Joint Modeling of Imaging and Genetics

  • Nematollah K. Batmanghelich
  • Adrian V. Dalca
  • Mert R. Sabuncu
  • Polina Golland
Conference paper

DOI: 10.1007/978-3-642-38868-2_64

Volume 7917 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Batmanghelich N.K., Dalca A.V., Sabuncu M.R., Golland P. (2013) Joint Modeling of Imaging and Genetics. 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

Abstract

We propose a unified Bayesian framework for detecting genetic variants associated with a disease while exploiting image-based features as an intermediate phenotype. Traditionally, imaging genetics methods comprise two separate steps. First, image features are selected based on their relevance to the disease phenotype. Second, a set of genetic variants are identified to explain the selected features. In contrast, our method performs these tasks simultaneously to ultimately assign probabilistic measures of relevance to both genetic and imaging markers. We derive an efficient approximate inference algorithm that handles high dimensionality of imaging genetic data. We evaluate the algorithm on synthetic data and show that it outperforms traditional models. We also illustrate the application of the method on ADNI data.

Keywords

Imaging Genetics Bayesian Models Variational Inference Probabilistic Graphical Model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nematollah K. Batmanghelich
    • 1
  • Adrian V. Dalca
    • 1
  • Mert R. Sabuncu
    • 2
  • Polina Golland
    • 1
  1. 1.Computer Science and Artificial Intelligence LaboratoryMITCambridgeUSA
  2. 2.Martinos Center for Biomedical ImagingCharlestownUSA