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      Changed Cerebral White Matter Structural Network Topological Characters and Its Correlation with Cognitive Behavioral Abnormalities in Narcolepsy Type 1

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          Abstract

          Objective

          In the current study we investigated topological abnormalities of the cerebral white matter networks in narcolepsy type 1 (NT1) patients and its relationship with their cognitive abnormalities using diffusion tensor imaging (DTI) technology.

          Methods

          DTI and the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) were applied to 30 NT1 patients and 30 age-matched healthy controls. DTI studies were also carried using the 3T MRI system. Next, DTI data was used to establish a cerebral white matter network for all subjects and graph theory was applied to analyze the topological characteristics of the white matter structural network. Topographical parameters (such as local efficiency (Eloc), global efficiency (Eglob) and small-world (σ)) between NT1 patients and controls were then compared. The correlation between MoCA-BJ scores and topological parameters was also analyzed.

          Results

          MoCA-BJ scores in NT1 patients were lower than those in the healthy controls. Compared with healthy controls, the global efficiency of the white matter network and attributes of the small world network were significantly reduced in NT1 patients. Finally, the global efficiency of the white matter structural network was related to the MoCA-BJ score of NT1 patients.

          Conclusion

          The abnormal topological characteristics of the white matter structural network in NT1 patients may be associated with their cognitive impairment.

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          Most cited references46

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          The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.

          To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia. Validation study. A community clinic and an academic center. Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score > or =17), and 90 healthy elderly controls (NC). The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD. Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively). MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.
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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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              A new method for measuring daytime sleepiness: the Epworth sleepiness scale.

              The development and use of a new scale, the Epworth sleepiness scale (ESS), is described. This is a simple, self-administered questionnaire which is shown to provide a measurement of the subject's general level of daytime sleepiness. One hundred and eighty adults answered the ESS, including 30 normal men and women as controls and 150 patients with a range of sleep disorders. They rated the chances that they would doze off or fall asleep when in eight different situations commonly encountered in daily life. Total ESS scores significantly distinguished normal subjects from patients in various diagnostic groups including obstructive sleep apnea syndrome, narcolepsy and idiopathic hypersomnia. ESS scores were significantly correlated with sleep latency measured during the multiple sleep latency test and during overnight polysomnography. In patients with obstructive sleep apnea syndrome ESS scores were significantly correlated with the respiratory disturbance index and the minimum SaO2 recorded overnight. ESS scores of patients who simply snored did not differ from controls.
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                Author and article information

                Journal
                Nat Sci Sleep
                Nat Sci Sleep
                nss
                Nature and Science of Sleep
                Dove
                1179-1608
                02 February 2022
                2022
                : 14
                : 165-173
                Affiliations
                [1 ]Department of Radiology, Shengjing Hospital of China Medical University , Shenyang, 110004, People’s Republic of China
                [2 ]Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University , Shenyang, 110004, People’s Republic of China
                [3 ]Sleep Medicine Center, Shengjing Hospital of China Medical University , Shenyang, 110004, People’s Republic of China
                Author notes
                Correspondence: Bing Yu, Department of Radiology, Shengjing Hospital of China Medical University , Shenyang, 110004,People’s Republic of China, Tel +86-24-96615-73266, Fax +86-24-23929902, Email yubingcmu@163.com; Li Xiao, Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University , Shenyang, 110004, People’s Republic of China, Tel +86-24-96615-61713, Fax +86-24-22780097, Email xiaolicmu@163.com
                [*]

                These authors contributed equally to this work

                Article
                336967
                10.2147/NSS.S336967
                8818963
                6fd34925-4f1c-4440-9f41-3f61a4e4673d
                © 2022 Ni et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 11 October 2021
                : 19 January 2022
                Page count
                Figures: 2, Tables: 4, References: 49, Pages: 9
                Funding
                Funded by: the Key Research and Development Program Project Fund of Liaoning Province;
                Funded by: the 345 Talent Project of Shengjing Hospital of China Medical University;
                This study was funded by the Key Research and Development Program Project Fund of Liaoning Province (2019JH8/10300006) and the 345 Talent Project of Shengjing Hospital of China Medical University.
                Categories
                Original Research

                cognitive dysfunction,graph theory analysis,narcolepsy type 1,diffusion tensor imaging,montreal cognitive assessment beijing edition

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