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

Author:

Kesler Shelli R.1ORCID,Acton Paul2,Rao Vikram1,Ray William J.2

Affiliation:

1. Department of Neuro-oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA

2. Neurodegeneration Consortium, Institute for Applied Cancer Science, University of Texas MD Anderson Cancer Center, Houston, TX, USA

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.

Funder

The Neurodegeneration Consortium

The MD Anderson Foundation

National Cancer Institute

National Institute of Nursing Research

Publisher

MIT Press - Journals

Subject

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

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