European Radiology

, Volume 28, Issue 8, pp 3296–3305 | Cite as

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



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.

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.


Classification Grey matter Magnetic resonance imaging Parkinson disease Structural network 



Analysis of covariance


Cerebrospinal fluid


Diffeomorphic anatomical registration exponentiated lie algebra


Grey matter volume

HY stage

Hoehn and Yahr stages


Independent component analysis


Leave-one-out cross-validation


Montreal Neurological Institute


Multivariate exploratory linear optimized decomposition into independent components


Parkinson’s disease


Receiver operator characteristic


Schwab and England activities of daily living scale


Structural covariance networks


Unified Parkinson’s disease rating scale


Voxel-based morphometry


White matter



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.

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.

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.


• prospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

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


  1. 1.
    Sveinbjornsdottir S (2016) The clinical symptoms of Parkinson's disease. J Neurochem 139:318–324CrossRefPubMedGoogle Scholar
  2. 2.
    Chaudhuri KR, Healy DG, Schapira AH, National Institute for Clinical Excellence (2006) Non-motor symptoms of Parkinson's disease: diagnosis and management. Lancet Neurol 5:235–245CrossRefPubMedGoogle Scholar
  3. 3.
    Luo C, Song W, Chen Q et al (2014) Reduced functional connectivity in early-stage drug-naive Parkinson's disease: a resting-state fMRI study. Neurobiol Aging 35:431–441CrossRefPubMedGoogle Scholar
  4. 4.
    Pan PL, Song W, Shang HF (2012) Voxel-wise meta-analysis of gray matter abnormalities in idiopathic Parkinson's disease. Eur J Neurol 19:199–206CrossRefPubMedGoogle Scholar
  5. 5.
    Tessitore A, Amboni M, Cirillo G et al (2012) Regional gray matter atrophy in patients with Parkinson disease and freezing of gait. AJNR Am J Neuroradiol 33:1804–1809CrossRefPubMedGoogle Scholar
  6. 6.
    Tessitore A, Santangelo G, De Micco R et al (2016) Cortical thickness changes in patients with Parkinson's disease and impulse control disorders. Parkinsonism Relat Disord 24:119–125CrossRefPubMedGoogle Scholar
  7. 7.
    Mak E, Su L, Williams GB et al (2015) Baseline and longitudinal grey matter changes in newly diagnosed Parkinson's disease: ICICLE-PD study. Brain 138:2974–2986CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Mechelli A, Friston KJ, Frackowiak RS, Price CJ (2005) Structural covariance in the human cortex. J Neurosci 25:8303–8310CrossRefPubMedGoogle Scholar
  9. 9.
    Chou KH, Lin WC, Lee PL et al (2015) Structural covariance networks of striatum subdivision in patients with Parkinson's disease. Hum Brain Mapp 36:1567–1584CrossRefPubMedGoogle Scholar
  10. 10.
    Alexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14:322–336CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Pagani M, Bifone A, Gozzi A (2016) Structural covariance networks in the mouse brain. NeuroImage 129:55–63CrossRefPubMedGoogle Scholar
  12. 12.
    Paviour DC, Price SL, Stevens JM, Lees AJ, Fox NC (2005) Quantitative MRI measurement of superior cerebellar peduncle in progressive supranuclear palsy. Neurology 64:675–679CrossRefPubMedGoogle Scholar
  13. 13.
    Marquand AF, Filippone M, Ashburner J et al (2013) Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach. PLoS One 8:e69237CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Lin WC, Chou KH, Lee PL et al (2017) Parkinson's disease: diagnostic utility of volumetric imaging. Neuroradiology 59:367–377CrossRefPubMedGoogle Scholar
  15. 15.
    Fornito A, Zalesky A, Breakspear M (2015) The connectomics of brain disorders. Nat Rev Neurosci 16:159–172CrossRefPubMedGoogle Scholar
  16. 16.
    Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD (2009) Neurodegenerative diseases target large-scale human brain networks. Neuron 62:42–52CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Willette AA, Calhoun VD, Egan JM, Kapogiannis D, Alzheimers Disease Neuroimaging Initiative (2014) Prognostic classification of mild cognitive impairment and Alzheimer's disease: MRI independent component analysis. Psychiatry Res 224:81–88CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Hafkemeijer A, Moller C, Dopper EG et al (2016) Differences in structural covariance brain networks between behavioral variant frontotemporal dementia and Alzheimer's disease. Hum Brain Mapp 37:978–988CrossRefPubMedGoogle Scholar
  19. 19.
    Zeighami Y, Ulla M, Iturria-Medina Y et al (2015) Network structure of brain atrophy in de novo Parkinson's disease. elife.
  20. 20.
    Rektorova I, Biundo R, Marecek R, Weis L, Aarsland D, Antonini A (2014) Grey matter changes in cognitively impaired Parkinson's disease patients. PLoS One 9:e85595CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Li X, Xing Y, Schwarz ST, Auer DP (2017) Limbic grey matter changes in early Parkinson's disease. Hum Brain Mapp.
  22. 22.
    Postuma RB, Berg D, Stern M et al (2015) MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord 30:1591–1601CrossRefPubMedGoogle Scholar
  23. 23.
