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

Publication ,  Journal Article
Badea, A; Wu, W; Shuff, J; Wang, M; Anderson, RJ; Qi, Y; Johnson, GA; Wilson, JG; Koudoro, S; Garyfallidis, E; Colton, CA; Dunson, DB
Published in: Front Neuroinform
2019

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.

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Published In

Front Neuroinform

DOI

ISSN

1662-5196

Publication Date

2019

Volume

13

Start / End Page

72

Location

Switzerland

Related Subject Headings

  • 4611 Machine learning
  • 4601 Applied computing
  • 3209 Neurosciences
  • 1702 Cognitive Sciences
  • 1109 Neurosciences
 

Citation

APA
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MLA
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Badea, A., Wu, W., Shuff, J., Wang, M., Anderson, R. J., Qi, Y., … Dunson, D. B. (2019). Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease. Front Neuroinform, 13, 72. https://doi.org/10.3389/fninf.2019.00072
Badea, Alexandra, Wenlin Wu, Jordan Shuff, Michele Wang, Robert J. Anderson, Yi Qi, G Allan Johnson, et al. “Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease.Front Neuroinform 13 (2019): 72. https://doi.org/10.3389/fninf.2019.00072.
Badea A, Wu W, Shuff J, Wang M, Anderson RJ, Qi Y, et al. Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease. Front Neuroinform. 2019;13:72.
Badea, Alexandra, et al. “Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease.Front Neuroinform, vol. 13, 2019, p. 72. Pubmed, doi:10.3389/fninf.2019.00072.
Badea A, Wu W, Shuff J, Wang M, Anderson RJ, Qi Y, Johnson GA, Wilson JG, Koudoro S, Garyfallidis E, Colton CA, Dunson DB. Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease. Front Neuroinform. 2019;13:72.

Published In

Front Neuroinform

DOI

ISSN

1662-5196

Publication Date

2019

Volume

13

Start / End Page

72

Location

Switzerland

Related Subject Headings

  • 4611 Machine learning
  • 4601 Applied computing
  • 3209 Neurosciences
  • 1702 Cognitive Sciences
  • 1109 Neurosciences