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      Resting brain dynamics at different timescales capture distinct aspects of human behavior

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          Abstract

          Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The behavioral counterparts of static functional connectivity (FC), at the resolution of several minutes, have been studied but behavioral correlates of dynamic measures of FC at the resolution of a few seconds remain unclear. Here, using resting-state fMRI and 58 phenotypic measures from the Human Connectome Project, we find that dynamic FC captures task-based phenotypes (e.g., processing speed or fluid intelligence scores), whereas self-reported measures (e.g., loneliness or life satisfaction) are equally well explained by static and dynamic FC. Furthermore, behaviorally relevant dynamic FC emerges from the interconnections across all resting-state networks, rather than within or between pairs of networks. Our findings shed new light on the timescales of cognitive processes involved in distinct facets of behavior.

          Abstract

          An individual’s pattern of resting state brain connectivity, as measured with fMRI, has been shown to predict cognitive and behavioral traits. Here, the authors show that different traits are predicted by different time-scales of resting state activity (dynamic vs. static).

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          The Jackknife, the Bootstrap and Other Resampling Plans

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            Searching for a baseline: functional imaging and the resting human brain.

            Functional brain imaging in humans has revealed task-specific increases in brain activity that are associated with various mental activities. In the same studies, mysterious, task-independent decreases have also frequently been encountered, especially when the tasks of interest have been compared with a passive state, such as simple fixation or eyes closed. These decreases have raised the possibility that there might be a baseline or resting state of brain function involving a specific set of mental operations. We explore this possibility, including the manner in which we might define a baseline and the implications of such a baseline for our understanding of brain function.
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              Dynamic reconfiguration of human brain networks during learning.

              Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes--flexibility and selection--must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.
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                Author and article information

                Contributors
                Raphael.Liegeois@epfl.ch
                Thomas.Yeo@nus.edu.sg
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                24 May 2019
                24 May 2019
                2019
                : 10
                : 2317
                Affiliations
                [1 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, , National University of Singapore, ; Singapore, 117583 Singapore
                [2 ]ISNI 0000000121839049, GRID grid.5333.6, Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, ; 1015 Lausanne, Switzerland
                [3 ]ISNI 0000 0001 2322 4988, GRID grid.8591.5, Department of Radiology and Medical Informatics, , University of Geneva, ; 1205 Geneva, Switzerland
                [4 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, ; Boston, MA 02114 USA
                [5 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, ; Charlestown, MA 02129 USA
                [6 ]ISNI 000000041936877X, GRID grid.5386.8, School of Electrical and Computer Engineering, , Cornell University, ; Ithaca, NY 14853 USA
                [7 ]ISNI 0000 0004 0385 0924, GRID grid.428397.3, Centre for Cognitive Neuroscience, Duke-NUS Medical School, ; Singapore, 169857 Singapore
                [8 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, NUS Graduate School for Integrative Sciences and Engineering, , National University of Singapore, ; Singapore, 119077 Singapore
                Author information
                http://orcid.org/0000-0003-3985-3898
                http://orcid.org/0000-0002-6395-8801
                http://orcid.org/0000-0001-9133-3561
                http://orcid.org/0000-0002-2879-3861
                Article
                10317
                10.1038/s41467-019-10317-7
                6534566
                31127095
                3dec354d-f18c-43d9-8267-8f6b0c974fd5
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 November 2018
                : 3 May 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001352, National University of Singapore (NUS);
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                neuroscience,cognitive neuroscience,computational neuroscience,human behaviour
                Uncategorized
                neuroscience, cognitive neuroscience, computational neuroscience, human behaviour

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