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Xie A, Li C, Chou CH, Li T, Dai C, Lan N. A hybrid sensory feedback system for thermal nociceptive warning and protection in prosthetic hand. Front Neurosci 2024; 18:1351348. [PMID: 38650624 PMCID: PMC11033464 DOI: 10.3389/fnins.2024.1351348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
Background Advanced prosthetic hands may embed nanosensors and microelectronics in their cosmetic skin. Heat influx may cause damage to these delicate structures. Protecting the integrity of the prosthetic hand becomes critical and necessary to ensure sustainable function. This study aims to mimic the sensorimotor control strategy of the human hand in perceiving nociceptive stimuli and triggering self-protective mechanisms and to investigate how similar neuromorphic mechanisms implemented in prosthetic hand can allow amputees to both volitionally release a hot object upon a nociceptive warning and achieve reinforced release via a bionic withdrawal reflex. Methods A steady-state temperature prediction algorithm was proposed to shorten the long response time of a thermosensitive temperature sensor. A hybrid sensory strategy for transmitting force and a nociceptive temperature warning using transcutaneous electrical nerve stimulation based on evoked tactile sensations was designed to reconstruct the nociceptive sensory loop for amputees. A bionic withdrawal reflex using neuromorphic muscle control technology was used so that the prosthetic hand reflexively opened when a harmful temperature was detected. Four able-bodied subjects and two forearm amputees randomly grasped a tube at the different temperatures based on these strategies. Results The average prediction error of temperature prediction algorithm was 8.30 ± 6.00%. The average success rate of six subjects in perceiving force and nociceptive temperature warnings was 86.90 and 94.30%, respectively. Under the reinforcement control mode in Test 2, the median reaction time of all subjects was 1.39 s, which was significantly faster than the median reaction time of 1.93 s in Test 1, in which two able-bodied subjects and two amputees participated. Results demonstrated the effectiveness of the integration of nociceptive sensory strategy and withdrawal reflex control strategy in a closed loop and also showed that amputees restored the warning of nociceptive sensation while also being able to withdraw from thermal danger through both voluntary and reflexive protection. Conclusion This study demonstrated that it is feasible to restore the sensorimotor ability of amputees to warn and react against thermal nociceptive stimuli. Results further showed that the voluntary release and withdrawal reflex can work together to reinforce heat protection. Nevertheless, fusing voluntary and reflex functions for prosthetic performance in activities of daily living awaits a more cogent strategy in sensorimotor control.
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Affiliation(s)
- Anran Xie
- Laboratory of NeuroRehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chen Li
- Laboratory of NeuroRehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chih-hong Chou
- Laboratory of NeuroRehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, School of Biomedical Engineering Shanghai Jiao Tong University, Shanghai, China
| | - Tie Li
- i-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), Suzhou, China
| | - Chenyun Dai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ning Lan
- Laboratory of NeuroRehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, School of Biomedical Engineering Shanghai Jiao Tong University, Shanghai, China
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, United States
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Housley SN, Powers RK, Nardelli P, Lee S, Blum K, Bewick GS, Banks RW, Cope TC. Biophysical model of muscle spindle encoding. Exp Physiol 2024; 109:55-65. [PMID: 36966478 PMCID: PMC10988694 DOI: 10.1113/ep091099] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/09/2023] [Indexed: 03/27/2023]
Abstract
Muscle spindles encode mechanosensory information by mechanisms that remain only partially understood. Their complexity is expressed in mounting evidence of various molecular mechanisms that play essential roles in muscle mechanics, mechanotransduction and intrinsic modulation of muscle spindle firing behaviour. Biophysical modelling provides a tractable approach to achieve more comprehensive mechanistic understanding of such complex systems that would be difficult/impossible by more traditional, reductionist means. Our objective here was to construct the first integrative biophysical model of muscle spindle firing. We leveraged current knowledge of muscle spindle neuroanatomy and in vivo electrophysiology to develop and validate a biophysical model that reproduces key in vivo muscle spindle encoding characteristics. Crucially, to our knowledge, this is the first computational model of mammalian muscle spindle that integrates the asymmetric distribution of known voltage-gated ion channels (VGCs) with neuronal architecture to generate realistic firing profiles, both of which seem likely to be of great biophysical importance. Results predict that particular features of neuronal architecture regulate specific characteristics of Ia encoding. Computational simulations also predict that the asymmetric distribution and ratios of VGCs is a complementary and, in some instances, orthogonal means to regulate Ia encoding. These results generate testable hypotheses and highlight the integral role of peripheral neuronal structure and ion channel composition and distribution in somatosensory signalling.
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Affiliation(s)
| | - Randal K. Powers
- Department of Physiology and BiophysicsUniversity of WashingtonSeattleWAUSA
| | - Paul Nardelli
- School of Biological SciencesGeorgia Institute of TechnologyAtlantaGA
| | - Sebinne Lee
- School of Biological SciencesGeorgia Institute of TechnologyAtlantaGA
| | - Kyle Blum
- Department of Physiology, Feinberg School of MedicineNorthwestern UniversityChicagoILUSA
| | - Guy S. Bewick
- Institute of Medical ScienceUniversity of AberdeenAberdeenUK
| | | | - Timothy C. Cope
- School of Biological SciencesGeorgia Institute of TechnologyAtlantaGA
- W. H. Coulter Department of Biomedical EngineeringEmory University and Georgia Institute of Technology, Georgia Institute of TechnologyAtlantaGA
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Luo Q, Bai M, Chen S, Gao K, Yin L, Du R. Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 12:66-75. [PMID: 38088991 PMCID: PMC10712672 DOI: 10.1109/jtehm.2023.3320715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 09/13/2023] [Accepted: 09/21/2023] [Indexed: 12/18/2023]
Abstract
Prosthetic hands are frequently rejected due to frustrations in daily uses. By adopting principles of human neuromuscular control, it could potentially achieve human-like compliance in hand functions, thereby improving functionality in prosthetic hand. Previous studies have confirmed the feasibility of real-time emulation of neuromuscular reflex for prosthetic control. This study further to explore the effect of feedforward electromyograph (EMG) decoding and proprioception on the biomimetic controller. The biomimetic controller included a feedforward Bayesian model for decoding alpha motor commands from stump EMG, a muscle model, and a closed-loop component with a model of muscle spindle modified with spiking afferents. Real-time control was enabled by neuromorphic hardware to accelerate evaluation of biologically inspired models. This allows us to investigate which aspects in the controller could benefit from biological properties for improvements on force control performance. 3 non-disabled and 3 amputee subjects were recruited to conduct a "press-without-break" task, subjects were required to press a transducer till the pressure stabilized in an expected range without breaking the virtual object. We tested whether introducing more complex but biomimetic models could enhance the task performance. Data showed that when replacing proportional feedback with the neuromorphic spindle, success rates of amputees increased by 12.2% and failures due to breakage decreased by 26.3%. More prominently, success rates increased by 55.5% and failures decreased by 79.3% when replacing a linear model of EMG with the Bayesian model in the feedforward EMG processing. Results suggest that mimicking biological properties in feedback and feedforward control may improve the manipulation of objects by amputees using prosthetic hands. Clinical and Translational Impact Statement: This control approach may eventually assist amputees to perform fine force control when using prosthetic hands, thereby improving the motor performance of amputees. It highlights the promising potential of the biomimetic controller integrating biological properties implemented on neuromorphic models as a viable approach for clinical application in prosthetic hands.
