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Prediction of Cognitive Progression Due to Alzheimer's Disease in Normal Subjects Based on Individual Default Mode Network Metabolic Connectivity Strength

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机构： [1]School of Communication & Information Engineering, Shanghai University, Shanghai, China, 200444 [2]School of Life Sciences, Shanghai University, Shanghai, China, 200444 [3]Shanghai University of Traditional Chinese Medicine, Shanghai, China, 201203 [4]Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China, 100053 [5]School of Biomedical Engineering, Hainan University, Haikou, China, 570228 [6]Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China, 100053 [7]National Clinical Research Center for Geriatric Diseases, Beijing, China, 100053

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关键词： Cognitively unimpaired FDG PET Default mode network Metabolic connectivity Alzheimer's disease Cognitive decline

摘要：

Predicting cognitive decline in those already Aβ positive or Tau positive among the aging population poses clinical challenges. In Alzheimer's disease (AD) research, intra-default mode network (DMN) connections play a pivotal role in diagnosis. This paper proposes metabolic connectivity within the DMN as a supplementary biomarker to the AT(N) framework.Extracting data from 1292 subjects in the Alzheimer's Disease Neuroimaging Initiative, we collected paired T1-weighted structural MRI and 18F-labeled-fluorodeoxyglucose positron emission computed tomography (PET) scans. Individual metabolic DMN networks were constructed, and metabolic connectivity (MC) strength in DMN was assessed. In the cognitively unimpaired (CU) group, the Cox model identified CU(MC+), high-risk subjects, with Kaplan-Meier survival analyses and hazard ratio (HR) revealing MC strength's predictive performance. Spearman correlation analyses explored relationships between MC strength, AT(N) biomarkers, and clinical scales. DMN standard uptake value ratio (SUVR) provided comparative insights in the analyses.Both MC strength and SUVR exhibit gradual declines with cognitive deterioration, displaying significant intergroup differences. Survival analyses indicate enhanced Aβ and Tau prediction with both metrics, with MC strength outperforming SUVR. Combined MC strength and Aβ yield optimal predictive performance (HR = 9.29), followed by MC strength and Tau (HR = 8.92). In CU(MC+), MC strength correlates significantly with CSF Aβ42 and AV45 PET SUVR (r = 0.22, -0.19). Generally, MC strength's correlation with AT(N) biomarkers exceeded SUVR.Individuals with normal cognition and disrupted DMN metabolic connectivity face an elevated cognitive decline risk linked to Aβ, preceding metabolic issues.Copyright © 2024. Published by Elsevier Inc.

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出版当年[2023]版：

大类 | 2 区医学

小类 | 2 区神经科学

最新[2025]版：

大类 | 2 区医学

小类 | 2 区神经科学

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Q1 NEUROSCIENCES

最新[2024]版：

Q1 NEUROSCIENCES Q1 PSYCHIATRY

影响因子： 4.7 最新[2024版] 5.5 最新五年平均 5.9 出版当年[2022版] 6.5 出版当年五年平均 6.05 出版前一年[2021版] 5.7 出版后一年[2023版]

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第一作者机构： [1]School of Communication & Information Engineering, Shanghai University, Shanghai, China, 200444 [2]School of Life Sciences, Shanghai University, Shanghai, China, 200444

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通讯作者：

通讯机构： [2]School of Life Sciences, Shanghai University, Shanghai, China, 200444 [4]Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China, 100053 [5]School of Biomedical Engineering, Hainan University, Haikou, China, 570228 [6]Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China, 100053 [7]National Clinical Research Center for Geriatric Diseases, Beijing, China, 100053 [*1]Institute of Biomedical Engineering, School of Life Science, Shanghai University, No.99 Shangda Road, Shanghai, China, 200444 [*2]Department of Neurology, XuanWu Hospital of Capital Medical University, No.45 Changchun Street, Beijing, China, 100053

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Prediction of Cognitive Progression Due to Alzheimer's Disease in Normal Subjects Based on Individual Default Mode Network Metabolic Connectivity Strength

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