Objective: To investigate the association between β-amyloid (Aβ) load and postmortem structural network topology in decedents without dementia.
Methods: Fourteen decedents (mean age at death 72.6 ± 7.2 years) without known clinical diagnosis of neurodegenerative disease and meeting pathology criteria only for no or low Alzheimer disease (AD) pathologic change were selected from the Normal Aging Brain Collection Amsterdam database. In situ brain MRI included 3D T1-weighted images for anatomical registration and diffusion tensor imaging for probabilistic tractography with subsequent structural network construction. Network topologic measures of centrality (degree), integration (global efficiency), and segregation (clustering and local efficiency) were calculated. Tissue sections from 12 cortical regions were sampled and immunostained for Aβ and hyperphosphorylated tau (p-tau), and histopathologic burden was determined. Linear mixed effect models were used to assess the relationship between Aβ and p-tau load and network topologic measures.
Results: Aβ was present in 79% of cases and predominantly consisted of diffuse plaques; p-tau was sparsely present. Linear mixed effect models showed independent negative associations between Aβ load and global efficiency (β = -0.83 × 10, = 0.014), degree (β = -0.47, = 0.034), and clustering (β = -0.55 × 10, = 0.043). A positive association was present between Aβ load and local efficiency (β = 3.16 × 10, = 0.035). Regionally, these results were significant in the posterior cingulate cortex (PCC) for degree (β = -2.22, < 0.001) and local efficiency (β = 1.01 × 10, = 0.014) and precuneus for clustering (β = -0.91 × 10, = 0.017). There was no relationship between p-tau and network topology.
Conclusion: This study in deceased adults with AD-related pathologic change provides evidence for a relationship among early Aβ accumulation, predominantly of the diffuse type, and structural network topology, specifically of the PCC and precuneus.
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http://dx.doi.org/10.1212/WNL.0000000000009910 | DOI Listing |
Bioinformatics
January 2025
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
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January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Sichuan 611756, China.
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January 2025
Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren University, Hangzhou, 310015, China.
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View Article and Find Full Text PDFPhysiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
View Article and Find Full Text PDFActa Crystallogr B Struct Sci Cryst Eng Mater
February 2025
CSIRO Division of Mineral Products, Port Melbourne, Victoria, Australia.
The crystallographic phase change from tetragonal litharge (α-PbO; P4/nmm) to orthorhombic massicot (β-PbO; Pbcm) has been studied by full-matrix Rietveld analysis of high-temperature neutron powder diffraction data collected in equal steps from ambient temperature up to 925 K and back down to 350 K. The phase transformation takes place between 850 and 925 K, with the coexisting phases having equal abundance by weight at 885 K. The product massicot remains metastable on cooling to near ambient temperature.
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