Hierarchical cluster analysis of multimodal imaging data identifies brain atrophy and cognitive patterns in Parkinson's disease.
Aged
Aged, 80 and over
Atrophy
/ pathology
Cluster Analysis
Cognitive Dysfunction
/ classification
Diffusion Tensor Imaging
/ methods
Female
Gray Matter
/ diagnostic imaging
Humans
Magnetic Resonance Imaging
/ methods
Male
Middle Aged
Multimodal Imaging
Parkinson Disease
/ classification
White Matter
/ diagnostic imaging
Cluster analysis
DTI
Gray matter volume
Magnetic resonance imaging
Parkinson disease
Journal
Parkinsonism & related disorders
ISSN: 1873-5126
Titre abrégé: Parkinsonism Relat Disord
Pays: England
ID NLM: 9513583
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
received:
17
01
2020
revised:
15
09
2020
accepted:
10
11
2020
pubmed:
24
11
2020
medline:
23
11
2021
entrez:
23
11
2020
Statut:
ppublish
Résumé
Parkinson's disease (PD) is a heterogeneous condition. Cluster analysis based on cortical thickness has been used to define distinct patterns of brain atrophy in PD. However, the potential of other neuroimaging modalities, such as white matter (WM) fractional anisotropy (FA), which has also been demonstrated to be altered in PD, has not been investigated. We aim to characterize PD subtypes using a multimodal clustering approach based on cortical and subcortical gray matter (GM) volumes and FA measures. We included T1-weighted and diffusion-weighted MRI data from 62 PD patients and 33 healthy controls. We extracted mean GM volumes from 48 cortical and 17 subcortical regions using FSL-VBM, and the mean FA from 20 WM tracts using Tract-Based Spatial Statistics (TBSS). Hierarchical cluster analysis was performed with the PD sample using Ward's linkage method. Whole-brain voxel-wise intergroup comparisons of VBM and TBSS data were also performed using FSL. Neuropsychological and demographic statistical analyses were conducted using IBM SPSS Statistics 25.0. We identified three PD subtypes, with prominent differences in GM patterns and little WM involvement. One group (n = 15) with widespread cortical and subcortical GM volume and WM FA reductions and pronounced cognitive deficits; a second group (n = 21) with only cortical atrophy limited to frontal and temporal regions and more specific neuropsychological impairment, and a third group (n = 26) without detectable atrophy or cognition impairment. Multimodal MRI data allows classifying PD patients into groups according to GM and WM patterns, which in turn are associated with the cognitive profile.
Sections du résumé
BACKGROUND
Parkinson's disease (PD) is a heterogeneous condition. Cluster analysis based on cortical thickness has been used to define distinct patterns of brain atrophy in PD. However, the potential of other neuroimaging modalities, such as white matter (WM) fractional anisotropy (FA), which has also been demonstrated to be altered in PD, has not been investigated.
OBJECTIVE
We aim to characterize PD subtypes using a multimodal clustering approach based on cortical and subcortical gray matter (GM) volumes and FA measures.
METHODS
We included T1-weighted and diffusion-weighted MRI data from 62 PD patients and 33 healthy controls. We extracted mean GM volumes from 48 cortical and 17 subcortical regions using FSL-VBM, and the mean FA from 20 WM tracts using Tract-Based Spatial Statistics (TBSS). Hierarchical cluster analysis was performed with the PD sample using Ward's linkage method. Whole-brain voxel-wise intergroup comparisons of VBM and TBSS data were also performed using FSL. Neuropsychological and demographic statistical analyses were conducted using IBM SPSS Statistics 25.0.
RESULTS
We identified three PD subtypes, with prominent differences in GM patterns and little WM involvement. One group (n = 15) with widespread cortical and subcortical GM volume and WM FA reductions and pronounced cognitive deficits; a second group (n = 21) with only cortical atrophy limited to frontal and temporal regions and more specific neuropsychological impairment, and a third group (n = 26) without detectable atrophy or cognition impairment.
CONCLUSION
Multimodal MRI data allows classifying PD patients into groups according to GM and WM patterns, which in turn are associated with the cognitive profile.
Identifiants
pubmed: 33227683
pii: S1353-8020(20)30860-9
doi: 10.1016/j.parkreldis.2020.11.010
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
16-23Informations de copyright
Copyright © 2020. Published by Elsevier Ltd.