Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/161243
Title: Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography
Author: Abós, Alexandra
Baggio, Hugo César
Segura i Fàbregas, Bàrbara
Campabadal, Anna
Uribe Codesal, Carme
Giraldo, Darly M.
Pérez Soriano, Alexandra
Muñoz, Esteban
Compta, Yaroslau
Junqué i Plaja, Carme, 1955-
Martí Domènech, Ma. Josep
Keywords: Malalties neurodegeneratives
Imatges per ressonància magnètica
Xarxes neuronals (Neurobiologia)
Malaltia de Parkinson
Neurodegenerative Diseases
Magnetic resonance imaging
Neural networks (Neurobiology)
Parkinson's disease
Issue Date: 11-Nov-2019
Publisher: Nature Publishing Group
Abstract: Recent studies combining difusion 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 classifcation performance of subcortical FA and MD was also evaluated to compare the discriminant ability between difusion tensor-derived metrics and NOS. Using difusion-weighted images acquired in a 3T 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 classifcation procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classifed. NOS features outperformed the discrimination performance obtained with FA and MD. Our fndings 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 difusion tensor-derived metrics for the detection of MSA.
Note: Reproducció del document publicat a: https://doi.org/10.1038/s41598-019-52829-8
It is part of: Scientific Reports, 2019, vol. 9, num. 1, p. 16488
URI: http://hdl.handle.net/2445/161243
Related resource: https://doi.org/10.1038/s41598-019-52829-8
ISSN: 2045-2322
Appears in Collections:Articles publicats en revistes (Medicina)
Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)

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