The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson's Disease.

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_43BF05B7A397
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson's Disease.
Journal
Frontiers in aging neuroscience
Author(s)
Lorio S., Sambataro F., Bertolino A., Draganski B., Dukart J.
ISSN
1663-4365 (Print)
ISSN-L
1663-4365
Publication state
Published
Issued date
2019
Peer-reviewed
Oui
Volume
11
Pages
57
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
There is an increasing interest in identifying non-invasive biomarkers of disease severity and prognosis in idiopathic Parkinson's disease (PD). Dopamine-transporter SPECT (DAT-SPECT), diffusion tensor imaging (DTI), and structural magnetic resonance imaging (sMRI) provide unique information about the brain's neurotransmitter and microstructural properties. In this study, we evaluate the relative and combined capability of these imaging modalities to predict symptom severity and clinical progression in de novo PD patients. To this end, we used MRI, SPECT, and clinical data of de novo drug-naïve PD patients (n = 205, mean age 61 ± 10) and age-, sex-matched healthy controls (n = 105, mean age 58 ± 12) acquired at baseline. Moreover, we employed clinical data acquired at 1 year follow-up for PD patients with or without L-Dopa treatment in order to predict the progression symptoms severity. Voxel-based group comparisons and covariance analyses were applied to characterize baseline disease-related alterations for DAT-SPECT, DTI, and sMRI. Cortical and subcortical alterations in de novo PD patients were found in all evaluated imaging modalities, in line with previously reported midbrain-striato-cortical network alterations. The combination of these imaging alterations was reliably linked to clinical severity and disease progression at 1 year follow-up in this patient population, providing evidence for the potential use of these modalities as imaging biomarkers for disease severity and prognosis that can be integrated into clinical trials.
Keywords
Parkinson’s disease, covariance analysis, symptoms severity, voxel-based morphometry, voxel-based quantification
Pubmed
Web of science
Open Access
Yes
Create date
07/04/2019 15:58
Last modification date
20/08/2019 14:47
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