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      Frontoparietal network resilience is associated with protection against cognitive decline in Parkinson’s disease

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          Abstract

          Though Parkinson’s disease is primarily defined as a movement disorder, it is also characterized by a range of non-motor symptoms, including cognitive decline. The onset and progression of cognitive decline in individuals with Parkinson’s disease is variable, and the neurobiological mechanisms that contribute to, or protect against, cognitive decline in Parkinson’s disease are poorly understood. Using resting-state functional magnetic resonance imaging data collected from individuals with Parkinson’s disease with and without cognitive decline, we examined the relationship between topological brain-network resilience and cognition in Parkinson’s disease. By leveraging network attack analyses, we demonstrate that relative to individuals with Parkinson’s disease experiencing cognitive decline, the frontoparietal network in cognitively stable individuals with Parkinson’s disease is significantly more resilient to network perturbation. Our findings suggest that the topological robustness of the frontoparietal network is associated with the absence of cognitive decline in individuals with Parkinson’s disease.

          Abstract

          Arianna Cascone et al. examine the relationship between frontoparietal brain network resilience and cognitive decline in Parkinson’s disease (PD). Their results suggest that individuals with PD and cognitive decline exhibit reduced tolerance to network "attacks" or perturbation in the frontoparietal network, compared to both individuals with PD with normal cognition and healthy, non-PD controls.

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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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            Re-epithelialization and immune cell behaviour in an ex vivo human skin model

            A large body of literature is available on wound healing in humans. Nonetheless, a standardized ex vivo wound model without disruption of the dermal compartment has not been put forward with compelling justification. Here, we present a novel wound model based on application of negative pressure and its effects for epidermal regeneration and immune cell behaviour. Importantly, the basement membrane remained intact after blister roof removal and keratinocytes were absent in the wounded area. Upon six days of culture, the wound was covered with one to three-cell thick K14+Ki67+ keratinocyte layers, indicating that proliferation and migration were involved in wound closure. After eight to twelve days, a multi-layered epidermis was formed expressing epidermal differentiation markers (K10, filaggrin, DSG-1, CDSN). Investigations about immune cell-specific manners revealed more T cells in the blister roof epidermis compared to normal epidermis. We identified several cell populations in blister roof epidermis and suction blister fluid that are absent in normal epidermis which correlated with their decrease in the dermis, indicating a dermal efflux upon negative pressure. Together, our model recapitulates the main features of epithelial wound regeneration, and can be applied for testing wound healing therapies and investigating underlying mechanisms.
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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
                eran_dayan@med.unc.edu
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                1 September 2021
                1 September 2021
                2021
                : 4
                : 1021
                Affiliations
                [1 ]Neuroscience Curriculum, University of North at Chapel Hill, Chapel Hill, NC United States
                [2 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Psychology and Neuroscience, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC United States
                [3 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Neurology, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC United States
                [4 ]GRID grid.10698.36, ISNI 0000000122483208, Department of Radiology and Biomedical Research Imaging Center, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC United States
                Author information
                http://orcid.org/0000-0001-5948-2831
                http://orcid.org/0000-0001-6031-4763
                http://orcid.org/0000-0001-9710-9210
                Article
                2478
                10.1038/s42003-021-02478-3
                8410800
                34471211
                385631a1-ec20-4524-a38c-8cbd0c9b7362
                © The Author(s) 2021

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

                History
                : 18 March 2021
                : 22 July 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000049, U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging);
                Award ID: R01AG062590
                Award Recipient :
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                © The Author(s) 2021

                parkinson's disease,network models
                parkinson's disease, network models

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