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Huang Y, Yao K, Zhang Q, Huang X, Chen Z, Zhou Y, Yu X. Bioelectronics for electrical stimulation: materials, devices and biomedical applications. Chem Soc Rev 2024; 53:8632-8712. [PMID: 39132912 DOI: 10.1039/d4cs00413b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Bioelectronics is a hot research topic, yet an important tool, as it facilitates the creation of advanced medical devices that interact with biological systems to effectively diagnose, monitor and treat a broad spectrum of health conditions. Electrical stimulation (ES) is a pivotal technique in bioelectronics, offering a precise, non-pharmacological means to modulate and control biological processes across molecular, cellular, tissue, and organ levels. This method holds the potential to restore or enhance physiological functions compromised by diseases or injuries by integrating sophisticated electrical signals, device interfaces, and designs tailored to specific biological mechanisms. This review explains the mechanisms by which ES influences cellular behaviors, introduces the essential stimulation principles, discusses the performance requirements for optimal ES systems, and highlights the representative applications. From this review, we can realize the potential of ES based bioelectronics in therapy, regenerative medicine and rehabilitation engineering technologies, ranging from tissue engineering to neurological technologies, and the modulation of cardiovascular and cognitive functions. This review underscores the versatility of ES in various biomedical contexts and emphasizes the need to adapt to complex biological and clinical landscapes it addresses.
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Affiliation(s)
- Ya Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Kuanming Yao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Qiang Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Xingcan Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Zhenlin Chen
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Yu Zhou
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Xinge Yu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
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2
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Tostado-Marcos P, Arneodo EM, Ostrowski L, Brown DE, Perez XA, Kadwory A, Stanwicks LL, Alothman A, Gentner TQ, Gilja V. Neural population dynamics in songbird RA and HVC during learned motor-vocal behavior. ARXIV 2024:arXiv:2407.06244v1. [PMID: 39040642 PMCID: PMC11261980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Complex, learned motor behaviors involve the coordination of large-scale neural activity across multiple brain regions, but our understanding of the population-level dynamics within different regions tied to the same behavior remains limited. Here, we investigate the neural population dynamics underlying learned vocal production in awake-singing songbirds. We use Neuropixels probes to record the simultaneous extracellular activity of populations of neurons in two regions of the vocal motor pathway. In line with observations made in non-human primates during limb-based motor tasks, we show that the population-level activity in both the premotor nucleus HVC and the motor nucleus RA is organized on low-dimensional neural manifolds upon which coordinated neural activity is well described by temporally structured trajectories during singing behavior. Both the HVC and RA latent trajectories provide relevant information to predict vocal sequence transitions between song syllables. However, the dynamics of these latent trajectories differ between regions. Our state-space models suggest a unique and continuous-over-time correspondence between the latent space of RA and vocal output, whereas the corresponding relationship for HVC exhibits a higher degree of neural variability. We then demonstrate that comparable high-fidelity reconstruction of continuous vocal outputs can be achieved from HVC and RA neural latents and spiking activity. Unlike those that use spiking activity, however, decoding models using neural latents generalize to novel sub-populations in each region, consistent with the existence of preserved manifolds that confine vocal-motor activity in HVC and RA.
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Affiliation(s)
- Pablo Tostado-Marcos
- Department of Bioengineering
- Department of Electrical and Computer Engineering
- Department of Psychology
| | | | - Lauren Ostrowski
- Neurosciences Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Daril E Brown
- Department of Electrical and Computer Engineering
- Department of Psychology
| | | | - Adam Kadwory
- Department of Electrical and Computer Engineering
| | - Lauren L Stanwicks
- Neurosciences Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | | | - Timothy Q Gentner
- Department of Psychology
- Department of Neurobiology
- Neurosciences Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Vikash Gilja
- Department of Electrical and Computer Engineering
- Neurosciences Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
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3
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Xu J, Mawase F, Schieber MH. Evolution, biomechanics, and neurobiology converge to explain selective finger motor control. Physiol Rev 2024; 104:983-1020. [PMID: 38385888 DOI: 10.1152/physrev.00030.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/16/2024] [Accepted: 02/15/2024] [Indexed: 02/23/2024] Open
Abstract
Humans use their fingers to perform a variety of tasks, from simple grasping to manipulating objects, to typing and playing musical instruments, a variety wider than any other species. The more sophisticated the task, the more it involves individuated finger movements, those in which one or more selected fingers perform an intended action while the motion of other digits is constrained. Here we review the neurobiology of such individuated finger movements. We consider their evolutionary origins, the extent to which finger movements are in fact individuated, and the evolved features of neuromuscular control that both enable and limit individuation. We go on to discuss other features of motor control that combine with individuation to create dexterity, the impairment of individuation by disease, and the broad extent of capabilities that individuation confers on humans. We comment on the challenges facing the development of a truly dexterous bionic hand. We conclude by identifying topics for future investigation that will advance our understanding of how neural networks interact across multiple regions of the central nervous system to create individuated movements for the skills humans use to express their cognitive activity.
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Affiliation(s)
- Jing Xu
- Department of Kinesiology, University of Georgia, Athens, Georgia, United States
| | - Firas Mawase
- Department of Biomedical Engineering, Israel Institute of Technology, Haifa, Israel
| | - Marc H Schieber
- Departments of Neurology and Neuroscience, University of Rochester, Rochester, New York, United States
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4
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Liu J, Younk R, Drahos LM, Nagrale SS, Yadav S, Widge AS, Shoaran M. Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597165. [PMID: 38895388 PMCID: PMC11185693 DOI: 10.1101/2024.06.06.597165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Objective Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors. Approach We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance. Main results Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference. Significance Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.
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Affiliation(s)
- Jinhan Liu
- Institute of Electrical and Micro Engineering, EPFL, Lausanne, Switzerland
- Neuro-X Institute, EPFL, Geneva, Switzerland
| | - Rebecca Younk
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Lauren M Drahos
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Sumedh S Nagrale
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Shreya Yadav
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
- These authors jointly supervised this work
| | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering, EPFL, Lausanne, Switzerland
- Neuro-X Institute, EPFL, Geneva, Switzerland
- These authors jointly supervised this work
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5
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Wang Y, Sun QQ. A prefrontal motor circuit initiates persistent movement. Nat Commun 2024; 15:5264. [PMID: 38898065 PMCID: PMC11187183 DOI: 10.1038/s41467-024-49615-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/11/2024] [Indexed: 06/21/2024] Open
Abstract
Persistence reinforces continuous action, which benefits animals in many aspects. Diverse external or internal signals may trigger animals to start a persistent movement. However, it is unclear how the brain decides to persist with current actions by selecting specific information. Using single-unit extracellular recordings and opto-tagging in awake mice, we demonstrated that a group of dorsal mPFC (dmPFC) motor cortex projecting (MP) neurons initiate a persistent movement by selectively encoding contextual information rather than natural valence. Inactivation of dmPFC MP neurons impairs the initiation and reduces neuronal activity in the insular and motor cortex. After the persistent movement is initiated, the dmPFC MP neurons are not required to maintain it. Finally, a computational model suggests that a successive sensory stimulus acts as an input signal for the dmPFC MP neurons to initiate a persistent movement. These results reveal a neural initiation mechanism on the persistent movement.
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Affiliation(s)
- Yihan Wang
- Graduate Neuroscience Program, University of Wyoming, Laramie, WY, 82071, USA
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY, 82071, USA
| | - Qian-Quan Sun
- Graduate Neuroscience Program, University of Wyoming, Laramie, WY, 82071, USA.
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY, 82071, USA.
- Wyoming Sensory Biology Center of Biomedical Research Excellence, University of Wyoming, Laramie, WY, 82071, USA.
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Herrera-Arcos G, Song H, Yeon SH, Ghenand O, Gutierrez-Arango S, Sinha S, Herr H. Closed-loop optogenetic neuromodulation enables high-fidelity fatigue-resistant muscle control. Sci Robot 2024; 9:eadi8995. [PMID: 38776378 DOI: 10.1126/scirobotics.adi8995] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 04/25/2024] [Indexed: 05/25/2024]
Abstract
Closed-loop neuroprostheses show promise in restoring motion in individuals with neurological conditions. However, conventional activation strategies based on functional electrical stimulation (FES) fail to accurately modulate muscle force and exhibit rapid fatigue because of their unphysiological recruitment mechanism. Here, we present a closed-loop control framework that leverages physiological force modulation under functional optogenetic stimulation (FOS) to enable high-fidelity muscle control for extended periods of time (>60 minutes) in vivo. We first uncovered the force modulation characteristic of FOS, showing more physiological recruitment and significantly higher modulation ranges (>320%) compared with FES. Second, we developed a neuromuscular model that accurately describes the highly nonlinear dynamics of optogenetically stimulated muscle. Third, on the basis of the optogenetic model, we demonstrated real-time control of muscle force with improved performance and fatigue resistance compared with FES. This work lays the foundation for fatigue-resistant neuroprostheses and optogenetically controlled biohybrid robots with high-fidelity force modulation.
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Affiliation(s)
- Guillermo Herrera-Arcos
- K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA
- Program in Media Arts and Sciences, MIT Media Lab, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Hyungeun Song
- K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA
- Harvard-MIT Division of Health Sciences and Technology (HST), MIT, Cambridge, MA, USA
| | - Seong Ho Yeon
- K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA
- Program in Media Arts and Sciences, MIT Media Lab, Cambridge, MA, USA
| | - Omkar Ghenand
- K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Samantha Gutierrez-Arango
- K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA
- Program in Media Arts and Sciences, MIT Media Lab, Cambridge, MA, USA
| | - Sapna Sinha
- K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Hugh Herr
- K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
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Xu S, Li C, Wei C, Kang X, Shu S, Liu G, Xu Z, Han M, Luo J, Tang W. Closed-Loop Wearable Device Network of Intrinsically-Controlled, Bilateral Coordinated Functional Electrical Stimulation for Stroke. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304763. [PMID: 38429890 PMCID: PMC11077660 DOI: 10.1002/advs.202304763] [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: 07/14/2023] [Revised: 01/28/2024] [Indexed: 03/03/2024]
Abstract
Innovative functional electrical stimulation has demonstrated effectiveness in enhancing daily walking and rehabilitating stroke patients with foot drop. However, its lack of precision in stimulating timing, individual adaptivity, and bilateral symmetry, resulted in diminished clinical efficacy. Therefore, a closed-loop wearable device network of intrinsically controlled functional electrical stimulation (CI-FES) system is proposed, which utilizes the personal surface myoelectricity, derived from the intrinsic neuro signal, as the switch to activate/deactivate the stimulation on the affected side. Simultaneously, it decodes the myoelectricity signal of the patient's healthy side to adjust the stimulation intensity, forming an intrinsically controlled loop with the inertial measurement units. With CI-FES assistance, patients' walking ability significantly improved, evidenced by the shift in ankle joint angle mean and variance from 105.53° and 28.84 to 102.81° and 17.71, and the oxyhemoglobin concentration tested by the functional near-infrared spectroscopy. In long-term CI-FES-assisted clinical testing, the discriminability in machine learning classification between patients and healthy individuals gradually decreased from 100% to 92.5%, suggesting a remarkable recovery tendency, further substantiated by performance on the functional movement scales. The developed CI-FES system is crucial for contralateral-hemiplegic stroke recovery, paving the way for future closed-loop stimulation systems in stroke rehabilitation is anticipated.
