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Targeting Neuroplasticity to Improve Motor Recovery after Stroke

Norman, Sumner L. and Wolpaw, Jonathan R. and Reinkensmeyer, David J. (2020) Targeting Neuroplasticity to Improve Motor Recovery after Stroke. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200911-133136049

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Abstract

After 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 (TNP) protocols interact with the 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 TNP 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 TNP protocol that improves recovery of extension force. After a simulated stroke, standard training produced abnormal bilateral cortical activation and suboptimal force recovery. To enhance recovery, we interdigitated standard trials with trials in which the teaching signal came from a targeted population of sub-optimized neurons. Targeting neurons in secondary motor areas on 5-20% of the total trials restored lateralized cortical activation and improved recovery of extension force. The results illuminate mechanisms underlying suboptimal cortical activity post-stroke; they enable identification and parameterization of the most promising TNP protocols. By providing initial guidance, computational models could facilitate and accelerate realization of new therapies that improve motor recovery.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2020.09.09.284620DOIDiscussion Paper
ORCID:
AuthorORCID
Norman, Sumner L.0000-0001-9945-697X
Wolpaw, Jonathan R.0000-0003-0805-1315
Reinkensmeyer, David J.0000-0002-3196-8706
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. We thank Dr. Peter Brunner for valuable comments on an earlier version of this manuscript. The National Center for Adaptive Neurotechnologies (NCAN) is a Biomedical Technology Resource Center (BTRC) of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH). Dr. Wolpaw’s research is supported by NIBIB/NIH Grant P41 EB018783-06, NINDS/NIH Grant R01 NS110577, VA Merit Award 5I01CX001812, and New York State Spinal Cord Injury Research Board (SCIRB) Grants C32236GG and DOH01-C33279GG-3450000. The authors declare no competing interests. Data availability. URL will be made available at time of publication. The authors have declared no competing interest.
Funders:
Funding AgencyGrant Number
NIHP41 EB018783-06
NIHR01 NS110577
Veterans Administration5I01CX001812
New York State Spinal Cord Injury Research BoardC32236GG
New York State Spinal Cord Injury Research BoardDOH01-C33279GG-3450000
Subject Keywords:Stroke, Motor Control, Operant Conditioning, Reinforcement Learning, Activity-Dependent Plasticity, Rehabilitation
Record Number:CaltechAUTHORS:20200911-133136049
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200911-133136049
Official Citation:Targeting Neuroplasticity to Improve Motor Recovery after Stroke Sumner L. Norman, Jonathan R. Wolpaw, David J. Reinkensmeyer bioRxiv 2020.09.09.284620; doi: https://doi.org/10.1101/2020.09.09.284620
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105341
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:14 Sep 2020 14:28
Last Modified:14 Sep 2020 14:28

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