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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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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