Literature DB >> 18479943

Functional neuroanatomical networks associated with expertise in motor imagery.

Aymeric Guillot1, Christian Collet, Vo An Nguyen, Francine Malouin, Carol Richards, Julien Doyon.   

Abstract

Although numerous behavioural studies provide evidence that there exist wide differences within individual motor imagery (MI) abilities, little is known with regards to the functional neuroanatomical networks that dissociate someone with good versus poor MI capacities. For the first time, we thus compared, through functional magnetic resonance imaging (fMRI), the pattern of cerebral activations in 13 skilled and 15 unskilled imagers during both physical execution and MI of a sequence of finger movements. Differences in MI abilities were assessed using well-established questionnaire and chronometric measures, as well as a new index based upon the subject's peripheral responses from the autonomic nervous system. As expected, both good and poor imagers activated the inferior and superior parietal lobules, as well as motor-related regions including the lateral and medial premotor cortex, the cerebellum and putamen. Inter-group comparisons revealed that good imagers activated more the parietal and ventrolateral premotor regions, which are known to play a critical role in the generation of mental images. By contrast, poor imagers recruited the cerebellum, orbito-frontal and posterior cingulate cortices. Consistent with findings from the motor sequence learning literature and Doyon and Ungerleider's model of motor learning [Doyon, J., Ungerleider, L.G., 2002. Functional anatomy of motor skill learning. In: Squire, L.R., Schacter, D.L. (Eds.), Neuropsychology of memory, Guilford Press, pp. 225-238], our results demonstrate that compared to skilled imagers, poor imagers not only need to recruit the cortico-striatal system, but to compensate with the cortico-cerebellar system during MI of sequential movements.

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Year:  2008        PMID: 18479943     DOI: 10.1016/j.neuroimage.2008.03.042

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  77 in total

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