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      Network structure of the human musculoskeletal system shapes neural interactions on multiple time scales

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          Abstract

          Changes in postural task result in a frequency-dependent reconfiguration of the multiplex muscle network.

          Abstract

          Human motor control requires the coordination of muscle activity under the anatomical constraints imposed by the musculoskeletal system. Interactions within the central nervous system are fundamental to motor coordination, but the principles governing functional integration remain poorly understood. We used network analysis to investigate the relationship between anatomical and functional connectivity among 36 muscles. Anatomical networks were defined by the physical connections between muscles, and functional networks were based on intermuscular coherence assessed during postural tasks. We found a modular structure of functional networks that was strongly shaped by the anatomical constraints of the musculoskeletal system. Changes in postural tasks were associated with a frequency-dependent reconfiguration of the coupling between functional modules. These findings reveal distinct patterns of functional interactions between muscles involved in flexibly organizing muscle activity during postural control. Our network approach to the motor system offers a unique window into the neural circuitry driving the musculoskeletal system.

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          Most cited references38

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          Community detection in graphs

          The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
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            Skeletal muscle performance and ageing

            Abstract The world population is ageing rapidly. As society ages, the incidence of physical limitations is dramatically increasing, which reduces the quality of life and increases healthcare expenditures. In western society, ~30% of the population over 55 years is confronted with moderate or severe physical limitations. These physical limitations increase the risk of falls, institutionalization, co‐morbidity, and premature death. An important cause of physical limitations is the age‐related loss of skeletal muscle mass, also referred to as sarcopenia. Emerging evidence, however, clearly shows that the decline in skeletal muscle mass is not the sole contributor to the decline in physical performance. For instance, the loss of muscle strength is also a strong contributor to reduced physical performance in the elderly. In addition, there is ample data to suggest that motor coordination, excitation–contraction coupling, skeletal integrity, and other factors related to the nervous, muscular, and skeletal systems are critically important for physical performance in the elderly. To better understand the loss of skeletal muscle performance with ageing, we aim to provide a broad overview on the underlying mechanisms associated with elderly skeletal muscle performance. We start with a system level discussion and continue with a discussion on the influence of lifestyle, biological, and psychosocial factors on elderly skeletal muscle performance. Developing a broad understanding of the many factors affecting elderly skeletal muscle performance has major implications for scientists, clinicians, and health professionals who are developing therapeutic interventions aiming to enhance muscle function and/or prevent mobility and physical limitations and, as such, support healthy ageing.
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              Locomotor primitives in newborn babies and their development.

              How rudimentary movements evolve into sophisticated ones during development remains unclear. It is often assumed that the primitive patterns of neural control are suppressed during development, replaced by entirely new patterns. Here we identified the basic patterns of lumbosacral motoneuron activity from multimuscle recordings in stepping neonates, toddlers, preschoolers, and adults. Surprisingly, we found that the two basic patterns of stepping neonates are retained through development, augmented by two new patterns first revealed in toddlers. Markedly similar patterns were observed also in the rat, cat, macaque, and guineafowl, consistent with the hypothesis that, despite substantial phylogenetic distances and morphological differences, locomotion in several animal species is built starting from common primitives, perhaps related to a common ancestral neural network.
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                Author and article information

                Journal
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                June 2018
                27 June 2018
                : 4
                : 6
                : eaat0497
                Affiliations
                [1 ]Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences and Institute for Brain and Behavior, Amsterdam, Netherlands.
                [2 ]QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
                [3 ]The University of Queensland, St. Lucia, Queensland 4072, Australia.
                [4 ]Queensland University of Technology, 2 George Street, Brisbane, Queensland 4000, Australia.
                [5 ]National Institute for Dementia Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Brisbane, Queensland 4006, Australia.
                [6 ]Metro North Mental Health Service, Brisbane, Queensland, Australia.
                [7 ]Black Dog Institute, University of New South Wales, Sydney, New South Wales, Australia.
                Author notes
                [* ]Corresponding author. Email: t.boonstra@ 123456unsw.edu.au
                Author information
                http://orcid.org/0000-0001-7543-8013
                http://orcid.org/0000-0001-9107-3552
                http://orcid.org/0000-0003-3505-9259
                http://orcid.org/0000-0002-6969-7243
                Article
                aat0497
                10.1126/sciadv.aat0497
                6021138
                29963631
                09289c4d-21bd-4e25-989a-21ee79637a0a
                Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 19 January 2018
                : 22 May 2018
                Funding
                Funded by: doi http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: APP1037196
                Funded by: doi http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: APP1110975
                Funded by: doi http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;
                Award ID: 45110-030
                Funded by: doi http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;
                Award ID: 016.156.346
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Network Science
                Neuroscience
                Neuroscience
                Custom metadata
                Rochelle Abragante

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