Cerebellar control of movements is dependent on mossy fiber input conveying information about sensory and premotor activity in the spinal cord. While much is known about spino-cerebellar systems, which provide the cerebellum with detailed sensory information, much less is known about systems conveying motor information. Individual motoneurones do not have projections to spino-cerebellar neurons. Instead, the fastest route is from last order spinal interneurons. In order to identify the networks that convey ascending premotor information from last order interneurons, we have focused on the lateral reticular nucleus (LRN), which provides the major mossy fiber input to cerebellum from spinal interneuronal systems. Three spinal ascending systems to the LRN have been investigated: the C3-C4 propriospinal neurones (PNs), the ipsilateral forelimb tract (iFT) and the bilateral ventral flexor reflex tract (bVFRT). Voluntary forelimb movements involve reaching and grasping together with necessary postural adjustments and each of these three interneuronal systems likely contribute to specific aspects of forelimb motor control. It has been demonstrated that the command for reaching can be mediated via C3-C4 PNs, while the command for grasping is conveyed via segmental interneurons in the forelimb segments. Our results reveal convergence of ascending projections from all three interneuronal systems in the LRN, producing distinct combinations of excitation and inhibition. We have also identified a separate descending control of LRN neurons exerted via a subgroup of cortico-reticular neurones. The LRN projections to the deep cerebellar nuclei exert a direct excitatory effect on descending motor pathways via the reticulospinal, vestibulospinal, and other supraspinal tracts, and might play a key role in cerebellar motor control. Our results support the hypothesis that the LRN provides the cerebellum with highly integrated information, enabling cerebellar control of complex forelimb movements.
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 August 2015.
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Outputs from Frontiers in Computational Neuroscience
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Outputs of similar age from Frontiers in Computational Neuroscience
#13
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