Brain network characteristics and cognitive performance in motor subtypes of Parkinson's disease: A resting state fMRI study.
Cognition
Magnetic resonance imaging
Parkinson's disease
Resting-state networks
Subtypes
fMRI
Journal
Parkinsonism & related disorders
ISSN: 1873-5126
Titre abrégé: Parkinsonism Relat Disord
Pays: England
ID NLM: 9513583
Informations de publication
Date de publication:
12 2022
12 2022
Historique:
received:
25
07
2022
revised:
15
10
2022
accepted:
23
10
2022
pubmed:
5
11
2022
medline:
7
12
2022
entrez:
4
11
2022
Statut:
ppublish
Résumé
Parkinson's disease (PD) is a heterogeneous disorder with great variability in motor and non-motor manifestations. It is hypothesized that different motor subtypes are characterized by different neuropsychiatric and cognitive symptoms, but the underlying correlates in cerebral connectivity remain unknown. Our aim is to compare brain network connectivity between the postural instability and gait disorder (PIGD) and tremor-dominant (TD) subtypes, using both a within- and between-network analysis. This cross-sectional resting-state fMRI study includes 81 PD patients, 54 belonging to the PIGD and 27 to the TD subgroup. Group-level spatial maps were created using independent component analysis. Differences in functional connectivity were investigated using dual regression analysis and inter-network connectivity analysis. An additional voxel-based morphometry analysis was performed to examine if results were influenced by grey matter atrophy. The PIGD subgroup scored worse than the TD subgroup on all cognitive domains. Resting-state fMRI network analyses suggested that the connection between the visual and sensorimotor network is a potential differentiator between PIGD and TD subgroups. However, after correcting for dopaminergic medication use these results were not significant anymore. There was no between-group difference in grey matter volume. Despite clear motor and cognitive differences between the PIGD and TD subtypes, no significant differences were found in network connectivity. Methodological challenges, substantial symptom heterogeneity and many involved variables make analyses and hypothesis building around PD subtypes highly complex. More sensitive visualisation methods combined with machine learning approaches may be required in the search for characteristic underpinnings of PD subtypes.
Identifiants
pubmed: 36332290
pii: S1353-8020(22)00355-8
doi: 10.1016/j.parkreldis.2022.10.027
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
32-38Informations de copyright
Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.