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Affiliation(s)
- Shuxing Xu
- Beijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing101400China
- Center on Nanoenergy ResearchSchool of Physical Science & TechnologyGuangxi UniversityNanning530004China
- School of Nanoscience and TechnologyUniversity of Chinese Academy of SciencesBeijing100049China
| | - Chengyu Li
- Beijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing101400China
- School of Nanoscience and TechnologyUniversity of Chinese Academy of SciencesBeijing100049China
| | - Conghui Wei
- Rehabilitation Medicine DepartmentThe Second Affiliated Hospital of Nanchang UniversityNanchang City330006P. R. China
| | - Xinfang Kang
- Rehabilitation Medicine DepartmentThe Second Affiliated Hospital of Nanchang UniversityNanchang City330006P. R. China
| | - Sheng Shu
- Beijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing101400China
- School of Nanoscience and TechnologyUniversity of Chinese Academy of SciencesBeijing100049China
| | - Guanlin Liu
- Center on Nanoenergy ResearchSchool of Physical Science & TechnologyGuangxi UniversityNanning530004China
| | - Zijie Xu
- Beijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing101400China
- School of Nanoscience and TechnologyUniversity of Chinese Academy of SciencesBeijing100049China
| | - Mengdi Han
- Department of Biomedical EngineeringCollege of Future TechnologyPeking UniversityBeijing100871China
| | - Jun Luo
- Rehabilitation Medicine DepartmentThe Second Affiliated Hospital of Nanchang UniversityNanchang City330006P. R. China
| | - Wei Tang
- Beijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing101400China
- School of Nanoscience and TechnologyUniversity of Chinese Academy of SciencesBeijing100049China
- Institute of Applied NanotechnologyJiaxingZhejiang314031China
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8
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Churchland MM, Shenoy KV. Preparatory activity and the expansive null-space. Nat Rev Neurosci 2024; 25:213-236. [PMID: 38443626 DOI: 10.1038/s41583-024-00796-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2024] [Indexed: 03/07/2024]
Abstract
The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.
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Affiliation(s)
- Mark M Churchland
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
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9
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Wang Y, Sun QQ. A prefrontal motor circuit initiates persistent movement. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.11.548619. [PMID: 38585867 PMCID: PMC10996565 DOI: 10.1101/2023.07.11.548619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Persistence reinforces continuous action, which benefits animals in many aspects. Diverse information may trigger animals to start a persistent movement. However, it is unclear how the brain decides to persist with current actions by selecting specific information. Using single-unit extracellular recordings and opto-tagging in awake mice, we demonstrated that a group of dorsal mPFC (dmPFC) motor cortex projecting (MP) neurons initiate a persistent movement selectively encoding contextual information rather than natural valence. Inactivation of dmPFC MP neurons impairs the initiation and reduces neuronal activity in the insular and motor cortex. Finally, a computational model suggests that a successive sensory stimulus acts as an input signal for the dmPFC MP neurons to initiate a persistent movement. These results reveal a neural initiation mechanism on the persistent movement.
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Affiliation(s)
- Yihan Wang
- Graduate Neuroscience Program, University of Wyoming, Laramie, WY82071, USA
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY82071, USA
| | - Qian-Quan Sun
- Graduate Neuroscience Program, University of Wyoming, Laramie, WY82071, USA
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY82071, USA
- Wyoming Sensory Biology Center of Biomedical Research Excellence, University of Wyoming, Laramie, WY82071, USA
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10
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Losanno E, Badi M, Roussinova E, Bogaard A, Delacombaz M, Shokur S, Micera S. An Investigation of Manifold-Based Direct Control for a Brain-to-Body Neural Bypass. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:271-280. [PMID: 38766541 PMCID: PMC11100864 DOI: 10.1109/ojemb.2024.3381475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 02/06/2024] [Accepted: 03/11/2024] [Indexed: 05/22/2024] Open
Abstract
Objective: Brain-body interfaces (BBIs) have emerged as a very promising solution for restoring voluntary hand control in people with upper-limb paralysis. The BBI module decoding motor commands from brain signals should provide the user with intuitive, accurate, and stable control. Here, we present a preliminary investigation in a monkey of a brain decoding strategy based on the direct coupling between the activity of intrinsic neural ensembles and output variables, aiming at achieving ease of learning and long-term robustness. Results: We identified an intrinsic low-dimensional space (called manifold) capturing the co-variation patterns of the monkey's neural activity associated to reach-to-grasp movements. We then tested the animal's ability to directly control a computer cursor using cortical activation along the manifold axes. By daily recalibrating only scaling factors, we achieved rapid learning and stable high performance in simple, incremental 2D tasks over more than 12 weeks of experiments. Finally, we showed that this brain decoding strategy can be effectively coupled to peripheral nerve stimulation to trigger voluntary hand movements. Conclusions: These results represent a proof of concept of manifold-based direct control for BBI applications.
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Affiliation(s)
- E. Losanno
- The Biorobotics Institute and Department of Excellence in Robotics and AIScuola Superiore Sant'Anna56025PisaItaly
- Modular Implantable Neuroprostheses (MINE) LaboratoryUniversità Vita-Salute San Raffaele and Scuola Superiore Sant'AnnaMilanItaly
| | - M. Badi
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - E. Roussinova
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - A. Bogaard
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and MedicineUniversity of Fribourg1700FribourgSwitzerland
| | - M. Delacombaz
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and MedicineUniversity of Fribourg1700FribourgSwitzerland
| | - S. Shokur
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - S. Micera
- The Biorobotics Institute and Department of Excellence in Robotics and AIScuola Superiore Sant'Anna56025PisaItaly
- Modular Implantable Neuroprostheses (MINE) LaboratoryUniversità Vita-Salute San Raffaele and Scuola Superiore Sant'AnnaMilanItaly
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
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11
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Carè M, Chiappalone M, Cota VR. Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function. Front Neurosci 2024; 18:1363128. [PMID: 38516316 PMCID: PMC10954825 DOI: 10.3389/fnins.2024.1363128] [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: 12/29/2023] [Accepted: 02/22/2024] [Indexed: 03/23/2024] Open
Abstract
Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.
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Affiliation(s)
- Marta Carè
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Michela Chiappalone
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, Genova, Italy
- Rehab Technologies Lab, Istituto Italiano di Tecnologia, Genova, Italy
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12
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Barra B, Kumar R, Gopinath C, Mirzakhalili E, Lempka SF, Gaunt RA, Fisher LE. High-frequency amplitude-modulated sinusoidal stimulation induces desynchronized yet controllable neural firing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.14.580219. [PMID: 38405798 PMCID: PMC10888888 DOI: 10.1101/2024.02.14.580219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Regaining sensory feedback is pivotal for people living with limb amputation. Electrical stimulation of sensory fibers in peripheral nerves has been shown to restore focal percepts in the missing limb. However, conventional rectangular current pulses induce sensations often described as unnatural. This is likely due to the synchronous and periodic nature of activity evoked by these pulses. Here we introduce a fast-oscillating amplitude-modulated sinusoidal (FAMS) stimulation waveform that desynchronizes evoked neural activity. We used a computational model to show that sinusoidal waveforms evoke asynchronous and irregular firing and that firing patterns are frequency dependent. We designed the FAMS waveform to leverage both low- and high-frequency effects and found that membrane non-linearities enhance neuron-specific differences when exposed to FAMS. We implemented this waveform in a feline model of peripheral nerve stimulation and demonstrated that FAMS-evoked activity is more asynchronous than activity evoked by rectangular pulses, while being easily controllable with simple stimulation parameters. These results represent an important step towards biomimetic stimulation strategies useful for clinical applications to restore sensory feedback.
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Affiliation(s)
- Beatrice Barra
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Neuroscience Institute, New York University Langone Health, New York, USA
| | - Ritesh Kumar
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, USA
| | - Chaitanya Gopinath
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ehsan Mirzakhalili
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, USA
| | - Scott F. Lempka
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Robert A. Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, USA
- Department of Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, USA
| | - Lee E Fisher
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, USA
- Department of Physical Medicine & Rehabilitation, University of Pittsburgh, Pittsburgh, USA
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13
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Khan S, Anderson W, Constandinou T. Surgical Implantation of Brain Computer Interfaces. JAMA Surg 2024; 159:219-220. [PMID: 37991789 DOI: 10.1001/jamasurg.2023.2399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
This article discusses the function and capabilities of brain computer interfaces as a novel approach to rehabilitation for a variety of neurological disorders.
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Affiliation(s)
- Shujhat Khan
- Department of Bioengineering, Imperial College London, South Kensington, London, United Kingdom
- Association of Surgeons of Great Britain and Ireland, London, United Kingdom
| | - William Anderson
- Department of Neurosurgery, Johns Hopkins hospital, Baltimore, Maryland
| | - Timothy Constandinou
- Department of Electrical & Electronic Engineering, Imperial College London, South Kensington, London, United Kingdom
- Care Research & Technology Centre at Imperial College London, UK Dementia Research Institute, London, United Kingdom
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14
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Deo DR, Willett FR, Avansino DT, Hochberg LR, Henderson JM, Shenoy KV. Brain control of bimanual movement enabled by recurrent neural networks. Sci Rep 2024; 14:1598. [PMID: 38238386 PMCID: PMC10796685 DOI: 10.1038/s41598-024-51617-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/07/2024] [Indexed: 01/22/2024] Open
Abstract
Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. Here, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders. Through simulations, we show that NNs leverage a neural 'laterality' dimension to distinguish between left and right-hand movements as neural tuning to both hands become increasingly correlated. In training recurrent neural networks (RNNs) for two-cursor control, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously. Our results suggest that neural network decoders may be advantageous for multi-effector decoding, provided they are designed to transfer to the online setting.
