Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease

Shelli R. Kesler, Paul Acton, Vikram Rao, William J. Ray

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Neurodegeneration in Alzheimer’s disease (AD) is associated with amyloid-beta peptide accumulation into insoluble amyloid plaques. The five-familial AD (5XFAD) transgenic mouse model exhibits accelerated amyloid-beta deposition, neuronal dysfunction, and cognitive impairment. We aimed to determine whether connectome properties of these mice parallel those observed in patients with AD. We obtained diffusion tensor imaging and resting-state functional magnetic resonance imaging data for four transgenic and four nontransgenic male mice. We constructed both structural and functional connectomes and measured their topological properties by applying graph theoretical analysis. We compared connectome properties between groups using both binarized and weighted networks. Transgenic mice showed higher characteristic path length in weighted structural connectomes and functional connectomes at minimum density. Normalized clustering and modularity were lower in transgenic mice across the upper densities of the structural connectome. Transgenic mice also showed lower small-worldness index in higher structural connectome densities and in weighted structural networks. Hyper-correlation of structural and functional connectivity was observed in transgenic mice compared with nontransgenic controls. These preliminary findings suggest that 5XFAD mouse connectomes may provide useful models for investigating the molecular mechanisms of AD pathogenesis and testing the effectiveness of potential treatments.

Original languageEnglish (US)
Pages (from-to)241-258
Number of pages18
JournalNetwork Neuroscience
Volume2
Issue number2
DOIs
StatePublished - Jan 1 2018

Keywords

  • Alzheimer’s disease
  • Connectome
  • Diffusion tensor imaging
  • Neuroimaging
  • fMRI

ASJC Scopus subject areas

  • General Neuroscience
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

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