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Bayesian Longitudinal Modeling of Early Stage Parkinson’s Disease Using DaTscan Images

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Information Processing in Medical Imaging (IPMI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11492))

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Abstract

This paper proposes a disease progression model for early stage Parkinson’s Disease (PD) based on DaTscan images. The model has two novel aspects: first, the model is fully coupled across the two caudates and putamina. Second, the model uses a new constraint called model mirror symmetry (MMS). A full Bayesian analysis, with collapsed Gibbs sampling using conjugate priors, is used to obtain posterior samples of the model parameters. The model identifies PD progression subtypes and reveals novel fast modes of PD progression.

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Acknowledgements

This research is supported by the NIH grant R01NS107328. We gratefully acknowledge discussions with Prof. Sule Tinaz of the Dept. of Neurology Yale University.

The data used in the preparation of this article was obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (up-to-date information is available at http://www.ppmi-info.org). PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and multiple funding partners. The full list of PPMI funding partners can be found at ppmi- info.org/fundingpartners.

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Authors and Affiliations

  1. Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06510, USA

    Yuan Zhou & Hemant D. Tagare

  2. Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA

    Hemant D. Tagare

Authors
  1. Yuan Zhou
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  2. Hemant D. Tagare
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Corresponding author

Correspondence to Yuan Zhou .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

    Albert C. S. Chung

  2. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    James C. Gee

  3. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    Paul A. Yushkevich

  4. Department of Natural Language Processing, Baidu Inc., Shenzhen, China

    Siqi Bao

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Zhou, Y., Tagare, H.D. (2019). Bayesian Longitudinal Modeling of Early Stage Parkinson’s Disease Using DaTscan Images. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_31

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  • DOI: https://doi.org/10.1007/978-3-030-20351-1_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20350-4

  • Online ISBN: 978-3-030-20351-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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