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Affiliation(s)
- Darrel R Deo
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Francis R Willett
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | - Donald T Avansino
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- School of Engineering, Brown University, Providence, RI, USA
- Carney Institute for Brain Science, Brown University, Providence, RI, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jaimie M Henderson
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
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15
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Rizzoglio F, Altan E, Ma X, Bodkin KL, Dekleva BM, Solla SA, Kennedy A, Miller LE. From monkeys to humans: observation-basedEMGbrain-computer interface decoders for humans with paralysis. J Neural Eng 2023; 20:056040. [PMID: 37844567 PMCID: PMC10618714 DOI: 10.1088/1741-2552/ad038e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/02/2023] [Accepted: 10/16/2023] [Indexed: 10/18/2023]
Abstract
Objective. Intracortical brain-computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients' paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on 'observation-based' decoding in which the patient watches a moving cursor while mentally envisioning making the movement. However, this reliance on observed target motion for decoder development precludes its application to the prediction of unobservable motor output like muscle activity. Here, we ask whether recordings of muscle activity from a surrogate individual performing the same movement as the iBCI patient can be used as target for an iBCI decoder.Approach. We test two possible approaches, each using data from a human iBCI user and a monkey, both performing similar motor actions. In one approach, we trained a decoder to predict the electromyographic (EMG) activity of a monkey from neural signals recorded from a human. We then contrast this to a second approach, based on the hypothesis that the low-dimensional 'latent' neural representations of motor behavior, known to be preserved across time for a given behavior, might also be preserved across individuals. We 'transferred' an EMG decoder trained solely on monkey data to the human iBCI user after using Canonical Correlation Analysis to align the human latent signals to those of the monkey.Main results. We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human.Significance. Our findings suggest that these latent representations of behavior are consistent across animals and even primate species. These methods are an important initial step in the development of iBCI decoders that generate EMG predictions that could serve as signals for a biomimetic decoder controlling motion and impedance of a prosthetic arm, or even muscle force directly through functional electrical stimulation.
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Affiliation(s)
- Fabio Rizzoglio
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Ege Altan
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America
| | - Xuan Ma
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Kevin L Bodkin
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Brian M Dekleva
- Rehab Neural Engineering Labs, Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Sara A Solla
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, United States of America
| | - Ann Kennedy
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Lee E Miller
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America
- Shirley Ryan AbilityLab, Chicago, IL, United States of America
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States of America
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16
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Pellot-Cestero JE, Herring EZ, Graczyk EL, Memberg WD, Kirsch RF, Ajiboye AB, Miller JP. Implanted Electrodes for Functional Electrical Stimulation to Restore Upper and Lower Extremity Function: History and Future Directions. Neurosurgery 2023; 93:965-970. [PMID: 37288972 DOI: 10.1227/neu.0000000000002561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/03/2023] [Indexed: 06/09/2023] Open
Abstract
Functional electrical stimulation (FES) to activate nerves and muscles in paralyzed extremities has considerable promise to improve outcome after neurological disease or injury, especially in individuals who have upper motor nerve dysfunction due to central nervous system pathology. Because technology has improved, a wide variety of methods for providing electrical stimulation to create functional movements have been developed, including muscle stimulating electrodes, nerve stimulating electrodes, and hybrid constructs. However, in spite of decades of success in experimental settings with clear functional improvements for individuals with paralysis, the technology has not yet reached widespread clinical translation. In this review, we outline the history of FES techniques and approaches and describe future directions in evolution of the technology.
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Affiliation(s)
- Joel E Pellot-Cestero
- Department of Neurosurgery, School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Neurosurgery, The Neurological Institute, University Hospital Cleveland Medical Center, Cleveland , Ohio , USA
| | - Eric Z Herring
- Department of Neurosurgery, School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Neurosurgery, The Neurological Institute, University Hospital Cleveland Medical Center, Cleveland , Ohio , USA
| | - Emily L Graczyk
- Department of Neurosurgery, School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland , Ohio , USA
| | - William D Memberg
- Department of Neurosurgery, School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland , Ohio , USA
| | - Robert F Kirsch
- Department of Neurosurgery, School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland , Ohio , USA
| | - A Bolu Ajiboye
- Department of Neurosurgery, School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland , Ohio , USA
| | - Jonathan P Miller
- Department of Neurosurgery, School of Medicine, Case Western Reserve University, Cleveland , Ohio , USA
- Department of Neurosurgery, The Neurological Institute, University Hospital Cleveland Medical Center, Cleveland , Ohio , USA
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland , Ohio , USA
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17
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Du L, Hao H, Ding Y, Gabros A, Mier TCE, Van der Spiegel J, Lucas TH, Aflatouni F, Richardson AG, Allen MG. An implantable, wireless, battery-free system for tactile pressure sensing. MICROSYSTEMS & NANOENGINEERING 2023; 9:130. [PMID: 37829157 PMCID: PMC10564885 DOI: 10.1038/s41378-023-00602-3] [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: 01/25/2023] [Revised: 08/28/2023] [Accepted: 09/06/2023] [Indexed: 10/14/2023]
Abstract
The sense of touch is critical to dexterous use of the hands and thus an essential component of efforts to restore hand function after amputation or paralysis. Prosthetic systems have addressed this goal with wearable tactile sensors. However, such wearable sensors are suboptimal for neuroprosthetic systems designed to reanimate a patient's own paralyzed hand. Here, we developed an implantable tactile sensing system intended for subdermal placement. The system is composed of a microfabricated capacitive pressure sensor, a custom integrated circuit supporting wireless powering and data transmission, and a laser-fused hermetic silica package. The miniature device was validated through simulations, benchtop assessment, and testing in a primate hand. The sensor implanted in the fingertip accurately measured applied skin forces with a resolution of 4.3 mN. The output from this novel sensor could be encoded in the brain with microstimulation to provide tactile feedback. More broadly, the materials, system design, and fabrication approach establish new foundational capabilities for various applications of implantable sensing systems.
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Affiliation(s)
- Lin Du
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Han Hao
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
| | - Yixiao Ding
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
| | - Andrew Gabros
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Thomas C. E. Mier
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
| | - Jan Van der Spiegel
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
| | - Timothy H. Lucas
- Departments of Neurosurgery and Biomedical Engineering, Ohio State University, Columbus, OH USA
| | - Firooz Aflatouni
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
| | - Andrew G. Richardson
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Mark G. Allen
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
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18
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Rouzitalab A, Boulay CB, Park J, Sachs AJ. Intracortical brain-computer interfaces in primates: a review and outlook. Biomed Eng Lett 2023; 13:375-390. [PMID: 37519868 PMCID: PMC10382423 DOI: 10.1007/s13534-023-00286-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 05/04/2023] [Accepted: 05/14/2023] [Indexed: 08/01/2023] Open
Abstract
Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.
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Affiliation(s)
- Alireza Rouzitalab
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5 Canada
- The Ottawa Hospital Research Institute, Ottawa, ON Canada
| | | | - Jeongwon Park
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5 Canada
- Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV 89557 USA
| | - Adam J. Sachs
- The Ottawa Hospital Research Institute, Ottawa, ON Canada
- The University of Ottawa Brain and Mind Research Institute, Ottawa, ON Canada
- Division of Neurosurgery, Department of Surgery, The Ottawa Hospital, Ottawa, ON Canada
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19
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Guan C, Aflalo T, Kadlec K, Gámez de Leon J, Rosario ER, Bari A, Pouratian N, Andersen RA. Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex. J Neural Eng 2023; 20:036020. [PMID: 37160127 PMCID: PMC10209510 DOI: 10.1088/1741-2552/acd3b1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/24/2023] [Accepted: 05/09/2023] [Indexed: 05/11/2023]
Abstract
Objective. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis.Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified attempted finger movements from neural firing rates using linear discriminant analysis with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain-machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands.Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). In MC and in one PPC array, a factorized code linked corresponding finger movements of the contralateral and ipsilateral hands.Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.
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Affiliation(s)
- Charles Guan
- California Institute of Technology, Pasadena, CA, United States of America
| | - Tyson Aflalo
- California Institute of Technology, Pasadena, CA, United States of America
- T&C Chen Brain-Machine Interface Center at Caltech, Pasadena, CA, United States of America
| | - Kelly Kadlec
- California Institute of Technology, Pasadena, CA, United States of America
| | | | - Emily R Rosario
- Casa Colina Hospital and Centers for Healthcare, Pomona, CA, United States of America
| | - Ausaf Bari
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States of America
| | - Nader Pouratian
- University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Richard A Andersen
- California Institute of Technology, Pasadena, CA, United States of America
- T&C Chen Brain-Machine Interface Center at Caltech, Pasadena, CA, United States of America
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20
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Deo DR, Willett FR, Avansino DT, Hochberg LR, Henderson JM, Shenoy KV. Translating deep learning to neuroprosthetic control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.21.537581. [PMID: 37131830 PMCID: PMC10153231 DOI: 10.1101/2023.04.21.537581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Advances in deep learning have given rise to neural network models of the relationship between movement and brain activity that appear to far outperform prior approaches. Brain-computer interfaces (BCIs) that enable people with paralysis to control external devices, such as robotic arms or computer cursors, might stand to benefit greatly from these advances. We tested recurrent neural networks (RNNs) on a challenging nonlinear BCI problem: decoding continuous bimanual movement of two computer cursors. Surprisingly, we found that although RNNs appeared to perform well in offline settings, they did so by overfitting to the temporal structure of the training data and failed to generalize to real-time neuroprosthetic control. In response, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously, far outperforming standard linear methods. Our results provide evidence that preventing models from overfitting to temporal structure in training data may, in principle, aid in translating deep learning advances to the BCI setting, unlocking improved performance for challenging applications.
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21
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Wang Y, Sun QQ. Persistence is driven by a prefrontal motor circuit. RESEARCH SQUARE 2023:rs.3.rs-2739144. [PMID: 37131668 PMCID: PMC10153365 DOI: 10.21203/rs.3.rs-2739144/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Persistence provides a long-lasting effect on actions, including avoiding predators and storing energy, and hence is crucial for the survival (Adolphs and Anderson, 2018). However, how the brain loads persistence on movements is unknown. Here, we demonstrate that being persistent is determined at the initial phase of movement, and this persistency will be sustained until the terminal signaling. The neural coding of persistent movement phases (initial or terminal) is independent from the judgement (i.e. valence) (Li et al., 2022; Wang et al., 2018) upon the external stimuli. Next, we identify a group of dorsal medial prefrontal cortex (dmPFC) motor cortex projecting (MP) neurons (Wang and Sun, 2021), which encodes the initial phase of a persistent movement rather than the valence. Inactivation of dmPFC MP neurons impairs the initiation of persistency and reduce the neural activity in the insular and motor cortex. Finally, a MP network-based computational model suggests that an intact, successive sensory stimulus acts as a triggering signal to direct the initiation of persistent movements. These findings reveal a neural mechanism that transforms the brain state from neutral to persistent during a movement.