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Affiliation(s)
- Qi Luo
- School of Automotive and Mechanical EngineeringChangsha University of Science and TechnologyChangsha410114China
| | - Minglei Bai
- School of Biomedical SciencesThe Chinese University of Hong KongHong Kong999077China
| | - Shuhan Chen
- School of Automotive and Mechanical EngineeringChangsha University of Science and TechnologyChangsha410114China
| | - Kai Gao
- School of Automotive and Mechanical EngineeringChangsha University of Science and TechnologyChangsha410114China
| | - Lairong Yin
- School of Automotive and Mechanical EngineeringChangsha University of Science and TechnologyChangsha410114China
| | - Ronghua Du
- School of Automotive and Mechanical EngineeringChangsha University of Science and TechnologyChangsha410114China
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4
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Zhang Z, Zhang J, Luo Q, Chou CH, Xie A, Niu CM, Hao M, Lan N. A Biorealistic Computational Model Unfolds Human-Like Compliant Properties for Control of Hand Prosthesis. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:150-161. [PMID: 36712316 PMCID: PMC9870270 DOI: 10.1109/ojemb.2022.3215726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/17/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Objective: Human neuromuscular reflex control provides a biological model for a compliant hand prosthesis. Here we present a computational approach to understanding the emerging human-like compliance, force and position control, and stiffness adaptation in a prosthetic hand with a replica of human neuromuscular reflex. Methods: A virtual twin of prosthetic hand was constructed in the MuJoCo environment with a tendon-driven anthropomorphic hand structure. Biorealistic mathematic models of muscle, spindle, spiking-neurons and monosynaptic reflex were implemented in neuromorphic chips to drive the virtual hand for real-time control. Results: Simulation showed that the virtual hand acquired human-like ability to control fingertip position, force and stiffness for grasp, as well as the capacity to interact with soft objects by adaptively adjusting hand stiffness. Conclusion: The biorealistic neuromorphic reflex model restores human-like neuromuscular properties for hand prosthesis to interact with soft objects.
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Affiliation(s)
- Zhuozhi Zhang
- Laboratory of Neurorehabilitation Engineering, School of Biomedical EngineeringShanghai Jiao Tong University Shanghai 200240 China
| | - Jie Zhang
- Laboratory of Neurorehabilitation Engineering, School of Biomedical EngineeringShanghai Jiao Tong University Shanghai 200240 China
| | - Qi Luo
- Laboratory of Neurorehabilitation Engineering, School of Biomedical EngineeringShanghai Jiao Tong University Shanghai 200240 China
| | - Chih-Hong Chou
- Laboratory of Neurorehabilitation Engineering, School of Biomedical EngineeringShanghai Jiao Tong University Shanghai 200240 China
- Institute of Medical RoboticsShanghai Jiao Tong University Shanghai 200240 China
| | - Anran Xie
- Laboratory of Neurorehabilitation Engineering, School of Biomedical EngineeringShanghai Jiao Tong University Shanghai 200240 China
| | - Chuanxin M Niu
- Laboratory of Neurorehabilitation Engineering, School of Biomedical EngineeringShanghai Jiao Tong University Shanghai 200240 China
- Institute of Medical RoboticsShanghai Jiao Tong University Shanghai 200240 China
| | - Manzhao Hao
- Laboratory of Neurorehabilitation Engineering, School of Biomedical EngineeringShanghai Jiao Tong University Shanghai 200240 China
- Institute of Medical RoboticsShanghai Jiao Tong University Shanghai 200240 China
| | - Ning Lan
- Laboratory of Neurorehabilitation Engineering, School of Biomedical EngineeringShanghai Jiao Tong University Shanghai 200240 China
- Institute of Medical RoboticsShanghai Jiao Tong University Shanghai 200240 China
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5
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Luo Q, Niu CM, Chou CH, Liang W, Deng X, Hao M, Lan N. Biorealistic Control of Hand Prosthesis Augments Functional Performance of Individuals With Amputation. Front Neurosci 2021; 15:783505. [PMID: 34970115 PMCID: PMC8712573 DOI: 10.3389/fnins.2021.783505] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 11/15/2021] [Indexed: 11/27/2022] Open
Abstract
The human hand has compliant properties arising from muscle biomechanics and neural reflexes, which are absent in conventional prosthetic hands. We recently proved the feasibility to restore neuromuscular reflex control (NRC) to prosthetic hands using real-time computing neuromorphic chips. Here we show that restored NRC augments the ability of individuals with forearm amputation to complete grasping tasks, including standard Box and Blocks Test (BBT), Golf Balls Test (GBT), and Potato Chips Test (PCT). The latter two were more challenging, but novel to prosthesis tests. Performance of a biorealistic controller (BC) with restored NRC was compared to that of a proportional linear feedback (PLF) controller. Eleven individuals with forearm amputation were divided into two groups: one with experience of myocontrol of a prosthetic hand and another without any. Controller performances were evaluated by success rate, failure (drop/break) rate in each grasping task. In controller property tests, biorealistic control achieved a better compliant property with a 23.2% wider range of stiffness adjustment than that of PLF control. In functional grasping tests, participants could control prosthetic hands more rapidly and steadily with neuromuscular reflex. For participants with myocontrol experience, biorealistic control yielded 20.4, 39.4, and 195.2% improvements in BBT, GBT, and PCT, respectively, compared to PLF control. Interestingly, greater improvements were achieved by participants without any myocontrol experience for BBT, GBT, and PCT at 27.4, 48.9, and 344.3%, respectively. The functional gain of biorealistic control over conventional control was more dramatic in more difficult grasp tasks of GBT and PCT, demonstrating the advantage of NRC. Results support the hypothesis that restoring neuromuscular reflex in hand prosthesis can improve neural motor compatibility to human sensorimotor system, hence enabling individuals with amputation to perform delicate grasps that are not tested with conventional prosthetic hands.
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Affiliation(s)
- Qi Luo
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chuanxin M. Niu
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chih-Hong Chou
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Wenyuan Liang
- National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Xiaoqian Deng
- Guangdong Work Injury Rehabilitation Hospital, Guangzhou, China
| | - Manzhao Hao
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Ning Lan
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
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6
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Sohn WJ, Sanger TD. Constraint-induced intervention as an emergent phenomenon from synaptic competition in biological systems. J Comput Neurosci 2021; 49:175-188. [PMID: 33825082 PMCID: PMC8046695 DOI: 10.1007/s10827-021-00782-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 11/20/2020] [Accepted: 02/10/2021] [Indexed: 01/03/2023]
Abstract
The principle of constraint-induced therapy is widely practiced in rehabilitation. In hemiplegic cerebral palsy (CP) with impaired contralateral corticospinal projection due to unilateral injury, function improves after imposing a temporary constraint on limbs from the less affected hemisphere. This type of partially-reversible impairment in motor control by early brain injury bears a resemblance to the experience-dependent plastic acquisition and modification of neuronal response selectivity in the visual cortex. Previously, such mechanism was modeled within the framework of BCM (Bienenstock-Cooper-Munro) theory, a rate-based synaptic modification theory. Here, we demonstrate a minimally complex yet sufficient neural network model which provides a fundamental explanation for inter-hemispheric competition using a simplified spike-based model of information transmission and plasticity. We emulate the restoration of function in hemiplegic CP by simulating the competition between cells of the ipsilateral and contralateral corticospinal tracts. We use a high-speed hardware neural simulation to provide realistic numbers of spikes and realistic magnitudes of synaptic modification. We demonstrate that the phenomenon of constraint-induced partial reversal of hemiplegia can be modeled by simplified neural descending tracts with 2 layers of spiking neurons and synapses with spike-timing-dependent plasticity (STDP). We further demonstrate that persistent hemiplegia following unilateral cortical inactivation or deprivation is predicted by the STDP-based model but is inconsistent with BCM model. Although our model is a highly simplified and limited representation of the corticospinal system, it offers an explanation of how constraint as an intervention can help the system to escape from a suboptimal solution. This is a display of an emergent phenomenon from the synaptic competition.
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Affiliation(s)
- Won J Sohn
- Department of Neurology, University of California at Irvine, 200 S. Manchester Ave, Orange, CA, 92868, USA
| | - Terence D Sanger
- Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA. .,Department of Biokinesiology, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA. .,Department of Neurology, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA.
