Great word salad but I see nothing that is going to get survivors recovered.
Targeting neuroplasticity to improve motor recovery after stroke: an artificial neural network model
Sumner L. Norman1,2,*, Jonathan R. Wolpaw3, and David J. Reinkensmeyer2
1Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
2Mechanical and Aerospace Engineering, University of California: Irvine, Irvine, CA, USA
3National Center for Adaptive Neurotechnologies, Stratton VA Medical Center and State University of
New York, Albany, NY, USA
*Correspondence to: Sumner L. Norman, Ph.D., 1200 E California Blvd, MC 216-76, Pasadena, CA 91125
USA, sumnern@caltech.edu
1Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
2Mechanical and Aerospace Engineering, University of California: Irvine, Irvine, CA, USA
3National Center for Adaptive Neurotechnologies, Stratton VA Medical Center and State University of
New York, Albany, NY, USA
*Correspondence to: Sumner L. Norman, Ph.D., 1200 E California Blvd, MC 216-76, Pasadena, CA 91125
USA, sumnern@caltech.edu
Abstract
After a neurological injury, people develop abnormal patterns of neural activity that limit motor recovery. Traditional rehabilitation, which concentrates on practicing impaired skills, is seldom fully effective. New targeted neuroplasticity protocols interact with the central nervous system (CNS) to induce beneficial plasticity in key sites and thereby enable wider beneficial plasticity. They can complement traditional therapy and enhance recovery. However, their development and validation is difficult because many different targeted neuroplasticity protocols are conceivable, and evaluating even one of them is lengthy, laborious, and expensive. Computational models can address this problem by triaging numerous candidate protocols rapidly and effectively. Animal and human empirical testing can then concentrate on the most promising ones. Here we simulate a neural network of corticospinal neurons that control motoneurons eliciting unilateral finger extension. We use this network to (1) study the mechanisms and patterns of cortical reorganization after a stroke, and (2) identify and parameterize a targeted neuroplasticity protocol that improves recovery of extension torque. After a simulated stroke, standard training produced abnormal bilateral cortical activation and suboptimal torque recovery. To enhance recovery, we interdigitated standard training with trials in which the network was given feedback only from a targeted population of sub-optimized neurons. Targeting neurons in secondary motor areas on ~20% of the total trials restored lateralized cortical activation and improved recovery of extension torque. The results illuminate mechanisms underlying suboptimal cortical activity post-stroke; they enablePage 1 of 38
https://mc.manuscriptcentral.com/braincom
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