The neural representation of force across grasp types in motor cortex of humans with tetraplegia
The neural representation of force across grasp types in motor cortex of humans with tetraplegia
Abstract Intracortical brain-computer interfaces (iBCIs) have the potential to restore hand grasping and object interaction to individuals with tetraplegia. Optimal grasping and object interaction require simultaneous production of both force and grasp outputs. However, since overlapping neural populations are modulated by both parameters, grasp type could affect how well forces are decoded from motor cortex in a closed-loop force iBCI. Therefore, this work quantified the neural representation and offline decoding performance of discrete hand grasps and force levels in two participants with tetraplegia. Participants attempted to produce three discrete forces (light, medium, hard) using up to five hand grasp configurations. A two-way Welch ANOVA was implemented on multiunit neural features to assess their modulation to force and grasp. Demixed principal component analysis was used to assess for population-level tuning to force and grasp and to predict these parameters from neural activity. Three major findings emerged from this work: 1) Force information was neurally represented and could be decoded across multiple hand grasps (and, in one participant, across attempted elbow extension as well); 2) Grasp type affected force representation within multi-unit neural features and offline force classification accuracy; and 3) Grasp was classified more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time iBCI systems is feasible across multiple hand grasps if the decoder also accounts for grasp type. Significance StatementIntracortical brain-computer interfaces (iBCIs) have emerged as a promising technology to potentially restore hand grasping and object interaction in people with tetraplegia. This study is among the first to quantify the degree to which hand grasp affects force-related – or kinetic – neural activity and decoding performance in individuals with tetraplegia. The study results enhance our overall understanding of how the brain encodes kinetic parameters across varying kinematic behaviors -- and in particular, the degree to which these parameters have independent versus interacting neural representations. Such investigations are a critical first step to incorporating force control into human-operated iBCI systems, which would move the technology towards restoring more functional and naturalistic tasks.
Rezaii Paymon G.、Stavisky Sergey D.、Walter Benjamin L.、Henderson Jaimie M.、Rastogi Anisha、Willett Francis R.、Murphy Brian A.、Vargas-Irwin Carlos E.、Miller Jonathan P.、Sweet Jennifer、Ajiboye A. Bolu、Kirsch Robert F.、Memberg William D.、Shenoy Krishna V.、Crowder Douglas C.、Abreu Jessica、Hochberg Leigh R.
Department of Neurosurgery, Stanford UniversityDepartment of Neurosurgery, Stanford University||Department of Electrical Engineering, Stanford UniversityLouis Stokes Cleveland Dept. of VA Med. Ctr.||Department of Neurology, UH Cleveland Med. Ctr.Department of Neurosurgery, Stanford UniversityDepartment of Biomedical Engineering, Case Western Reserve UniversityDepartment of Neurosurgery, Stanford University||Department of Electrical Engineering, Stanford UniversityDepartment of Biomedical Engineering, Case Western Reserve University||Louis Stokes Cleveland Dept. of VA Med. Ctr.Department of Neuroscience, Brown University||Robert J. and Nancy D. Carney Institute for Brain Sciences, Brown UniversityLouis Stokes Cleveland Dept. of VA Med. Ctr.||Department of Neurological Surgery, UH Cleveland Med. Ctr.||Department of Neurological Surgery, CWRU School of MedicineDepartment of Neurological Surgery, UH Cleveland Med. Ctr.||Department of Neurological Surgery, CWRU School of MedicineDepartment of Biomedical Engineering, Case Western Reserve University||Louis Stokes Cleveland Dept. of VA Med. Ctr.Department of Biomedical Engineering, Case Western Reserve University||Louis Stokes Cleveland Dept. of VA Med. Ctr.Department of Biomedical Engineering, Case Western Reserve UniversityDepartment of Electrical Engineering, Stanford University||Department of Bioengineering, Stanford University||Department of Neurobiology, Stanford University||Howard Hughes Medical Institute at Stanford University||Wu Tsai Neuroscience Institute, Bio-X Program, Stanford UniversityDepartment of Biomedical Engineering, Case Western Reserve University||Louis Stokes Cleveland Dept. of VA Med. Ctr.Department of Biomedical Engineering, Case Western Reserve University||Louis Stokes Cleveland Dept. of VA Med. Ctr.Robert J. and Nancy D. Carney Institute for Brain Sciences, Brown University||VA RR&D Center for Neurorestoration and Neurotechnology||School of Engineering, Brown University||Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital||Department of Neurology, Harvard Medical School
基础医学神经病学、精神病学生物科学研究方法、生物科学研究技术
forcekineticmotor cortexbrain-computer interfacegrasp
Rezaii Paymon G.,Stavisky Sergey D.,Walter Benjamin L.,Henderson Jaimie M.,Rastogi Anisha,Willett Francis R.,Murphy Brian A.,Vargas-Irwin Carlos E.,Miller Jonathan P.,Sweet Jennifer,Ajiboye A. Bolu,Kirsch Robert F.,Memberg William D.,Shenoy Krishna V.,Crowder Douglas C.,Abreu Jessica,Hochberg Leigh R..The neural representation of force across grasp types in motor cortex of humans with tetraplegia[EB/OL].(2025-03-28)[2025-05-17].https://www.biorxiv.org/content/10.1101/2020.06.01.126755.点此复制
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