Fascicle localisation within peripheral nerves through evoked activity recordings: A comparison between electrical impedance tomography and multi-electrode arrays.

Electrical impedance tomography Fascicular anatomy Image reconstruction Multi-electrode arrays Peripheral nerves

Journal

Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558

Informations de publication

Date de publication:
01 07 2021
Historique:
received: 19 09 2020
revised: 07 03 2021
accepted: 12 03 2021
pubmed: 29 3 2021
medline: 1 7 2021
entrez: 28 3 2021
Statut: ppublish

Résumé

The lack of understanding of fascicular organisation in peripheral nerves limits the potential of vagus nerve stimulation therapy. Two promising methods may be employed to identify the functional anatomy of fascicles within the nerve: fast neural electrical impedance tomography (EIT), and penetrating multi-electrode arrays (MEA). These could provide a means to image the compound action potential within fascicles in the nerve. We compared the ability to localise fascicle activity between silicon shanks (SS) and carbon fibre (CF) multi-electrode arrays and fast neural EIT, with micro-computed tomography (MicroCT) as an independent reference. Fast neural EIT in peripheral nerves was only recently developed and MEA technology has been used only sparingly in nerves and not for source localisation. Assessment was performed in rat sciatic nerves while evoking neural activity in the tibial and peroneal fascicles. Recorded compound action potentials were larger with CF compared to SS (∼700 μV vs ∼300 μV); however, background noise was greater (6.3 μV vs 1.7 μV) leading to lower SNR. Maximum spatial discrimination between Centres-of-Mass of fascicular activity was achieved by fast neural EIT (402 ± 30 μm) and CF MEA (414 ± 123 μm), with no statistical difference between MicroCT (625 ± 17 μm) and CF (p > 0.05) and between CF and EIT (p > 0.05). Compared to CF MEAs, SS MEAs had a lower discrimination power (103 ± 51 μm, p < 0.05). EIT and CF MEAs showed localisation power closest to MicroCT. Silicon MEAs adopted in this study failed to discriminate fascicle location. Re-design of probe geometry may improve results. Nerve EIT is an accurate tool for assessment of fascicular position within nerves. Accuracy of EIT and CF MEA is similar to the reference method. We give technical recommendations for performing multi-electrode recordings in nerves.

Sections du résumé

BACKGROUND
The lack of understanding of fascicular organisation in peripheral nerves limits the potential of vagus nerve stimulation therapy. Two promising methods may be employed to identify the functional anatomy of fascicles within the nerve: fast neural electrical impedance tomography (EIT), and penetrating multi-electrode arrays (MEA). These could provide a means to image the compound action potential within fascicles in the nerve.
NEW METHOD
We compared the ability to localise fascicle activity between silicon shanks (SS) and carbon fibre (CF) multi-electrode arrays and fast neural EIT, with micro-computed tomography (MicroCT) as an independent reference. Fast neural EIT in peripheral nerves was only recently developed and MEA technology has been used only sparingly in nerves and not for source localisation. Assessment was performed in rat sciatic nerves while evoking neural activity in the tibial and peroneal fascicles.
RESULTS
Recorded compound action potentials were larger with CF compared to SS (∼700 μV vs ∼300 μV); however, background noise was greater (6.3 μV vs 1.7 μV) leading to lower SNR. Maximum spatial discrimination between Centres-of-Mass of fascicular activity was achieved by fast neural EIT (402 ± 30 μm) and CF MEA (414 ± 123 μm), with no statistical difference between MicroCT (625 ± 17 μm) and CF (p > 0.05) and between CF and EIT (p > 0.05). Compared to CF MEAs, SS MEAs had a lower discrimination power (103 ± 51 μm, p < 0.05).
COMPARISON WITH EXISTING METHODS
EIT and CF MEAs showed localisation power closest to MicroCT. Silicon MEAs adopted in this study failed to discriminate fascicle location. Re-design of probe geometry may improve results.
CONCLUSIONS
Nerve EIT is an accurate tool for assessment of fascicular position within nerves. Accuracy of EIT and CF MEA is similar to the reference method. We give technical recommendations for performing multi-electrode recordings in nerves.

