Bains Amarpreet Singh, Schweighofer Nicolas
Neuroscience Graduate Program, University of Southern California, Los Angeles, California;
Neuroscience Graduate Program, University of Southern California, Los Angeles, California; Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, California; and M2H Laboratory, Euromov, University of Montpellier I, Montpellier, France.
J Neurophysiol. 2014 Dec 15;112(12):3240-50. doi: 10.1152/jn.00433.2013. Epub 2014 Oct 1.
Together with Hebbian plasticity, homeoplasticity presumably plays a significant, yet unclear, role in recovery postlesion. Here, we undertake a simulation study addressing the role of homeoplasticity and rehabilitation timing poststroke. We first hypothesize that homeoplasticity is essential for recovery and second that rehabilitation training delivered too early, before homeoplasticity has compensated for activity disturbances postlesion, is less effective for recovery than training delivered after a delay. We developed a neural network model of the sensory cortex driven by muscle spindle inputs arising from a six-muscle arm. All synapses underwent Hebbian plasticity, while homeoplasticity adjusted cell excitability to maintain a desired firing distribution. After initial training, the network was lesioned, leading to areas of hyper- and hypoactivity due to the loss of lateral synaptic connections. The network was then retrained through rehabilitative arm movements. We found that network recovery was unsuccessful in the absence of homeoplasticity, as measured by reestablishment of lesion-affected inputs. We also found that a delay preceding rehabilitation led to faster network recovery during the rehabilitation training than no delay. Our simulation results thus suggest that homeoplastic restoration of prelesion activity patterns is essential to functional network recovery via Hebbian plasticity.
与赫布可塑性一起,顺势可塑性可能在损伤后恢复中发挥重要但尚不清楚的作用。在此,我们进行了一项模拟研究,探讨顺势可塑性和中风后康复时机的作用。我们首先假设顺势可塑性对恢复至关重要,其次假设在顺势可塑性补偿损伤后活动干扰之前过早进行康复训练,其恢复效果不如延迟后进行训练。我们开发了一个感觉皮层的神经网络模型,由来自六肌肉手臂的肌梭输入驱动。所有突触都经历赫布可塑性,而顺势可塑性则调整细胞兴奋性以维持所需的放电分布。初始训练后,网络受损,由于横向突触连接的丧失导致出现活动亢进和减退区域。然后通过康复性手臂运动对网络进行再训练。我们发现,通过重新建立受损伤影响的输入来衡量,在没有顺势可塑性的情况下网络恢复不成功。我们还发现,康复前的延迟导致在康复训练期间网络恢复比无延迟时更快。因此,我们的模拟结果表明,损伤前活动模式的顺势恢复对于通过赫布可塑性实现功能网络恢复至关重要。