Brain Metabolic Network Covariance and Aging in a Mouse Model of Alzheimer’s Disease

Abstract

INTRODUCTION: Alzheimer's disease (AD), the leading cause of dementia worldwide, represents a human and financial impact for which few effective drugs exist to treat the disease. Advances in molecular imaging have enabled assessment of cerebral glycolytic metabolism, and network modeling of brain region have linked to alterations in metabolic activity to AD stage. METHODS: We performed 18F-FDG Positron Emission Tomography (PET) imaging in 4, 6, and 12 month old 5XFAD and littermate controls (WT) of both sexes, and analyzed region data via brain metabolic covariance analysis. RESULTS: 5XFAD model mice show age related changes glucose uptake relative to WT mice. Analysis of community structure of covariance networks was different across age and sex, with a disruption of metabolic coupling in the 5XFAD model. DISCUSSION: The current study replicates clinical AD findings and indicates that metabolic network covariance modeling provides a translational tool to assess disease progression in AD models.

Levetiracetam Modulates Brain Metabolic Networks and Transcriptomic Signatures in the 5XFAD Mouse Model of Alzheimer’s disease

“…In this model system, dynamic range was determined to have a 1.5-fold difference in 18 F-FDG uptake between WT and 5XFAD mice (Oblak et al, 2021), thus supporting its use for drug discovery studies. Significance was seen at the brain region level across treatments (Onos et al, 2022), but pairwise interregional interactions were not explained by per-region significance, so a network approach (Jeub et al, 2018;Veronese et al, 2019;Chumin et al, 2023) was applied to further investigate interregional metabolic alterations.…”

“…To measure metabolic changes at a network level, we utilized covariance and connectomic analyses (Jeub et al, 2018;Veronese et al, 2019;Chumin et al, 2023). First, we measured the degree of functional metabolic connectivity on a single-region basis, and extrapolated nodal degree values to a global distribution.…”

“…Metabolic covariance networks were assessed via the MRCC community detection algorithm with 10,000 permutations to establish a rigorous partition for covariance networks focused on positive covariance between regions (Veronese et al, 2019;Chumin et al, 2023) where partitions, or communities, differed between sexes and treatments. Female modularity ranged from two to five communities within a network, with the number of partitions generally increasing linearly as a function of dose.…”

“…Consensus regions which explained 80% of the variance observed across all regions studied were selected for regional interrogation via MANOVA, correcting for multiple comparisons with a Bonferroni correction. To assess whole brain and sub-network changes in 18 F-FDG images, metabolic covariance analysis was conducted according to our recent work and open source tools (Chumin et al, 2023). Pearson’s correlation between z-scores of region pairs was calculated for all pairwise interactions within each cohort to generate a covariance adjacency matrix (see Figures.…”

“…Recent work from our group has applied a metabolic network covariance modeling approach in the 5XFAD model across lifespan (i.e. 4, 6, and 12 mos) (Chumin et al, 2023). This work permits the interrogation of network changes at the whole brain and sub-network levels, allowing greater insights into the network changes that occur with disease progression.…”