Reorganization and plasticity in the adult brain during learning of motor skills

神经可塑性 心理学 可塑性 运动皮层 初级运动皮层 认知心理学
作者
Julien Doyon,Habib Benali
出处
期刊:International Joint Conference on Neural Network 卷期号:3: 1523-1524 被引量:6
标识
DOI:10.1109/ijcnn.2005.1556102
摘要

In this presentation, the functional anatomy and the cerebral plasticity associated with both motor sequence learning and motor adaptation is discussed based in our work and that of others using brain imaging techniques. A new model of motor skill learning (Doyon & Ungerleider, 2002, 2003; Doyon et al., 2003; Ungerleider et al., 2002) is then presented. Briefly, this model suggests that interactions between the CS and CC systems are critical for establishing the motor routines necessary to learn skilled motor behavior. When consolidation has occurred, the subject has achieved asymptotic performance and its performance has become automatic, however, the neural representation of a new motor skill is then believed to be distributed in a network of structures that involves the CS or CC circuit depending on the type of motor learning acquired. At this stage, we suggest that for motor adaptation, the striatum is no longer necessary for the retention and execution of the acquired skill; regions representing the skill are now involving the cerebellum and related cortical regions. By contrast, a reverse pattern of plasticity is thought to occur in motor sequence learning, such that with extended practice, the cerebellum is no longer essential, and the long lasting retention of the skill is now believed to involve representational changes in the striatum and associated motor cortical regions (Doyon & Ungerleider, 2002). Finally, the results of recent experiments confirming some of the predictions of this model is discussed using both standard peak detection methods for analyzing functional data, as well as new and innovative data driven mathematical approaches designed to measure, not only the spatial relationships between activated regions, but the changes observed in these networks over time (i.e. dynamic functional connectivity) (Bellec, 2003, Perlbarg, 2003).
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