Skip to main content
SpringerLink
Log in

Group-Constrained Laplacian Eigenmaps: Longitudinal AD Biomarker Learning

  • Conference paper
  • First Online:
Machine Learning in Medical Imaging (MLMI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9352))

Included in the following conference series:

Abstract

Longitudinal modeling of biomarkers to assess a subject’s risk of developing Alzheimers disease (AD) or determine the current state in the disease trajectory has recently received increased attention.Here, a new method to estimate the time-to-conversion (TTC) of mild cognitive impaired (MCI) subjects to AD from a low-dimensional representation of the data is proposed. This is achieved via a combination of multilevel feature selection followed by a novel formulation of the Laplacian Eigenmaps manifold learning algorithm that allows the incorporation of group constraints.Feature selection is performed using Magnetic Resonance (MR) images that have been aligned at different detail levels to a template. The suggested group constraints are added to the construction of the neighborhood matrix which is used to calculate the graph Laplacian in the Laplacian Eigenmaps algorithm.The proposed formulation yields relevant improvements for the prediction of the TTC and for the three-way classification (control/MCI/AD) on the ADNI database.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems 14, 585–591 (2002)

    Google Scholar 

  2. Tenenbaum, J.B., Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  3. Jenkins, O.C., Matarić, M.J.: A spatio-temporal extension to Isomap nonlinear dimension reduction. In: International Conference on Machine Learning, pp. 441–448 (2004)

    Google Scholar 

  4. Lewandowski, M., Martinez-del-Rincon, J., Makris, D., Nebel, J.: Temporal extension of laplacian eigenmaps for unsupervised dimensionality reduction of time series. In: International Conference on Pattern Recognition, pp. 161–164 (2010)

    Google Scholar 

  5. Misra, C., Fan, Y., Davatzikos, C.: Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. NeuroImage 44(4), 141522 (2009)

    Article  Google Scholar 

  6. Li, Y., Wang, Y., Wu, G., Shi, F., Zhou, L., Lin, W., Shen, D.: Discriminant analysis of longitudinal cortical thickness changes in Alzheimers disease using dynamic and network features. Neurobiology of Aging 33(2), 427.e1530 (2012)

    Article  Google Scholar 

  7. Wolz, R., Aljabar, P., Hajnal, J.V., Rueckert, D.: Manifold learning for biomarker discovery in MR imaging. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds.) MLMI 2010. LNCS, vol. 6357, pp. 116–123. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  8. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B 67, 301–320 (2005)

    Article  MathSciNet  Google Scholar 

  9. Meinshausen, N., Bühlmann, P.: Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72(4), 417–473 (2010)

    Article  MathSciNet  Google Scholar 

  10. Guerrero, R., Wolz, R., Rao, A.W., Rueckert, D.: Manifold population modeling as a neuro-imaging biomarker: Application to ADNI and ADNI-GO. NeuroImage 94C, 275–286 (2014)

    Article  Google Scholar 

  11. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)

    Article  Google Scholar 

  12. Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)

    Article  Google Scholar 

  13. Heckemann, R.A., Ledig, C., Aljabar, P., Gray, K.R., Rueckert, D., Hajnal, J.V., Hammers, A.: Label propagation using group agreement. In: MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling, pp. 75–78 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biomedical Image Analysis Group, Imperial College London, London, UK

    R. Guerrero, C. Ledig, A. Schmidt-Richberg & D. Rueckert

Authors
  1. R. Guerrero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Ledig
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Schmidt-Richberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. Rueckert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Guerrero .

Editor information

Editors and Affiliations

  1. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

    Luping Zhou

  2. Radiology and BRIC, University of North Carolina, Chapel Hill, NC, USA

    Li Wang

  3. Biomedical Engineering, Shanghai Jiaotong University, Shanghai, China

    Qian Wang

  4. Computer Science, Nanjing University, Nanjing, China

    Yinghuan Shi

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Guerrero, R., Ledig, C., Schmidt-Richberg, A., Rueckert, D. (2015). Group-Constrained Laplacian Eigenmaps: Longitudinal AD Biomarker Learning. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24888-2_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24887-5

  • Online ISBN: 978-3-319-24888-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation