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Functional Connectivity of the Cognitive Cerebellum

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FRONTIERS IN SYSTEMS NEUROSCIENCE
卷 15, 期 -, 页码 -

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FRONTIERS MEDIA SA
DOI: 10.3389/fnsys.2021.642225

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neocerebellum; crus 1– 2; cognition; functional connectivity; resting-state; intrinsic networks

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Research has firmly established the major role of the (neo-)cerebellum in cognition and emotion, with the expansion of the associative cortices in the brain being associated with an increased number of interconnections with the neocerebellar cortex, especially lobule VII. This complex organization enables the neocerebellum to monitor and synchronize the main networks involved in cognition and emotion.
Anatomical tracing, human clinical data, and stimulation functional imaging have firmly established the major role of the (neo-)cerebellum in cognition and emotion. Telencephalization characterized by the great expansion of associative cortices, especially the prefrontal one, has been associated with parallel expansion of the neocerebellar cortex, especially the lobule VII, and by an increased number of interconnections between these two cortical structures. These anatomical modifications underlie the implication of the neocerebellum in cognitive control of complex motor and non-motor tasks. In humans, resting state functional connectivity has been used to determine a thorough anatomo-functional parcellation of the neocerebellum. This technique has identified central networks involving the neocerebellum and subserving its cognitive function. Neocerebellum participates in all intrinsic connected networks such as central executive, default mode, salience, dorsal and ventral attentional, and language-dedicated networks. The central executive network constitutes the main circuit represented within the neocerebellar cortex. Cerebellar zones devoted to these intrinsic networks appear multiple, interdigitated, and spatially ordered in three gradients. Such complex neocerebellar organization enables the neocerebellum to monitor and synchronize the main networks involved in cognition and emotion, likely by computing internal models.

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