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Subtype Identification of Parkinson’s Disease Using Sparse Canonical Correlation and Clustering Analysis of Multimodal Neuroimaging

  • Ji Hye Won
  • Mansu Kim
  • Jinyoung Yoon
  • Hyunjin ParkEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1057)

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder with heterogeneity, which indicates that there are subtypes within PD. Identification of subtypes in PD is important because it may provide a better understanding of PD and improved therapy planning. Our aim was to find and characterize the subtypes of PD using multimodal neuroimaging. We computed structural neuroimaging and structural connectivity information from 193 patients. The structural connectivity information was computed through connectivity analysis derived from tractography of diffusion tensor imaging. A three-way sparse canonical correlation analysis was applied to reduce the dimension of three modalities into three latent variables. A clustering analysis with four clusters using the resulting latent variables was conducted. We regarded each cluster as subtypes of PD and showed that each subtype had distinct patterns of correlation with important known clinical scores in PD. The clinical scores were unified Parkinson’s disease rating scale, mini-mental state examination, and standardized uptake value of putamen calculated using positron-emission tomography. The distinct correlation patterns of subtypes supported the existence of subtypes in PD and showed that the subtypes could be effectively identified by clustering a few features obtained with dimensionality reduction.

Keywords

Parkinson’s disease Sparse canonical correlation analysis Clustering analysis 

Notes

Acknowledgments

This study was supported by the Institute for Basic Science (grant number IBS-R015-D1), the National Research Foundation of Korea (grant number NRF-2019R1H1A2079721), the Ministry of Science and ICT of Korea under the ITRC program (grant number IITP-2019-2018-0-01798), and IITP grant funded by the Korean government under the AI Graduate School Support Program (No. 2019-0-00421).

References

  1. 1.
    Foltynie, T., Brayne, C., Barker, R.A.: The heterogeneity of idiopathic Parkinson’s disease. J. Neurol. 249, 138–145 (2002)CrossRefGoogle Scholar
  2. 2.
    Atkinson-Clement, C., Pinto, S., Eusebio, A., Coulon, O.: Diffusion tensor imaging in Parkinson’s disease: review and meta-analysis. NeuroImage Clin. 16, 98–110 (2017).  https://doi.org/10.1016/j.nicl.2017.07.011CrossRefGoogle Scholar
  3. 3.
    Lo, C.-Y., Wang, P.-N., Chou, K.-H., Wang, J., He, Y., Lin, C.-P.: Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. J. Neurosci. 30, 16876–16885 (2010).  https://doi.org/10.1523/JNEUROSCI.4136-10.2010CrossRefGoogle Scholar
  4. 4.
    van den Heuvel, M.P., Mandl, R.C.W., Stam, C.J., Kahn, R.S., Hulshoff Pol, H.E.: Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. J. Neurosci. 30, 15915–15926 (2010).  https://doi.org/10.1523/JNEUROSCI.2874-10.2010CrossRefGoogle Scholar
  5. 5.
    Burges, C.J.C.: Dimension reduction: a guided tour. Found. Trends® Mach. Learn. 2, 275–364 (2009).  https://doi.org/10.1561/2200000002CrossRefzbMATHGoogle Scholar
  6. 6.
    Rosa, M.J., et al.: Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging. Front. Neurosci. 9, 366 (2015).  https://doi.org/10.3389/fnins.2015.00366CrossRefGoogle Scholar
  7. 7.
    van Rooden, S.M., Heiser, W.J., Kok, J.N., Verbaan, D., van Hilten, J.J., Marinus, J.: The identification of Parkinson’s disease subtypes using cluster analysis: a systematic review. Mov. Disord. 25, 969–978 (2010).  https://doi.org/10.1002/mds.23116CrossRefGoogle Scholar
  8. 8.
    Lawton, M., et al.: Developing and validating Parkinson’s disease subtypes and their motor and cognitive progression. J. Neurol. Neurosurg. Psychiatry 89, 1279–1287 (2018).  https://doi.org/10.1136/jnnp-2018-318337CrossRefGoogle Scholar
  9. 9.
    Fereshtehnejad, S.-M., Romenets, S.R., Anang, J.B.M., Latreille, V., Gagnon, J.-F., Postuma, R.B.: New clinical subtypes of Parkinson disease and their longitudinal progression. JAMA Neurol. 72, 863 (2015).  https://doi.org/10.1001/jamaneurol.2015.0703CrossRefGoogle Scholar
  10. 10.
    Witten, D.M., Tibshirani, R.J.: Extensions of sparse canonical correlation analysis with applications to genomic data. Stat. Appl. Genet. Mol. Biol. 8 (2009).  https://doi.org/10.2202/1544-6115.1470. Article 28MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hao, X.: Alzheimer’s disease neuroimaging initiative: mining outcome-relevant brain imaging genetic associations via three-way sparse canonical correlation analysis in Alzheimer’s disease. Sci. Rep. 7, 44272 (2017).  https://doi.org/10.1038/srep44272CrossRefGoogle Scholar
  12. 12.
    Kim, D.-E., et al.: Single photon emission computerized tomography and neuropsychological tests that predict a good response to donepezil therapy for Alzheimer’s Disease. Dement. Neurocognitive Disord. 14, 106 (2015).  https://doi.org/10.12779/dnd.2015.14.3.106CrossRefGoogle Scholar
  13. 13.
    Yildiz, D., et al.: Impaired cognitive performance and hippocampal atrophy in Parkinson disease. Turkish J. Med. Sci. 45, 1173–1177 (2015)CrossRefGoogle Scholar
  14. 14.
    Prakash, K.G., Bannur, B.M., Chavan, M.D., Saniya, K., Sailesh, K.S., Rajagopalan, A.: Neuroanatomical changes in Parkinson’s disease in relation to cognition: An update. J. Adv. Pharm. Technol. Res. 7, 123–126 (2016).  https://doi.org/10.4103/2231-4040.191416CrossRefGoogle Scholar
  15. 15.
    Gattellaro, G., et al.: White matter involvement in idiopathic Parkinson disease: a diffusion tensor imaging study. AJNR Am. J. Neuroradiol. 30, 1222–1226 (2009).  https://doi.org/10.3174/ajnr.A1556CrossRefGoogle Scholar
  16. 16.
    Borzì, L., et al.: Home monitoring of motor fluctuations in Parkinson’s disease patients. J. Reliab. Intell. Environ. 5, 145–162 (2019).  https://doi.org/10.1007/s40860-019-00086-xCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwonKorea
  2. 2.Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonKorea
  3. 3.Department of NeurologySungkyunkwan University School of MedicineSeoulKorea
  4. 4.Neuroscience CenterSamsung Medical CenterSeoulKorea
  5. 5.School of Electronic and Electrical EngineeringSungkyunkwan UniversitySeoulKorea

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