    Gasser T, Bressman S, Durr A, Higgins J, Klockgether T, Myers RH (2003) State of the art review: molecular diagnosis of inherited movement disorders. Movement Disorders Society task force on molecular diagnosis. Mov Disord 18:3–18CrossRefPubMedGoogle Scholar
  24. 24.
    Goetz CG, Poewe W, Rascol O et al (2004) Movement Disorder Society Task Force report on the Hoehn and Yahr staging scale: status and recommendations. Mov Disord 19:1020–1028CrossRefPubMedGoogle Scholar
  25. 25.
    Schwab RS, Engeland A (1969) Projection technique for evaluating surgery in Parkinson's disease. Livingstone, EdinburghGoogle Scholar
  26. 26.
    Ashburner J, Friston KJ (2000) Voxel-based morphometry–the methods. NeuroImage 11:805–821CrossRefPubMedGoogle Scholar
  27. 27.
    Yang FC, Chou KH, Fuh JL et al (2013) Altered gray matter volume in the frontal pain modulation network in patients with cluster headache. Pain 154:801–807CrossRefPubMedGoogle Scholar
  28. 28.
    Douaud G, Groves AR, Tamnes CK et al (2014) A common brain network links development, aging, and vulnerability to disease. Proc Natl Acad Sci U S A 111:17648–17653CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Segall JM, Allen EA, Jung RE et al (2012) Correspondence between structure and function in the human brain at rest. Front Neuroinform 6:10CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Smith SM, Fox PT, Miller KL et al (2009) Correspondence of the brain's functional architecture during activation and rest. Proc Natl Acad Sci U S A 106:13040–13045CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Filippini N, MacIntosh BJ, Hough MG et al (2009) Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci U S A 106:7209–7214CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Perkins NJ, Schisterman EF (2006) The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol 163:670–675CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    van Stralen KJ, Stel VS, Reitsma JB, Dekker FW, Zoccali C, Jager KJ (2009) Diagnostic methods I: sensitivity, specificity, and other measures of accuracy. Kidney Int 75:1257–1263CrossRefPubMedGoogle Scholar
  34. 34.
    Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691–692CrossRefGoogle Scholar
  35. 35.
    Ortiz A, Munilla J, Alvarez-Illan I, Gorriz JM, Ramirez J, Alzheimer's Disease Neuroimaging Initiative (2015) Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis. Front Comput Neurosci 9:132CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Ng B, Varoquaux G, Poline JB, Thirion B, Greicius MD, Poston KL (2017) Distinct alterations in Parkinson's medication-state and disease-state connectivity. Neuroimage Clin 16:575–585CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Zhang D, Liu X, Chen J, Liu B (2014) Distinguishing patients with Parkinson's disease subtypes from normal controls based on functional network regional efficiencies. PLoS One 9:e115131CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Wang JJ, Lin WY, Lu CS et al (2011) Parkinson disease: diagnostic utility of diffusion kurtosis imaging. Radiology 261:210–217CrossRefPubMedGoogle Scholar
  39. 39.
    Mahlknecht P, Krismer F, Poewe W, Seppi K (2017) Meta-analysis of dorsolateral nigral hyperintensity on magnetic resonance imaging as a marker for Parkinson's disease. Mov Disord 32:619–623CrossRefPubMedGoogle Scholar
  40. 40.
    Szewczyk-Krolikowski K, Menke RA, Rolinski M et al (2014) Functional connectivity in the basal ganglia network differentiates PD patients from controls. Neurology 83:208–214CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Wu T, Hallett M (2013) The cerebellum in Parkinson's disease. Brain 136:696–709CrossRefPubMedGoogle Scholar
  42. 42.
    Lenka A, Naduthota RM, Jha M et al (2016) Freezing of gait in Parkinson's disease is associated with altered functional brain connectivity. Parkinsonism Relat Disord 24:100–106CrossRefPubMedGoogle Scholar
  43. 43.
    Leh SE, Petrides M, Strafella AP (2010) The neural circuitry of executive functions in healthy subjects and Parkinson's disease. Neuropsychopharmacology 35:70–85CrossRefPubMedGoogle Scholar
  44. 44.
    Carlesimo GA, Piras F, Assogna F, Pontieri FE, Caltagirone C, Spalletta G (2012) Hippocampal abnormalities and memory deficits in Parkinson disease: a multimodal imaging study. Neurology 78:1939–1945CrossRefPubMedGoogle Scholar
  45. 45.
    Cardoso EF, Fregni F, Maia FM et al (2010) Abnormal visual activation in Parkinson's disease patients. Mov Disord 25:1590–1596CrossRefPubMedGoogle Scholar
  46. 46.
    Luppino G, Matelli M, Camarda R, Rizzolatti G (1993) Corticocortical connections of area F3 (SMA-proper) and area F6 (pre-SMA) in the macaque monkey. J Comp Neurol 338:114–140CrossRefPubMedGoogle Scholar
  47. 47.
    Cerasa A, Pugliese P, Messina D et al (2012) Prefrontal alterations in Parkinson's disease with levodopa-induced dyskinesia during fMRI motor task. Mov Disord 27:364–371CrossRefPubMedGoogle Scholar
  48. 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
  49. 49.
    Monchi O, Petrides M, Mejia-Constain B, Strafella AP (2007) Cortical activity in Parkinson's disease during executive processing depends on striatal involvement. Brain 130:233–244CrossRefPubMedGoogle Scholar

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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