Extraction of large-scale structural covariance networks from grey matter volume for Parkinson’s disease classification
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To identify disease-related spatial covariance patterns of grey matter volume as an aid in the classification of Parkinson’s disease (PD).
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.
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.
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.
• 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.
KeywordsClassification Grey matter Magnetic resonance imaging Parkinson disease Structural network
Analysis of covariance
Diffeomorphic anatomical registration exponentiated lie algebra
Grey matter volume
- HY stage
Hoehn and Yahr stages
Independent component analysis
Montreal Neurological Institute
Multivariate exploratory linear optimized decomposition into independent components
Receiver operator characteristic
Schwab and England activities of daily living scale
Structural covariance networks
Unified Parkinson’s disease rating scale
Compliance with ethical standards
The scientific guarantor of this publication is Wei-Che Lin in Kaohsiung Chang Gung Memorial Hospital.
Conflict of interest
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.
Written informed consent was obtained from all subjects (patients) in this study.
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.
• diagnostic or prognostic study
• performed at one institution
- 19.Zeighami Y, Ulla M, Iturria-Medina Y et al (2015) Network structure of brain atrophy in de novo Parkinson's disease. elife. https://doi.org/10.7554/eLife.08440
- 21.Li X, Xing Y, Schwarz ST, Auer DP (2017) Limbic grey matter changes in early Parkinson's disease. Hum Brain Mapp. https://doi.org/10.1002/hbm.23610
- 25.Schwab RS, Engeland A (1969) Projection technique for evaluating surgery in Parkinson's disease. Livingstone, EdinburghGoogle Scholar
- 48.Sawada M, Imamura K, Nagatsu T (2006) Role of cytokines in inflammatory process in Parkinson's disease. J Neural Transm Suppl: 373–381Google Scholar