Brain network fingerprints of Alzheimer's disease risk factors in mouse models with humanized APOE alleles.

Autor: Winter S; Statistical Science, Trinity School, Duke University, Durham, NC 27710, USA., Mahzarnia A; Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA., Anderson RJ; Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA., Han ZY; Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA., Tremblay J; Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA., Stout JA; Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA; Duke UNC Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710, USA., Moon HS; Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA; Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC 27710, USA., Marcellino D; Department of Medical and Translational Biology, Umeå University, Umeå 901 87, Sweden; Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund 22184, Sweden., Dunson DB; Statistical Science, Trinity School, Duke University, Durham, NC 27710, USA., Badea A; Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA; Duke UNC Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710, USA; Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC 27710, USA; Department of Neurology, Duke University School of Medicine, Durham, NC 27710, USA. Electronic address: Alexandra.badea@duke.edu.
Jazyk: angličtina
Zdroj: Magnetic resonance imaging [Magn Reson Imaging] 2024 Dec; Vol. 114, pp. 110251. Date of Electronic Publication: 2024 Oct 01.
DOI: 10.1016/j.mri.2024.110251
Abstrakt: Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic and modifiable risk factors influence disease susceptibility are under intense investigation, with APOE being the major genetic risk factor for late onset AD. Yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear. To model multiple risk factors, including APOE genotype, age, sex, diet, and immunity we used a cross sectional design, leveraging mice expressing human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. We used network topological and GraphClass analyses of brain connectomes derived from accelerated diffusion-weighted MRI to assess the global and local impact of risk factors, in the absence of AD pathology. Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles. Sparse Canonical Correlation Analysis (CCA) including spatial memory as a risk factor resulted in a network comprising 80 edges, showing significant overlap with risk-associated networks from GraphClass. The largest overlaps were observed with networks impacted by diet (47 edges), immunity (39 edges), APOE3 vs 4 (26 edges), sex (23 edges), and age (19 edges), the resulting networks supporting the use of sensory cues in spatial memory retrieval. These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.
Competing Interests: Declaration of competing interest The authors report no competing interests.
(Copyright © 2024. Published by Elsevier Inc.)
Databáze: MEDLINE
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