Characterization of Cortical Networks and Corticocortical Functional Connectivity Mediating Arbitrary Visuomotor Mapping

Abstract

Adaptive behaviors are built on the arbitrary linkage of sensory inputs to actions and goals. Although the sensorimotor and associative frontostriatal circuits are known to mediate arbitrary visuomotor mappings, the underlying corticocortico dynamics remain elusive. Here, we take a novel approach exploiting gamma-band neural activity to study the human cortical networks and corticocortical functional connectivity mediating arbitrary visuomotor mapping. Single-trial gamma-power time courses were estimated for all Brodmann areas by combing magnetoencephalographic and MRI data with spectral analysis and beam-forming techniques. Linear correlation and Granger causality analyses were performed to investigate functional connectivity between cortical regions. The performance of visuomotor associations was characterized by an increase in gamma-power and functional connectivity over the sensorimotor and frontoparietal network, in addition to medial prefrontal areas. The superior parietal area played a driving role in the network, exerting Granger causality on the dorsal premotor area. Premotor areas acted as relay from parietal to medial prefrontal cortices, which played a receiving role in the network. Link community analysis further revealed that visuomotor mappings reflect the coordination of multiple subnetworks with strong overlap over motor and frontoparietal areas. We put forward an associative account of the underlying cognitive processes and corticocortical functional connectivity. Overall, our approach and results provide novel perspectives toward a better understanding of how distributed brain activity coordinates adaptive behaviors.

Integration and segregation of large-scale brain networks during short-term task automatization

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Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy

“…Thus, several variants of connectivity have been applied to epilepsy (Amini et al , 2011, Epstein et al , 2014, Coito et al , 2015. It is interesting to note that Granger causality as implemented with auto-regressive models (Brovelli et al , 2004) or with a covariance-based approach (Brovelli et al , 2015) may not be radically different from a classical correlation analysis (Davey et al , 2013). What is likely to be a more influential choice is the fact of considering pair-wise or multivariate methods, which could remove indirect correlation arising from a common driving node (Kuś et al , 2004).…”

Dynamic Reconfiguration of Visuomotor-Related Functional Connectivity Networks

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