Effective Connectivity of the Human Cerebellum during Visual Attention

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  2. Dr Andreas Finkelmeyer
Author(s)Kellermann T, Regenbogen C, DeVos M, Mossnang C, Finkelmeyer A, Habel U
Publication type Article
JournalJournal of Neuroscience
Year2012
Volume32
Issue33
Pages11453-11460
ISSN (print)0270-6474
ISSN (electronic)1529-2401
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Insights from both lesion and neuroimaging studies increasingly substantiate the view that the human cerebellum not only serves motor control but also supports various cognitive processes. Higher cognitive functions like working memory or executive control have been associated with the phylogenetically younger parts of the cerebellum, crus I and crus II. Functional connectivity studies corroborate this notion as activation of the cerebellum correlates with activity in numerous areas of the cerebral cortex. Moreover, these cerebrocerebellar loops were shown to be topographically organized. We used an attention-to-motion paradigm to elaborate on the effective connectivity of cerebellar crus I during visual attention. Psychophysiological interaction analyses demonstrated enhanced connectivity of the cerebellum-during attention-with dorsal visual stream regions including posterior parietal cortex (PPC) and left secondary visual cortex (V5). Dynamic causal modeling revealed a modulation of the connections from V5 to PPC and from crus I to V5 by attention. Remarkably, the influence which V5 exerted on PPC was reduced during attention, resulting in a suppression of the sensitivity of PPC to bottom-up information. Moreover, the sensitivity of V5 populations to inputs from crus I was increased under attention. This might underscore the presumed role of the cerebellum as a state estimator that provides hierarchically lower regions (V5) with top-down predictions, which in turn might be based on endogenous inputs from PPC to the cerebellum. These results are in line with formulations of attention in predictive coding, where attention increases the precision or sensitivity of hierarchically lower neuronal populations that may encode prediction error.
PublisherSociety for Neuroscience
URLhttp://dx.doi.org/10.1523/JNEUROSCI.0678-12.2012
DOI10.1523/JNEUROSCI.0678-12.2012
PubMed id22895727
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