Nathaniel R Bridges1, Michael Meyers1, Jonathan Garcia1, Patricia A Shewokis2, Karen A Moxon3. 1. Drexel University, School of Biomedical Engineering, Science and Health Systems, 3141 Chestnut Street, Philadelphia, PA, 19104, USA. 2. Drexel University, School of Biomedical Engineering, Science and Health Systems, 3141 Chestnut Street, Philadelphia, PA, 19104, USA; Drexel University, Nutrition Sciences Department, College of Nursing and Health Professions, 1601 Cherry St., 382 Parkway Building, Philadelphia, PA, 19102, USA. 3. Drexel University, School of Biomedical Engineering, Science and Health Systems, 3141 Chestnut Street, Philadelphia, PA, 19104, USA; University of California Davis, Department of Biomedical Engineering, 451 E. Health Sciences Drive, GBSF 2303, Davis, CA, 95616, USA. Electronic address: moxon@ucdavis.edu.
Abstract
BACKGROUND: Most brain machine interfaces (BMI) focus on upper body function in non-injured animals, not addressing the lower limb functional needs of those with paraplegia. A need exists for a novel BMI task that engages the lower body and takes advantage of well-established rodent spinal cord injury (SCI) models to study methods to improve BMI performance. NEW METHOD: A tilt BMI task was designed that randomly applies different types of tilts to a platform, decodes the tilt type applied and rights the platform if the decoder correctly classifies the tilt type. The task was tested on female rats and is relatively natural such that it does not require the animal to learn a new skill. It is self-rewarding such that there is no need for additional rewards, eliminating food or water restriction, which can be especially hard on spinalized rats. Finally, task difficulty can be adjusted by making the tilt parameters. RESULTS: This novel BMI task bilaterally engages the cortex without visual feedback regarding limb position in space and animals learn to improve their performance both pre and post-SCI.Comparison with Existing Methods: Most BMI tasks primarily engage one hemisphere, are upper-body, rely heavily on visual feedback, do not perform investigations in animal models of SCI, and require nonnaturalistic extrinsic motivation such as water rewarding for performance improvement. Our task addresses these gaps. CONCLUSIONS: The BMI paradigm presented here will enable researchers to investigate the interaction of plasticity after SCI and plasticity during BMI training on performance.
BACKGROUND: Most brain machine interfaces (BMI) focus on upper body function in non-injured animals, not addressing the lower limb functional needs of those with paraplegia. A need exists for a novel BMI task that engages the lower body and takes advantage of well-established rodent spinal cord injury (SCI) models to study methods to improve BMI performance. NEW METHOD: A tilt BMI task was designed that randomly applies different types of tilts to a platform, decodes the tilt type applied and rights the platform if the decoder correctly classifies the tilt type. The task was tested on female rats and is relatively natural such that it does not require the animal to learn a new skill. It is self-rewarding such that there is no need for additional rewards, eliminating food or water restriction, which can be especially hard on spinalized rats. Finally, task difficulty can be adjusted by making the tilt parameters. RESULTS: This novel BMI task bilaterally engages the cortex without visual feedback regarding limb position in space and animals learn to improve their performance both pre and post-SCI.Comparison with Existing Methods: Most BMI tasks primarily engage one hemisphere, are upper-body, rely heavily on visual feedback, do not perform investigations in animal models of SCI, and require nonnaturalistic extrinsic motivation such as water rewarding for performance improvement. Our task addresses these gaps. CONCLUSIONS: The BMI paradigm presented here will enable researchers to investigate the interaction of plasticity after SCI and plasticity during BMI training on performance.
Authors: J Wessberg; C R Stambaugh; J D Kralik; P D Beck; M Laubach; J K Chapin; J Kim; S J Biggs; M A Srinivasan; M A Nicolelis Journal: Nature Date: 2000-11-16 Impact factor: 49.962
Authors: Mijail D Serruya; Nicholas G Hatsopoulos; Liam Paninski; Matthew R Fellows; John P Donoghue Journal: Nature Date: 2002-03-14 Impact factor: 49.962
Authors: Jose M Carmena; Mikhail A Lebedev; Roy E Crist; Joseph E O'Doherty; David M Santucci; Dragan F Dimitrov; Parag G Patil; Craig S Henriquez; Miguel A L Nicolelis Journal: PLoS Biol Date: 2003-10-13 Impact factor: 8.029