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      Repeated unilateral handgrip contractions alter functional connectivity and improve contralateral limb response times

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          Abstract

          In humans, motor learning is underpinned by changes in sensorimotor network functional connectivity (FC). Unilateral contractions increase FC in the ipsilateral primary motor cortex (M1) and supplementary motor area (SMA); areas involved in motor planning and execution of the contralateral hand. Therefore, unilateral contractions are a promising approach to augment motor performance in the contralateral hand. In a within-participant, randomized, cross-over design, 15 right-handed adults had two magnetic resonance imaging (MRI) sessions, where functional-MRI and MR-Spectroscopic Imaging were acquired before and after repeated right-hand contractions at either 5% or 50% maximum voluntary contraction (MVC). Before and after scanning, response times (RTs) were determined in both hands. Nine minutes of 50% MVC contractions resulted in decreased handgrip force in the contracting hand, and decreased RTs and increased handgrip force in the contralateral hand. This improved motor performance in the contralateral hand was supported by significant neural changes: increased FC between SMA-SMA and increased FC between right M1 and right Orbitofrontal Cortex. At a neurochemical level, the degree of GABA decline in left M1, left and right SMA correlated with subsequent behavioural improvements in the left-hand. These results support the use of repeated handgrip contractions as a potential modality for improving motor performance in the contralateral hand.

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          The assessment and analysis of handedness: The Edinburgh inventory

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            FSL.

            FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Fast robust automated brain extraction.

              An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                Justin.andrushko@usask.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 April 2023
                20 April 2023
                2023
                : 13
                : 6437
                Affiliations
                [1 ]GRID grid.25152.31, ISNI 0000 0001 2154 235X, College of Kinesiology, , University of Saskatchewan, ; Saskatoon, Canada
                [2 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, FMRIB, Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, , University of Oxford, ; Oxford, UK
                [3 ]GRID grid.94365.3d, ISNI 0000 0001 2297 5165, Section on Functional Imaging Methods, National Institutes of Mental Health, , National Institutes of Health, ; Bethesda, MD USA
                [4 ]GRID grid.1034.6, ISNI 0000 0001 1555 3415, Thompson Institute, , University of the Sunshine Coast, ; Sippy Downs, Australia
                [5 ]GRID grid.83440.3b, ISNI 0000000121901201, Department of Clinical and Movement Neurosciences, , University College London, ; London, UK
                [6 ]GRID grid.169077.e, ISNI 0000 0004 1937 2197, School of Health Sciences, College of Health and Human Sciences, , Purdue University, ; West Lafayette, USA
                Article
                33106
                10.1038/s41598-023-33106-1
                10119116
                37081073
                0ca9cd88-a02c-400b-a40f-1de63eba13d0
                © The Author(s) 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 September 2022
                : 7 April 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004489, Mitacs;
                Award ID: Globalink
                Award Recipient :
                Funded by: Natural Sciences and Engineering Research Council of Canada
                Award ID: Alexander Graham Bell Canada Graduate Scholarship - Doctoral Award
                Award ID: RGPIN 2016-0529
                Award Recipient :
                Funded by: National Institutes of Health Oxford-Cambridge Scholar Fellowship
                Funded by: International Biomedial Research Alliance
                Funded by: National Institute for Health Research (NIHR)
                Funded by: Oxford Health Biomedical Research Centre
                Funded by: FundRef http://dx.doi.org/10.13039/501100004789, John Fell Fund, University of Oxford;
                Funded by: Wellcome Trust,United Kingdom
                Award ID: 203139/Z/16/Z
                Award Recipient :
                Funded by: Wellcome Trust and Royal Society
                Award ID: 102584/Z/13/Z
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                neuroscience,motor control,sensorimotor processing
                Uncategorized
                neuroscience, motor control, sensorimotor processing

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