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Predicting Cognitive Data from Medical Images Using Sparse Linear Regression

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Information Processing in Medical Imaging (IPMI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7917))

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Abstract

We present a new framework for predicting cognitive or other continuous-variable data from medical images. Current methods of probing the connection between medical images and other clinical data typically use voxel-based mass univariate approaches. These approaches do not take into account the multivariate, network-based interactions between the various areas of the brain and do not give readily interpretable metrics that describe how strongly cognitive function is related to neuroanatomical structure. On the other hand, high-dimensional machine learning techniques do not typically provide a direct method for discovering which parts of the brain are used for making predictions. We present a framework, based on recent work in sparse linear regression, that addresses both drawbacks of mass univariate approaches, while preserving the direct spatial interpretability that they provide. In addition, we present a novel optimization algorithm that adapts the conjugate gradient method for sparse regression on medical imaging data. This algorithm produces coefficients that are more interpretable than existing sparse regression techniques.

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Author information

Authors and Affiliations

  1. Department of Bioengineering, University of Pennsylvania, USA

    Benjamin M. Kandel

  2. Department of Neurology and Penn Memory Center, University of Pennsylvania, USA

    David A. Wolk

  3. Department of Radiology, University of Pennsylvania, USA

    James C. Gee & Brian Avants

  4. Penn Image Computing and Science Laboratory (PICSL), USA

    Benjamin M. Kandel, James C. Gee & Brian Avants

Authors
  1. Benjamin M. Kandel
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  2. David A. Wolk
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  3. James C. Gee
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  4. Brian Avants
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Editor information

Editors and Affiliations

  1. Department of Radiology, University of Pennsylvania, 3600 Market Street, 19104, Philadelphia, PA, USA

    James C. Gee  & Kilian M. Pohl  & 

  2. Department of Bioengineering, University of Utah, 72 S Central Campus Drive, 84112, Salt Lake City, UT, USA

    Sarang Joshi

  3. Department of Radiology, Harvard Medical School and Brigham and Women’s Hospital, 75 Francis Street, 02115, Boston, MA, USA

    William M. Wells

  4. A.A. Martinos Center for Biomedical Imaging, Harvard Medical School and Massachussetts General Hospital, 149 Thirteenth Street, 02129, Chalrestown, MA, USA

    Lilla Zöllei

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© 2013 Springer-Verlag Berlin Heidelberg

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Kandel, B.M., Wolk, D.A., Gee, J.C., Avants, B. (2013). Predicting Cognitive Data from Medical Images Using Sparse Linear Regression. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds) Information Processing in Medical Imaging. IPMI 2013. Lecture Notes in Computer Science, vol 7917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38868-2_8

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  • DOI: https://doi.org/10.1007/978-3-642-38868-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38867-5

  • Online ISBN: 978-3-642-38868-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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