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Divergent Connectivity Changes in Gray Matter Structural Covariance Networks in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease

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机构: [1]School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China [2]Department of Neurology, Tangshan Gongren Hospital, Tangshan, China [3]Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China [4]School of Electrical Engineering, Yanshan University, Qinhuangdao, China [5]Measurement Technology and Instrumentation Key Laboratory of Hebei Province, Qinhuangdao, China [6]School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China [7]Biomedical Engineering Institute, Hainan University, Haikou, China [8]Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China [9]National Clinical Research Center for Geriatric Disorders, Beijing, China
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关键词: structural covariance network subjective cognitive decline structural MRI default mode network amnestic mild cognitive impairment Alzheimer's disease

摘要:
Alzheimer's disease (AD) has a long preclinical stage that can last for decades prior to progressing toward amnestic mild cognitive impairment (aMCI) and/or dementia. Subjective cognitive decline (SCD) is characterized by self-experienced memory decline without any evidence of objective cognitive decline and is regarded as the later stage of preclinical AD. It has been reported that the changes in structural covariance patterns are affected by AD pathology in the patients with AD and aMCI within the specific large-scale brain networks. However, the changes in structural covariance patterns including normal control (NC), SCD, aMCI, and AD are still poorly understood. In this study, we recruited 42 NCs, 35 individuals with SCD, 43 patients with aMCI, and 41 patients with AD. Gray matter (GM) volumes were extracted from 10 readily identifiable regions of interest involved in high-order cognitive function and AD-related dysfunctional structures. The volume values were used to predict the regional densities in the whole brain by using voxel-based statistical and multiple linear regression models. Decreased structural covariance and weakened connectivity strength were observed in individuals with SCD compared with NCs. Structural covariance networks (SCNs) seeding from the default mode network (DMN), salience network, subfields of the hippocampus, and cholinergic basal forebrain showed increased structural covariance at the early stage of AD (referring to aMCI) and decreased structural covariance at the dementia stage (referring to AD). Moreover, the SCN seeding from the executive control network (ECN) showed a linearly increased extent of the structural covariance during the early and dementia stages. The results suggest that changes in structural covariance patterns as the order of NC-SCD-aMCI-AD are divergent and dynamic, and support the structural disconnection hypothesis in individuals with SCD.

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基金编号: 81972160 61633018 82020108013 82001773 81622025 F2019203515

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出版当年[2020]版:
大类 | 2 区 医学
小类 | 2 区 老年医学 3 区 神经科学
最新[2025]版:
大类 | 3 区 医学
小类 | 3 区 老年医学 3 区 神经科学
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出版当年[2019]版:
Q1 GERIATRICS & GERONTOLOGY Q2 NEUROSCIENCES
最新[2023]版:
Q2 GERIATRICS & GERONTOLOGY Q2 NEUROSCIENCES

影响因子: 最新[2023版] 最新五年平均 出版当年[2019版] 出版当年五年平均 出版前一年[2018版] 出版后一年[2020版]

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第一作者机构: [1]School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
通讯作者:
通讯机构: [3]Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China [7]Biomedical Engineering Institute, Hainan University, Haikou, China [8]Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China [9]National Clinical Research Center for Geriatric Disorders, Beijing, China
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