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Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography.

ABSTRACT: Recent studies combining diffusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson's disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classification performance of subcortical FA and MD was also evaluated to compare the discriminant ability between diffusion tensor-derived metrics and NOS. Using diffusion-weighted images acquired in a 3?T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classification procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classified. NOS features outperformed the discrimination performance obtained with FA and MD. Our findings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than diffusion tensor-derived metrics for the detection of MSA.

SUBMITTER: Abos A

PROVIDER: S-EPMC6848175 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography.

Abos Alexandra A Baggio Hugo C HC Segura Barbara B Campabadal Anna A Uribe Carme C Giraldo Darly Milena DM Perez-Soriano Alexandra A Muñoz Esteban E Compta Yaroslau Y Junque Carme C Marti Maria Jose MJ

Scientific reports 20191111 1

Recent studies combining diffusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson's disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be use ...[more]

PMID: 31712681

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Project description:Synucleinopathies are neurodegenerative diseases that are associated with the misfolding and aggregation of α-synuclein, including Parkinson's disease, dementia with Lewy bodies and multiple system atrophy1. Clinically, it is challenging to differentiate Parkinson's disease and multiple system atrophy, especially at the early stages of disease2. Aggregates of α-synuclein in distinct synucleinopathies have been proposed to represent different conformational strains of α-synuclein that can self-propagate and spread from cell to cell3-6. Protein misfolding cyclic amplification (PMCA) is a technique that has previously been used to detect α-synuclein aggregates in samples of cerebrospinal fluid with high sensitivity and specificity7,8. Here we show that the α-synuclein-PMCA assay can discriminate between samples of cerebrospinal fluid from patients diagnosed with Parkinson's disease and samples from patients with multiple system atrophy, with an overall sensitivity of 95.4%. We used a combination of biochemical, biophysical and biological methods to analyse the product of α-synuclein-PMCA, and found that the characteristics of the α-synuclein aggregates in the cerebrospinal fluid could be used to readily distinguish between Parkinson's disease and multiple system atrophy. We also found that the properties of aggregates that were amplified from the cerebrospinal fluid were similar to those of aggregates that were amplified from the brain. These findings suggest that α-synuclein aggregates that are associated with Parkinson's disease and multiple system atrophy correspond to different conformational strains of α-synuclein, which can be amplified and detected by α-synuclein-PMCA. Our results may help to improve our understanding of the mechanism of α-synuclein misfolding and the structures of the aggregates that are implicated in different synucleinopathies, and may also enable the development of a biochemical assay to discriminate between Parkinson's disease and multiple system atrophy.

Dataset Information

Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography.

Publications

Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography.

Similar Datasets

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