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7
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Niu CM, Luo Q, Chou CH, Liu J, Hao M, Lan N. Neuromorphic Model of Reflex for Realtime Human-Like Compliant Control of Prosthetic Hand. Ann Biomed Eng 2020; 49:673-688. [PMID: 32816166 PMCID: PMC7851042 DOI: 10.1007/s10439-020-02596-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 08/11/2020] [Indexed: 12/18/2022]
Abstract
Current control of prosthetic hands is ineffective when grasping deformable, irregular, or heavy objects. In humans, grasping is achieved under spinal reflexive control of the musculotendon skeletal structure, which produces a hand stiffness commensurate with the task. We hypothesize that mimicking reflex on a prosthetic hand may improve grasping performance and safety when interacting with human. Here, we present a design of compliant controller for prosthetic hand with a neuromorphic model of human reflex. The model includes 6 motoneuron pools containing 768 spiking neurons, 1 muscle spindle with 128 spiking afferents, and 1 modified Hill-type muscle. Models are implemented using neuromorphic hardware with 1 kHz real-time computing. Experimental tests showed that the prosthetic hand could sustain a 40 N load compared to 95 N for an adult. Stiffness range was adjustable from 60 to 640 N/m, about 46.6% of that of human hand. The grasping velocity could be ramped up to 14.4 cm/s, or 24% of the human peak velocity. The complaint control could switch between free movement and contact force when pressing a deformable beam. The amputee can achieve a 47% information throughput of healthy humans. Overall, the reflex-enabled prosthetic hand demonstrated the attributes of human compliant grasping with the neuromorphic model of spinal neuromuscular reflex.
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Affiliation(s)
- Chuanxin M Niu
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Med-X Research Institute, Rm 405 (South), Shanghai, China
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Qi Luo
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Med-X Research Institute, Rm 405 (South), Shanghai, China
| | - Chih-Hong Chou
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Med-X Research Institute, Rm 405 (South), Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jiayue Liu
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Med-X Research Institute, Rm 405 (South), Shanghai, China
| | - Manzhao Hao
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Med-X Research Institute, Rm 405 (South), Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Ning Lan
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Med-X Research Institute, Rm 405 (South), Shanghai, China.
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
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Wang R, Herman P, Ekeberg Ö, Gäverth J, Fagergren A, Forssberg H. Neural and non-neural related properties in the spastic wrist flexors: An optimization study. Med Eng Phys 2017; 47:198-209. [PMID: 28694106 DOI: 10.1016/j.medengphy.2017.06.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Revised: 06/14/2017] [Accepted: 06/14/2017] [Indexed: 10/19/2022]
Abstract
Quantifying neural and non-neural contributions to increased joint resistance in spasticity is essential for a better understanding of its pathophysiological mechanisms and evaluating different intervention strategies. However, direct measurement of spasticity-related manifestations, e.g., motoneuron and biophysical properties in humans, is extremely challenging. In this vein, we developed a forward neuromusculoskeletal model that accounts for dynamics of muscle spindles, motoneuron pools, muscle activation and musculotendon of wrist flexors and relies on the joint angle and resistant torque as the only input measurement variables. By modeling the stretch reflex pathway, neural and non-neural related properties of the spastic wrist flexors were estimated during the wrist extension test. Joint angle and resistant torque were collected from 17 persons with chronic stroke and healthy controls using NeuroFlexor, a motorized force measurement device during the passive wrist extension test. The model was optimized by tuning the passive and stretch reflex-related parameters to fit the measured torque in each participant. We found that persons with moderate and severe spasticity had significantly higher stiffness than controls. Among subgroups of stroke survivors, the increased neural component was mainly due to a lower muscle spindle rate at 50% of the motoneuron recruitment. The motoneuron pool threshold was highly correlated to the motoneuron pool gain in all subgroups. The model can describe the overall resistant behavior of the wrist joint during the test. Compared to controls, increased resistance was predominantly due to higher elasticity and neural components. We concluded that in combination with the NeuroFlexor measurement, the proposed neuromusculoskeletal model and optimization scheme served as suitable tools for investigating potential parameter changes along the stretch-reflex pathway in persons with spasticity.
