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Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments over Progressions

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (MICCAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11767))

Abstract

Incomplete or inconsistent temporal neuroimaging records of patients over time pose a major challenge to accurately predict clinical scores for diagnosing Alzheimer’s Disease (AD). In this paper, we present an unsupervised method to learn enriched imaging biomarker representations that can simultaneously capture the information conveyed by all the baseline neuroimaging measures and the progressive variations of the available follow-up measurements of every participant. Our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show improved performance in predicting cognitive outcomes thereby demonstrating the effectiveness of our proposed method.

ADNI—Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (ad-ni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: https://adni.loni.usc.edu/wp-content/uploads/how to apply/ADNI Acknowledgement List.pdf.

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Acknowledgements

L. Lu, S. Elbeleidy, L. Baker and H. Wang were partially supported by the National Science Foundation (NSF) under the grants of IIS 1652943 and IIS 1849359; H. Huang was partially supported by the National Institutes of Health (NIH) under the grant of NIH R01 AG049371 and by the NSF under the grants of NSF IIS 1836938, DBI 1836866, IIS 1845666, IIS 1852606, IIS 1838627, IIS 1837956; L. Shen was partially supported by the NIH under the grant of NIH R01 EB022574 and by the NSF under the grant of NSF IIS 1837964.

Author information

Authors and Affiliations

  1. Department of Computer Science, Colorado School of Mines, Golden, CO, USA

    Lyujian Lu, Saad Elbeleidy, Lauren Baker & Hua Wang

  2. Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA

    Heng Huang

  3. JD Finance America Corporation, Mountain View, CA, USA

    Heng Huang

  4. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Li Shen

Authors
  1. Lyujian Lu
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  2. Saad Elbeleidy
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  3. Lauren Baker
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  4. Hua Wang
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  5. Heng Huang
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  6. Li Shen
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for the ADNI

Corresponding author

Correspondence to Hua Wang .

Editor information

Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dinggang Shen

  2. University of Georgia, Athens, GA, USA

    Tianming Liu

  3. Western University, London, ON, Canada

    Terry M. Peters

  4. Yale University, New Haven, CT, USA

    Lawrence H. Staib

  5. University of Strasbourg, Illkirch, France

    Caroline Essert

  6. United Imaging Intelligence, Shanghai, China

    Sean Zhou

  7. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Pew-Thian Yap

  8. Western University, London, ON, Canada

    Ali Khan

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Lu, L. et al. (2019). Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments over Progressions. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_16

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  • DOI: https://doi.org/10.1007/978-3-030-32251-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32250-2

  • Online ISBN: 978-3-030-32251-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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