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the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI.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.
Bibliographical noteFunding Information:
This work was funded by the Craig H. Neilsen Foundation Grant 224308, the National Institutes of Health (NIH) NS067092 (A.R.F.), NS069537 (A.R.F), NS038079 (J.C.B. and M.S.B.), NS031193 and AG032518 (M.S.B. and J.C.B.), NS079030 (J.L.N.), NS032000 (W.Y.; M.S.B. site PI, multicentre study); NYSCoRE CO19772 (M.S.B. and J.C.B.), the Department of Defense (DoD) grants W81XWH-10-1-0910 (M.S.B.) and W81XWH-13-1-0297 (M.S.B.), and Wings for Life Foundation grants WFLUS008/12 and WFLUS006/ 14 (A.R.F.). We thank J.R. Huie and J. Haefeli for useful comments, and A. Lin and S. Visuthikraisee for technical support.