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Riley M, Tala FNU, Johnson KJ, Johnson BC. Multi-Channel Microscale Nerve Cuffs for Spatially Selective Neuromodulation. MICROMACHINES 2024; 15:1036. [PMID: 39203687 PMCID: PMC11356344 DOI: 10.3390/mi15081036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 08/08/2024] [Accepted: 08/12/2024] [Indexed: 09/03/2024]
Abstract
Peripheral nerve modulation via electrical stimulation shows promise for treating several diseases, but current approaches lack selectivity, leading to side effects. Exploring selective neuromodulation with commercially available nerve cuffs is impractical due to their high cost and limited spatial resolution. While custom cuffs reported in the literature achieve high spatial resolutions, they require specialized microfabrication equipment and significant effort to produce even a single design. This inability to rapidly and cost-effectively prototype novel cuff designs impedes research into selective neuromodulation therapies in acute studies. To address this, we developed a reproducible method to easily create multi-channel epineural nerve cuffs for selective fascicular neuromodulation. Leveraging commercial flexible printed circuit (FPC) technology, we created cuffs with high spatial resolution (50 μm) and customizable parameters like electrode size, channel count, and cuff diameter. We designed cuffs to accommodate adult mouse or rat sciatic nerves (300-1500 μm diameter). We coated the electrodes with PEDOT:PSS to improve the charge injection capacity. We demonstrated selective neuromodulation in both rats and mice, achieving preferential activation of the tibialis anterior (TA) and lateral gastrocnemius (LG) muscles. Selectivity was confirmed through micro-computed tomography (μCT) and quantified through a selectivity index. These results demonstrate the potential of this fabrication method for enabling selective neuromodulation studies while significantly reducing production time and costs compared to traditional approaches.
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Affiliation(s)
- Morgan Riley
- Biomedical Engineering Doctoral Program, Boise State University, Boise, ID 83725, USA
| | - FNU Tala
- Department of Electrical and Computer Engineering, Boise State University, Boise, ID 83725, USA
| | | | - Benjamin C. Johnson
- Department of Electrical and Computer Engineering, Boise State University, Boise, ID 83725, USA
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Hwang YCE, Genov R, Zariffa J. Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices. IEEE Trans Biomed Eng 2024; 71:631-639. [PMID: 37672367 DOI: 10.1109/tbme.2023.3312361] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
BACKGROUND Closed-loop functional electrical stimulation can use recorded nerve signals to create implantable systems that make decisions regarding nerve stimulation in real-time. Previous work demonstrated convolutional neural network (CNN) discrimination of activity from different neural pathways recorded by a high-density multi-contact nerve cuff electrode, achieving state-of-the-art performance but requiring too much data storage and power for a practical implementation on surgically implanted hardware. OBJECTIVE To reduce resource utilization for an implantable implementation, with minimal performance loss for CNNs that can discriminate between neural pathways in multi-contact cuff electrode recordings. METHODS Neural networks (NNs) were evaluated using rat sciatic nerve recordings previously collected using 56-channel cuff electrodes to capture spatiotemporal neural activity patterns. NNs were trained to classify individual, natural compound action potentials (nCAPs) elicited by sensory stimuli. Three architectures were explored: the previously reported ESCAPE-NET, a fully convolutional network, and a recurrent neural network. Variations of each architecture were evaluated based on F1-score, number of weights, and floating-point operations (FLOPs). RESULTS NNs were identified that, when compared to ESCAPE-NET, require 1,132-1,787x fewer weights, 389-995x less memory, and 6-11,073x fewer FLOPs, while maintaining macro F1-scores of 0.70-0.71 compared to a baseline of 0.75. Memory requirements range from 22.69 KB to 58.11 KB, falling within on-chip memory sizes from published deep learning accelerators fabricated in ASIC technology. CONCLUSION Reduced versions of ESCAPE-NET require significantly fewer resources without significant accuracy loss, thus can be more easily incorporated into a surgically implantable device that performs closed-loop responsive neural stimulation.
