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      Multilayer network switching rate predicts brain performance

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          Significance

          The human brain comprises multiple distinct and highly complex networks responsible for specific functions. Most of our current understanding about functional brain networks comes from studies treating the brain as a static entity, and the spatiotemporal configuration of brain networks remains poorly understood. Using a multilayer network model, we show that brain regions, particularly the lateral frontal and parietal brain areas, transit between different network configurations at a high rate (i.e., have high network switching). This network switching rate predicts performance of higher-order cognitive functions including working memory, planning, and reasoning. In other words, efficient brain network switching appears to be an important aspect of optimal brain function.

          Abstract

          Large-scale brain dynamics are characterized by repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood, and whether switching between these states is important for behavior has been little studied. Our aim was to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved fMRI connectivity in 1,003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data were used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching, which we define as the rate at which each brain region transits between different networks. We found ( i) an inverse relationship between network switching and connectivity dynamics, where the latter was defined in terms of time-resolved fMRI connections with variance in time that significantly exceeded phase-randomized surrogate data; ( ii) brain connectivity was lower during intervals of network switching; ( iii) brain areas with frequent network switching had greater temporal complexity; ( iv) brain areas with high network switching were located in association cortices; and ( v) using cross-validated elastic net regression, network switching predicted intersubject variation in working memory performance, planning/reasoning, and amount of sleep. Our findings shed light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

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          Most cited references29

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          Rich-club organization of the human connectome.

          The human brain is a complex network of interlinked regions. Recent studies have demonstrated the existence of a number of highly connected and highly central neocortical hub regions, regions that play a key role in global information integration between different parts of the network. The potential functional importance of these "brain hubs" is underscored by recent studies showing that disturbances of their structural and functional connectivity profile are linked to neuropathology. This study aims to map out both the subcortical and neocortical hubs of the brain and examine their mutual relationship, particularly their structural linkages. Here, we demonstrate that brain hubs form a so-called "rich club," characterized by a tendency for high-degree nodes to be more densely connected among themselves than nodes of a lower degree, providing important information on the higher-level topology of the brain network. Whole-brain structural networks of 21 subjects were reconstructed using diffusion tensor imaging data. Examining the connectivity profile of these networks revealed a group of 12 strongly interconnected bihemispheric hub regions, comprising the precuneus, superior frontal and superior parietal cortex, as well as the subcortical hippocampus, putamen, and thalamus. Importantly, these hub regions were found to be more densely interconnected than would be expected based solely on their degree, together forming a rich club. We discuss the potential functional implications of the rich-club organization of the human connectome, particularly in light of its role in information integration and in conferring robustness to its structural core.
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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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              Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

              Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly-connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows one to study community structure in a very general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                26 December 2018
                13 December 2018
                13 December 2018
                : 115
                : 52
                : 13376-13381
                Affiliations
                [1] aThe Florey Institute of Neuroscience and Mental Health, The University of Melbourne , Melbourne, VIC 3010, Australia;
                [2] bDepartment of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne , Melbourne, VIC 3010, Australia;
                [3] cDepartment of Neurology, Austin Health , Heidelberg, VIC 3084, Australia
                Author notes
                1To whom correspondence should be addressed. Email: mangor.pedersen@ 123456florey.edu.au .

                Edited by Olaf Sporns, Indiana University, Bloomington, IN, and accepted by Editorial Board Member Michael S. Gazzaniga November 15, 2018 (received for review August 29, 2018)

                Author contributions: M.P., A.Z., A.O., and G.D.J. designed research; M.P., A.Z., A.O., and G.D.J. performed research; M.P. and A.O. analyzed data; and M.P., A.Z., A.O., and G.D.J. wrote the paper.

                Article
                201814785
                10.1073/pnas.1814785115
                6310789
                30545918
                894d1671-7f3c-4a6d-8cc9-d297005e7e46
                Copyright © 2018 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 6
                Funding
                Funded by: Department of Health | National Health and Medical Research Council (NHMRC) 501100000925
                Award ID: 628952
                Award Recipient : Andrew Zalesky Award Recipient : Graeme D Jackson
                Funded by: Department of Health | National Health and Medical Research Council (NHMRC) 501100000925
                Award ID: 1060312
                Award Recipient : Andrew Zalesky Award Recipient : Graeme D Jackson
                Funded by: Department of Health | National Health and Medical Research Council (NHMRC) 501100000925
                Award ID: 1136649
                Award Recipient : Andrew Zalesky Award Recipient : Graeme D Jackson
                Categories
                Biological Sciences
                Neuroscience

                multilayer networks,switching,dynamic functional connectivity,fmri,brain performance

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