Neural circuitries of motor learning as a target to modulate sensorimotor recovery after stroke

中风后，大脑从微观到网络层面都发生了可塑性，对功能恢复有积极的影响，也有消极的影响。为什么中风后的可塑性采取了有益的或不适应的方向仍然没有完全理解。由于感觉运动学习的生物学机制与恢复过程中观察到的机制相似，因此学习机制可能是卒中后神经可塑性的潜在调节剂，并对感觉运动功能的恢复产生离散的不良/适应性影响。然而，目前尚不完全清楚程序性学习过程中的过程是否与恢复机制共享相同的大脑回路，以及它们如何与中风后运动功能的恢复相关。 该项目旨在表征恢复和程序性学习在多大程度上依赖于相同的脑回路动力学，并进一步了解在感觉运动恢复过程中适应性与适应不良可塑性的相关机制。 我们提出了一个国家的最先进的分析方法，以任务为基础的功能性磁共振成像（fMRI）数据的参与者和没有感觉运动障碍的创新实施。具体而言，使用多变量模式分析方法将用于（1）识别程序学习期间定义的皮质-皮质下回路内的网络动态的相似性和差异，以及（2）将这些个体网络动态与学习任务中的行为变化和卒中后恢复的描述符联系起来。 该项目的预期结果将提供新的见解，了解中风后与完整的大脑相比，新的感觉运动信息如何整合到大脑中。识别脑卒中患者学习过程中的脑网络改变可能具有潜在的诊断相关性，并可作为指导个体化治疗和未来康复干预设计的标志。

After a stroke, plasticity occurs in the brain from microscopic to network-level with positive but also negative consequences for functional recovery. Why post-stroke plasticity takes a beneficial or a maladaptive direction is still incompletely understood. Because the biological mechanisms underlying sensorimotor learning parallel those observed during recovery, learning mechanisms could be potential modifiers of post-stroke neuroplasticity and have a discrete mal-/adaptive impact on the recovery of sensorimotor function. However, it is not completely clear whether the processes during procedural learning share the same brain circuits with recovery mechanisms and how they relate to recovery of motor function post-stroke. This project aims to characterize the extent to which recovery and procedural learning rely on the same brain circuit dynamic and to further the understanding of the mechanisms underlying adaptive in contrast to maladaptive plasticity during sensorimotor recovery. We propose the innovative implementation of a state-of-the-art analysis method for task-based functional magnetic resonance imaging (fMRI) data for participants with and without sensorimotor impairment. Specifically, using a multivariate pattern analysis approach will serve (1) to identify similarities and differences of network dynamics within defined cortico-subcortical circuits during procedural learning and (2) to link these individual network dynamics to behavioral change in the learning task and descriptors of recovery post-stroke. The project’s expected results will give novel insight into how new sensorimotor information is integrated in the brain after a stroke compared to the intact brain. Identifying brain network alterations during learning in stroke patients could be of potential diagnostic relevance and serve as a marker to guide individualized therapy and the design of future rehabilitative interventions.