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Extraction of large-scale structural covariance networks from grey matter volume for Parkinson’s disease classification

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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.

Author information

Author notes

  1. Wei-Che Lin and Ching-Po Lin contributed equally to this work and share the role of corresponding author.

Authors and Affiliations

  1. Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan

    Pei-Lin Lee & Ching-Po Lin

  2. Brain Research Center, National Yang-Ming University, Taipei, Taiwan

    Kun-Hsien Chou & Ching-Po Lin

  3. Institute of Neuroscience, National Yang-Ming University, 155 Li-Nong St., Sec. 2, Peitou, Taipei, Taiwan

    Kun-Hsien Chou & Ching-Po Lin

  4. Department of Neurology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan

    Cheng-Hsien Lu & Nai-Wen Tsai

  5. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan

    Hsiu-Ling Chen & Ai-Ling Hsu

  6. Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, 123 Ta-Pei Road, Niao-Sung, Kaohsiung, Taiwan

    Meng-Hsiang Chen & Wei-Che Lin

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  1. Pei-Lin Lee
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Corresponding authors

Correspondence to Wei-Che Lin or Ching-Po Lin.

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Guarantor

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.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

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.

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• 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

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