Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivity

Objectives: Investigating biomarkers to demonstrate progression of Parkinson's disease (PD) is of high priority. We investigated the association of brain structural properties with progression of clinical outcomes and their ability to differentiate clinical subtypes of PD.Methods: A comprehensi...

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Main Authors: Nooshin Abbasi, Seyed-Mohammad Fereshtehnejad, Yashar Zeighami, Kevin Michel-Herve Larcher, Ronald B. Postuma, Alain Dagher
Format: Article
Language:English
Published: Elsevier 2020-01-01
Series:NeuroImage: Clinical
Online Access:http://www.sciencedirect.com/science/article/pii/S2213158219304589
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spelling doaj-0a04beb361d54d5bb25dd7c710ece2cf2020-11-25T02:25:55ZengElsevierNeuroImage: Clinical2213-15822020-01-0125Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivityNooshin Abbasi0Seyed-Mohammad Fereshtehnejad1Yashar Zeighami2Kevin Michel-Herve Larcher3Ronald B. Postuma4Alain Dagher5McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St., Montreal, Quebec H3A 2B4, Canada; Corresponding author.Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada; Division of Neurology, Department of Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada; Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institute, Stockholm, SwedenMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St., Montreal, Quebec H3A 2B4, CanadaBiospective Inc., Montreal, Quebec, CanadaDepartment of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada; Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, CanadaMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St., Montreal, Quebec H3A 2B4, CanadaObjectives: Investigating biomarkers to demonstrate progression of Parkinson's disease (PD) is of high priority. We investigated the association of brain structural properties with progression of clinical outcomes and their ability to differentiate clinical subtypes of PD.Methods: A comprehensive set of clinical features was evaluated at baseline and 4.5-year follow-up for 144 de-novo PD patients from the Parkinson's Progression Markers Initiative. We created a global composite outcome (GCO) by combining z-scores of non-motor and motor symptoms, motor signs, overall activities of daily living and global cognition, as a single numeric indicator of prognosis. We classified patients into three subtypes based on multi-domain clinical criteria: ‘mild motor-predominant’, ‘intermediate’ and ‘diffuse-malignant’. We analyzed diffusion-weighted scans at the early drug-naïve stage and extracted fractional anisotropy and mean diffusivity (MD) of basal ganglia and cortical sub-regions. Then, we employed graph theory to calculate network properties and used network-based statistic to investigate our primary hypothesis.Results: Baseline MD of globus pallidus was associated with worsening of motor severity, cognition, and GCO after 4.5 years of follow-up. Connectivity disruption at baseline was correlated with decline in cognition, and increase in GCO. Baseline MD of nucleus accumbens, globus pallidus and basal-ganglia were linked to clinical subtypes at 4.5-year of follow-up. Disruption in sub-cortical networks associated with being subtyped as ‘diffuse-malignant’ versus ‘mild motor-predominant’ after 4.5 years.Conclusions: Diffusion imaging analysis at the early de-novo stage of PD was able to differentiate clinical sub-types of PD after 4.5 years and was highly associated with future clinical outcomes of PD.http://www.sciencedirect.com/science/article/pii/S2213158219304589
collection DOAJ
language English
format Article
sources DOAJ
author Nooshin Abbasi
Seyed-Mohammad Fereshtehnejad
Yashar Zeighami
Kevin Michel-Herve Larcher
Ronald B. Postuma
Alain Dagher
spellingShingle Nooshin Abbasi
Seyed-Mohammad Fereshtehnejad
Yashar Zeighami
Kevin Michel-Herve Larcher
Ronald B. Postuma
Alain Dagher
Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivity
NeuroImage: Clinical
author_facet Nooshin Abbasi
Seyed-Mohammad Fereshtehnejad
Yashar Zeighami
Kevin Michel-Herve Larcher
Ronald B. Postuma
Alain Dagher
author_sort Nooshin Abbasi
title Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivity
title_short Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivity
title_full Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivity
title_fullStr Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivity
title_full_unstemmed Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivity
title_sort predicting severity and prognosis in parkinson's disease from brain microstructure and connectivity
publisher Elsevier
series NeuroImage: Clinical
issn 2213-1582
publishDate 2020-01-01
description Objectives: Investigating biomarkers to demonstrate progression of Parkinson's disease (PD) is of high priority. We investigated the association of brain structural properties with progression of clinical outcomes and their ability to differentiate clinical subtypes of PD.Methods: A comprehensive set of clinical features was evaluated at baseline and 4.5-year follow-up for 144 de-novo PD patients from the Parkinson's Progression Markers Initiative. We created a global composite outcome (GCO) by combining z-scores of non-motor and motor symptoms, motor signs, overall activities of daily living and global cognition, as a single numeric indicator of prognosis. We classified patients into three subtypes based on multi-domain clinical criteria: ‘mild motor-predominant’, ‘intermediate’ and ‘diffuse-malignant’. We analyzed diffusion-weighted scans at the early drug-naïve stage and extracted fractional anisotropy and mean diffusivity (MD) of basal ganglia and cortical sub-regions. Then, we employed graph theory to calculate network properties and used network-based statistic to investigate our primary hypothesis.Results: Baseline MD of globus pallidus was associated with worsening of motor severity, cognition, and GCO after 4.5 years of follow-up. Connectivity disruption at baseline was correlated with decline in cognition, and increase in GCO. Baseline MD of nucleus accumbens, globus pallidus and basal-ganglia were linked to clinical subtypes at 4.5-year of follow-up. Disruption in sub-cortical networks associated with being subtyped as ‘diffuse-malignant’ versus ‘mild motor-predominant’ after 4.5 years.Conclusions: Diffusion imaging analysis at the early de-novo stage of PD was able to differentiate clinical sub-types of PD after 4.5 years and was highly associated with future clinical outcomes of PD.
url http://www.sciencedirect.com/science/article/pii/S2213158219304589
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