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Affiliation(s)
- Yihan Wang
- Graduate Neuroscience Program, University of Wyoming, Laramie, WY82071, USA
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY82071, USA
| | - Qian-Quan Sun
- Graduate Neuroscience Program, University of Wyoming, Laramie, WY82071, USA
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY82071, USA
- Wyoming Sensory Biology Center of Biomedical Research Excellence, University of Wyoming, Laramie, WY82071, USA
- Lead contact
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22
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Zuccaroli I, Lucke-Wold B, Palla A, Eremiev A, Sorrentino Z, Zakare-Fagbamila R, McNulty J, Christie C, Chandra V, Mampre D. Neural Bypasses: Literature Review and Future Directions in Developing Artificial Neural Connections. OBM NEUROBIOLOGY 2023; 7:158. [PMID: 36908763 PMCID: PMC9997488 DOI: 10.21926/obm.neurobiol.2301158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Reported neuro-modulation schemes in the literature are typically classified as closed-loop or open-loop. A novel group of recently developed neuro-modulation devices may be better described as a neural bypass, which attempts to transmit neural data from one location of the nervous system to another. The most common form of neural bypasses in the literature utilize EEG recordings of cortical information paired with functional electrical stimulation for effector muscle output, most commonly for assistive applications and rehabilitation in spinal cord injury or stroke. Other neural bypass locations that have also been described, or may soon be in development, include cortical-spinal bypasses, cortical-cortical bypasses, autonomic bypasses, peripheral-central bypasses, and inter-subject bypasses. The most common recording devices include EEG, ECoG, and microelectrode arrays, while stimulation devices include both invasive and noninvasive electrodes. Several devices are in development to improve the temporal and spatial resolution and biocompatibility for neuronal recording and stimulation. A major barrier to entry includes neuroplasticity and current decoding mechanisms that regularly require retraining. Neural bypasses are a unique class of neuro-modulation. Continued advancement of neural recording and stimulating devices with high spatial and temporal resolution, combined with decoding mechanisms uninhibited by neuroplasticity, can expand the therapeutic capability of neural bypassing. Overall, neural bypasses are a promising modality to improve the treatment of common neurologic disorders, including stroke, spinal cord injury, peripheral nerve injury, brain injury and more.
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Affiliation(s)
| | | | | | - Alexander Eremiev
- Department of Neurosurgery, New York University School of Medicine, New York, USA
| | | | | | - Jack McNulty
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA
| | - Carlton Christie
- Department of Neurosurgery, University of Florida, Gainesville, USA
| | - Vyshak Chandra
- Department of Neurosurgery, University of Florida, Gainesville, USA
| | - David Mampre
- Johns Hopkins University, Baltimore, USA
- Department of Neurosurgery, University of Florida, Gainesville, USA
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23
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Du L, Hao H, Ding Y, Gabros A, Mier TCE, Van der Spiegel J, Lucas TH, Aflatouni F, Richardson AG, Allen MG. An implantable wireless tactile sensing system. RESEARCH SQUARE 2023:rs.3.rs-2515082. [PMID: 36778258 PMCID: PMC9915765 DOI: 10.21203/rs.3.rs-2515082/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The sense of touch is critical to dexterous use of the hands and thus an essential component to efforts to restore hand function after amputation or paralysis. Prosthetic systems have focused on wearable tactile sensors. But wearable sensors are suboptimal for neuroprosthetic systems designed to reanimate a patient's own paralyzed hand. Here, we developed an implantable tactile sensing system intended for subdermal placement. The system is composed of a microfabricated capacitive force sensor, a custom integrated circuit supporting wireless powering and data transmission, and a laser-fused hermetic silica package. The miniature device was validated through simulations, benchtop testing, and ex vivo testing in a primate hand. The sensor implanted in the fingertip accurately measured skin forces with a resolution of 4.3 mN. The output from this novel sensor could be encoded in the brain with microstimulation to provide tactile feedback. More broadly, the materials, system design, and fabrication approach establish new foundational capabilities for various applications of implantable sensing systems.
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Affiliation(s)
- Lin Du
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Han Hao
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yixiao Ding
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew Gabros
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Thomas C. E. Mier
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jan Van der Spiegel
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy H. Lucas
- Departments of Neurosurgery and Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Firooz Aflatouni
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew G. Richardson
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Mark G. Allen
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
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24
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Wang Y, Yang X, Zhang X, Wang Y, Pei W. Implantable intracortical microelectrodes: reviewing the present with a focus on the future. MICROSYSTEMS & NANOENGINEERING 2023; 9:7. [PMID: 36620394 PMCID: PMC9814492 DOI: 10.1038/s41378-022-00451-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 08/08/2022] [Accepted: 08/22/2022] [Indexed: 06/17/2023]
Abstract
Implantable intracortical microelectrodes can record a neuron's rapidly changing action potentials (spikes). In vivo neural activity recording methods often have either high temporal or spatial resolution, but not both. There is an increasing need to record more neurons over a longer duration in vivo. However, there remain many challenges to overcome before achieving long-term, stable, high-quality recordings and realizing comprehensive, accurate brain activity analysis. Based on the vision of an idealized implantable microelectrode device, the performance requirements for microelectrodes are divided into four aspects, including recording quality, recording stability, recording throughput, and multifunctionality, which are presented in order of importance. The challenges and current possible solutions for implantable microelectrodes are given from the perspective of each aspect. The current developments in microelectrode technology are analyzed and summarized.
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Affiliation(s)
- Yang Wang
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Xinze Yang
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Xiwen Zhang
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Yijun Wang
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Chinese Institute for Brain Research, 102206 Beijing, China
| | - Weihua Pei
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
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25
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Obara K, Kaneshige M, Suzuki M, Yokoyama O, Tazoe T, Nishimura Y. Corticospinal interface to restore voluntary control of joint torque in a paralyzed forearm following spinal cord injury in non-human primates. Front Neurosci 2023; 17:1127095. [PMID: 36960166 PMCID: PMC10028188 DOI: 10.3389/fnins.2023.1127095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/23/2023] [Indexed: 03/09/2023] Open
Abstract
The corticospinal tract plays a major role in the control of voluntary limb movements, and its damage impedes voluntary limb control. We investigated the feasibility of closed-loop brain-controlled subdural spinal stimulation through a corticospinal interface for the modulation of wrist torque in the paralyzed forearm of monkeys with spinal cord injury at C4/C5. Subdural spinal stimulation of the preserved cervical enlargement activated multiple muscles on the paralyzed forearm and wrist torque in the range from flexion to ulnar-flexion. The magnitude of the evoked torque could be modulated by changing current intensity. We then employed the corticospinal interface designed to detect the firing rate of an arbitrarily selected "linked neuron" in the forearm territory of the primary motor cortex (M1) and convert it in real time to activity-contingent electrical stimulation of a spinal site caudal to the lesion. Linked neurons showed task-related activity that modulated the magnitude of the evoked torque and the activation of multiple muscles depending on the required torque. Unlinked neurons, which were independent of spinal stimulation and located in the vicinity of the linked neurons, exhibited task-related or -unrelated activity. Thus, monkeys were able to modulate the wrist torque of the paralyzed forearm by modulating the firing rate of M1 neurons including unlinked and linked neurons via the corticospinal interface. These results suggest that the corticospinal interface can replace the function of the corticospinal tract after spinal cord injury.
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Affiliation(s)
- Kei Obara
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Division of Neural Engineering, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Miki Kaneshige
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Michiaki Suzuki
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Osamu Yokoyama
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Toshiki Tazoe
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Yukio Nishimura
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Division of Neural Engineering, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
- *Correspondence: Yukio Nishimura,
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26
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Ma Y, Gong A, Nan W, Ding P, Wang F, Fu Y. Personalized Brain-Computer Interface and Its Applications. J Pers Med 2022; 13:46. [PMID: 36675707 PMCID: PMC9861730 DOI: 10.3390/jpm13010046] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Brain-computer interfaces (BCIs) are a new technology that subverts traditional human-computer interaction, where the control signal source comes directly from the user's brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.
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Affiliation(s)
- Yixin Ma
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China
| | - Anmin Gong
- School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xian 710086, China
| | - Wenya Nan
- Department of Psychology, College of Education, Shanghai Normal University, Shanghai 200234, China
| | - Peng Ding
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China
| | - Fan Wang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China
| | - Yunfa Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China
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27
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Cometa A, Falasconi A, Biasizzo M, Carpaneto J, Horn A, Mazzoni A, Micera S. Clinical neuroscience and neurotechnology: An amazing symbiosis. iScience 2022; 25:105124. [PMID: 36193050 PMCID: PMC9526189 DOI: 10.1016/j.isci.2022.105124] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In the last decades, clinical neuroscience found a novel ally in neurotechnologies, devices able to record and stimulate electrical activity in the nervous system. These technologies improved the ability to diagnose and treat neural disorders. Neurotechnologies are concurrently enabling a deeper understanding of healthy and pathological dynamics of the nervous system through stimulation and recordings during brain implants. On the other hand, clinical neurosciences are not only driving neuroengineering toward the most relevant clinical issues, but are also shaping the neurotechnologies thanks to clinical advancements. For instance, understanding the etiology of a disease informs the location of a therapeutic stimulation, but also the way stimulation patterns should be designed to be more effective/naturalistic. Here, we describe cases of fruitful integration such as Deep Brain Stimulation and cortical interfaces to highlight how this symbiosis between clinical neuroscience and neurotechnology is closer to a novel integrated framework than to a simple interdisciplinary interaction.
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Affiliation(s)
- Andrea Cometa
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Antonio Falasconi
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
- Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Marco Biasizzo
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Jacopo Carpaneto
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Andreas Horn
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology, 10117 Berlin, Germany
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Silvestro Micera
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
- Translational Neural Engineering Lab, School of Engineering, École Polytechnique Fèdèrale de Lausanne, 1015 Lausanne, Switzerland
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28
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Huang Y, Gong Y, Liu Y, Lu J. Global trends and hot topics in electrical stimulation of skeletal muscle research over the past decade: A bibliometric analysis. Front Neurol 2022; 13:991099. [PMID: 36277916 PMCID: PMC9581161 DOI: 10.3389/fneur.2022.991099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 09/13/2022] [Indexed: 11/25/2022] Open
Abstract
Background Over the past decade, numerous advances have been made in the research on electrical stimulation of skeletal muscle. However, the developing status and future direction of this field remain unclear. This study aims to visualize the evolution and summarize global research hot topics and trends based on quantitative and qualitative evidence from bibliometrics. Methods Literature search was based on the Web of Science Core Collection (WoSCC) database from 2011 to 2021. CiteSpace and VOSviewer, typical bibliometric tools, were used to perform analysis and visualization. Results A total of 3,059 documents were identified. The number of literature is on the rise in general. Worldwide, researchers come primarily from North America and Europe, represented by the USA, France, Switzerland, and Canada. The Udice French Research Universities is the most published affiliation. Millet GY and Maffiuletti NA are the most prolific and the most co-cited authors, respectively. Plos One is the most popular journal, and the Journal of Applied Physiology is the top co-cited journal. The main keywords are muscle fatigue, neuromuscular electrical stimulation, spinal cord injury, tissue engineering, and atrophy. Moreover, this study systematically described the hotspots in this field. Conclusion As the first bibliometric analysis of electrical stimulation of skeletal muscle research over the past decade, this study can help scholars recognize hot topics and trends and provide a reference for further exploration in this field.
