A computational model to design neural interfaces for lower-limb sensory neuroprostheses
dc.contributor.author
Zelechowski, Marek
dc.contributor.author
Valle, Giacomo
dc.contributor.author
Raspopovic, Stanisa
dc.date.accessioned
2021-04-06T14:52:07Z
dc.date.available
2021-04-06T14:52:07Z
dc.date.issued
2020-02-19
dc.identifier.issn
1743-0003
dc.identifier.other
10.1186/s12984-020-00657-7
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/477585
dc.identifier.doi
10.3929/ethz-b-000403140
dc.description.abstract
Background
Leg amputees suffer the lack of sensory feedback from a prosthesis, which is connected to their low confidence during walking, falls and low mobility. Electrical peripheral nerve stimulation (ePNS) of upper-limb amputee’s residual nerves has shown the ability to restore the sensations from the missing limb via intraneural (TIME) and epineural (FINE) neural interfaces. Physiologically plausible stimulation protocols targeting lower limb sciatic nerve hold promise to induce sensory feedback restoration that should facilitate close-to-natural sensorimotor integration and therefore walking corrections. The sciatic nerve, innervating the foot and lower leg, has very different dimensions in respect to upper-limb nerves. Therefore, there is a need to develop a computational model of its behavior in response to the ePNS.
Methods
We employed a hybrid FEM-NEURON model framework for the development of anatomically correct sciatic nerve model. Based on histological images of two distinct sciatic nerve cross-sections, we reconstructed accurate FEM models for testing neural interfaces. Two different electrode types (based on TIME and FINE) with multiple active sites configurations were tested and evaluated for efficiency (selective recruitment of fascicles). We also investigated different policies of stimulation (monopolar and bipolar), as well as the optimal number of implants. Additionally, we optimized the existing simulation framework significantly reducing the computational load.
Results
The main findings achieved through our modelling study include electrode manufacturing and surgical placement indications, together with beneficial stimulation policy of use. It results that TIME electrodes with 20 active sites are optimal for lower limb and the same number has been obtained for FINE electrodes. To interface the huge sciatic nerve, model indicates that 3 TIMEs is the optimal number of surgically implanted electrodes. Through the bipolar policy of stimulation, all studied configurations were gaining in the efficiency. Also, an indication for the optimized computation is given, which decreased the computation time by 80%.
Conclusions
This computational model suggests the optimal interfaces to use in human subjects with lower limb amputation, their surgical placement and beneficial bipolar policy of stimulation. It will potentially enable the clinical translation of the sensory neuroprosthetics towards the lower limb applications.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Sensory
en_US
dc.subject
Neuroprosthesis
en_US
dc.subject
Lower limb
en_US
dc.subject
Hybrid computational model
en_US
dc.subject
Neural interfacing
en_US
dc.subject
Neural stimulation
en_US
dc.title
A computational model to design neural interfaces for lower-limb sensory neuroprostheses
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-02-19
ethz.journal.title
Journal of NeuroEngineering and Rehabilitation
ethz.journal.volume
17
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
J. Neuroeng. Rehabilitat.
ethz.pages.start
24
en_US
ethz.size
13 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Restoring natural feelings from missing or damaged peripheral nervous system by model-driven neuroprosthesis
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::09632 - Raspopovic, Stanisa (ehemalig) / Raspopovic, Stanisa (former)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::09632 - Raspopovic, Stanisa (ehemalig) / Raspopovic, Stanisa (former)
ethz.grant.agreementno
759998
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
H2020
ethz.date.deposited
2020-03-05T03:17:31Z
ethz.source
SCOPUS
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-04-06T14:52:20Z
ethz.rosetta.lastUpdated
2024-02-02T13:27:46Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/403140
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/437218
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