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

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 t...

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Autores principales: Lorio, Sara, Sambataro, Fabio, Bertolino, Alessandro, Draganski, Bogdan, Dukart, Juergen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428714/
https://www.ncbi.nlm.nih.gov/pubmed/30930768
http://dx.doi.org/10.3389/fnagi.2019.00057
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author Lorio, Sara
Sambataro, Fabio
Bertolino, Alessandro
Draganski, Bogdan
Dukart, Juergen
author_facet Lorio, Sara
Sambataro, Fabio
Bertolino, Alessandro
Draganski, Bogdan
Dukart, Juergen
author_sort Lorio, Sara
collection PubMed
description 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.
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spelling pubmed-64287142019-03-29 The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson’s Disease Lorio, Sara Sambataro, Fabio Bertolino, Alessandro Draganski, Bogdan Dukart, Juergen Front Aging Neurosci Neuroscience 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. Frontiers Media S.A. 2019-03-15 /pmc/articles/PMC6428714/ /pubmed/30930768 http://dx.doi.org/10.3389/fnagi.2019.00057 Text en Copyright © 2019 Lorio, Sambataro, Bertolino, Draganski and Dukart. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Lorio, Sara
Sambataro, Fabio
Bertolino, Alessandro
Draganski, Bogdan
Dukart, Juergen
The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson’s Disease
title The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson’s Disease
title_full The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson’s Disease
title_fullStr The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson’s Disease
title_full_unstemmed The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson’s Disease
title_short The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson’s Disease
title_sort combination of dat-spect, structural and diffusion mri predicts clinical progression in parkinson’s disease
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428714/
https://www.ncbi.nlm.nih.gov/pubmed/30930768
http://dx.doi.org/10.3389/fnagi.2019.00057
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