Identifiants

pubmed: 33774053
pii: S0165-0270(21)00075-3
doi: 10.1016/j.jneumeth.2021.109140
pmc: PMC8249910
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

109140

Subventions

Organisme : NIH HHS
ID : OT2 OD026545
Pays : United States
Organisme : Medical Research Council
ID : MR/R01213X/1
Pays : United Kingdom

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

Références

J Neural Eng. 2019 Nov 11;16(6):066041
pubmed: 31536974
Eur Heart J. 2011 Apr;32(7):847-55
pubmed: 21030409
J Neural Eng. 2013 Aug;10(4):046016
pubmed: 23860226
J Neurosurg. 2011 Dec;115(6):1248-55
pubmed: 21838505
Muscle Nerve. 2010 Aug;42(2):192-201
pubmed: 20544926
Acta Neurol Scand. 2016 Mar;133(3):173-82
pubmed: 26190515
Muscle Nerve. 2003 Nov;28(5):525-41
pubmed: 14571454
Sci Rep. 2020 Mar 2;10(1):3830
pubmed: 32123283
Neuron. 2011 Dec 8;72(5):847-58
pubmed: 22153379
Neurotherapeutics. 2017 Jul;14(3):716-727
pubmed: 28585221
Neurology. 2015 Apr 28;84(17):1782-7
pubmed: 25841030
Brain Res. 1980 Oct 6;198(2):253-69
pubmed: 7407598
J Neural Eng. 2016 Dec;13(6):066002
pubmed: 27705958
Neuroimage. 2016 Jan 1;124(Pt A):204-213
pubmed: 26348559
Physiol Meas. 2019 Dec 03;40(11):115007
pubmed: 31694004
Front Neurosci. 2018 May 28;12:350
pubmed: 29910705
J Neural Eng. 2016 Jun;13(3):036001
pubmed: 27001946
J Neural Eng. 2018 Oct;15(5):056025
pubmed: 30070261
J Neurophysiol. 2010 May;103(5):2315-7
pubmed: 20220081
J Neural Eng. 2019 Feb;16(1):016001
pubmed: 30444215
J Physiol. 2020 Sep;598(17):3569-3576
pubmed: 32538473
IEEE Trans Biomed Eng. 2015 Jan;62(1):126-37
pubmed: 25069109
J Neural Eng. 2020 Apr 09;17(2):026022
pubmed: 32108590
Nat Rev Neurosci. 2013 Nov;14(11):770-85
pubmed: 24135696
J Neural Eng. 2018 Feb;15(1):016010
pubmed: 28905812
Biomed Phys Eng Express. 2017;3:
pubmed: 29568573
Nat Rev Neurosci. 2012 May 18;13(6):407-20
pubmed: 22595786
J Neurosci Methods. 2019 Sep 1;325:108325
pubmed: 31260728
J Neurosci Methods. 2020 May 15;338:108652
pubmed: 32179090
Front Neural Circuits. 2012 Dec 20;6:105
pubmed: 23267316
J Clin Neurophysiol. 2001 Sep;18(5):415-8
pubmed: 11709646
J Neural Eng. 2020 Apr 29;17(2):026037
pubmed: 32209743
Neuroimage. 2009 Aug 15;47(2):514-22
pubmed: 19426819
J Integr Neurosci. 2017;16(1):107-126
pubmed: 28891502
Sensors (Basel). 2017 Jan 31;17(2):
pubmed: 28146122
Physiol Meas. 2014 Jun;35(6):1095-109
pubmed: 24845144
Proc Natl Acad Sci U S A. 2016 Jul 19;113(29):8284-9
pubmed: 27382171
Sci Rep. 2018 May 22;8(1):7997
pubmed: 29789596
Sci Rep. 2018 Sep 20;8(1):14149
pubmed: 30237487
Med Biol Eng Comput. 2011 May;49(5):593-604
pubmed: 21448692
Front Neural Circuits. 2016 Dec 15;10:101
pubmed: 28018180
Sci Rep. 2020 Sep 23;10(1):15501
pubmed: 32968177
Nat Commun. 2020 Dec 7;11(1):6241
pubmed: 33288760
Clin Neurophysiol. 2010 May;121(5):777-83
pubmed: 20110193
Sci Rep. 2019 Jul 31;9(1):11145
pubmed: 31366940
IEEE Trans Neural Syst Rehabil Eng. 2016 Jan;24(1):20-7
pubmed: 26087496
Front Neurosci. 2015 Jan 06;8:423
pubmed: 25610364
Acta Biomater. 2014 Nov;10(11):4650-4660
pubmed: 25042798
Brain Res. 2009 Jul 28;1282:183-200
pubmed: 19486899
J Neural Eng. 2015 Aug;12(4):046009
pubmed: 26035638
J Neurosci Methods. 2021 Mar 15;352:109079
pubmed: 33516735

Auteurs

Enrico Ravagli (E)

Medical Physics and Biomedical Engineering, University College London, UK. Electronic address: e.ravagli@ucl.ac.uk.

Svetlana Mastitskaya (S)

Medical Physics and Biomedical Engineering, University College London, UK.

Nicole Thompson (N)

Medical Physics and Biomedical Engineering, University College London, UK.

Elissa J Welle (EJ)

Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.

Cynthia A Chestek (CA)

Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.

Kirill Aristovich (K)

Medical Physics and Biomedical Engineering, University College London, UK.

David Holder (D)

Medical Physics and Biomedical Engineering, University College London, UK.

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