Reconfiguration of brain network architectures between resting-state and complexity-dependent cognitive reasoning

LJ Hearne; L Cocchi; A Zalesky; JB Mattingley

Journal article

Reconfiguration of brain network architectures between resting-state and complexity-dependent cognitive reasoning

LJ Hearne, L Cocchi, A Zalesky, JB Mattingley

Journal of Neuroscience | Published : 2017

DOI: 10.1523/JNEUROSCI.0485-17.2017

Abstract

Our capacity for higher cognitive reasoning has a measurable limit. This limit is thought to arise from the brain’s capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic “resting-state” network architecture. Here we combined resting-state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting-states can, followed..

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University of Melbourne Researchers

Andrew Zalesky Author

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Grants

Awarded by National Health and Medical Research Council

Funding Acknowledgements

This work was supported by the Australian Research Council (ARC) Special Research Initiatives Science of Learning Research Centre (SR120300015), and the ARC Centre of Excellence for Integrative Brain Function (ARC Centre Grant CE140100007). J.B.M. was supported by an ARC Australian Laureate Fellowship (FL110100103). L.C. was supported by a National Health and Medical Research Council (NHMRC) grant (APP1099082). A.Z. was supported by an NHMRC Career Development Fellowship (GNT1047648). L.J.H. was supported by an Australian Postgraduate Award. We thank Oscar Jacoby and Zoie Nott for data-collection assistance, Tong Wu for assistance with imaging analysis, and Dr. Kieran O'Brien, Associate Professor Markus Barth, and Dr. Steffen Bollmann for performing the sequence optimizations.