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      Functional connectivity and topology in patients with restless legs syndrome: a case–control resting‐state functional magnetic resonance imaging study

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          Abstract

          Background and purpose

          Functional connectivity studies revealed alterations within thalamic, salience, and default mode networks in restless legs syndrome patients.

          Methods

          Eighty‐two patients with restless legs syndrome (untreated, n = 30; on dopaminergic medication, n = 42; on alpha‐2‐delta ligands as mono‐ or polytherapy combined with dopaminergic medication, n = 10), and 82 individually age‐ and gender‐matched healthy controls were studied with resting‐state functional magnetic resonance imaging. Connectivity of 12 resting‐state networks was investigated with independent component analysis, and network topology was studied with graph methods among 410 brain regions.

          Results

          Patients with restless legs syndrome showed significantly higher connectivity within salience ( p = 0.029), executive ( p = 0.001), and cerebellar ( p = 0.041) networks, as well as significantly lower ( p < 0.05) cerebello‐frontal communication compared to controls. In addition, they had a significantly higher ( p < 0.05) clustering coefficient and local efficiency in motor and frontal regions; lower clustering coefficient in the central sulcus; and lower local efficiency in the central opercular cortex, temporal, parieto‐occipital, cuneus, and occipital regions compared to controls. Untreated patients had significantly lower ( p < 0.05) cerebello‐parietal communication compared to healthy controls. Connectivity between the thalamus and frontal regions was significantly increased ( p < 0.05) in patients on dopaminergic medication compared to untreated patients and controls.

          Conclusions

          Networks with higher intranetwork connectivity (i.e., salience, executive, cerebellar) and lower cerebello‐frontal connectivity in the restless legs syndrome patients, as well as lower cerebello‐parietal connectivity in untreated patients, correspond to regions associated with attention, response inhibitory control, and processing of sensory information. Intact cerebello‐parietal communication and increased thalamic connectivity to the prefrontal regions in patients on dopaminergic medication suggests a treatment effect on thalamus.

          Abstract

          This resting‐state functional magnetic resonance imaging study applied graph methods, which revealed dopaminergic‐related alterations in a broad array of brain networks in restless legs syndrome (RLS) patients. In particular, lower cerebello‐parietal connectivity was present in untreated RLS, but not in medicated patients. RLS patients treated solely with dopaminergic medication presented with higher thalamo‐frontal connectivity compared to untreated patients.

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          Most cited references52

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          Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

          An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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            Collective dynamics of 'small-world' networks.

            Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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              Complex network measures of brain connectivity: uses and interpretations.

              Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                christoph.scherfler@i-med.ac.at
                Journal
                Eur J Neurol
                Eur J Neurol
                10.1111/(ISSN)1468-1331
                ENE
                European Journal of Neurology
                John Wiley and Sons Inc. (Hoboken )
                1351-5101
                1468-1331
                05 November 2020
                February 2021
                : 28
                : 2 ( doiID: 10.1111/ene.v28.2 )
                : 448-458
                Affiliations
                [ 1 ] Department of Psychiatry, Psychotherapy and Psychosomatics Division of Psychiatry I Medical University of Innsbruck Innsbruck Austria
                [ 2 ] Department of Neurology Medical University of Innsbruck Innsbruck Austria
                [ 3 ] Neurologie 1 Kepler Universitätsklinikum GmbH Neuromed Campus Linz Austria
                [ 4 ] Johannes Kepler University Linz Austria
                [ 5 ] Department of Neurology University Hospital Münster Münster Germany
                [ 6 ] Analytical Neurophysiology Lab Montreal Neurological Institute & Hospital McGill University Montreal Quebec Canada
                [ 7 ] Neuroimaging Research Core Facility Medical University of Innsbruck Innsbruck Austria
                [ 8 ] Department of Neuroradiology Medical University of Innsbruck Innsbruck Austria
                Author notes
                [*] [* ] Correspondence: C. Scherfler, Department of Neurology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria (tel.: +4351250426276; fax: +4351250423852; e‐mail: christoph.scherfler@ 123456i-med.ac.at ).

                [*]

                N. Tuovinen and A. Stefani contributed equally as co–first authors.

                [†]

                B. Högl and C. Scherfler contributed equally as co–senior authors.

                Author information
                https://orcid.org/0000-0002-8835-5281
                https://orcid.org/0000-0003-4259-8824
                https://orcid.org/0000-0001-6859-8377
                https://orcid.org/0000-0003-4367-0971
                https://orcid.org/0000-0003-1894-7641
                https://orcid.org/0000-0002-4885-5265
                Article
                ENE14577
                10.1111/ene.14577
                7820983
                33032390
                469e9115-de27-425a-b751-f005baec0630
                © 2020 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 29 July 2020
                : 29 September 2020
                : 05 October 2020
                Page count
                Figures: 4, Tables: 1, Pages: 12, Words: 7034
                Funding
                Funded by: Government of Tirol, Austria
                Award ID: Translational Research Fund
                Categories
                Original Article
                Movement Disorders
                Custom metadata
                2.0
                February 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.6 mode:remove_FC converted:22.01.2021

                Neurology
                brain connectivity,functional magnetic resonance imaging,restless legs syndrome,sleep wake disorders

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