Norman Sumner L, Wolpaw Jonathan R, Reinkensmeyer David J
Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
Mechanical and Aerospace Engineering, University of California: Irvine, Irvine, CA 92697, USA.
Brain Commun. 2022 Oct 21;4(6):fcac264. doi: 10.1093/braincomms/fcac264. eCollection 2022.
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.
神经损伤后，人们会形成限制运动恢复的异常神经活动模式。专注于练习受损技能的传统康复方法很少能完全有效。新的靶向神经可塑性方案与中枢神经系统相互作用，在关键部位诱导有益的可塑性，从而实现更广泛的有益可塑性。它们可以补充传统疗法并促进恢复。然而，它们的开发和验证很困难，因为可以设想出许多不同的靶向神经可塑性方案，而且评估其中任何一个都耗时、费力且昂贵。计算模型可以通过快速有效地筛选众多候选方案来解决这个问题。然后动物和人体实证测试可以集中在最有前景的方案上。在这里，我们模拟了一个控制运动神经元引发单侧手指伸展的皮质脊髓神经元神经网络。我们使用这个网络来（i）研究中风后皮质重组的机制和模式；以及（ii）识别并参数化一种能改善伸展扭矩恢复的靶向神经可塑性方案。模拟中风后，标准训练产生了异常的双侧皮质激活和次优的扭矩恢复。为了促进恢复，我们将标准训练与仅从一组经过优化的目标神经元给予网络反馈的试验交叉进行。在约20%的总试验中靶向次级运动区的神经元恢复了皮质激活的偏侧化并改善了伸展扭矩的恢复。这些结果阐明了中风后皮质活动次优的潜在机制；它们能够识别并参数化最有前景的靶向神经可塑性方案。通过提供初步指导，计算模型可以促进并加速实现改善运动恢复的新疗法。
