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Abstract #3058

Towards a better understanding of Alzheimer’s Disease: Rasch transformation of cognitive assessment data yields better linear description of cognition using neurometabolite concentrations as explanatory variables

Ariane Fillmer1, Jeanette Melin2, Leslie Pendrill2, Laura Göschel3,4, Stefan Cano5, Semiha Aydin1, Theresa Köbe3,4,6,7, Agnes Flöel3,4,8, and Bernd Ittermann1

1Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany, 2RISE Metrology, Gothenburg, Sweden, 3Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Klinik und Hochschulambulanz für Neurologie, Charité - Universitätsmedizin Berlin, Berlin, Germany, 4Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany, 5Modus Outcomes, Letchworth Garden City, United Kingdom, 6Studies on Prevention of Alzheimer's Disease (StOP-AD) Centre, Douglas Mental Health University Institute, Montreal, QC, Canada, 7Department of Psychiatry, McGill University, Montreal, QC, Canada, 8Department of Neurology, University Medicine Greifswald, Greifswald, Germany

Due to its non-invasive nature, magnetic resonance spectroscopy is a promising tool for investigating neurochemical disease processes, monitoring potential therapy responses, and diagnosis of Alzheimer’s disease (AD). Changes of γ-amino-butyric acid (GABA) and glutamate (Glu) concentrations have been associated with AD, however, their relationship to other disease parameters is still unknown. This work aims to investigate the relationship of GABA and Glu with cognitive measures and demonstrates that the application of Rasch transformation to cognitive assessment data yields more reliable descriptions of cognitive outcome using metabolite concentrations as explanatory variables.

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