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Takeuchi M, Tokutake K, Watanabe K, Ito N, Aoyama T, Saeki S, Kurimoto S, Hirata H, Hasegawa Y. A Wirelessly Powered 4-Channel Neurostimulator for Reconstructing Walking Trajectory. SENSORS (BASEL, SWITZERLAND) 2022; 22:7198. [PMID: 36236295 PMCID: PMC9572656 DOI: 10.3390/s22197198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/10/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
A wirelessly powered four-channel neurostimulator was developed for applying selective Functional Electrical Stimulation (FES) to four peripheral nerves to control the ankle and knee joints of a rat. The power of the neurostimulator was wirelessly supplied from a transmitter device, and the four nerves were connected to the receiver device, which controlled the ankle and knee joints in the rat. The receiver device had functions to detect the frequency of the transmitter signal from the transmitter coil. The stimulation site of the nerves was selected according to the frequency of the transmitter signal. The rat toe position was controlled by changing the angles of the ankle and knee joints. The joint angles were controlled by the stimulation current applied to each nerve independently. The stimulation currents were adjusted by the Proportional Integral Differential (PID) and feed-forward control method through a visual feedback control system, and the walking trajectory of a rat's hind leg was reconstructed. This study contributes to controlling the multiple joints of a leg and reconstructing functional motions such as walking using the robotic control technology.
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Affiliation(s)
- Masaru Takeuchi
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, Japan
| | - Katsuhiro Tokutake
- Department of Human Enhancement and Hand Surgery, Nagoya University, Nagoya 464-8601, Japan
| | - Keita Watanabe
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, Japan
| | - Naoyuki Ito
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, Japan
| | - Tadayoshi Aoyama
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, Japan
| | - Sota Saeki
- Department of Human Enhancement and Hand Surgery, Nagoya University, Nagoya 464-8601, Japan
| | - Shigeru Kurimoto
- Department of Human Enhancement and Hand Surgery, Nagoya University, Nagoya 464-8601, Japan
| | - Hitoshi Hirata
- Department of Human Enhancement and Hand Surgery, Nagoya University, Nagoya 464-8601, Japan
| | - Yasuhisa Hasegawa
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, Japan
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Gelenitis K, Foglyano K, Lombardo L, Triolo R. Selective neural stimulation methods improve cycling exercise performance after spinal cord injury: a case series. J Neuroeng Rehabil 2021; 18:117. [PMID: 34301286 PMCID: PMC8301730 DOI: 10.1186/s12984-021-00912-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 07/15/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Exercise after paralysis can help prevent secondary health complications, but achieving adequate exercise volumes and intensities is difficult with loss of motor control. Existing electrical stimulation-driven cycling systems involve the paralyzed musculature but result in rapid force decline and muscle fatigue, limiting their effectiveness. This study explores the effects of selective stimulation patterns delivered through multi-contact nerve cuff electrodes on functional exercise output, with the goal of increasing work performed and power maintained within each bout of exercise. METHODS Three people with spinal cord injury and implanted stimulation systems performed cycling trials using conventional (S-Max), low overlap (S-Low), low duty cycle (C-Max), and/or combined low overlap and low duty cycle (C-Low) stimulation patterns. Outcome measures include total work (W), end power (Pend), power fluctuation indices (PFI), charge accumulation (Q), and efficiency (η). Mann-Whitney tests were used for statistical comparisons of W and Pend between a selective pattern and S-Max. Welch's ANOVAs were used to evaluate differences in PFIs among all patterns tested within a participant (n ≥ 90 per stimulation condition). RESULTS At least one selective pattern significantly (p < 0.05) increased W and Pend over S-Max in each participant. All selective patterns also reduced Q and increased η compared with S-Max for all participants. C-Max significantly (p < 0.01) increased PFI, indicating a decrease in ride smoothness with low duty cycle patterns. CONCLUSIONS Selective stimulation patterns can increase work performed and power sustained by paralyzed muscles prior to fatigue with increased stimulation efficiency. While still effective, low duty cycle patterns can cause inconsistent power outputs each pedal stroke, but this can be managed by utilizing optimized stimulation levels. Increasing work and sustained power each exercise session has the potential to ultimately improve the physiological benefits of stimulation-driven exercise.
