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

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, Gunnar E. Carlsson, Geoffrey T. Manley, Wise Young, Michael S. Beattie, Jacqueline C. Bresnahan and Adam R. Ferguson ()
Additional contact information
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.
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

Nature Communications, 2015, vol. 6, issue 1, 1-12

Abstract: 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the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI.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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9581

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DOI: 10.1038/ncomms9581

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