Targeting neuroplasticity to improve motor recovery after stroke: an artificial neural network model

Author:

Norman Sumner L12ORCID,Wolpaw Jonathan R3,Reinkensmeyer David J4

Affiliation:

1. Biology and Biological Engineering, California Institute of Technology , Pasadena, CA 91125 , USA

2. Mechanical and Aerospace Engineering, University of California: Irvine , Irvine, CA 92697 , USA

3. National Center for Adaptive Neurotechnologies, Stratton VA Medical Center and State University of New York , Albany, NY 12208 , USA

4. Mechanical and Aerospace Engineering, Anatomy and Neurobiology, University of California: Irvine, Irvine , CA 92697 , USA

Abstract

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 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 (i) study the mechanisms and patterns of cortical reorganization after a stroke; and (ii) 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 enable the identification and parameterization of the most promising targeted neuroplasticity protocols. By providing initial guidance, computational models could facilitate and accelerate the realization of new therapies that improve motor recovery.

Funder

National Institutes of Health

Eunice Kennedy Shriver National Institute of Child Health and Human Development

National Institute of Biomedical Imaging and Bioengineering

National Institute of Neurological Disorders and Stroke

Publisher

Oxford University Press (OUP)

Subject

Neurology,Cellular and Molecular Neuroscience,Biological Psychiatry,Psychiatry and Mental health

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