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Predicting disease progression in behavioral variant frontotemporal dementia
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 o...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719425/ https://www.ncbi.nlm.nih.gov/pubmed/35005196 http://dx.doi.org/10.1002/dad2.12262 |
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author | Anderl‐Straub, Sarah Lausser, Ludwig Lombardi, Jolina Uttner, Ingo Fassbender, Klaus Fliessbach, Klaus Huppertz, Hans‐Jürgen Jahn, Holger Kornhuber, Johannes Obrig, Hellmuth Schneider, Anja Semler, Elisa Synofzik, Matthis Danek, Adrian Prudlo, Johannes Kassubek, Jan Landwehrmeyer, Bernhard Lauer, Martin Volk, Alexander E. Wiltfang, Jens Diehl‐Schmid, Janine Ludolph, Albert C. Schroeter, Matthias L. Kestler, Hans A. Otto, Markus |
author_facet | Anderl‐Straub, Sarah Lausser, Ludwig Lombardi, Jolina Uttner, Ingo Fassbender, Klaus Fliessbach, Klaus Huppertz, Hans‐Jürgen Jahn, Holger Kornhuber, Johannes Obrig, Hellmuth Schneider, Anja Semler, Elisa Synofzik, Matthis Danek, Adrian Prudlo, Johannes Kassubek, Jan Landwehrmeyer, Bernhard Lauer, Martin Volk, Alexander E. Wiltfang, Jens Diehl‐Schmid, Janine Ludolph, Albert C. Schroeter, Matthias L. Kestler, Hans A. Otto, Markus |
author_sort | Anderl‐Straub, Sarah |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8719425 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87194252022-01-07 Predicting disease progression in behavioral variant frontotemporal dementia Anderl‐Straub, Sarah Lausser, Ludwig Lombardi, Jolina Uttner, Ingo Fassbender, Klaus Fliessbach, Klaus Huppertz, Hans‐Jürgen Jahn, Holger Kornhuber, Johannes Obrig, Hellmuth Schneider, Anja Semler, Elisa Synofzik, Matthis Danek, Adrian Prudlo, Johannes Kassubek, Jan Landwehrmeyer, Bernhard Lauer, Martin Volk, Alexander E. Wiltfang, Jens Diehl‐Schmid, Janine Ludolph, Albert C. Schroeter, Matthias L. Kestler, Hans A. Otto, Markus Alzheimers Dement (Amst) Diagnostic Assessment & Prognosis 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. John Wiley and Sons Inc. 2021-12-31 /pmc/articles/PMC8719425/ /pubmed/35005196 http://dx.doi.org/10.1002/dad2.12262 Text en © 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Diagnostic Assessment & Prognosis Anderl‐Straub, Sarah Lausser, Ludwig Lombardi, Jolina Uttner, Ingo Fassbender, Klaus Fliessbach, Klaus Huppertz, Hans‐Jürgen Jahn, Holger Kornhuber, Johannes Obrig, Hellmuth Schneider, Anja Semler, Elisa Synofzik, Matthis Danek, Adrian Prudlo, Johannes Kassubek, Jan Landwehrmeyer, Bernhard Lauer, Martin Volk, Alexander E. Wiltfang, Jens Diehl‐Schmid, Janine Ludolph, Albert C. Schroeter, Matthias L. Kestler, Hans A. Otto, Markus Predicting disease progression in behavioral variant frontotemporal dementia |
title | Predicting disease progression in behavioral variant frontotemporal dementia |
title_full | Predicting disease progression in behavioral variant frontotemporal dementia |
title_fullStr | Predicting disease progression in behavioral variant frontotemporal dementia |
title_full_unstemmed | Predicting disease progression in behavioral variant frontotemporal dementia |
title_short | Predicting disease progression in behavioral variant frontotemporal dementia |
title_sort | predicting disease progression in behavioral variant frontotemporal dementia |
topic | Diagnostic Assessment & Prognosis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719425/ https://www.ncbi.nlm.nih.gov/pubmed/35005196 http://dx.doi.org/10.1002/dad2.12262 |
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