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29
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Awasthi P, Lin TH, Bae J, Miller LE, Danziger ZC. Validation of a non-invasive, real-time, human-in-the-loop model of intracortical brain-computer interfaces. J Neural Eng 2022; 19:056038. [PMID: 36198278 PMCID: PMC9855658 DOI: 10.1088/1741-2552/ac97c3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 10/05/2022] [Indexed: 01/26/2023]
Abstract
Objective. Despite the tremendous promise of invasive brain-computer interfaces (iBCIs), the associated study costs, risks, and ethical considerations limit the opportunity to develop and test the algorithms that decode neural activity into a user's intentions. Our goal was to address this challenge by designing an iBCI model capable of testing many human subjects in closed-loop.Approach. We developed an iBCI model that uses artificial neural networks (ANNs) to translate human finger movements into realistic motor cortex firing patterns, which can then be decoded in real time. We call the model the joint angle BCI, or jaBCI. jaBCI allows readily recruited, healthy subjects to perform closed-loop iBCI tasks using any neural decoder, preserving subjects' control-relevant short-latency error correction and learning dynamics.Main results. We validated jaBCI offline through emulated neuron firing statistics, confirming that emulated neural signals have firing rates, low-dimensional PCA geometry, and rotational jPCA dynamics that are quite similar to the actual neurons (recorded in monkey M1) on which we trained the ANN. We also tested jaBCI in closed-loop experiments, our single study examining roughly as many subjects as have been tested world-wide with iBCIs (n= 25). Performance was consistent with that of the paralyzed, human iBCI users with implanted intracortical electrodes. jaBCI allowed us to imitate the experimental protocols (e.g. the same velocity Kalman filter decoder and center-out task) and compute the same seven behavioral measures used in three critical studies.Significance. These encouraging results suggest the jaBCI's real-time firing rate emulation is a useful means to provide statistically robust sample sizes for rapid prototyping and optimization of decoding algorithms, the study of bi-directional learning in iBCIs, and improving iBCI control.
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Affiliation(s)
- Peeyush Awasthi
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States of Amercia
| | - Tzu-Hsiang Lin
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States of Amercia
| | - Jihye Bae
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY, United States
| | - Lee E Miller
- Department of Neuroscience, Physical Medicine, and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Zachary C Danziger
- Department of Biomedical Engineering, Florida International University, Miami, FL, United States of Amercia,Author to whom any correspondence should be addressed
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30
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Liu F, Meamardoost S, Gunawan R, Komiyama T, Mewes C, Zhang Y, Hwang E, Wang L. Deep learning for neural decoding in motor cortex. J Neural Eng 2022; 19. [PMID: 36148535 DOI: 10.1088/1741-2552/ac8fb5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/06/2022] [Indexed: 11/12/2022]
Abstract
Objective. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep learning to decode movement trajectories from the activity of motor cortical neurons.Approach. In this paper, we assessed the performance of deep learning methods in three different decoding schemes, concurrent, time-delay, and spatiotemporal. In the concurrent decoding scheme where the input to the network is the neural activity coincidental to the movement, deep learning networks including artificial neural network (ANN) and long-short term memory (LSTM) were applied to decode movement and compared with traditional machine learning algorithms. Both ANN and LSTM were further evaluated in the time-delay decoding scheme in which temporal delays are allowed between neural signals and movements. Lastly, in the spatiotemporal decoding scheme, we trained convolutional neural network (CNN) to extract movement information from images representing the spatial arrangement of neurons, their activity, and connectomes (i.e. the relative strengths of connectivity between neurons) and combined CNN and ANN to develop a hybrid spatiotemporal network. To reveal the input features of the CNN in the hybrid network that deep learning discovered for movement decoding, we performed a sensitivity analysis and identified specific regions in the spatial domain.Main results. Deep learning networks (ANN and LSTM) outperformed traditional machine learning algorithms in the concurrent decoding scheme. The results of ANN and LSTM in the time-delay decoding scheme showed that including neural data from time points preceding movement enabled decoders to perform more robustly when the temporal relationship between the neural activity and movement dynamically changes over time. In the spatiotemporal decoding scheme, the hybrid spatiotemporal network containing the concurrent ANN decoder outperformed single-network concurrent decoders.Significance. Taken together, our study demonstrates that deep learning could become a robust and effective method for the neural decoding of behavior.
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Affiliation(s)
- Fangyu Liu
- Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States of America
| | - Saber Meamardoost
- Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY 14260, United States of America
| | - Rudiyanto Gunawan
- Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY 14260, United States of America
| | - Takaki Komiyama
- Department of Neurobiology, Center for Neural Circuits and Behavior, and Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, United States of America
| | - Claudia Mewes
- Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, United States of America
| | - Ying Zhang
- Department of Cell and Molecular Biology, University of Rhode Island, Kingston, RI 02881, United States of America
| | - EunJung Hwang
- Department of Neurobiology, Center for Neural Circuits and Behavior, and Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, United States of America.,Cell Biology and Anatomy Discipline, Center for Brain Function and Repair, Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60064, United States of America
| | - Linbing Wang
- Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States of America
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31
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A low-power stretchable neuromorphic nerve with proprioceptive feedback. Nat Biomed Eng 2022; 7:511-519. [PMID: 35970931 DOI: 10.1038/s41551-022-00918-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 06/29/2022] [Indexed: 11/08/2022]
Abstract
By relaying neural signals from the motor cortex to muscles, devices for neurorehabilitation can enhance the movement of limbs in which nerves have been damaged as a consequence of injuries affecting the spinal cord or the lower motor neurons. However, conventional neuroprosthetic devices are rigid and power-hungry. Here we report a stretchable neuromorphic implant that restores coordinated and smooth motions in the legs of mice with neurological motor disorders, enabling the animals to kick a ball, walk or run. The neuromorphic implant acts as an artificial efferent nerve by generating electrophysiological signals from excitatory post-synaptic signals and by providing proprioceptive feedback. The device operates at low power (~1/150 that of a typical microprocessor system), and consists of hydrogel electrodes connected to a stretchable transistor incorporating an organic semiconducting nanowire (acting as an artificial synapse), connected via an ion gel to an artificial proprioceptor incorporating a carbon nanotube strain sensor (acting as an artificial muscle spindle). Stretchable electronics with proprioceptive feedback may inspire the further development of advanced neuromorphic devices for neurorehabilitation.
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32
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Samejima S, Henderson R, Pradarelli J, Mondello SE, Moritz CT. Activity-dependent plasticity and spinal cord stimulation for motor recovery following spinal cord injury. Exp Neurol 2022; 357:114178. [PMID: 35878817 DOI: 10.1016/j.expneurol.2022.114178] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/22/2022] [Accepted: 07/16/2022] [Indexed: 02/07/2023]
Abstract
Spinal cord injuries lead to permanent physical impairment despite most often being anatomically incomplete disruptions of the spinal cord. Remaining connections between the brain and spinal cord create the potential for inducing neural plasticity to improve sensorimotor function, even many years after injury. This narrative review provides an overview of the current evidence for spontaneous motor recovery, activity-dependent plasticity, and interventions for restoring motor control to residual brain and spinal cord networks via spinal cord stimulation. In addition to open-loop spinal cord stimulation to promote long-term neuroplasticity, we also review a more targeted approach: closed-loop stimulation. Lastly, we review mechanisms of spinal cord neuromodulation to promote sensorimotor recovery, with the goal of advancing the field of rehabilitation for physical impairments following spinal cord injury.
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Affiliation(s)
- Soshi Samejima
- International Collaboration on Repair Discoveries, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Medicine, Division of Physical Medicine and Rehabilitation, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Richard Henderson
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA
| | - Jared Pradarelli
- Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA
| | - Sarah E Mondello
- Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA
| | - Chet T Moritz
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA; Center for Neurotechnology, Seattle, WA, USA; Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA.
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33
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Grani F, Soto Sanchez C, Farfan FD, Alfaro A, Grima MD, Rodil Doblado A, Fernandez E. Time stability and connectivity analysis with an intracortical 96-channel microelectrode array inserted in human visual cortex. J Neural Eng 2022; 19. [PMID: 35817011 DOI: 10.1088/1741-2552/ac801d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 07/11/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Microstimulation via electrodes that penetrate the visual cortex creates visual perceptions called phosphenes. Besides providing electrical stimulation to induce perceptions, each electrode can be used to record the brain signals from the cortex region under the electrode which contains brain state information. Since the future visual prosthesis interfaces will be implanted chronically in the visual cortex of blind people, it is important to study the long-term stability of the signals acquired from the electrodes. Here, we studied the changes over time and the repercussions of electrical stimulation on the brain signals acquired with an intracortical 96-channel microelectrode array implanted in the visual cortex of a blind volunteer for 6 months. APPROACH We used variance, power spectral density, correlation, coherence, and phase coherence to study the brain signals acquired in resting condition before and after the administration of electrical stimulation during a period of 6 months. MAIN RESULTS Variance and power spectral density up to 750 Hz do not show any significant trend in the 6 months, but correlation coherence and phase coherence significantly decrease over the implantation time and increase after electrical stimulation. SIGNIFICANCE The stability of variance and power spectral density in time is important for long-term clinical applications based on the intracortical signals collected by the electrodes. The decreasing trends of correlation, coherence, and phase coherence might be related to plasticity changes in the visual cortex due to electrical microstimulation.
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Affiliation(s)
- Fabrizio Grani
- Universidad Miguel Hernandez de Elche, Avinguda de la Universitat d'Elx, Elche, 03206, SPAIN
| | - Cristina Soto Sanchez
- Universidad Miguel Hernandez de Elche, Avinguda de la Universitat d'Elx, Elche, 03206, SPAIN
| | - Fernando Daniel Farfan
- Departmento de Bioingenieria Fac de Ciencias Exactas y Technologia, Universidad Nacional de Tucuman, Av. Independencia 1800, San Miguel de Tucumán, Tucumán, 4000, ARGENTINA
| | - Arantxa Alfaro
- Institute of Bioengineering, Universidad Miguel Hernandez de Elche, Fac. Medicina, San Juan, Alicante , 03550, SPAIN
| | - Maria Dolores Grima
- Universidad Miguel Hernandez de Elche, Avinguda de la Universitat d'Elx, ELCHE, Elche, 03206, SPAIN
| | - Alfonso Rodil Doblado
- Universidad Miguel Hernandez de Elche, Avinguda de la Universitat d'Elx, Elche, 03206, SPAIN
| | - Eduardo Fernandez
- Institute of Bioengineering, Universidad Miguel Hernandez de Elche, Unidad de Neuroingeniería Biomédica, Avda de la Universidad s/n, Elche, ALicante, 03202, SPAIN
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34
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Barra B, Conti S, Perich MG, Zhuang K, Schiavone G, Fallegger F, Galan K, James ND, Barraud Q, Delacombaz M, Kaeser M, Rouiller EM, Milekovic T, Lacour S, Bloch J, Courtine G, Capogrosso M. Epidural electrical stimulation of the cervical dorsal roots restores voluntary upper limb control in paralyzed monkeys. Nat Neurosci 2022; 25:924-934. [PMID: 35773543 DOI: 10.1038/s41593-022-01106-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 05/19/2022] [Indexed: 11/09/2022]
Abstract
Regaining arm control is a top priority for people with paralysis. Unfortunately, the complexity of the neural mechanisms underlying arm control has limited the effectiveness of neurotechnology approaches. Here, we exploited the neural function of surviving spinal circuits to restore voluntary arm and hand control in three monkeys with spinal cord injury, using spinal cord stimulation. Our neural interface leverages the functional organization of the dorsal roots to convey artificial excitation via electrical stimulation to relevant spinal segments at appropriate movement phases. Stimulation bursts targeting specific spinal segments produced sustained arm movements, enabling monkeys with arm paralysis to perform an unconstrained reach-and-grasp task. Stimulation specifically improved strength, task performances and movement quality. Electrophysiology suggested that residual descending inputs were necessary to produce coordinated movements. The efficacy and reliability of our approach hold realistic promises of clinical translation.
