Abstract
Objectives
To identify disease-related spatial covariance patterns of grey matter volume as an aid in the classification of Parkinson’s disease (PD).
Methods
Seventy structural covariance networks (SCNs) based on grey matter volume covariance patterns were defined using independent component analysis with T1-weighted structural MRI scans (discovery sample, 70 PD patients and 70 healthy controls). An image-based classifier was constructed from SCNs using a multiple logistic regression analysis with a leave-one-out cross-validation-based feature selection scheme. A validation sample (26 PD patients and 26 healthy controls) was further collected to evaluate the generalization ability of the constructed classifier.
Results
In the discovery sample, 13 SCNs, including the cerebellum, anterior temporal poles, parahippocampal gyrus, parietal operculum, occipital lobes, supramarginal gyri, superior parietal lobes, paracingulate gyri and precentral gyri, had higher classification performance for PD. In the validation sample, the classifier had moderate generalization ability, with a mean sensitivity of 81%, specificity of 69% and overall accuracy of 75%. Furthermore, certain individual SCNs were also associated with disease severity.
Conclusions
Although not applicable for routine care at present, our results provide empirical evidence that disease-specific, large-scale structural networks can provide a foundation for the further improvement of diagnostic MRI in movement disorders.
Key Points
• Disease-specific, large-scale SCNs can be identified from structural MRI.
• A new network-based framework for PD classification is proposed.
• An SCN-based classifier had moderate generalization ability in PD classification.
• The selected SCNs provide valuable functional information regarding PD patients.
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Abbreviations
- ANCOVA:
-
Analysis of covariance
- CSF:
-
Cerebrospinal fluid
- DARTEL:
-
Diffeomorphic anatomical registration exponentiated lie algebra
- GMV:
-
Grey matter volume
- HY stage:
-
Hoehn and Yahr stages
- ICA:
-
Independent component analysis
- LOOCV:
-
Leave-one-out cross-validation
- MNI:
-
Montreal Neurological Institute
- MELODIC:
-
Multivariate exploratory linear optimized decomposition into independent components
- PD:
-
Parkinson’s disease
- ROC:
-
Receiver operator characteristic
- SE-ADL:
-
Schwab and England activities of daily living scale
- SCNs:
-
Structural covariance networks
- UPDRS:
-
Unified Parkinson’s disease rating scale
- VBM:
-
Voxel-based morphometry
- WM:
-
White matter
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Funding
This work was supported by funds from the National Science Council (MOST 103-2314-B-182A-010 -MY3 to W-C Lin, MOST 104-2221-E-010-013- to C-P Lin, and MOST 104-2314-B-182A-053 to H-L Chen) and Chang Gung Memorial Hospital (CMRPG891511 and CMRPG8B0831 to W-C Lin, and CMRPG8E0621 to M-H Chen). The authors would like to thank the patients affected by PD and their families for their involvement and altruism. The authors declare no competing financial interests.
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The scientific guarantor of this publication is Wei-Che Lin in Kaohsiung Chang Gung Memorial Hospital.
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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
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Written informed consent was obtained from all subjects (patients) in this study.
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We declare that all human and animal studies have been approved by the Institutional Review Board of Chang Gung Memorial Hospital and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. We declare that all patients gave informed consent prior to inclusion in this study.
Methodology
• prospective
• diagnostic or prognostic study
• performed at one institution
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Lee, PL., Chou, KH., Lu, CH. et al. Extraction of large-scale structural covariance networks from grey matter volume for Parkinson’s disease classification. Eur Radiol 28, 3296–3305 (2018). https://doi.org/10.1007/s00330-018-5342-1
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DOI: https://doi.org/10.1007/s00330-018-5342-1