Brain metabolic network covariance and aging in a mouse model of Alzheimer's disease

Evgeny J. Chumin, Charles P. Burton, Rebecca Silvola, Ethan W. Miner, Scott C. Persohn, Mattia Veronese, Paul R. Territo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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: The 5XFAD model mice showed age-related changes in 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.

Original languageEnglish
JournalAlzheimer's and Dementia
Early online date30 Nov 2023
DOIs
Publication statusE-pub ahead of print - 30 Nov 2023

Keywords

  • Alzheimer's disease
  • connectomics
  • metabolic covariance networks
  • preclinical models

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