computational modelling Finite element method models Current flow models Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Non-invasive brain stimulation Transcutaneous auricular vagus nerve stimulation Auricular branch of the vagus nerve Computational modelling Finite element method models Current flow models Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Erica Kreisberg Zeinab Esmaeilpour Devin Adair Niranjan Khadka Abhishek Datta Bashar W. Badran J. Douglas Bremner Marom Bikson High-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)
description	Background: Transcutaneous auricular Vagus Nerve Stimulation (taVNS) applies low-intensity electrical current to the ear with the intention of activating the auricular branch of the Vagus nerve. The sensitivity and selectivity of stimulation applied to the ear depends on current flow pattern produced by a given electrode montage (size and placement). Objective: We compare different electrodes designs for taVNS considering both the predicted peak electric fields (sensitivity) and their spatial distribution (selectivity). Methods: Based on optimized high-resolution (0.47 mm) T1 and T2 weighted MRI, we developed an anatomical model of the left ear and the surrounding head tissues including brain, CSF/meninges, skull, muscle, blood vessels, fat, cartilage, and skin. The ear was further segmented into 6 regions of interest (ROI) based on various nerve densities: cavum concha, cymba concha, crus of helix, tragus, antitragus, and earlobe. A range of taVNS electrode montages were reproduced spanning varied electrodes sizes and placements over the tragus, cymba concha, earlobe, cavum concha, and crus of helix. Electric field across the ear (from superficial skin to cartilage) for each montage at 1 mA or 2 mA taVNS, assuming an activation threshold of 6.15 V/m, 12.3 V/m or 24.6 V/m was predicted using a Finite element method (FEM). Finally, considering every ROI, we calculated the sensitivity and selectivity of each montage. Results: Current flow patterns through the ear were highly specific to the electrode montage. Electric field was maximal at the ear regions directly under the electrodes, and for a given total current, increases with decreasing electrode size. Depending on the applied current and nerves threshold, activation may also occur in the regions between multiple anterior surface electrodes. Each considered montage was selective for one or two regions of interest. For example, electrodes across the tragus restricted significant electric field to the tragus. Stimulation across the earlobe restricted significant electric field to the earlobe and the antitragus. Because of this relative selectivity, use of control ear montages in experimental studies, support testing of targeting. Relative targeting was robust across assumptions of activation threshold and tissue properties. Discussion: Computational models provide additional insight on how details in electrode shape and placement impact sensitivity (how much current is needed) and selectivity (spatial distribution), thereby supporting analysis of existing approaches and optimization of new devices. Our result suggest taVNS current patterns and relative target are robust across individuals, though (variance in) axon morphology was not represented.
format	article
author	Erica Kreisberg Zeinab Esmaeilpour Devin Adair Niranjan Khadka Abhishek Datta Bashar W. Badran J. Douglas Bremner Marom Bikson
author_facet	Erica Kreisberg Zeinab Esmaeilpour Devin Adair Niranjan Khadka Abhishek Datta Bashar W. Badran J. Douglas Bremner Marom Bikson
author_sort	Erica Kreisberg
title	High-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)
title_short	High-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)
title_full	High-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)
title_fullStr	High-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)
title_full_unstemmed	High-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)
title_sort	high-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular vagus nerve stimulation (tavns)
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/7d2c4e3d19f24e35b8b02c778cfef700
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High-resolution computational modeling of the current flow in the outer ear during transcutaneous auricular Vagus Nerve Stimulation (taVNS)

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