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Predicting disease progression in behavioral variant frontotemporal dementia

MPG-Autoren
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Obrig,  Hellmuth
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Schroeter,  Matthias L.
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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AnderlStraub_2021.pdf
(Verlagsversion), 742KB

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Zitation

Anderl‐Straub, S., Lausser, L., Lombardi, J., Uttner, I., Fassbender, K., Fliessbach, K., et al. (2021). Predicting disease progression in behavioral variant frontotemporal dementia. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1): e12262. doi:10.1002/dad2.12262.


Zitierlink: https://hdl.handle.net/21.11116/0000-0009-E54E-E
Zusammenfassung
Introduction: The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline.

Methods: One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups ("fast progressors" vs. "slow progressors"), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models.

Results: We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models.

Discussion: Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model.