Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load.

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License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_3A58877533DB
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load.
Journal
Journal of magnetic resonance imaging
Author(s)
Fang E., Ann C.N., Maréchal B., Lim J.X., Tan SYZ, Li H., Gan J., Tan E.K., Chan L.L.
ISSN
1522-2586 (Electronic)
ISSN-L
1053-1807
Publication state
Published
Issued date
03/2020
Peer-reviewed
Oui
Volume
51
Number
3
Pages
748-756
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
Periventricular leukoaraiosis may be an important pathological change in postural instability gait disorder (PIGD), a motor subtype of Parkinson's disease (PD). Clinical diagnosis of PIGD may be challenging for the general neurologist.
To evaluate 1) the utility of a fully automated volume-based morphometry (Vol-BM) in characterizing imaging diagnostic markers in PD and PIGD, including, 2) novel deep gray nuclear lesion load (GMab), and 3) discriminatory performance of a Vol-BM model construct in classifying the PIGD subtype.
Prospective.
In all, 23 PIGD, 21 PD, and 20 age-matched healthy controls (HC) underwent MRI brain scans and clinical assessments.
3.0T, sagittal 3D-magnetization-prepared rapid gradient echo (MPRAGE), and fluid-attenuated inversion recovery imaging (FLAIR) sequences.
Clinical assessment was conducted by a movement disorder neurologist. The MR brain images were then segmented using an automated multimodal Vol-BM algorithm (MorphoBox) and reviewed by two authors independently.
Brain segmentation and clinical parameter differences and dependence were assessed using analysis of variance (ANOVA) and regression analysis, respectively. Logistic regression was performed to differentiate PIGD from PD, and discriminative reliability was evaluated using receiver operating characteristic (ROC) analysis.
Significantly higher white matter lesion load (WMab) (P < 0.01), caudate GMab (P < 0.05), and lateral and third ventricular (P < 0.05) volumetry were found in PIGD, compared with PD and HC. WMab, caudate and putamen GMab, and caudate, lateral, and third ventricular volumetry showed significant coefficients (P < 0.005) in linear regressions with balance and gait assessments in both patient groups. A model incorporating WMab, caudate GMab, and caudate GM discriminated PIGD from PD and HC with a sensitivity = 0.83 and specificity = 0.76 (AUC = 0.84).
Fast, unbiased quantification of microstructural brain changes in PD and PIGD is feasible using automated Vol-BM. Composite lesion load in the white matter and caudate, and caudate volumetry discriminated PIGD from PD and HC, and showed potential in classification of these disorders using supervised machine learning.
1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:748-756.
Keywords
Parkinson's disease, abnormal deep gray matter, automated segmentation, postural instability gait disorder, volume-based morphometry
Pubmed
Web of science
Open Access
Yes
Create date
19/08/2019 8:36
Last modification date
15/01/2021 7:08
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