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Woodington BJ, Lei J, Carnicer-Lombarte A, Güemes-González A, Naegele TE, Hilton S, El-Hadwe S, Trivedi RA, Malliaras GG, Barone DG. Flexible circumferential bioelectronics to enable 360-degree recording and stimulation of the spinal cord. SCIENCE ADVANCES 2024; 10:eadl1230. [PMID: 38718109 PMCID: PMC11078185 DOI: 10.1126/sciadv.adl1230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 04/04/2024] [Indexed: 05/12/2024]
Abstract
The spinal cord is crucial for transmitting motor and sensory information between the brain and peripheral systems. Spinal cord injuries can lead to severe consequences, including paralysis and autonomic dysfunction. We introduce thin-film, flexible electronics for circumferential interfacing with the spinal cord. This method enables simultaneous recording and stimulation of dorsal, lateral, and ventral tracts with a single device. Our findings include successful motor and sensory signal capture and elicitation in anesthetized rats, a proof-of-concept closed-loop system for bridging complete spinal cord injuries, and device safety verification in freely moving rodents. Moreover, we demonstrate potential for human application through a cadaver model. This method sees a clear route to the clinic by using materials and surgical practices that mitigate risk during implantation and preserve cord integrity.
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Affiliation(s)
- Ben J. Woodington
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Jiang Lei
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Amparo Güemes-González
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Tobias E. Naegele
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Sam Hilton
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Salim El-Hadwe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Rikin A. Trivedi
- Division of Neurosurgery, Addenbrookes Hospital, Hills Road, Cambridge, UK
| | - George G. Malliaras
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Damiano G. Barone
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, UK
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Fan J, Li X, Wang P, Yang F, Zhao B, Yang J, Zhao Z, Li X. A Hyperflexible Electrode Array for Long-Term Recording and Decoding of Intraspinal Neuronal Activity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2303377. [PMID: 37870208 PMCID: PMC10667843 DOI: 10.1002/advs.202303377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 09/23/2023] [Indexed: 10/24/2023]
Abstract
Neural interfaces for stable access to the spinal cord (SC) electrical activity can benefit patients with motor dysfunctions. Invasive high-density electrodes can directly extract signals from SC neuronal populations that can be used for the facilitation, adjustment, and reconstruction of motor actions. However, developing neural interfaces that can achieve high channel counts and long-term intraspinal recording remains technically challenging. Here, a biocompatible SC hyperflexible electrode array (SHEA) with an ultrathin structure that minimizes mechanical mismatch between the interface and SC tissue and enables stable single-unit recording for more than 2 months in mice is demonstrated. These results show that SHEA maintains stable impedance, signal-to-noise ratio, single-unit yield, and spike amplitude after implantation into mouse SC. Gait analysis and histology show that SHEA implantation induces negligible behavioral effects and Inflammation. Additionally, multi-unit signals recorded from the SC ventral horn can predict the mouse's movement trajectory with a high decoding coefficient of up to 0.95. Moreover, during step cycles, it is found that the neural trajectory of spikes and low-frequency local field potential (LFP) signal exhibits periodic geometry patterns. Thus, SHEA can offer an efficient and reliable SC neural interface for monitoring and potentially modulating SC neuronal activity associated with motor dysfunctions.
