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Dynamic causal modeling of the effective connectivity between the cerebrum and cerebellum in social mentalizing across five studies

In this analysis we explored the effective connectivity of the cerebellum with the cerebrum in social mentalizing, across five studies ( n = 91) involving abstract and complex forms of mentalizing, such as (a) person and group impression formation, based on behavioral descriptions, and (b) construct...

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Bibliographic Details
Published in:Cognitive, affective, & behavioral neuroscience affective, & behavioral neuroscience, 2019-02, Vol.19 (1), p.211-223
Main Authors: Van Overwalle, Frank, Van de Steen, Frederik, Mariën, Peter
Format: Article
Language:English
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Summary:In this analysis we explored the effective connectivity of the cerebellum with the cerebrum in social mentalizing, across five studies ( n = 91) involving abstract and complex forms of mentalizing, such as (a) person and group impression formation, based on behavioral descriptions, and (b) constructing personal counterfactual events. Connectivity was analyzed by applying dynamic causal model analysis, which revealed effective connectivity between the mentalizing areas of the cerebellum and cerebrum. The results revealed a significant pattern of bidirectional (closed-loop) connectivity linking the right posterior cerebellum with bilateral temporo-parietal junction (TPJ), associated with behavior understanding. These connections are consistent with known anatomical data on closed loops between the cerebellum and cerebrum, although contralateral closed loops typically dominate. This analysis improves on an earlier psychophysiological interaction analysis of this dataset, which had failed to reveal such evidence of closed loops. Within the cerebrum, there were connections between the bilateral areas of TPJ, as well as connections between bilateral TPJ and the (ventral and dorsal) medial prefrontal cortex. The discussion centers on the function of cerebro-cerebellar connections in generating internal cerebellar “forward” models, potentially serving the automatic understanding, prediction, and error correction of behavioral sequences.
ISSN:1530-7026
1531-135X
DOI:10.3758/s13415-018-00659-y