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Affiliation(s)
- Beatrice Barra
- Platform of Translational Neuroscience, Department of Neuroscience and Movement Sciences, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland.,Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sara Conti
- Platform of Translational Neuroscience, Department of Neuroscience and Movement Sciences, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Matthew G Perich
- Department of Fundamental Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Katie Zhuang
- Platform of Translational Neuroscience, Department of Neuroscience and Movement Sciences, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Giuseppe Schiavone
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Microengineering, Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Florian Fallegger
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Microengineering, Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Katia Galan
- Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (NeuroRestore), University Hospital Lausanne (CHUV), University of Lausanne (UNIL) and École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Nicholas D James
- Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Quentin Barraud
- Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (NeuroRestore), University Hospital Lausanne (CHUV), University of Lausanne (UNIL) and École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maude Delacombaz
- Platform of Translational Neuroscience, Department of Neuroscience and Movement Sciences, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland.,Defitech Center for Interventional Neurotherapies (NeuroRestore), University Hospital Lausanne (CHUV), University of Lausanne (UNIL) and École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Mélanie Kaeser
- Platform of Translational Neuroscience, Department of Neuroscience and Movement Sciences, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Eric M Rouiller
- Platform of Translational Neuroscience, Department of Neuroscience and Movement Sciences, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Tomislav Milekovic
- Department of Fundamental Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Defitech Center for Interventional Neurotherapies (NeuroRestore), University Hospital Lausanne (CHUV), University of Lausanne (UNIL) and École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Stephanie Lacour
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Microengineering, Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Jocelyne Bloch
- Defitech Center for Interventional Neurotherapies (NeuroRestore), University Hospital Lausanne (CHUV), University of Lausanne (UNIL) and École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Grégoire Courtine
- Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (NeuroRestore), University Hospital Lausanne (CHUV), University of Lausanne (UNIL) and École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Marco Capogrosso
- Platform of Translational Neuroscience, Department of Neuroscience and Movement Sciences, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland. .,Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA. .,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
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Wu W, Nguyen T, Ordaz JD, Zhang Y, Liu NK, Hu X, Liu Y, Ping X, Han Q, Wu X, Qu W, Gao S, Shields CB, Jin X, Xu XM. Transhemispheric cortex remodeling promotes forelimb recovery after spinal cord injury. JCI Insight 2022; 7:e158150. [PMID: 35552276 PMCID: PMC9309060 DOI: 10.1172/jci.insight.158150] [Citation(s) in RCA: 5] [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: 01/06/2022] [Accepted: 05/10/2022] [Indexed: 11/28/2022] Open
Abstract
Understanding the reorganization of neural circuits spared after spinal cord injury in the motor cortex and spinal cord would provide insights for developing therapeutics. Using optogenetic mapping, we demonstrated a transhemispheric recruitment of neural circuits in the contralateral cortical M1/M2 area to improve the impaired forelimb function after a cervical 5 right-sided hemisection in mice, a model mimicking the human Brown-Séquard syndrome. This cortical reorganization can be elicited by a selective cortical optogenetic neuromodulation paradigm. Areas of whisker, jaw, and neck, together with the rostral forelimb area, on the motor cortex ipsilateral to the lesion were engaged to control the ipsilesional forelimb in both stimulation and nonstimulation groups 8 weeks following injury. However, significant functional benefits were only seen in the stimulation group. Using anterograde tracing, we further revealed a robust sprouting of the intact corticospinal tract in the spinal cord of those animals receiving optogenetic stimulation. The intraspinal corticospinal axonal sprouting correlated with the forelimb functional recovery. Thus, specific neuromodulation of the cortical neural circuits induced massive neural reorganization both in the motor cortex and spinal cord, constructing an alternative motor pathway in restoring impaired forelimb function.
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Affiliation(s)
- Wei Wu
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tyler Nguyen
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Josue D. Ordaz
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Yiping Zhang
- Norton Neuroscience Institute, Norton Healthcare, Louisville, Kentucky, USA
| | - Nai-Kui Liu
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Xinhua Hu
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Yuxiang Liu
- Mechanical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
| | - Xingjie Ping
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Qi Han
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Xiangbing Wu
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Wenrui Qu
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Sujuan Gao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Xiaoming Jin
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Xiao-Ming Xu
- Spinal Cord and Brain Injury Research Group, Stark Neurosciences Research Institute, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA
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36
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Hasse BA, Sheets DEG, Holly NL, Gothard KM, Fuglevand AJ. Restoration of complex movement in the paralyzed upper limb. J Neural Eng 2022; 19. [PMID: 35728568 DOI: 10.1088/1741-2552/ac7ad7] [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: 03/19/2022] [Accepted: 06/21/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Functional electrical stimulation (FES) involves artificial activation of skeletal muscles to reinstate motor function in paralyzed individuals. While FES applied to the upper limb has improved the ability of tetraplegics to perform activities of daily living, there are key shortcomings impeding its widespread use. One major limitation is that the range of motor behaviors that can be generated is restricted to a small set of simple, preprogrammed movements. This limitation stems from the substantial difficulty in determining the patterns of stimulation across many muscles required to produce more complex movements. Therefore, the objective of this study was to use machine learning to flexibly identify patterns of muscle stimulation needed to evoke a wide array of multi-joint arm movements. APPROACH Arm kinematics and electromyographic activity from 29 muscles were recorded while a 'trainer' monkey made an extensive range of arm movements. Those data were used to train an artificial neural network that predicted patterns of muscle activity associated with a new set of movements. Those patterns were converted into trains of stimulus pulses that were delivered to upper limb muscles in two other temporarily paralyzed monkeys. RESULTS Machine-learning based prediction of EMG was good for within-subject predictions but appreciably poorer for across-subject predictions. Evoked responses matched the desired movements with good fidelity only in some cases. Means to mitigate errors associated with FES-evoked movements are discussed. SIGNIFICANCE Because the range of movements that can be produced with our approach is virtually unlimited, this system could greatly expand the repertoire of movements available to individuals with high level paralysis.
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Affiliation(s)
- Brady A Hasse
- Department of Physiology, The University of Arizona College of Medicine Tucson, 1501 N Campbell Avenue, Tucson, Arizona, 85724-5051, UNITED STATES
| | - Drew E G Sheets
- Department of Organismal Biology & Anatomy, University of Chicago Biological Sciences Division, Anatomy, 1027 E 57th Street Chicago, IL 60637, Chicago, Illinois, 60637-5416, UNITED STATES
| | - Nicole L Holly
- Physiology, The University of Arizona College of Medicine Tucson, 1501 N Campbell Avenue, Tucson, Arizona, 85724-5051, UNITED STATES
| | - Katalin M Gothard
- Physiology, The University of Arizona College of Medicine Tucson, 1501 N Campbell Ave, Tucson, Arizona, 85724-5051, UNITED STATES
| | - Andrew J Fuglevand
- Department of Physiology, University of Arizona, Arizona Health Sciences Center, 1501 N. Campbell Ave, Tucson, Arizona, 85724-5051, UNITED STATES
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37
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An H, Nason-Tomaszewski SR, Lim J, Kwon K, Willsey MS, Patil PG, Kim HS, Sylvester D, Chestek CA, Blaauw D. A Power-Efficient Brain-Machine Interface System With a Sub-mw Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:395-408. [PMID: 35594208 PMCID: PMC9375520 DOI: 10.1109/tbcas.2022.3175926] [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] [Indexed: 06/15/2023]
Abstract
Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD's real-time predictions, the monkey achieved 100% success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 μW. To predict two-dimensional finger movements, the NeuRAD consumed 588 μW to enable the monkey to achieve a 96% success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis.
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38
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Wimalasena LN, Braun JF, Keshtkaran MR, Hofmann D, Gallego JÁ, Alessandro C, Tresch MC, Miller LE, Pandarinath C. Estimating muscle activation from EMG using deep learning-based dynamical systems models. J Neural Eng 2022; 19:10.1088/1741-2552/ac6369. [PMID: 35366649 PMCID: PMC9628781 DOI: 10.1088/1741-2552/ac6369] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 04/01/2022] [Indexed: 11/11/2022]
Abstract
Objective. To study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. One approach is to try extracting the underlying neural command signal to muscles by applying latent variable modeling methods to electromyographic (EMG) recordings. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded EMG signals. Common approaches estimate each muscle's activation independently or require manual tuning of model hyperparameters to preserve behaviorally-relevant features.Approach. Here, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks to model the spatial and temporal regularities that underlie multi-muscle activation.Main results. We first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also applied AutoLFADS to monkey forearm muscle activity recorded during an isometric wrist force task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than were other tested approaches.Significance.This method leverages dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles. Ultimately, the approach can be used for further studies of multi-muscle coordination and its control by upstream brain areas, and for improving brain-machine interfaces that rely on myoelectric control signals.
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Affiliation(s)
- Lahiru N. Wimalasena
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Jonas F. Braun
- Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Mohammad Reza Keshtkaran
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - David Hofmann
- Department of Physics, Emory University, Atlanta, GA, USA
- Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, GA, USA
| | | | - Cristiano Alessandro
- Department of Physiology, Northwestern University, Chicago, IL, USA
- School of Medicine and Surgery/Sport and Exercise Medicine, University of Milano-Bicocca, Milan, Italy
| | - Matthew C. Tresch
- Department of Physiology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
- Shirley Ryan AbilityLab, Chicago, IL, USA
| | - Lee E. Miller
- Department of Physiology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
- Shirley Ryan AbilityLab, Chicago, IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
| | - Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Department of Neurosurgery, Emory University, Atlanta, GA, USA
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Niu CM, Chou CH, Bao Y, Wang T, Gu L, Zhang X, Cui L, Xuan Z, Zhuang C, Li S, Chen Z, Lan N, Xie Q. A Pilot Study of Synergy-Based FES for Upper-Extremity Poststroke Rehabilitation. Neurosci Lett 2022; 780:136621. [PMID: 35395324 DOI: 10.1016/j.neulet.2022.136621] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 03/21/2022] [Accepted: 04/04/2022] [Indexed: 11/25/2022]
Abstract
A previous study indicated that synergy-based functional electrical stimulation (FES) may improve instantaneous upper-limb motor performance for stroke survivors. However, it remains unclear whether the improvements will sustain over time to achieve functional gains associated with a task-oriented training (TOT). This pilot study was designed to investigate whether there is any promising sign of functional benefits. A TOT protocol with repeated forward and lateral reaching movements assisted by synergy-based FES was conducted in 16 patients (9 FES, 7 Sham) with post-stroke hemiparesis. FES stimuli were applied to 7 upper-extremity muscles of elbow and shoulder during patient movements. Envelopes of stimuli were individualized by re-composing the muscle synergies extracted from a healthy subject. After a five-day training for one hour each day, synergy-based FES induced higher increases in Fugl-Meyer scores (6.67±5.20) than did the Sham (2.00±2.38, p<0.05). Peak velocity of forward reaching movements increased with a slope 73% steeper in FES group than Sham. In lateral reaching movements, the change in synergy similarity correlated with the change in elbow flexion for the FES group, but not the Sham group. Our results indicate that synergy-based FES therapy induced clinically traceable signs of improvements in poststroke motor performance. The muscle activation in patients also showed promising sign of alteration by FES. Results suggest that a larger scale clinical trial of synergy-based FES may be feasible towards an individualized therapeutic regimen.
