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Nat Commun. 2015 Oct 14;6:8581. doi: 10.1038/ncomms9581.

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

Author information

1
Department of Neurosurgery, Brain and Spinal Injury Center, University of California, San Francisco, 1001 Potrero Avenue, Building 1, Room 101, San Francisco, California 94143, USA.
2
Tagb.io, 1 Quartz Way, San Francisco, California 94131, USA.
3
Department of Neuroscience, Ohio State University, 460 West 12th Avenue, 670 Biomedical Research Tower, Columbus, Ohio 43210, USA.
4
Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai city, Miyagi prefecture 980-0856, Japan.
5
Department of Neurology, San Francisco VA Medical Center, University of California San Francisco, San Francisco, California 94110, USA.
6
Department of Physiology, Spinal Cord and Brain Injury Research Center, Chandler Medical Center, University of Kentucky Lexington, B463 Biomedical &Biological Sciences Research Building, 741 South Limestone Street, Kentucky 40536, USA.
7
Ayasdi Inc., 4400 Bohannon Drive Suite #200, Menlo Park, California 94025, USA.
8
GenePeeks, Inc., 777 Avenue of the Americas, New York, New York 10001, USA.
9
Capella Biosciences, 550 Hamilton Avenue, Palo Alto, California 94301, USA.
10
Department of Mathematics, Stanford University, Building 380, Stanford, California, 94305, USA.
11
Department of Cell Biology and Neuroscience, W.M. Keck Center for Collaborative Neuroscience, Rutgers University, Piscataway, New Jersey 08854, USA.
12
Department of Neurosurgery, San Francisco VA Medical Center, University of California San Francisco, San Francisco, California 94110, USA.

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syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI.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

PMID:
26466022
PMCID:
PMC4634208
DOI:
10.1038/ncomms9581
[Indexed for MEDLINE]
Free PMC Article

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