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Affiliation(s)
- R Wang
- Department of Mechanics, Royal Institute of Technology, Stockholm, Sweden; KTH Biomex Center, Royal Institute of Technology, Stockholm, Sweden; Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.
| | - P Herman
- Dept. of Computational Science and Technology, Royal Institute of Technology, Stockholm, Sweden.
| | - Ö Ekeberg
- Dept. of Computational Science and Technology, Royal Institute of Technology, Stockholm, Sweden.
| | - J Gäverth
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.
| | | | - H Forssberg
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.
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Niu CM, Jalaleddini K, Sohn WJ, Rocamora J, Sanger TD, Valero-Cuevas FJ. Neuromorphic meets neuromechanics, part I: the methodology and implementation. J Neural Eng 2017; 14:025001. [PMID: 28084217 PMCID: PMC5540665 DOI: 10.1088/1741-2552/aa593c] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
OBJECTIVE One goal of neuromorphic engineering is to create 'realistic' robotic systems that interact with the physical world by adopting neuromechanical principles from biology. Critical to this is the methodology to implement the spinal circuitry responsible for the behavior of afferented muscles. At its core, muscle afferentation is the closed-loop behavior arising from the interactions among populations of muscle spindle afferents, alpha and gamma motoneurons, and muscle fibers to enable useful behaviors. APPROACH We used programmable very- large-scale-circuit (VLSI) hardware to implement simple models of spiking neurons, skeletal muscles, muscle spindle proprioceptors, alpha-motoneuron recruitment, gamma motoneuron control of spindle sensitivity, and the monosynaptic circuitry connecting them. This multi-scale system of populations of spiking neurons emulated the physiological properties of a pair of antagonistic afferented mammalian muscles (each simulated by 1024 alpha- and gamma-motoneurones) acting on a joint via long tendons. MAIN RESULTS This integrated system was able to maintain a joint angle, and reproduced stretch reflex responses even when driving the nonlinear biomechanics of an actual cadaveric finger. Moreover, this system allowed us to explore numerous values and combinations of gamma-static and gamma-dynamic gains when driving a robotic finger, some of which replicated some human pathological conditions. Lastly, we explored the behavioral consequences of adopting three alternative models of isometric muscle force production. We found that the dynamic responses to rate-coded spike trains produce force ramps that can be very sensitive to tendon elasticity, especially at high force output. SIGNIFICANCE Our methodology produced, to our knowledge, the first example of an autonomous, multi-scale, neuromorphic, neuromechanical system capable of creating realistic reflex behavior in cadaveric fingers. This research platform allows us to explore the mechanisms behind healthy and pathological sensorimotor function in the physical world by building them from first principles, and it is a precursor to neuromorphic robotic systems.
