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Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease

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      Abstract

      Alzheimer's disease (AD) has a preclinical phase that can last for decades prior to clinical dementia onset. Subjective cognitive decline (SCD) is regarded as the last preclinical AD stage prior to the development of amnestic mild cognitive decline (aMCI) and AD dementia (d-AD). The analysis of brain structural networks based on diffusion tensor imaging (DTI) has identified the so-called 'rich club', a set of cortical regions highly connected to each other, with other regions referred to as peripheral. It has been reported that rich club architecture is affected by regional atrophy and connectivity, which are reduced in patients with aMCI and d-AD.

      Methods: We recruited 62 normal controls, 47 SCD patients, 60 aMCI patients and 55 d-AD patients and collected DTI data to analyze rich-club organization.

      Results: We demonstrated that rich club organization was disrupted, with reduced structural connectivity among rich club nodes, in aMCI and d-AD patients but remained stable in SCD patients. In addition, SCD, aMCI and d-AD patients showed similar patterns of disrupted peripheral regions and reduced connectivity involving these regions, suggesting that peripheral regions might contribute to cognitive decline and that disruptions here could be regarded as an early marker of SCD. This organization could provide the fundamental structural architecture for complex cognitive functions and explain the low prevalence of cognitive problems in SCD patients.

      Conclusions: These findings reveal a disrupted pattern of the AD connectome that starts in peripheral regions and then hierarchically propagates to rich club regions, when patients show clinical symptoms. This pattern provides evidence that disruptions in rich club organization are a key factor in the progression of AD that can dynamically reflect the progression of AD, thus representing a potential biomarker for early diagnosis.

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      Most cited references 81

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      Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease.

      Clinical criteria for the diagnosis of Alzheimer's disease include insidious onset and progressive impairment of memory and other cognitive functions. There are no motor, sensory, or coordination deficits early in the disease. The diagnosis cannot be determined by laboratory tests. These tests are important primarily in identifying other possible causes of dementia that must be excluded before the diagnosis of Alzheimer's disease may be made with confidence. Neuropsychological tests provide confirmatory evidence of the diagnosis of dementia and help to assess the course and response to therapy. The criteria proposed are intended to serve as a guide for the diagnosis of probable, possible, and definite Alzheimer's disease; these criteria will be revised as more definitive information become available.
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        Complex brain networks: graph theoretical analysis of structural and functional systems.

        Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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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

            Affiliations
            [1 ]School of Life Science, Beijing Institute of Technology, Beijing, China;
            [2 ]Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, China;
            [3 ]Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.
            [4 ]Banner Alzheimer's Institute and Banner Good Samaritan PET center, Phoenix, AZ, USA;
            [5 ]Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, USA.
            [6 ]Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China;
            [7 ]Beijing Institute of Geriatrics, Beijing, China;
            [8 ]National Clinical Research Center for Geriatric Disorders, Beijing, China.
            Author notes
            ✉ Corresponding authors: Tianyi Yan, Email: yantianyi@ 123456bit.edu.cn , Tel: 13070188777; Ying Han, Email: hanying@ 123456xwh.ccmu.edu.cn , Tel: 18515692701

            *These authors have contributed equally to this work

            Competing Interests: The authors have declared that no competing interest exists.

            Journal
            Theranostics
            Theranostics
            thno
            Theranostics
            Ivyspring International Publisher (Sydney )
            1838-7640
            2018
            11 May 2018
            : 8
            : 12
            : 3237-3255
            6010989
            10.7150/thno.23772
            thnov08p3237
            © Ivyspring International Publisher

            This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license ( https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.

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            Research Paper

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