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Affiliation(s)
- Jie Fan
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of NeuroscienceChinese Academy of SciencesShanghai200031P. R. China
| | - Xiaocheng Li
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of NeuroscienceChinese Academy of SciencesShanghai200031P. R. China
| | - Peiyu Wang
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of NeuroscienceChinese Academy of SciencesShanghai200031P. R. China
| | - Fan Yang
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of NeuroscienceChinese Academy of SciencesShanghai200031P. R. China
| | - Bingzhen Zhao
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of NeuroscienceChinese Academy of SciencesShanghai200031P. R. China
| | - Jianing Yang
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of NeuroscienceChinese Academy of SciencesShanghai200031P. R. China
| | - Zhengtuo Zhao
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of NeuroscienceChinese Academy of SciencesShanghai200031P. R. China
| | - Xue Li
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of NeuroscienceChinese Academy of SciencesShanghai200031P. R. China
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Fathi Y, Erfanian A. Decoding Bilateral Hindlimb Kinematics From Cat Spinal Signals Using Three-Dimensional Convolutional Neural Network. Front Neurosci 2022; 16:801818. [PMID: 35401098 PMCID: PMC8990134 DOI: 10.3389/fnins.2022.801818] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/02/2022] [Indexed: 11/13/2022] Open
Abstract
To date, decoding limb kinematic information mostly relies on neural signals recorded from the peripheral nerve, dorsal root ganglia (DRG), ventral roots, spinal cord gray matter, and the sensorimotor cortex. In the current study, we demonstrated that the neural signals recorded from the lateral and dorsal columns within the spinal cord have the potential to decode hindlimb kinematics during locomotion. Experiments were conducted using intact cats. The cats were trained to walk on a moving belt in a hindlimb-only condition, while their forelimbs were kept on the front body of the treadmill. The bilateral hindlimb joint angles were decoded using local field potential signals recorded using a microelectrode array implanted in the dorsal and lateral columns of both the left and right sides of the cat spinal cord. The results show that contralateral hindlimb kinematics can be decoded as accurately as ipsilateral kinematics. Interestingly, hindlimb kinematics of both legs can be accurately decoded from the lateral columns within one side of the spinal cord during hindlimb-only locomotion. The results indicated that there was no significant difference between the decoding performances obtained using neural signals recorded from the dorsal and lateral columns. The results of the time-frequency analysis show that event-related synchronization (ERS) and event-related desynchronization (ERD) patterns in all frequency bands could reveal the dynamics of the neural signals during movement. The onset and offset of the movement can be clearly identified by the ERD/ERS patterns. The results of the mutual information (MI) analysis showed that the theta frequency band contained significantly more limb kinematics information than the other frequency bands. Moreover, the theta power increased with a higher locomotion speed.
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Affiliation(s)
- Yaser Fathi
- Department of Biomedical Engineering, School of Electrical Engineering, Iran Neural Technology Research Centre, Iran University of Science and Technology, Tehran, Iran
| | - Abbas Erfanian
- Department of Biomedical Engineering, School of Electrical Engineering, Iran Neural Technology Research Centre, Iran University of Science and Technology, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- *Correspondence: Abbas Erfanian,
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Kiang L, Woodington B, Carnicer-Lombarte A, Malliaras G, Barone DG. Spinal cord bioelectronic interfaces: opportunities in neural recording and clinical challenges. J Neural Eng 2022; 19. [PMID: 35320780 DOI: 10.1088/1741-2552/ac605f] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 03/23/2022] [Indexed: 11/11/2022]
Abstract
Bioelectronic stimulation of the spinal cord has demonstrated significant progress in restoration of motor function in spinal cord injury (SCI). The proximal, uninjured spinal cord presents a viable target for the recording and generation of control signals to drive targeted stimulation. Signals have been directly recorded from the spinal cord in behaving animals and correlated with limb kinematics. Advances in flexible materials, electrode impedance and signal analysis will allow SCR to be used in next-generation neuroprosthetics. In this review, we summarize the technological advances enabling progress in SCR and describe systematically the clinical challenges facing spinal cord bioelectronic interfaces and potential solutions, from device manufacture, surgical implantation to chronic effects of foreign body reaction and stress-strain mismatches between electrodes and neural tissue. Finally, we establish our vision of bi-directional closed-loop spinal cord bioelectronic bypass interfaces that enable the communication of disrupted sensory signals and restoration of motor function in SCI.