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Affiliation(s)
- Chuanxin M Niu
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China. Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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10
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Jalaleddini K, Minos Niu C, Chakravarthi Raja S, Joon Sohn W, Loeb GE, Sanger TD, Valero-Cuevas FJ. Neuromorphic meets neuromechanics, part II: the role of fusimotor drive. J Neural Eng 2017; 14:025002. [PMID: 28094764 DOI: 10.1088/1741-2552/aa59bd] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE We studied the fundamentals of muscle afferentation by building a Neuro-mechano-morphic system actuating a cadaveric finger. This system is a faithful implementation of the stretch reflex circuitry. It allowed the systematic exploration of the effects of different fusimotor drives to the muscle spindle on the closed-loop stretch reflex response. APPROACH As in Part I of this work, sensory neurons conveyed proprioceptive information from muscle spindles (with static and dynamic fusimotor drive) to populations of α-motor neurons (with recruitment and rate coding properties). The motor commands were transformed into tendon forces by a Hill-type muscle model (with activation-contraction dynamics) via brushless DC motors. Two independent afferented muscles emulated the forces of flexor digitorum profundus and the extensor indicis proprius muscles, forming an antagonist pair at the metacarpophalangeal joint of a cadaveric index finger. We measured the physical response to repetitions of bi-directional ramp-and-hold rotational perturbations for 81 combinations of static and dynamic fusimotor drives, across four ramp velocities, and three levels of constant cortical drive to the α-motor neuron pool. MAIN RESULTS We found that this system produced responses compatible with the physiological literature. Fusimotor and cortical drives had nonlinear effects on the reflex forces. In particular, only cortical drive affected the sensitivity of reflex forces to static fusimotor drive. In contrast, both static fusimotor and cortical drives reduced the sensitivity to dynamic fusimotor drive. Interestingly, realistic signal-dependent motor noise emerged naturally in our system without having been explicitly modeled. SIGNIFICANCE We demonstrate that these fundamental features of spinal afferentation sufficed to produce muscle function. As such, our Neuro-mechano-morphic system is a viable platform to study the spinal mechanisms for healthy muscle function-and its pathologies such as dystonia and spasticity. In addition, it is a working prototype of a robust biomorphic controller for compliant robotic limbs and exoskeletons.
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Affiliation(s)
- Kian Jalaleddini
- Division of Biokinesiology and Physical Therapy, University of Southern California, CA, United States of America
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Sohn WJ, Niu CM, Sanger TD. A neuromorphic model of motor overflow in focal hand dystonia due to correlated sensory input. J Neural Eng 2016; 13:055001. [PMID: 27578228 DOI: 10.1088/1741-2560/13/5/055001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Motor overflow is a common and frustrating symptom of dystonia, manifested as unintentional muscle contraction that occurs during an intended voluntary movement. Although it is suspected that motor overflow is due to cortical disorganization in some types of dystonia (e.g. focal hand dystonia), it remains elusive which mechanisms could initiate and, more importantly, perpetuate motor overflow. We hypothesize that distinct motor elements have low risk of motor overflow if their sensory inputs remain statistically independent. But when provided with correlated sensory inputs, pre-existing crosstalk among sensory projections will grow under spike-timing-dependent-plasticity (STDP) and eventually produce irreversible motor overflow. APPROACH We emulated a simplified neuromuscular system comprising two anatomically distinct digital muscles innervated by two layers of spiking neurons with STDP. The synaptic connections between layers included crosstalk connections. The input neurons received either independent or correlated sensory drive during 4 days of continuous excitation. The emulation is critically enabled and accelerated by our neuromorphic hardware created in previous work. MAIN RESULTS When driven by correlated sensory inputs, the crosstalk synapses gained weight and produced prominent motor overflow; the growth of crosstalk synapses resulted in enlarged sensory representation reflecting cortical reorganization. The overflow failed to recede when the inputs resumed their original uncorrelated statistics. In the control group, no motor overflow was observed. SIGNIFICANCE Although our model is a highly simplified and limited representation of the human sensorimotor system, it allows us to explain how correlated sensory input to anatomically distinct muscles is by itself sufficient to cause persistent and irreversible motor overflow. Further studies are needed to locate the source of correlation in sensory input.