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Affiliation(s)
- Chuanxin M Niu
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; 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
| | - Yong Bao
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Tong Wang
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Lin Gu
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiao Zhang
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lijun Cui
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi Xuan
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Cheng Zhuang
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Si Li
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi Chen
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ning Lan
- Laboratory of Neurorehabilitation Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qing Xie
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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40
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Pandarinath C, Bensmaia SJ. The science and engineering behind sensitized brain-controlled bionic hands. Physiol Rev 2022; 102:551-604. [PMID: 34541898 PMCID: PMC8742729 DOI: 10.1152/physrev.00034.2020] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/13/2022] Open
Abstract
Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.
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Affiliation(s)
- Chethan Pandarinath
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
- Department of Neurosurgery, Emory University, Atlanta, Georgia
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, Illinois
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41
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Asan AS, McIntosh JR, Carmel JB. Targeting Sensory and Motor Integration for Recovery of Movement After CNS Injury. Front Neurosci 2022; 15:791824. [PMID: 35126040 PMCID: PMC8813971 DOI: 10.3389/fnins.2021.791824] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 12/27/2021] [Indexed: 12/18/2022] Open
Abstract
The central nervous system (CNS) integrates sensory and motor information to acquire skilled movements, known as sensory-motor integration (SMI). The reciprocal interaction of the sensory and motor systems is a prerequisite for learning and performing skilled movement. Injury to various nodes of the sensorimotor network causes impairment in movement execution and learning. Stimulation methods have been developed to directly recruit the sensorimotor system and modulate neural networks to restore movement after CNS injury. Part 1 reviews the main processes and anatomical interactions responsible for SMI in health. Part 2 details the effects of injury on sites critical for SMI, including the spinal cord, cerebellum, and cerebral cortex. Finally, Part 3 reviews the application of activity-dependent plasticity in ways that specifically target integration of sensory and motor systems. Understanding of each of these components is needed to advance strategies targeting SMI to improve rehabilitation in humans after injury.
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Affiliation(s)
| | | | - Jason B. Carmel
- Departments of Neurology and Orthopedics, Columbia University, New York, NY, United States
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42
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Schroeder KE, Perkins SM, Wang Q, Churchland MM. Cortical Control of Virtual Self-Motion Using Task-Specific Subspaces. J Neurosci 2022; 42:220-239. [PMID: 34716229 PMCID: PMC8802935 DOI: 10.1523/jneurosci.2687-20.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 09/18/2021] [Accepted: 10/17/2021] [Indexed: 11/21/2022] Open
Abstract
Brain-machine interfaces (BMIs) for reaching have enjoyed continued performance improvements, yet there remains significant need for BMIs that control other movement classes. Recent scientific findings suggest that the intrinsic covariance structure of neural activity depends strongly on movement class, potentially necessitating different decode algorithms across classes. To address this possibility, we developed a self-motion BMI based on cortical activity as monkeys cycled a hand-held pedal to progress along a virtual track. Unlike during reaching, we found no high-variance dimensions that directly correlated with to-be-decoded variables. This was due to no neurons having consistent correlations between their responses and kinematic variables. Yet we could decode a single variable-self-motion-by nonlinearly leveraging structure that spanned multiple high-variance neural dimensions. Resulting online BMI-control success rates approached those during manual control. These findings make two broad points regarding how to build decode algorithms that harmonize with the empirical structure of neural activity in motor cortex. First, even when decoding from the same cortical region (e.g., arm-related motor cortex), different movement classes may need to employ very different strategies. Although correlations between neural activity and hand velocity are prominent during reaching tasks, they are not a fundamental property of motor cortex and cannot be counted on to be present in general. Second, although one generally desires a low-dimensional readout, it can be beneficial to leverage a multidimensional high-variance subspace. Fully embracing this approach requires highly nonlinear approaches tailored to the task at hand, but can produce near-native levels of performance.SIGNIFICANCE STATEMENT Many brain-machine interface decoders have been constructed for controlling movements normally performed with the arm. Yet it is unclear how these will function beyond the reach-like scenarios where they were developed. Existing decoders implicitly assume that neural covariance structure, and correlations with to-be-decoded kinematic variables, will be largely preserved across tasks. We find that the correlation between neural activity and hand kinematics, a feature typically exploited when decoding reach-like movements, is essentially absent during another task performed with the arm: cycling through a virtual environment. Nevertheless, the use of a different strategy, one focused on leveraging the highest-variance neural signals, supported high performance real-time brain-machine interface control.
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Affiliation(s)
- Karen E Schroeder
- Department of Neuroscience, Columbia University Medical Center, New York, New York
- Zuckerman Institute, Columbia University, New York, New York
| | - Sean M Perkins
- Zuckerman Institute, Columbia University, New York, New York
- Department of Biomedical Engineering, Columbia University, New York, New York
| | - Qi Wang
- Department of Biomedical Engineering, Columbia University, New York, New York
| | - Mark M Churchland
- Department of Neuroscience, Columbia University Medical Center, New York, New York
- Zuckerman Institute, Columbia University, New York, New York
- Kavli Institute for Brain Science, Columbia University Medical Center, New York, New York
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York
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43
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Losanno E, Badi M, Wurth S, Borgognon S, Courtine G, Capogrosso M, Rouiller EM, Micera S. Bayesian optimization of peripheral intraneural stimulation protocols to evoke distal limb movements. J Neural Eng 2021; 18. [PMID: 34874320 DOI: 10.1088/1741-2552/ac3f6c] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 12/02/2021] [Indexed: 11/12/2022]
Abstract
Objective.Motor neuroprostheses require the identification of stimulation protocols that effectively produce desired movements. Manual search for these protocols can be very time-consuming and often leads to suboptimal solutions, as several stimulation parameters must be personalized for each subject for a variety of target motor functions. Here, we present an algorithm that efficiently tunes peripheral intraneural stimulation protocols to elicit functionally relevant distal limb movements.Approach.We developed the algorithm using Bayesian optimization (BO) with multi-output Gaussian Processes (GPs) and defined objective functions based on coordinated muscle recruitment. We applied the algorithm offline to data acquired in rats for walking control and in monkeys for hand grasping control and compared different GP models for these two systems. We then performed a preliminary online test in a monkey to experimentally validate the functionality of our method.Main results.Offline, optimal intraneural stimulation protocols for various target motor functions were rapidly identified in both experimental scenarios. Using the model that performed best, the algorithm converged to stimuli that evoked functionally consistent movements with an average number of actions equal to 20% of the search space size in both the rat and monkey animal models. Online, the algorithm quickly guided the observations to stimuli that elicited functional hand gestures, although more selective motor outputs could have been achieved by refining the objective function used.Significance.These results demonstrate that BO can reliably and efficiently automate the tuning of peripheral neurostimulation protocols, establishing a translational framework to configure peripheral motor neuroprostheses in clinical applications. The proposed method can also potentially be applied to optimize motor functions using other stimulation modalities.
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Affiliation(s)
- E Losanno
- The Biorobotics Institute and Department of Excellent in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - M Badi
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - S Wurth
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - S Borgognon
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland.,Center for Neuroprosthetics and BrainMind Institute, School of Life Sciences, Eécole Polytechnique Feédeérale de Lausanne (EPFL), Lausanne, Switzerland
| | - G Courtine
- Center for Neuroprosthetics and BrainMind Institute, School of Life Sciences, Eécole Polytechnique Feédeérale de Lausanne (EPFL), Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL, University Hospital of Lausanne (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - M Capogrosso
- Department of Neurological Surgery, Rehabilitation and Neural Engineering Laboratories, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - E M Rouiller
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland
| | - S Micera
- The Biorobotics Institute and Department of Excellent in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy.,Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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44
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Tinkhauser G, Moraud EM. Controlling Clinical States Governed by Different Temporal Dynamics With Closed-Loop Deep Brain Stimulation: A Principled Framework. Front Neurosci 2021; 15:734186. [PMID: 34858126 PMCID: PMC8632004 DOI: 10.3389/fnins.2021.734186] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/18/2021] [Indexed: 02/05/2023] Open
Abstract
Closed-loop strategies for deep brain stimulation (DBS) are paving the way for improving the efficacy of existing neuromodulation therapies across neurological disorders. Unlike continuous DBS, closed-loop DBS approaches (cl-DBS) optimize the delivery of stimulation in the temporal domain. However, clinical and neurophysiological manifestations exhibit highly diverse temporal properties and evolve over multiple time-constants. Moreover, throughout the day, patients are engaged in different activities such as walking, talking, or sleeping that may require specific therapeutic adjustments. This broad range of temporal properties, along with inter-dependencies affecting parallel manifestations, need to be integrated in the development of therapies to achieve a sustained, optimized control of multiple symptoms over time. This requires an extended view on future cl-DBS design. Here we propose a conceptual framework to guide the development of multi-objective therapies embedding parallel control loops. Its modular organization allows to optimize the personalization of cl-DBS therapies to heterogeneous patient profiles. We provide an overview of clinical states and symptoms, as well as putative electrophysiological biomarkers that may be integrated within this structure. This integrative framework may guide future developments and become an integral part of next-generation precision medicine instruments.
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Affiliation(s)
- Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Eduardo Martin Moraud
- Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (.NeuroRestore), Ecole Polytechnique Fédérale de Lausanne and Lausanne University Hospital, Lausanne, Switzerland
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45
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Khan MA, Saibene M, Das R, Brunner IC, Puthusserypady S. Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review. J Neural Eng 2021; 18. [PMID: 34736239 DOI: 10.1088/1741-2552/ac36aa] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 11/04/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Stroke is one of the most common neural disorders, which causes physical disabilities and motor impairments among its survivors. Several technologies have been developed for providing stroke rehabilitation and to assist the survivors in performing their daily life activities. Currently, the use of flexible technology (FT) for stroke rehabilitation systems is on a rise that allows the development of more compact and lightweight wearable systems, which stroke survivors can easily use for long-term activities. APPROACH For stroke applications, FT mainly includes the "flexible/stretchable electronics", "e-textile (electronic textile)" and "soft robotics". Thus, a thorough literature review has been performed to report the practical implementation of FT for post-stroke application. MAIN RESULTS In this review, the highlights of the advancement of FT in stroke rehabilitation systems are dealt with. Such systems mainly involve the "biosignal acquisition unit", "rehabilitation devices" and "assistive systems". In terms of biosignals acquisition, electroencephalography (EEG) and electromyography (EMG) are comprehensively described. For rehabilitation/assistive systems, the application of functional electrical stimulation (FES) and robotics units (exoskeleton, orthosis, etc.) have been explained. SIGNIFICANCE This is the first review article that compiles the different studies regarding flexible technology based post-stroke systems. Furthermore, the technological advantages, limitations, and possible future implications are also discussed to help improve and advance the flexible systems for the betterment of the stroke community.