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Affiliation(s)
- Kristen Gelenitis
- Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA.
| | - Kevin Foglyano
- Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Cleveland, OH, 44106, USA
| | - Lisa Lombardo
- Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Cleveland, OH, 44106, USA
| | - Ronald Triolo
- Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA
- Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Cleveland, OH, 44106, USA
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Abstract
Peripheral nerve interfaces (PNIs) record and/or modulate neural activity of nerves, which are responsible for conducting sensory-motor information to and from the central nervous system, and for regulating the activity of inner organs. PNIs are used both in neuroscience research and in therapeutical applications such as precise closed-loop control of neuroprosthetic limbs, treatment of neuropathic pain and restoration of vital functions (e.g. breathing and bladder management). Implantable interfaces represent an attractive solution to directly access peripheral nerves and provide enhanced selectivity both in recording and in stimulation, compared to their non-invasive counterparts. Nevertheless, the long-term functionality of implantable PNIs is limited by tissue damage, which occurs at the implant-tissue interface, and is thus highly dependent on material properties, biocompatibility and implant design. Current research focuses on the development of mechanically compliant PNIs, which adapt to the anatomy and dynamic movements of nerves in the body thereby limiting foreign body response. In this paper, we review recent progress in the development of flexible and implantable PNIs, highlighting promising solutions related to materials selection and their associated fabrication methods, and integrated functions. We report on the variety of available interface designs (intraneural, extraneural and regenerative) and different modulation techniques (electrical, optical, chemical) emphasizing the main challenges associated with integrating such systems on compliant substrates.
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Affiliation(s)
- Valentina Paggi
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Microengineering, Institute of Bioengineering, Centre for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland. Equally contributing authors
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Gelenitis K, Freeberg M, Triolo R. Sum of phase-shifted sinusoids stimulation prolongs paralyzed muscle output. J Neuroeng Rehabil 2020; 17:49. [PMID: 32276627 PMCID: PMC7149858 DOI: 10.1186/s12984-020-00679-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 04/01/2020] [Indexed: 12/05/2022] Open
Abstract
Neuroprostheses that activate musculature of the lower extremities can enable standing and movement after paralysis. Current systems are functionally limited by rapid muscle fatigue induced by conventional, non-varying stimulus waveforms. Previous work has shown that sum of phase-shifted sinusoids (SOPS) stimulation, which selectively modulates activation of individual motor unit pools (MUPs) to lower the duty cycle of each while maintaining a high net muscle output, improves joint moment maintenance but introduces greater instability over conventional stimulation. In this case study, implementation of SOPS stimulation with a real-time feedback controller successfully decreased joint moment instability and further prolonged joint moment output with increased stimulation efficiency over open-loop approaches in one participant with spinal cord injury. These findings demonstrate the potential for closed-loop SOPS to improve functionality of neuroprosthetic systems.
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Affiliation(s)
- Kristen Gelenitis
- Department of Biomedical Engineering, Case Western Reserve University, 10,900 Euclid Avenue, Cleveland, OH, 44106, USA.
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA.
| | - Max Freeberg
- Department of Biomedical Engineering, Case Western Reserve University, 10,900 Euclid Avenue, Cleveland, OH, 44106, USA
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA
| | - Ronald Triolo
- Department of Biomedical Engineering, Case Western Reserve University, 10,900 Euclid Avenue, Cleveland, OH, 44106, USA
- Department of Orthopaedics, Case Western Reserve University, Cleveland, OH, USA
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA
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