Validity and value of metabolic connectivity in mouse models of β-amyloid and tauopathy
François Ruch,
Johannes Gnörich,
Karin Wind,
Mara Köhler,
Artem Zatcepin,
Thomas Wiedemann,
Franz-Joseph Gildehaus,
Simon Lindner,
Guido Boening,
Barbara von Ungern-Sternberg,
Leonie Beyer,
Jochen Herms,
Peter Bartenstein,
Matthias Brendel,
Florian Eckenweber
Affiliations
François Ruch
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
Johannes Gnörich
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
Karin Wind
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
Mara Köhler
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
Artem Zatcepin
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
Thomas Wiedemann
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
Franz-Joseph Gildehaus
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
Simon Lindner
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
Guido Boening
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
Barbara von Ungern-Sternberg
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
Leonie Beyer
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
Jochen Herms
German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Center of Neuropathology and Prion Research, University of Munich, Munich, Germany
Peter Bartenstein
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
Matthias Brendel
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Corresponding author at: Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany.
Florian Eckenweber
Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
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 (β-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-µPET 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 β-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 β-amyloid and tau mouse models. MC has the potential to serve as a read-out of pathological changes within neuronal networks in these models.