Citation

Nielson JL, Paquette J, Liu AW, Guandique CF, Tovar CA, Inoue T, Irvine KA, Gensel JC, Kloke J, Petrossian TC, Lum PY, Carlsson GE, Manley GT, Young W, Beattie MS, Bresnahan JC, Ferguson AR. Nat. Commun. 2015; 6: 8581.

Affiliation

Department of Neurosurgery, San Francisco VA Medical Center, University of California San Francisco, San Francisco, California 94110, USA.

Copyright

(Copyright © 2015, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1038/ncomms9581

PMID

26466022

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syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI.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

Language: en