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Affiliation(s)
- Muhammad Ahmed Khan
- Technical University of Denmark, Ørsteds Plads Building 345C, Room 215, Lyngby, 2800, DENMARK
| | - Matteo Saibene
- Technical University of Denmark, Ørsteds Plads, Building 345C, Lyngby, 2800, DENMARK
| | - Rig Das
- Technical University of Denmark, Ørsteds Plads Building 345C, Room 214, Lyngby, 2800, DENMARK
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46
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Johnson T, Taylor D. Improving reaching with functional electrical stimulation by incorporating stiffness modulation. J Neural Eng 2021; 18. [PMID: 34644693 DOI: 10.1088/1741-2552/ac2f7a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 10/13/2021] [Indexed: 11/12/2022]
Abstract
Objective.Intracortical recordings have now been combined with functional electrical stimulation (FES) of arm/hand muscles to demonstrate restoration of upper-limb function after spinal cord injury. However, for each desired limb position decoded from the brain, there are multiple combinations of muscle stimulation levels that can produce that position. The objective of this simulation study is to explore how modulating the amount of coactivation of antagonist muscles during FES can impact reaching performance and energy usage. Stiffening the limb by cocontracting antagonist muscles makes the limb more resistant to perturbation. Minimizing cocontraction saves energy and reduces fatigue.Approach.Prior demonstrations of reaching via FES used a fixed empirically-derived lookup table for each joint that defined the muscle stimulation levels that would position the limb at the desired joint angle decoded from the brain at each timestep. This study expands on that previous work by using simulations to: (a) test the feasibility of controlling arm reaching using asuiteof lookup tables with varying levels of cocontraction instead of a single fixed lookup table for each joint, (b) optimize a simple function for automatically switching between these different cocontraction tables using only the desired kinematic information already being decoded from the brain, and (c) compare energy savings and movement performance when using the optimized function to automatically modulate cocontraction during reaching versus using the best fixed level of cocontraction.Main results.Our data suggests energy usage and/or movement performance can be significantly improved by dynamically modulating limb stiffness using our multi-table method and a simple function that determines cocontraction level based on decoded endpoint speed and its derivative.Significance.By demonstrating how modulating cocontraction can reduce energy usage while maintaining or even improving movement performance, this study makes brain-controlled FES a more viable option for restoration of reaching after paralysis.
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Affiliation(s)
- Tyler Johnson
- Cleveland Clinic, Cleveland, OH, United States of America.,Case Western Reserve University, Cleveland, OH, United States of America.,Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States of America
| | - Dawn Taylor
- Cleveland Clinic, Cleveland, OH, United States of America.,Case Western Reserve University, Cleveland, OH, United States of America.,Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States of America
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47
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Badi M, Wurth S, Scarpato I, Roussinova E, Losanno E, Bogaard A, Delacombaz M, Borgognon S, C Vanc Ara P, Fallegger F, Su DK, Schmidlin E, Courtine G, Bloch J, Lacour SP, Stieglitz T, Rouiller EM, Capogrosso M, Micera S. Intrafascicular peripheral nerve stimulation produces fine functional hand movements in primates. Sci Transl Med 2021; 13:eabg6463. [PMID: 34705521 DOI: 10.1126/scitranslmed.abg6463] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Marion Badi
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Sophie Wurth
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Ilaria Scarpato
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Evgenia Roussinova
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Elena Losanno
- Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
| | - Andrew Bogaard
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
| | - Maude Delacombaz
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
| | - Simon Borgognon
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland.,Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, EPFL, 1015 Lausanne, Switzerland
| | - Paul C Vanc Ara
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, Bernstein Center Freiburg, and BrainLinks-BrainTools Center, University of Freiburg, 79110 Freiburg, Germany
| | - Florian Fallegger
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronics Interface, Institute of Microengineering, Institute of Bioengineering, Centre for Neuroprosthetics, 1202 Geneva, Switzerland
| | - David K Su
- Neurological Surgery, Harborview Medical Center, Seattle, WA 98104, USA
| | - Eric Schmidlin
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
| | - Grégoire Courtine
- Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, EPFL, 1015 Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL, University Hospital of Lausanne (CHUV), and University of Lausanne (UNIL), 1015 Lausanne, Switzerland
| | - Jocelyne Bloch
- Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL, University Hospital of Lausanne (CHUV), and University of Lausanne (UNIL), 1015 Lausanne, Switzerland
| | - Stéphanie P Lacour
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronics Interface, Institute of Microengineering, Institute of Bioengineering, Centre for Neuroprosthetics, 1202 Geneva, Switzerland
| | - Thomas Stieglitz
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, Bernstein Center Freiburg, and BrainLinks-BrainTools Center, University of Freiburg, 79110 Freiburg, Germany
| | - Eric M Rouiller
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
| | - Marco Capogrosso
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and Medicine, University of Fribourg, 1700 Fribourg, Switzerland.,Department of Neurological Surgery, Rehabilitation and Neural Engineering Laboratories, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Silvestro Micera
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.,Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
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48
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Nason SR, Mender MJ, Vaskov AK, Willsey MS, Ganesh Kumar N, Kung TA, Patil PG, Chestek CA. Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface. Neuron 2021; 109:3164-3177.e8. [PMID: 34499856 DOI: 10.1016/j.neuron.2021.08.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 06/07/2021] [Accepted: 08/10/2021] [Indexed: 11/27/2022]
Abstract
Modern brain-machine interfaces can return function to people with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a non-prehensile task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined. During online brain control, the ReFIT Kalman filter could predict individuated finger group movements with high performance. Next, training ridge regression decoders with individual movements was sufficient to predict untrained combined movements and vice versa. Finally, we compared the postural and movement tuning of finger-related cortical activity to find that individual cortical units simultaneously encode multiple behavioral dimensions. Our results suggest that linear decoders may be sufficient for brain-machine interfaces to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.
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Affiliation(s)
- Samuel R Nason
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew J Mender
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alex K Vaskov
- Robotics Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew S Willsey
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Nishant Ganesh Kumar
- Section of Plastic Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI 48109, USA
| | - Theodore A Kung
- Section of Plastic Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI 48109, USA
| | - Parag G Patil
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Cynthia A Chestek
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Robotics Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA.
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49
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Chandrasekaran S, Fifer M, Bickel S, Osborn L, Herrero J, Christie B, Xu J, Murphy RKJ, Singh S, Glasser MF, Collinger JL, Gaunt R, Mehta AD, Schwartz A, Bouton CE. Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications. Bioelectron Med 2021; 7:14. [PMID: 34548098 PMCID: PMC8456563 DOI: 10.1186/s42234-021-00076-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 08/29/2021] [Indexed: 11/10/2022] Open
Abstract
Almost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.
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Affiliation(s)
- Santosh Chandrasekaran
- Neural Bypass and Brain Computer Interface Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Matthew Fifer
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Stephan Bickel
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Luke Osborn
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Jose Herrero
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Breanne Christie
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Junqian Xu
- Departments of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, USA
| | - Rory K J Murphy
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Sandeep Singh
- Good Shepherd Rehabilitation Hospital, Allentown, PA, USA
| | - Matthew F Glasser
- Departments of Radiology and Neuroscience, Washington University in St Louis, Saint Louis, MO, USA
| | - Jennifer L Collinger
- Rehabilitation Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert Gaunt
- Rehabilitation Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashesh D Mehta
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Andrew Schwartz
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chad E Bouton
- Neural Bypass and Brain Computer Interface Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA.
- Department of Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA.
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50
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Driscoll N, Erickson B, Murphy BB, Richardson AG, Robbins G, Apollo NV, Mentzelopoulos G, Mathis T, Hantanasirisakul K, Bagga P, Gullbrand SE, Sergison M, Reddy R, Wolf JA, Chen HI, Lucas TH, Dillingham T, Davis KA, Gogotsi Y, Medaglia JD, Vitale F. MXene-infused bioelectronic interfaces for multiscale electrophysiology and stimulation. Sci Transl Med 2021; 13:eabf8629. [PMID: 34550728 PMCID: PMC8722432 DOI: 10.1126/scitranslmed.abf8629] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Soft bioelectronic interfaces for mapping and modulating excitable networks at high resolution and at large scale can enable paradigm-shifting diagnostics, monitoring, and treatment strategies. Yet, current technologies largely rely on materials and fabrication schemes that are expensive, do not scale, and critically limit the maximum attainable resolution and coverage. Solution processing is a cost-effective manufacturing alternative, but biocompatible conductive inks matching the performance of conventional metals are lacking. Here, we introduce MXtrodes, a class of soft, high-resolution, large-scale bioelectronic interfaces enabled by Ti3C2 MXene (a two-dimensional transition metal carbide nanomaterial) and scalable solution processing. We show that the electrochemical properties of MXtrodes exceed those of conventional materials and do not require conductive gels when used in epidermal electronics. Furthermore, we validate MXtrodes in applications ranging from mapping large-scale neuromuscular networks in humans to cortical neural recording and microstimulation in swine and rodent models. Last, we demonstrate that MXtrodes are compatible with standard clinical neuroimaging modalities.
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Affiliation(s)
- Nicolette Driscoll
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
| | - Brian Erickson
- Department of Psychology, Drexel University, Philadelphia, PA 19104, USA
| | - Brendan B. Murphy
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
| | - Andrew G. Richardson
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Gregory Robbins
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, PA 19104, USA
| | - Nicholas V. Apollo
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
| | - Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
| | - Tyler Mathis
- Department of Materials Science and Engineering, Drexel University, Philadelphia, PA 19104, USA
- A.J. Drexel Nanomaterials Institute, Drexel University, Philadelphia, PA 19104, USA
| | - Kanit Hantanasirisakul
- Department of Materials Science and Engineering, Drexel University, Philadelphia, PA 19104, USA
- A.J. Drexel Nanomaterials Institute, Drexel University, Philadelphia, PA 19104, USA
| | - Puneet Bagga
- Department of Radiology, Center for Magnetic Resonance and Optical Imaging, University of Pennsylvania, Philadelphia, PA 19104, USA
- Diagnostic Imaging, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Sarah E. Gullbrand
- Translational Musculoskeletal Research Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- McKay Orthopaedic Research Laboratory, Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Sergison
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ravinder Reddy
- Department of Radiology, Center for Magnetic Resonance and Optical Imaging, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John A. Wolf
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - H. Isaac Chen
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy H. Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy Dillingham
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, PA 19104, USA
| | - Kathryn A. Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yury Gogotsi
- Department of Materials Science and Engineering, Drexel University, Philadelphia, PA 19104, USA
- A.J. Drexel Nanomaterials Institute, Drexel University, Philadelphia, PA 19104, USA
| | - John D. Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Drexel University, Philadelphia, PA 19104, USA
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
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