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

  • Pei-Lin Lee
  • Kun-Hsien Chou
  • Cheng-Hsien Lu
  • Hsiu-Ling Chen
  • Nai-Wen Tsai
  • Ai-Ling Hsu
  • Meng-Hsiang Chen
  • Wei-Che Lin
  • Ching-Po Lin
Neuro
  • 85 Downloads

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.

Keywords

Classification Grey matter Magnetic resonance imaging Parkinson disease Structural network 

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

Notes

Compliance with ethical standards

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.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5342_MOESM1_ESM.docx (270 kb)
ESM 1 (DOCX 270 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Pei-Lin Lee
    • 1
  • Kun-Hsien Chou
    • 2
    • 3
  • Cheng-Hsien Lu
    • 4
  • Hsiu-Ling Chen
    • 5
  • Nai-Wen Tsai
    • 4
  • Ai-Ling Hsu
    • 5
  • Meng-Hsiang Chen
    • 6
  • Wei-Che Lin
    • 6
  • Ching-Po Lin
    • 1
    • 2
    • 3
  1. 1.Department of Biomedical Imaging and Radiological SciencesNational Yang-Ming UniversityTaipeiTaiwan
  2. 2.Brain Research CenterNational Yang-Ming UniversityTaipeiTaiwan
  3. 3.Institute of NeuroscienceNational Yang-Ming UniversityTaipeiTaiwan
  4. 4.Department of NeurologyKaohsiung Chang Gung Memorial Hospital and Chang Gung University College of MedicineKaohsiungTaiwan
  5. 5.Graduate Institute of Biomedical Electronics and BioinformaticsNational Taiwan UniversityTaipeiTaiwan
  6. 6.Department of Diagnostic RadiologyKaohsiung Chang Gung Memorial Hospital and Chang Gung University College of MedicineKaohsiungTaiwan

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