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 Göschel^3,4, Stefan Cano⁵, Semiha Aydin¹, Theresa Köbe^3,4,6,7, Agnes Flöel^3,4,8, 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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