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Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury

Author

Listed:
  • Jessica L. Nielson

    (Brain and Spinal Injury Center, University of California, San Francisco)

  • Jesse Paquette

    (Tagb.io)

  • Aiwen W. Liu

    (Brain and Spinal Injury Center, University of California, San Francisco)

  • Cristian F. Guandique

    (Brain and Spinal Injury Center, University of California, San Francisco)

  • C. Amy Tovar

    (Ohio State University)

  • Tomoo Inoue

    (Tohoku University Graduate School of Medicine)

  • Karen-Amanda Irvine

    (San Francisco VA Medical Center, University of California San Francisco)

  • John C. Gensel

    (Spinal Cord and Brain Injury Research Center, Chandler Medical Center, University of Kentucky Lexington)

  • Jennifer Kloke

    (Ayasdi Inc.)

  • Tanya C. Petrossian

    (GenePeeks, Inc.)

  • Pek Y. Lum

    (Capella Biosciences)

  • Gunnar E. Carlsson

    (Ayasdi Inc.
    Stanford University, Building 380, Stanford, California, 94305, USA)

  • Geoffrey T. Manley

    (Brain and Spinal Injury Center, University of California, San Francisco)

  • Wise Young

    (W.M. Keck Center for Collaborative Neuroscience, Rutgers University)

  • Michael S. Beattie

    (Brain and Spinal Injury Center, University of California, San Francisco)

  • Jacqueline C. Bresnahan

    (Brain and Spinal Injury Center, University of California, San Francisco)

  • Adam R. Ferguson

    (Brain and Spinal Injury Center, University of California, San Francisco
    San Francisco VA Medical Center, University of California San Francisco)

Abstract

Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis.

Suggested Citation

  • Jessica L. Nielson & Jesse Paquette & Aiwen W. Liu & Cristian F. Guandique & C. Amy Tovar & Tomoo Inoue & Karen-Amanda Irvine & John C. Gensel & Jennifer Kloke & Tanya C. Petrossian & Pek Y. Lum & Gun, 2015. "Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury," Nature Communications, Nature, vol. 6(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9581
    DOI: 10.1038/ncomms9581
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    Cited by:

    1. Hardeep Ryait & Edgar Bermudez-Contreras & Matthew Harvey & Jamshid Faraji & Behroo Mirza Agha & Andrea Gomez-Palacio Schjetnan & Aaron Gruber & Jon Doan & Majid Mohajerani & Gerlinde A S Metz & Ian Q, 2019. "Data-driven analyses of motor impairments in animal models of neurological disorders," PLOS Biology, Public Library of Science, vol. 17(11), pages 1-30, November.
    2. Hristo Todorov & Emily Searle-White & Susanne Gerber, 2020. "Applying univariate vs. multivariate statistics to investigate therapeutic efficacy in (pre)clinical trials: A Monte Carlo simulation study on the example of a controlled preclinical neurotrauma trial," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.

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