Validity and value of metabolic connectivity in mouse models of β-amyloid and tauopathy

Ruch, Francois; Gnoerich, Johannes; Wind, Karin; Koehler, Mara; Zatcepin, Artem; Wiedemann, Thomas; Gildehaus, Franz-Joseph; Lindner, Simon; Boening, Guido; von Ungern-Sternberg, Barbara; Beyer, Leonie; Herms, Jochen; Bartenstein, Peter; Brendel, Matthias und Eckenweber, Florian (2024): Validity and value of metabolic connectivity in mouse models of β-amyloid and tauopathy. In: Neuroimage, Bd. 286, 120513

Abstract

Among functional imaging methods, metabolic connectivity (MC) is increasingly used for investigation of regional network changes to examine the pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD) or movement disorders. Hitherto, MC was mostly used in clinical studies, but only a few studies demonstrated the usefulness of MC in the rodent brain. The goal of the current work was to analyze and validate metabolic regional network alterations in three different mouse models of neurodegenerative diseases (beta-amyloid and tau) by use of 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography (FDG-PET) imaging. We compared the results of FDG-mu PET MC with conventional VOI-based analysis and behavioral assessment in the Morris water maze (MWM). The impact of awake versus anesthesia conditions on MC read-outs was studied and the robustness of MC data deriving from different scanners was tested. MC proved to be an accurate and robust indicator of functional connectivity loss when sample sizes >= 12 were considered. MC readouts were robust across scanners and in awake/ anesthesia conditions. MC loss was observed throughout all brain regions in tauopathy mice, whereas beta-amyloid indicated MC loss mainly in spatial learning areas and subcortical networks. This study established a methodological basis for the utilization of MC in different beta-amyloid and tau mouse models. MC has the potential to serve as a read-out of pathological changes within neuronal networks in these models.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin > Munich Cluster for Systems Neurology (SyNergy)
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198