Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer’s Disease

Frontiers in Neuroinformatics

Robert J. Anderson¹  Alexandra Badea²  Jordan Shuff³  David B. Dunson⁴  Serge Koudoro⁴  Michele Wang⁵  Yi Qi⁶  Joan G. Wilson⁶  G. Allan Johnson⁶  Wenlin Wu⁷  Carol A. Colton⁸  Eleftherios Garyfallidis⁸ 

[1] Sciences, Duke University, Durham, NC, United States;Brain Imaging and Analysis Center, Duke University, Durham, NC, United States;Department of Biomedical Engineering, University of Delaware, Newark, NJ, United States;Department of Neurology, Duke University School of Medicine, Durham, NC, United States;;Department of Psychology and Neuroscience, Trinity College of Arts &Department of Radiology, Duke University, Durham, NC, United States;Pratt School of Engineering, Duke University, Durham, NC, United States;School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States;

关键词: mouse model;    Alzheimer’s disease;    neurodegeneration;    magnetic resonance imaging;    tractography;    tract based analysis;   

DOI  :  10.3389/fninf.2019.00072

来源: DOAJ

【 摘 要 】

The major genetic risk for late onset Alzheimer’s disease has been associated with the presence of APOE4 alleles. However, the impact of different APOE alleles on the brain aging trajectory, and how they interact with the brain local environment in a sex specific manner is not entirely clear. We sought to identify vulnerable brain circuits in novel mouse models with homozygous targeted replacement of the mouse ApoE gene with either human APOE3 or APOE4 gene alleles. These genes are expressed in mice that also model the human immune response to age and disease-associated challenges by expressing the human NOS2 gene in place of the mouse mNos2 gene. These mice had impaired learning and memory when assessed with the Morris water maze (MWM) and novel object recognition (NOR) tests. Ex vivo MRI-DTI analyses revealed global and local atrophy, and areas of reduced fractional anisotropy (FA). Using tensor network principal component analyses for structural connectomes, we inferred the pairwise connections which best separate APOE4 from APOE3 carriers. These involved primarily interhemispheric connections among regions of olfactory areas, the hippocampus, and the cerebellum. Our results also suggest that pairwise connections may be subdivided and clustered spatially to reveal local changes on a finer scale. These analyses revealed not just genotype, but also sex specific differences. Identifying vulnerable networks may provide targets for interventions, and a means to stratify patients.

【 授权许可】

Unknown