4.6 Article

Rehabilitative Training Promotes Rapid Motor Recovery but Delayed Motor Map Reorganization in a Rat Cortical Ischemic Infarct Model

期刊

NEUROREHABILITATION AND NEURAL REPAIR
卷 29, 期 5, 页码 472-482

出版社

SAGE PUBLICATIONS INC
DOI: 10.1177/1545968314543499

关键词

cerebral cortex; neuronal plasticity; stroke; rats; Long-Evans; rehabilitation

资金

  1. NIH [R37 NS030853]

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Background. In preclinical stroke models, improvement in motor performance is associated with reorganization of cortical motor maps. However, the temporal relationship between performance gains and map plasticity is not clear. Objective. This study was designed to assess the effects of rehabilitative training on the temporal dynamics of behavioral and neurophysiological endpoints in a rat model of focal cortical infarct. Methods. Eight days after an ischemic infarct in primary motor cortex, adult rats received either rehabilitative training or were allowed to recover spontaneously. Motor performance and movement quality of the paretic forelimb was assessed on a skilled reach task. Intracortical microstimulation mapping procedures were conducted to assess the topography of spared forelimb representations either at the end of training (post-lesion day 18) or at the end of a 3-week follow-up period (post-lesion day 38). Results. Rats receiving rehabilitative training demonstrated more rapid improvement in motor performance and movement quality during the training period that persisted through the follow-up period. Motor maps in both groups were unusually small on post-lesion day 18. On post-lesion day 38, forelimb motor maps in the rehabilitative training group were significantly enlarged compared with the no-rehab group, and within the range of normal maps. Conclusions. Postinfarct rehabilitative training rapidly improves motor performance and movement quality after an ischemic infarct in motor cortex. However, training-induced motor improvements are not reflected in spared motor maps until substantially later, suggesting that early motor training after stroke can help shape the evolving poststroke neural network.

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