A computational model to design neural interfaces for lower-limb sensory neuroprostheses
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 sensa...
| Những tác giả chính: | , , |
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| Định dạng: | Bài viết |
| Ngôn ngữ: | English |
| Được phát hành: |
BMC
2020-02-01
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| Loạt: | Journal of NeuroEngineering and Rehabilitation |
| Những chủ đề: | |
| Truy cập trực tuyến: | http://link.springer.com/article/10.1186/s12984-020-00657-7 |
| _version_ | 1828388808822882304 |
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| author | Marek Zelechowski Giacomo Valle Stanisa Raspopovic |
| author_facet | Marek Zelechowski Giacomo Valle Stanisa Raspopovic |
| author_sort | Marek Zelechowski |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-10T06:19:23Z |
| format | Article |
| id | doaj.art-39c901bae0e84a6d90b1ef8e818b37bb |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-12-10T06:19:23Z |
| publishDate | 2020-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-39c901bae0e84a6d90b1ef8e818b37bb2022-12-22T01:59:22ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032020-02-0117111310.1186/s12984-020-00657-7A computational model to design neural interfaces for lower-limb sensory neuroprosthesesMarek Zelechowski0Giacomo Valle1Stanisa Raspopovic2Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of BaselNeuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETHNeuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETHAbstract 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.http://link.springer.com/article/10.1186/s12984-020-00657-7SensoryNeuroprosthesisLower limbHybrid computational modelNeural interfacingNeural stimulation |
| spellingShingle | Marek Zelechowski Giacomo Valle Stanisa Raspopovic A computational model to design neural interfaces for lower-limb sensory neuroprostheses Journal of NeuroEngineering and Rehabilitation Sensory Neuroprosthesis Lower limb Hybrid computational model Neural interfacing Neural stimulation |
| title | A computational model to design neural interfaces for lower-limb sensory neuroprostheses |
| title_full | A computational model to design neural interfaces for lower-limb sensory neuroprostheses |
| title_fullStr | A computational model to design neural interfaces for lower-limb sensory neuroprostheses |
| title_full_unstemmed | A computational model to design neural interfaces for lower-limb sensory neuroprostheses |
| title_short | A computational model to design neural interfaces for lower-limb sensory neuroprostheses |
| title_sort | computational model to design neural interfaces for lower limb sensory neuroprostheses |
| topic | Sensory Neuroprosthesis Lower limb Hybrid computational model Neural interfacing Neural stimulation |
| url | http://link.springer.com/article/10.1186/s12984-020-00657-7 |
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