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Brain Topological Changes in Subjective Cognitive Decline and Associations with Amyloid Stages

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机构: [1]State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya, 572022, China. [2]German Center for Neurodegenerative Disease (DZNE), Bonn, 53127, Germany. [3]Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, 100053, China. [4]School of Psychology, Nanjing Normal University, Nanjing, 210023, China. [5]Clinical Functional Imaging Lab, Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, 53127, Germany. [6]State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya, 572022, China xiaochen.hu@uk-koeln.de fyuee@hotmail.com. [7]Collaborative Innovation Center of One Health, Hainan University. Haikou, 570228, China. [8]Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, 50924, Germany. [9]Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, 50924, Germany xiaochen.hu@uk-koeln.de fyuee@hotmail.com. [10]Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, 518132, China. [11]National Clinical Research Center for Geriatric Diseases, Beijing, China, 100053. [12]The Central Hospital of Karamay, Xinjiang, China, 834000.
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关键词: amyloid PET frequency-based staging resting-state functional MRI diffusion tensor imaging graph theory

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This study examined how amyloid burden affects structural and functional brain network topology in subjective cognitive decline (SCD), a risk condition for Alzheimer's disease (AD). Functional and structural brain networks were analyzed in 100 individuals with SCD and 86 normal controls (both sexes included) using resting-state functional MRI and diffusion tensor imaging. Topological properties of brain networks were evaluated as indicators of information exchange efficiency and network robustness. Amyloid burden in 55 SCD participants was measured using amyloid PET imaging and a frequency-based staging method, which defined global and regional amyloid burden for four anatomical stages. Compared to normal controls, individuals with SCD exhibited increased functional nodal efficiency and structural nodal betweenness in the left anterior and median cingulate gyri, with no differences in network-level properties. Amyloid staging revealed four cortical divisions: stage 1, fusiform and lateral temporal gyri; stage 2, occipital areas; stage 3, default mode network (DMN), midline brain and lateral frontotemporal areas; and stage 4, the remaining cortex. The global and regional amyloid burden of each cortical stage were positively associated with the node-level properties of a set of DMN hubs, with the left anterior and posterior cingulate gyri being congruently associated with all amyloid stages. These findings suggest that amyloid burden continuously influences network adaptations through DMN hubs, irrespective of local proximity to pathology. Increased nodal properties in cortical hubs may reflect heightened information-processing demands during early amyloid deposition in this population at risk for AD.Significance Statement Amyloid spreads throughout the cortex in AD. It is unclear whether early amyloid deposition may trigger system-level network reorganization in SCD who are at risk for AD. We examined the brain topology alterations in SCD and its relationship with amyloid deposition at different cortical stages. We found increased node-level topological properties, in the core default mode network region (i.e., the cingulate cortex) in SCD. Increasing regional amyloid load at all stages showed consistent associations with the increasing node-level topological properties of the cingulate cortex in SCD. Our findings suggest that amyloid deposition impacts the system-level network adaptation via the cingulate cortex already at the very early stage and is unlikely to have a local effect in this AD risk population.Copyright © 2025 the authors.

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大类 | 2 区 医学
小类 | 2 区 神经科学
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大类 | 2 区 医学
小类 | 2 区 神经科学
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第一作者机构: [1]State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya, 572022, China. [2]German Center for Neurodegenerative Disease (DZNE), Bonn, 53127, Germany.
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