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

Abstract

INTRODUCTIONAlzheimers 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. METHODSWe 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. RESULTS5XFAD model mice showed 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. DISCUSSIONThe current study replicates clinical AD findings and indicates that metabolic network covariance modeling provides a translational tool to assess disease progression in AD models. RESEARCH IN CONTEXTO_ST_ABSSYSTEMATIC REVIEWC_ST_ABSThe authors extensively reviewed literature (e.g., PubMed), meeting abstracts, and presentations on approaches to evaluate brain network analysis in animal models. Based on the available data, there were clear gaps in our understanding of how metabolic networks change with disease progression at the preclinical phase, thus limiting the utility of these measures for clinical comparison in Alzheimers disease (AD). INTERPRETATIONOur findings indicate that employing metabolic covariance modeling in mouse models of AD and littermate controls of both sexes with age provides a mechanism to evaluate brain changes in network function which align closely with previous clinical stages of AD. Moreover, utilizing open-source clinical tools from the Brain Connectivity Toolbox (BCT), we demonstrated that brain networks reorganize with AD progression at multiple levels, and these changes are consistent with previous reports in human AD studies. FUTURE DIRECTIONSThe open-source framework developed in the current work provides valuable tools for brain metabolic covariance modeling. Such tools can be used in both preclinical and clinical settings and they enable more direct translation of preclinical imaging studies to those in the clinic. When matched with an appropriate animal model, genetics, and/or treatments, this study will enable assessment of in vivo target engagement, translational pharmacodynamics, and insight into potential treatments of AD.