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Affiliation(s)
- Lei Kiang
- Orthopaedic Surgery, Singapore General Hospital, Outram Road, Singapore, Singapore, 169608, SINGAPORE
| | - Ben Woodington
- Department of Engineering, University of Cambridge, Electrical Engineering Division, 9 JJ Thomson Ave, Cambridge, Cambridge, CB2 1TN, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Alejandro Carnicer-Lombarte
- Clinical Neurosciences, University of Cambridge, Bioelectronics Laboratory, Cambridge, CB2 0PY, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - George Malliaras
- University of Cambridge, University of Cambridge, Cambridge, CB2 1TN, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Damiano G Barone
- Department of Engineering, University of Cambridge, Electrical Engineering Division, 9 JJ Thomson Ave, Cambridge, Cambridge, Cambridgeshire, CB2 1TN, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Fathi Y, Erfanian A. Decoding hindlimb kinematics from descending and ascending neural signals during cat locomotion. J Neural Eng 2021; 18. [PMID: 33395669 DOI: 10.1088/1741-2552/abd82a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 01/04/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The main objective of this research is to record both sensory and motor information from the ascending and descending tracts within the spinal cord for decoding the hindlimb kinematics during walking on the treadmill. APPROACH Two different experimental paradigms (i.e., active and passive) were used in the current study. During active experiments, five cats were trained to walk bipedally while their hands kept on the front frame of the treadmill for balance or to walk quadrupedally. During passive experiments, the limb was passively moved by the experimenter. Local field potential (LFP) activity was recorded using a microwire array implanted in the dorsal column (DC) and lateral column (LC) of the L3-L4 spinal segments. The amplitude and frequency components of the LFP formed the feature set and the elastic net regularization was used to decode the hindlimb joint angles. MAIN RESULTS The results show that there is no significant difference between the information content of the signals recorded from the DC and LC regions during walking on the treadmill, but the information content of the DC is significantly higher than that of the LC during passively applied movement of the hindlimb in the anesthetized cats. Moreover, the decoding performance obtained using the recorded signals from the DC is comparable with that from the LC during locomotion. But, the decoding performance obtained using the recording channels in the DC is significantly better than that obtained using the signals recorded from the LC. The long-term analysis shows that robust decoding performance can be achieved over 2-3 months without a significant decrease in performance. SIGNIFICANCE This work presents a promising approach to developing a natural and robust motor neuroprosthesis device using descending neural signals to execute the movement and ascending neural signals as the feedback information for control of the movement.
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Affiliation(s)
- Yaser Fathi
- Biomedical Engineering, Iran University of Science and Technology, Narmak, Resalat Square, Hengam Street, Iran University of Science and Technology, Tehran, Tehran, 16844, Iran (the Islamic Republic of)
| | - Abbas Erfanian
- Biomedical Engineering, Iran University of Science & Technology, Hengam Street, Narmak, Tehran 16844, Iran, Tehran, 16844, IRAN, ISLAMIC REPUBLIC OF
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Gok S, Sahin M. Prediction of Forelimb EMGs and Movement Phases from Corticospinal Signals in the Rat During the Reach-to-Pull Task. Int J Neural Syst 2019; 29:1950009. [DOI: 10.1142/s0129065719500096] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Brain-computer interfaces access the volitional command signals from various brain areas in order to substitute for the motor functions lost due to spinal cord injury or disease. As the final common pathway of the central nervous system (CNS) outputs, the descending tracts of the spinal cord offer an alternative site to extract movement-related command signals. Using flexible 2D microelectrode arrays, we have recorded the corticospinal tract (CST) signals in rats during a reach-to-pull task. The CST activity was then classified by the forelimb movement phases into two or three classes in a training dataset and cross validated in a test set. The average classification accuracies were [Formula: see text] (min: [Formula: see text] to max: [Formula: see text]) and [Formula: see text] (min: 43% to max: 71%) for two-class and three-class cases, respectively. The forelimb flexor and extensor EMG envelopes were also predicted from the CST signals using linear regression. The average correlation coefficient between the actual and predicted EMG signals was [Formula: see text] [Formula: see text], whereas the highest correlation was 0.81 for the biceps EMG. Although the forelimb motor function cannot be explained completely by the CST activity alone, the success rates obtained in reconstructing the EMG signals support the feasibility of a spinal-cord-computer interface as a concept.
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Affiliation(s)
- Sinan Gok
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey 07102, USA
| | - Mesut Sahin
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey 07102, USA
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Gok S, Sahin M. Prediction of forelimb muscle EMGs from the corticospinal signals in rats. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:2780-2783. [PMID: 28268895 DOI: 10.1109/embc.2016.7591307] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
To generate voluntary forearm movements, the information that is encoded in the activity of the cortical neurons has to travel through the spinal cord and activate the skeletal muscles. The axons carrying these signals are tightly bundled together in the descending tracts that control the spinal circuitry innervating the forearm muscles. In this paper, we show that corticospinal tract (CST) signals can be used to predict forearm electromyographic (EMG) activities that are recorded during an isometric-pull task. Rats were trained to pull on a metal bar through a window. A flexible-substrate multi-electrode array was chronically implanted into the dorsal column of the cervical spinal cord. Field potentials and multi-unit activities were recorded from the descending axons of the CST while the rat performed the task. Forelimb forces and EMG signals from a wrist extensor and a flexor, and the biceps and triceps were reconstructed using the neural signals in multiple sessions over three weeks. The regression coefficients found from the trial set were cross-validated on the other trials recorded on the same day. The maximum correlation coefficient between the actual and predicted signal was for the biceps (R=0.88). These results suggest the feasibility of an EMG-based spinal-cord-computer-interface (SCCI) for subjects with spinal cord injury.
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Effects of different fluences of low-level laser therapy in an experimental model of spinal cord injury in rats. Lasers Med Sci 2016; 32:343-349. [DOI: 10.1007/s10103-016-2120-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2016] [Accepted: 11/22/2016] [Indexed: 01/12/2023]
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Wang L, Freedman D, Sahin M, Ünlü MS, Knepper R. Active C4 Electrodes for Local Field Potential Recording Applications. SENSORS 2016; 16:198. [PMID: 26861324 PMCID: PMC4801575 DOI: 10.3390/s16020198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Revised: 01/26/2016] [Accepted: 01/31/2016] [Indexed: 11/16/2022]
Abstract
Extracellular neural recording, with multi-electrode arrays (MEAs), is a powerful method used to study neural function at the network level. However, in a high density array, it can be costly and time consuming to integrate the active circuit with the expensive electrodes. In this paper, we present a 4 mm × 4 mm neural recording integrated circuit (IC) chip, utilizing IBM C4 bumps as recording electrodes, which enable a seamless active chip and electrode integration. The IC chip was designed and fabricated in a 0.13 μm BiCMOS process for both in vitro and in vivo applications. It has an input-referred noise of 4.6 μVrms for the bandwidth of 10 Hz to 10 kHz and a power dissipation of 11.25 mW at 2.5 V, or 43.9 μW per input channel. This prototype is scalable for implementing larger number and higher density electrode arrays. To validate the functionality of the chip, electrical testing results and acute in vivo recordings from a rat barrel cortex are presented.
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Affiliation(s)
- Lu Wang
- Department of Electrical and Computer Engineering, Boston University, 8 Saint Mary's St, Boston 02215, MA, USA.
| | - David Freedman
- Department of Electrical and Computer Engineering, Boston University, 8 Saint Mary's St, Boston 02215, MA, USA.
| | - Mesut Sahin
- Department of Biomedical Engineering, New Jersey Institute of Technology, 323 Martin Luther King, Jr. Boulevard, University Heights Newark, Newark 07102, NJ, USA.
| | - M Selim Ünlü
- Department of Electrical and Computer Engineering, Boston University, 8 Saint Mary's St, Boston 02215, MA, USA.
- Department of Biomedical Engineering, Boston University, 44 Cummington St, Boston 02215, MA, USA.
| | - Ronald Knepper
- Department of Electrical and Computer Engineering, Boston University, 8 Saint Mary's St, Boston 02215, MA, USA.
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Mladinic M, Nistri A. Microelectrode arrays in combination with in vitro models of spinal cord injury as tools to investigate pathological changes in network activity: facts and promises. FRONTIERS IN NEUROENGINEERING 2013; 6:2. [PMID: 23459694 PMCID: PMC3586932 DOI: 10.3389/fneng.2013.00002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2012] [Accepted: 02/12/2013] [Indexed: 12/23/2022]
Abstract
Microelectrode arrays (MEAs) represent an important tool to study the basic characteristics of spinal networks that control locomotion in physiological conditions. Fundamental properties of this neuronal rhythmicity like burst origin, propagation, coordination, and resilience can, thus, be investigated at multiple sites within a certain spinal topography and neighboring circuits. A novel challenge will be to apply this technology to unveil the mechanisms underlying pathological processes evoked by spinal cord injury (SCI). To achieve this goal, it is necessary to fully identify spinal networks that make up the locomotor central pattern generator (CPG) and to understand their operational rules. In this review, the use of isolated spinal cord preparations from rodents, or organotypic spinal slice cultures is discussed to study rhythmic activity. In particular, this review surveys our recently developed in vitro models of SCI by evoking excitotoxic (or even hypoxic/dysmetabolic) damage to spinal networks and assessing the impact on rhythmic activity and cell survival. These pathological processes which evolve via different cell death mechanisms are discussed as a paradigm to apply MEA recording for detailed mapping of the functional damage and its time-dependent evolution.
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Affiliation(s)
- Miranda Mladinic
- Neuroscience Department, International School for Advanced Studies (SISSA) Trieste, Italy ; Spinal Person Injury Neurorehabilitation Applied Laboratory, Istituto di Medicina Fisica e Riabilitazione Udine, Italy ; Department of Biotechnology, University of Rijeka Rijeka, Croatia
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