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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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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-BC82-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-9FF4-7
Genre: Journal Article

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 Creators:
Ballarini, Tommaso1, Author              
Mueller, Karsten2, Author              
Albrecht, Franziska1, Author              
Růžička, Filip3, 4, Author
Bezdicek, Ondrej3, Author
Růžička, Evžen3, Author
Roth, Jan3, Author
Vymazal, Josef5, Author
Jech, Robert3, 5, Author
Schroeter, Matthias L.1, 4, 5, 6, Author              
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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Free keywords: Parkinson's disease; Dopaminergic therapy; Voxel-based morphometry; Support vector machine classification; Predictive models
 Abstract: 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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Language(s): eng - English
 Dates: 2018-08-302018-05-142018-12-092018-12-10
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.nicl.2018.101636
PMID: 30558868
Other: Epub ahead of print
 Degree: -

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

Source 1

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Title: NeuroImage: Clinical
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Elsevier
Pages: - Volume / Issue: 21 Sequence Number: 101636 Start / End Page: - Identifier: ISSN: 2213-1582
CoNE: https://pure.mpg.de/cone/journals/resource/2213-1582