Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 132–140Cite as

  1. Home
  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
  3. Conference paper
Genetic, Structural and Functional Imaging Biomarkers for Early Detection of Conversion from MCI to AD

Genetic, Structural and Functional Imaging Biomarkers for Early Detection of Conversion from MCI to AD

  • Nikhil Singh19,
  • Angela Y. Wang19,
  • Preethi Sankaranarayanan19,
  • P. Thomas Fletcher19 &
  • …
  • Sarang Joshi19 
  • Conference paper
  • 5712 Accesses

  • 12 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7510)

Abstract

With the advent of advanced imaging techniques, genotyping, and methods to assess clinical and biological progression, there is a growing need for a unified framework that could exploit information available from multiple sources to aid diagnosis and the identification of early signs of Alzheimer’s disease (AD). We propose a modeling strategy using supervised feature extraction to optimally combine high-dimensional imaging modalities with several other low-dimensional disease risk factors. The motivation is to discover new imaging biomarkers and use them in conjunction with other known biomarkers for prognosis of individuals at high risk of developing AD. Our framework also has the ability to assess the relative importance of imaging modalities for predicting AD conversion. We evaluate the proposed methodology on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to predict conversion of individuals with Mild Cognitive Impairment (MCI) to AD, only using information available at baseline.

Keywords

  • Positron Emission Tomography
  • Mild Cognitive Impairment
  • Partial Little Square
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Download conference paper PDF

References

  1. Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., Trojanowski, J.Q.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging 32(12), 2322.e19–2322.e27 (2011)

    CrossRef  Google Scholar 

  2. Lemoine, B., Rayburn, S., Benton, R.: Data Fusion and Feature Selection for Alzheimer’s Diagnosis. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS, vol. 6334, pp. 320–327. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  3. Weiner, M.W., et al.: The Alzheimers Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimer’s and Dementia, S1–S68 (2012)

    Google Scholar 

  4. Kohannim, O., et al.: Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiology of Aging 31(8), 1429–1442 (2010)

    CrossRef  Google Scholar 

  5. Younes, L., Arrate, F., Miller, M.: Evolutions equations in computational anatomy. NeuroImage 45(1S1), 40–50 (2009)

    CrossRef  Google Scholar 

  6. Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, 151–160 (2004)

    CrossRef  Google Scholar 

  7. Vialard, F.X., et al.: Diffeomorphic 3D image registration via geodesic shooting using an efficient adjoint calculation. IJCV, 1–13 (2011)

    Google Scholar 

  8. Minoshima, S., Frey, K.A., Koeppe, R.A., Foster, N.L., Kuhl, D.E.: A diagnostic approach in Alzheimers disease using three-dimensional stereotactic surface projections of Fluorine-18-FDG PET. J. of Nuclear Medicine 36(7), 1238–1248 (1995)

    Google Scholar 

  9. Bookstein, F.L.: Partial Least Squares: A dose-response model for measurement in the behavioral and brain sciences. Psycoloquy 5(23) (1994) (revised)

    Google Scholar 

  10. Singh, N., Fletcher, P.T., Preston, J.S., Ha, L., King, R., Marron, J.S., Wiener, M., Joshi, S.: Multivariate Statistical Analysis of Deformation Momenta Relating Anatomical Shape to Neuropsychological Measures. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 529–537. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Utah, Salt Lake City, UT, USA

    Nikhil Singh, Angela Y. Wang, Preethi Sankaranarayanan, P. Thomas Fletcher & Sarang Joshi

Authors
  1. Nikhil Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Angela Y. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Preethi Sankaranarayanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. Thomas Fletcher
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sarang Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Inria Sophia Antipolis, Project Team Asclepios, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139,, Cambridge,, MA, USA

    Polina Golland

  3. Information and Communication, Nagoya University, 464-8603, Headquarters, Nagoya, Japan

    Kensaku Mori

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Singh, N., Wang, A.Y., Sankaranarayanan, P., Fletcher, P.T., Joshi, S. (2012). Genetic, Structural and Functional Imaging Biomarkers for Early Detection of Conversion from MCI to AD. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_17

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33415-3_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33414-6

  • Online ISBN: 978-3-642-33415-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature