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  Predicting disease progression in behavioral variant frontotemporal dementia

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.

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Anderl‐Straub, Sarah1, Author
Lausser, Ludwig2, Author
Lombardi, Jolina1, Author
Uttner, Ingo1, Author
Fassbender, Klaus3, Author
Fliessbach, Klaus4, Author
Huppertz, Hans‐Jürgen5, Author
Jahn, Holger6, Author
Kornhuber, Johannes7, Author
Obrig, Hellmuth8, Author              
Schneider, Anja4, 9, Author
Semler, Elisa1, Author
Synofzik, Matthis10, 11, Author
Danek, Adrian12, Author
Prudlo, Johannes13, Author
Kassubek, Jan1, Author
Landwehrmeyer, Bernhard1, Author
Lauer, Martin14, Author
Volk, Alexander E.15, Author
Wiltfang, Jens16, 17, 18, Author
Diehl‐Schmid, Janine19, AuthorLudolph, Albert C.1, AuthorSchroeter, Matthias L.8, Author              Kestler, Hans A.2, AuthorOtto, Markus1, 20, AuthorFTLD Consortium, Author               more..
1Department of Neurology, Ulm University, Germany, ou_persistent22              
2Institute of Medical Systems Biology, Ulm University, Germany, ou_persistent22              
3Department of Neurology, Saarland University Homburg, Germany, ou_persistent22              
4Department of Neurodegenerative Disease and Geriatric Psychiatry, University Hospital Bonn, Germany, ou_persistent22              
5Swiss Epilepsy Centre, Zurich, Switzerland, ou_persistent22              
6Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Germany, ou_persistent22              
7Department of Psychology and Psychotherapy, Friedrich Alexander University Erlangen, Germany, ou_persistent22              
8Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
9German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, ou_persistent22              
10Hertie-Institute for Clinical Brain Research, Eberhard Karls University Tübingen, Germany, ou_persistent22              
11German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany, ou_persistent22              
12Department of Neurology, Ludwig Maximilians University Munich, Germany, ou_persistent22              
13Department of Neurology, University Medicine Rostock, Germany, ou_persistent22              
14Clinic for Psychiatry, Psychosomatic Medicine and Psychotherapy, Julius Maximilian University, Würzburg, Germany, ou_persistent22              
15Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Germany, ou_persistent22              
16Department of Psychiatry and Psychotherapy, Georg August University, Germany, ou_persistent22              
17German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany, ou_persistent22              
18 Neurosciences and Signaling Group Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Portugal, ou_persistent22              
19Department of Psychiatry and Psychotherapy, TU Munich, Germany, ou_persistent22              
20Department of Neurology, Martin Luther University Halle-Wittenberg, Germany, ou_persistent22              


Free keywords: Behavioral variant frontotemporal dementia; Brain volume; Classification models; Disease progression; Frontotemporal dementia; Prognosis
 Abstract: 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.


Language(s): eng - English
 Dates: 2021-12-31
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1002/dad2.12262
Other: eCollection 2021
PMID: 35005196
PMC: PMC8719425
 Degree: -



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Project name : -
Grant ID : 01ED1512; 01ED2008B
Funding program : Genfi-Prox
Funding organization : -
Project name : -
Grant ID : SFB1279; SCHR 774/5-1
Funding program : -
Funding organization : Deutsche Forschungsgemeinschaft (DFG)
Project name : -
Grant ID : D.5009
Funding program : -
Funding organization : Boehringer Ingelheim Ulm University BioCenter
Project name : -
Grant ID : UIDB/04501/2020
Funding program : Illídio Pinho Professorship
Funding organization : -

Source 1

Title: Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
Source Genre: Journal
Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 13 (1) Sequence Number: e12262 Start / End Page: - Identifier: ISSN: 2352-8729
CoNE: https://pure.mpg.de/cone/journals/resource/2352-8729