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Affiliation(s)
- Won Joon Sohn
- Department of Rehabilitation Medicine, Emory University, 1441 Clifton Rd, Atlanta, GA 30322, USA
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Malik P, Jabakhanji N, Jones KE. An Assessment of Six Muscle Spindle Models for Predicting Sensory Information during Human Wrist Movements. Front Comput Neurosci 2016; 9:154. [PMID: 26834618 PMCID: PMC4712307 DOI: 10.3389/fncom.2015.00154] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Accepted: 12/21/2015] [Indexed: 11/13/2022] Open
Abstract
Background: The muscle spindle is an important sensory organ for proprioceptive information, yet there have been few attempts to use Shannon information theory to quantify the capacity of human muscle spindles to encode sensory input. Methods: Computer simulations linked kinematics, to biomechanics, to six muscle spindle models that generated predictions of firing rate. The predicted firing rates were compared to firing rates of human muscle spindles recorded during a step-tracking (center-out) task to validate their use. The models were then used to predict firing rates during random movements with statistical properties matched to the ergonomics of human wrist movements. The data were analyzed for entropy and mutual information. Results: Three of the six models produced predictions that approximated the firing rate of human spindles during the step-tracking task. For simulated random movements these models predicted mean rates of 16.0 ± 4.1 imp/s (mean ± SD), peak firing rates <50 imp/s and zero firing rate during an average of 25% of the movement. The average entropy of the neural response was 4.1 ± 0.3 bits and is an estimate of the maximum information that could be carried by muscles spindles during ecologically valid movements. The information about tendon displacement preserved in the neural response was 0.10 ± 0.05 bits per symbol; whereas 1.25 ± 0.30 bits per symbol of velocity input were preserved in the neural response of the spindle models. Conclusions: Muscle spindle models, originally based on cat experiments, have predictive value for modeling responses of human muscle spindles with minimal parameter optimization. These models predict more than 10-fold more velocity over length information encoding during ecologically valid movements. These results establish theoretical parameters for developing neuroprostheses for proprioceptive function.
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Affiliation(s)
- Puja Malik
- Department of Biomedical Engineering, University of Alberta Edmonton, AB, Canada
| | - Nuha Jabakhanji
- Department of Biomedical Engineering, University of Alberta Edmonton, AB, Canada
| | - Kelvin E Jones
- Department of Biomedical Engineering, University of AlbertaEdmonton, AB, Canada; Faculty of Physical Education and Recreation, University of AlbertaEdmonton, AB, Canada; Neuroscience and Mental Health Institute, University of AlbertaEdmonton, AB, Canada
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Sohn WJ, Niu CM, Sanger TD. Increased long-latency reflex activity as a sufficient explanation for childhood hypertonic dystonia: a neuromorphic emulation study. J Neural Eng 2015; 12:036010. [PMID: 25946372 PMCID: PMC4475677 DOI: 10.1088/1741-2560/12/3/036010] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Childhood dystonia is a movement disorder that interferes with daily movements and can have a devastating effect on quality of life for children and their families. Although injury to basal ganglia is associated with dystonia, the neurophysiological mechanisms leading to the clinical manifestations of dystonia are not understood. Previous work suggested that long-latency stretch reflex (LLSR) is hyperactive in children with hypertonia due to secondary dystonia. We hypothesize that abnormal activity in motor cortices may cause an increase in the LLSR leading to hypertonia. APPROACH We modeled two possibilities of hyperactive LLSR by either creating a tonic involuntary drive to cortex, or increasing the synaptic gain in cortical neurons. Both models are emulated using programmable very-large-scale-integrated-circuit hardware to test their sufficiency for producing dystonic symptoms. The emulation includes a joint with two Hill-type muscles, realistic muscle spindles, and 2,304 Izhikevich-type spiking neurons. The muscles are regulated by a monosynaptic spinal pathway with 32 ms delay and a long-latency pathway with 64 ms loop-delay representing transcortical/supra-spinal connections. MAIN RESULTS When the limb is passively stretched, both models produce involuntary resistance with increased antagonist EMG responses similar to human data; also the muscle relaxation is delayed similar to human data. Both models predict reduced range of motion in voluntary movements. SIGNIFICANCE Although our model is a highly simplified and limited representation of reflex pathways, it shows that increased activity of the LLSR is by itself sufficient to cause many of the features of hypertonic dystonia.
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Affiliation(s)
- Won J. Sohn
- Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, California, 90089
| | - Chuanxin M. Niu
- Department of Rehabilitation, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Terence D. Sanger
- Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, California, 90089
- Department of Biokinesiology, University of Southern California, 1042 Downey Way, Los Angeles, California, 90089
- Department of Neurology, University of Southern California, 1042 Downey Way, Los Angeles, California, 90089
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