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| 001 | 910743 | ||
| 005 | 20221219115102.0 | ||
| 024 | 7 | _ | |a 10.1016/j.dadm.2019.01.011 |2 doi |
| 024 | 7 | _ | |a 2128/32629 |2 Handle |
| 024 | 7 | _ | |a 30984816 |2 pmid |
| 024 | 7 | _ | |a WOS:000723892800035 |2 WOS |
| 037 | _ | _ | |a FZJ-2022-04113 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Eimeren, Thilo |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Neuroimaging biomarkers for clinical trials in atypical parkinsonian disorders: Proposal for a Neuroimaging Biomarker Utility System |
| 260 | _ | _ | |a Hoboken, NJ |c 2019 |b Wiley |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1668667532_3489 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a IntroductionTherapeutic strategies targeting protein aggregations are ready for clinical trials in atypical parkinsonian disorders. Therefore, there is an urgent need for neuroimaging biomarkers to help with the early detection of neurodegenerative processes, the early differentiation of the underlying pathology, and the objective assessment of disease progression. However, there currently is not yet a consensus in the field on how to describe utility of biomarkers for clinical trials in atypical parkinsonian disorders.MethodsTo promote standardized use of neuroimaging biomarkers for clinical trials, we aimed to develop a conceptual framework to characterize in more detail the kind of neuroimaging biomarkers needed in atypical parkinsonian disorders, identify the current challenges in ascribing utility of these biomarkers, and propose criteria for a system that may guide future studies.ResultsAs a consensus outcome, we describe the main challenges in ascribing utility of neuroimaging biomarkers in atypical parkinsonian disorders, and we propose a conceptual framework that includes a graded system for the description of utility of a specific neuroimaging measure. We included separate categories for the ability to accurately identify an intention-to-treat patient population early in the disease (Early), to accurately detect a specific underlying pathology (Specific), and the ability to monitor disease progression (Progression).DiscussionWe suggest that the advancement of standardized neuroimaging in the field of atypical parkinsonian disorders will be furthered by a well-defined reference frame for the utility of biomarkers. The proposed utility system allows a detailed and graded description of the respective strengths of neuroimaging biomarkers in the currently most relevant areas of application in clinical trials.Keywords: Biomarker, Trials, PSP, MSA, CBD, CBS, Neurodegeneration, Biomarker, Multicentric, Multisite, Neuroimaging, Harmonization, PET, MRI |
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| 700 | 1 | _ | |a Berg, Daniela |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Bohnen, Nico |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Ceravolo, Roberto |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Drzezga, Alexander |0 P:(DE-Juel1)177611 |b 5 |
| 700 | 1 | _ | |a Höglinger, Günter U. |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Higuchi, Makoto |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Lehericy, Stephane |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Lewis, Simon |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Monchi, Oury |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Nestor, Peter |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Ondrus, Matej |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Pavese, Nicola |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Peralta, María Cecilia |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Piccini, Paola |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Pineda-Pardo, José Ángel |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Rektorová, Irena |0 P:(DE-HGF)0 |b 17 |
| 700 | 1 | _ | |a Rodríguez-Oroz, María |0 P:(DE-HGF)0 |b 18 |
| 700 | 1 | _ | |a Rominger, Axel |0 P:(DE-HGF)0 |b 19 |
| 700 | 1 | _ | |a Seppi, Klaus |0 P:(DE-HGF)0 |b 20 |
| 700 | 1 | _ | |a Stoessl, A. Jon |0 P:(DE-HGF)0 |b 21 |
| 700 | 1 | _ | |a Tessitore, Alessandro |0 P:(DE-HGF)0 |b 22 |
| 700 | 1 | _ | |a Thobois, Stephane |0 P:(DE-HGF)0 |b 23 |
| 700 | 1 | _ | |a Kaasinen, Valtteri |0 P:(DE-HGF)0 |b 24 |
| 700 | 1 | _ | |a Wenning, Gregor |0 P:(DE-HGF)0 |b 25 |
| 700 | 1 | _ | |a Siebner, Hartwig R. |0 P:(DE-HGF)0 |b 26 |
| 700 | 1 | _ | |a Strafella, Antonio P. |0 P:(DE-HGF)0 |b 27 |
| 700 | 1 | _ | |a Rowe, James B. |0 P:(DE-HGF)0 |b 28 |
| 773 | _ | _ | |a 10.1016/j.dadm.2019.01.011 |g Vol. 11, no. 1, p. 301 - 309 |0 PERI:(DE-600)2832898-X |n 1 |p 301 - 309 |t Alzheimer's & dementia / Diagnosis, assessment & disease monitoring |v 11 |y 2019 |x 2352-8729 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/910743/files/FZJ-2022-04113_Neuroimaging%20biomarkers%20for%20clinical_2019.pdf |y OpenAccess |
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