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  Regional gray matter changes and age predict individual treatment response in Parkinson's disease

Ballarini, T., Mueller, K., Albrecht, F., Růžička, F., Bezdicek, O., Růžička, E., et al. (2018). Regional gray matter changes and age predict individual treatment response in Parkinson's disease. NeuroImage: Clinical, 21: 101636. doi:10.1016/j.nicl.2018.101636.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0002-BC82-7 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0004-F32C-9
Genre: Zeitschriftenartikel

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Ballarini_Mueller_2018.pdf (Verlagsversion), 2MB
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 Urheber:
Ballarini, Tommaso1, Autor           
Mueller, Karsten2, Autor           
Albrecht, Franziska1, Autor           
Růžička, Filip3, 4, Autor
Bezdicek, Ondrej3, Autor
Růžička, Evžen3, Autor
Roth, Jan3, Autor
Vymazal, Josef5, Autor
Jech, Robert3, 5, Autor
Schroeter, Matthias L.1, 4, 5, 6, Autor           
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
3Department of Neurology, First Faculty of Medicine, Charles University, Prague, Czech Republic, ou_persistent22              
4Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
5Department of Radiology, Na Homolce Hospital, Prague, Czech Republic, ou_persistent22              
6FTLD Consortium, Germany, ou_persistent22              

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Schlagwörter: Parkinson's disease; Dopaminergic therapy; Voxel-based morphometry; Support vector machine classification; Predictive models
 Zusammenfassung: We aimed at testing the potential of biomarkers in predicting individual patient response to dopaminergic therapy for Parkinson's disease. Treatment efficacy was assessed in 30 Parkinson's disease patients as motor symptoms improvement from unmedicated to medicated state as assessed by the Unified Parkinson's Disease Rating Scale score III. Patients were stratified into weak and strong responders according to the individual treatment response. A multiple regression was implemented to test the prediction accuracy of age, disease duration and treatment dose and length. Univariate voxel-based morphometry was applied to investigate differences between the two groups on age-corrected T1-weighted magnetic resonance images. Multivariate support vector machine classification was used to predict individual treatment response based on neuroimaging data. Among clinical data, increasing age predicted a weaker treatment response. Additionally, weak responders presented greater brain atrophy in the left temporoparietal operculum. Support vector machine classification revealed that gray matter density in this brain region, including additionally the supplementary and primary motor areas and the cerebellum, was able to differentiate weak and strong responders with 74% accuracy. Remarkably, age and regional gray matter density of the left temporoparietal operculum predicted both and independently treatment response as shown in a combined regression analysis. In conclusion, both increasing age and reduced gray matter density are valid and independent predictors of dopaminergic therapy response in Parkinson's disease.

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Sprache(n): eng - English
 Datum: 2018-08-302018-05-142018-12-092018-12-10
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.nicl.2018.101636
PMID: 30558868
Anderer: Epub ahead of print
 Art des Abschluß: -

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Projektname : -
Grant ID : 16-13323S
Förderprogramm : -
Förderorganisation : Czech Science Foundation GAČR
Projektname : Czech Republic Progres Q27/LF1
Grant ID : -
Förderprogramm : -
Förderorganisation : Charles University
Projektname : German Consortium for Frontotemporal Lobar Degeneration
Grant ID : O1GI1007A
Förderprogramm : -
Förderorganisation : German Federal Ministry of Education, and Research (BMBF)
Projektname : -
Grant ID : SCHR 774/5-1
Förderprogramm : -
Förderorganisation : German Research Foundation (DFG)
Projektname : -
Grant ID : PDF-IRG-1307
Förderprogramm : -
Förderorganisation : Parkinson's Disease Foundation
Projektname : -
Grant ID : MJFF-11362
Förderprogramm : -
Förderorganisation : Michael J. Fox Foundation

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Titel: NeuroImage: Clinical
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Elsevier
Seiten: - Band / Heft: 21 Artikelnummer: 101636 Start- / Endseite: - Identifikator: ISSN: 2213-1582
CoNE: https://pure.mpg.de/cone/journals/resource/2213-1582