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

Norman, Sumner L. and Wolpaw, Jonathan R. and Reinkensmeyer, David J. (2022) Targeting neuroplasticity to improve motor recovery after stroke: an artificial neural network model. Brain Communications, 4 (6). Art. No. fcac264. ISSN 2632-1297. PMCID PMC9700163. doi:10.1093/braincomms/fcac264.

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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.

Item Type:Article
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URLURL TypeDescription
Norman, Sumner L.0000-0001-9945-697X
Wolpaw, Jonathan R.0000-0003-0805-1315
Reinkensmeyer, David J.0000-0002-3196-8706
Additional Information: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 Reinkensmeyer’s research is supported by the National Institutes of Health/Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) grant R01HD062744. Dr Wolpaw’s research is supported by National Institutes of Health (NIH)/National Institute of Biomedical Imaging and Bioengineering (NIBIB) grant P41 EB018783, National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NINDS) grant 1R01NS110577, The Veterans Affairs (VA) Merit Award 5I01CX001812, and New York State Spinal Cord Injury Research Board (SCIRB) grant C32236GG.
Funding AgencyGrant Number
NIHP41 EB018783
Veterans Administration5I01CX001812
New York State Spinal Cord Injury Research BoardC32236GG
Issue or Number:6
PubMed Central ID:PMC9700163
Record Number:CaltechAUTHORS:20230105-911538600.3
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118740
Deposited By: Research Services Depository
Deposited On:08 Feb 2023 02:11
Last Modified:08 Feb 2023 02:11

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