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

中风后，大脑发生了从微观到网络层面的可塑性，对功能恢复产生了积极但也消极的后果。为什么中风后的可塑性采取有益的或不适应的方向，目前仍不完全清楚。由于感觉运动学习背后的生物学机制与康复过程中观察到的机制相似，学习机制可能是中风后神经可塑性的潜在修饰者，并对感觉运动功能的恢复具有离散的不良/适应性影响。然而，目前还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，以及它们与中风后运动功能恢复的关系如何。 该项目旨在描述恢复和程序性学习在多大程度上依赖于相同的大脑回路动态，并进一步了解感觉运动恢复过程中适应与适应不良可塑性的潜在机制。 我们提出了一种先进的基于任务的功能性磁共振成像(FMRI)数据分析方法的创新实现，该方法适用于有和没有感觉运动障碍的参与者。具体地说，使用多变量模式分析方法将有助于(1)在程序性学习过程中确定已定义的皮质-皮质下回路中网络动力学的异同，以及(2)将这些个体网络动力学与学习任务中的行为变化和中风后康复的描述符联系起来。 该项目的预期结果将为中风后新的感觉运动信息如何整合到大脑中提供新的见解，而不是完整的大脑。识别中风患者学习过程中脑网络的变化可能具有潜在的诊断相关性，并可作为指导个性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.