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Cross-modal autoencoder framework learns holistic representations of cardiovascular state

Author

Listed:
  • Adityanarayanan Radhakrishnan

    (Massachusetts Institute of Technology)

  • Sam F. Friedman

    (Broad Institute of MIT and Harvard)

  • Shaan Khurshid

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital)

  • Kenney Ng

    (IBM T.J. Watson Research Center)

  • Puneet Batra

    (Broad Institute of MIT and Harvard)

  • Steven A. Lubitz

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital)

  • Anthony A. Philippakis

    (Broad Institute of MIT and Harvard)

  • Caroline Uhler

    (Massachusetts Institute of Technology
    Broad Institute of MIT and Harvard)

Abstract

A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.

Suggested Citation

  • Adityanarayanan Radhakrishnan & Sam F. Friedman & Shaan Khurshid & Kenney Ng & Puneet Batra & Steven A. Lubitz & Anthony A. Philippakis & Caroline Uhler, 2023. "Cross-modal autoencoder framework learns holistic representations of cardiovascular state," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38125-0
    DOI: 10.1038/s41467-023-38125-0
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    References listed on IDEAS

    as
    1. Karren Dai Yang & Anastasiya Belyaeva & Saradha Venkatachalapathy & Karthik Damodaran & Abigail Katcoff & Adityanarayanan Radhakrishnan & G. V. Shivashankar & Caroline Uhler, 2021. "Multi-domain translation between single-cell imaging and sequencing data using autoencoders," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    2. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
    3. Dan E Arking & M Juhani Junttila & Philippe Goyette & Adriana Huertas-Vazquez & Mark Eijgelsheim & Marieke T Blom & Christopher Newton-Cheh & Kyndaron Reinier & Carmen Teodorescu & Audrey Uy-Evanado &, 2011. "Identification of a Sudden Cardiac Death Susceptibility Locus at 2q24.2 through Genome-Wide Association in European Ancestry Individuals," PLOS Genetics, Public Library of Science, vol. 7(6), pages 1-9, June.
    4. James P. Pirruccello & Alexander Bick & Minxian Wang & Mark Chaffin & Samuel Friedman & Jie Yao & Xiuqing Guo & Bharath Ambale Venkatesh & Kent D. Taylor & Wendy S. Post & Stephen Rich & Joao A. C. Li, 2020. "Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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