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      Apolipoprotein E4 effects on topological brain network organization in mild cognitive impairment

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          Abstract

          The Apolipoprotein E isoform E4 (ApoE4) is consistently associated with an elevated risk of developing late-onset Alzheimer’s Disease (AD); however, less is known about the potential genetic modulation of the brain networks organization during prodromal stages like Mild Cognitive Impairment (MCI). To investigate this issue during this critical stage, we used a dataset with a cross-sectional sample of 253 MCI patients divided into ApoE4 -positive (‛Carriers’) and ApoE4 -negative (‘non-Carriers’). We estimated the cortical thickness (CT) from high-resolution T1-weighted structural magnetic images to calculate the correlation among anatomical regions across subjects and build the CT covariance networks ( CT-Nets). The topological properties of CT-Nets were described through the graph theory approach. Specifically, our results showed a significant decrease in characteristic path length, clustering-index, local efficiency, global connectivity, modularity, and increased global efficiency for Carriers compared to non-Carriers. Overall, we found that ApoE4 in MCI shaped the topological organization of CT-Nets. Our results suggest that in the MCI stage, the ApoE4 disrupting the CT correlation between regions may be due to adaptive mechanisms to sustain the information transmission across distant brain regions to maintain the cognitive and behavioral abilities before the occurrence of the most severe symptoms.

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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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              The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease

              The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of developing criteria for the symptomatic predementia phase of Alzheimer's disease (AD), referred to in this article as mild cognitive impairment due to AD. The workgroup developed the following two sets of criteria: (1) core clinical criteria that could be used by healthcare providers without access to advanced imaging techniques or cerebrospinal fluid analysis, and (2) research criteria that could be used in clinical research settings, including clinical trials. The second set of criteria incorporate the use of biomarkers based on imaging and cerebrospinal fluid measures. The final set of criteria for mild cognitive impairment due to AD has four levels of certainty, depending on the presence and nature of the biomarker findings. Considerable work is needed to validate the criteria that use biomarkers and to standardize biomarker analysis for use in community settings. Copyright © 2011 The Alzheimer's Association. All rights reserved.
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                Author and article information

                Contributors
                gretels.sanabria@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 January 2021
                12 January 2021
                2021
                : 11
                : 845
                Affiliations
                [1 ]GRID grid.8515.9, ISNI 0000 0001 0423 4662, Laboratoire de Recherche en Neuroimagerie (LREN), Département des neurosciences cliniques, , Centre Hospitalier Universitaire Vaudois (CHUV), ; Mont Paisible 16, 1011 Lausanne, Switzerland
                [2 ]GRID grid.8515.9, ISNI 0000 0001 0423 4662, Leenaards Memory Center, , Lausanne University Hospital (CHUV), ; Lausanne, Switzerland
                Article
                80909
                10.1038/s41598-020-80909-7
                7804004
                33436948
                c427782b-f271-4e8c-9750-f8000c5b8ca3
                © 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 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
                : 12 June 2020
                : 30 December 2020
                Funding
                Funded by: European Union Seventh Framework Programme (FP7/2007–2013)
                Award ID: 604102
                Funded by: European Union’s Horizon 2020 research and innovation programme
                Award ID: 720270 (HBP SGA1)
                Funded by: MORPHEMIC Grant
                Award ID: 871643
                Award Recipient :
                Funded by: Swiss National Science Foundation,NCCR Synapsy
                Award ID: 32003B_135679
                Award ID: 32003B_159780
                Award ID: CRSK-3_190185
                Award Recipient :
                Funded by: Leenaards Foundation
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                alzheimer's disease,network models,genetic predisposition to disease
                Uncategorized
                alzheimer's disease, network models, genetic predisposition to disease

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