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

Détails

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Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_43BF05B7A397
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson's Disease.
Périodique
Frontiers in aging neuroscience
Auteur⸱e⸱s
Lorio S., Sambataro F., Bertolino A., Draganski B., Dukart J.
ISSN
1663-4365 (Print)
ISSN-L
1663-4365
Statut éditorial
Publié
Date de publication
2019
Peer-reviewed
Oui
Volume
11
Pages
57
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
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.
Mots-clé
Parkinson’s disease, covariance analysis, symptoms severity, voxel-based morphometry, voxel-based quantification
Pubmed
Web of science
Open Access
Oui
Création de la notice
07/04/2019 14:58
Dernière modification de la notice
20/08/2019 13:47
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