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Towards Automated Hypothesis Testing in Neuroscience

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Heterogeneous Data Management, Polystores, and Analytics for Healthcare (DMAH 2019, Poly 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11721))

Abstract

Scientific data generation in the world is continuous. However, scientific studies once published do not take advantage of new data. In order to leverage this incoming flow of data, we present Neuro-DISK, an end-to-end framework to continuously process neuroscience data and update the assessment of a given hypothesis as new data become available. Our scope is within the ENIGMA consortium, a large international collaboration for neuro-imaging and genetics whose goal is to understand brain structure and function. Neuro-DISK includes an ontology and framework to organize datasets, cohorts, researchers, tools, working groups and organizations participating in multi-site studies, such as those of ENIGMA, and an automated discovery framework to continuously test hypotheses through the execution of scientific workflows. We illustrate the usefulness of our approach with an implemented example.

D. Garijo and S. Fakhraei—Co-first author.

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Notes

  1. 1.

    http://enigma.usc.edu.

  2. 2.

    https://w3id.org/enigma.

  3. 3.

    http://schema.org/.

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Acknowledgments

We are grateful to the KAVLI foundation for their support of ENIGMA Informatics (PIs: Jahanshad and Gil). We also acknowledge support from the National Science Foundation under awards IIS-1344272 (PI: Gil), ICER-1541029 (Co-PI: Gil), and IIS-1344272 (PI: Gil), and from the National Institutes of Health’s Big Data to Knowledge Grant U54EB020403 for support for ENIGMA (PI: Thompson) and High resolution mapping of the genetic risk for disease in the aging brain grant R01AG059874 (PI: Jahanshad). Data used in preparing this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), phases both 1 and 2. As such, many 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 is available online (http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf). We also used the DLBS (http://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html) dataset and the UK Biobank in this study. This research was conducted using the UK Biobank Resource under Application Number ‘11559’.

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Authors and Affiliations

  1. Information Sciences Institute, University of Southern California, Los Angeles, USA

    Daniel Garijo, Shobeir Fakhraei, Varun Ratnakar, Yibo Ma, Regina Wang & Yolanda Gil

  2. Department of Computer Science, University of Southern California, Los Angeles, USA

    Hanna Endrias & Yolanda Gil

  3. Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, USA

    Qifan Yang, Michael Bornstein, Joanna Bright & Neda Jahanshad

Authors
  1. Daniel Garijo
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  2. Shobeir Fakhraei
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  3. Varun Ratnakar
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  4. Qifan Yang
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  5. Hanna Endrias
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  6. Yibo Ma
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  7. Regina Wang
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  8. Michael Bornstein
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  9. Joanna Bright
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  10. Yolanda Gil
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  11. Neda Jahanshad
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Corresponding author

Correspondence to Shobeir Fakhraei .

Editor information

Editors and Affiliations

  1. Massachusetts Institute of Technology, Lexington, MA, USA

    Vijay Gadepally

  2. Intel Corporation, Portland, OR, USA

    Timothy Mattson

  3. Massachusetts Institute of Technology, Cambridge, MA, USA

    Michael Stonebraker

  4. Stony Brook University, Stony Brook, NY, USA

    Fusheng Wang

  5. University of Washington, Seattle, WA, USA

    Gang Luo

  6. Google, Mountain View, CA, USA

    Yanhui Laing

  7. Lucerne University of Applied Sciences, Rotkreuz, Switzerland

    Alevtina Dubovitskaya

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Garijo, D. et al. (2019). Towards Automated Hypothesis Testing in Neuroscience. In: Gadepally, V., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2019 2019. Lecture Notes in Computer Science(), vol 11721. Springer, Cham. https://doi.org/10.1007/978-3-030-33752-0_18

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  • DOI: https://doi.org/10.1007/978-3-030-33752-0_18

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  • Online ISBN: 978-3-030-33752-0

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