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

Jessica L Nielson,Jennifer Kloke,Karen-Amanda Irvine,Tanya C Petrossian,Jacqueline C Bresnahan,Gunnar E Carlsson,Michael S Beattie,C Amy Tovar,Aiwen W Liu,John C Gensel,Geoffrey T Manley,Pek Y Lum,Wise Young,Jesse Paquette,Adam R Ferguson,Cristian F Guandique,Tomoo Inoue

doi:10.1038/ncomms9581

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syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI.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

Highlights

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
topological data analysis (TDA) can apply principal components analysis (PCA) through singular value decomposition (SVD) to reveal the complex multivariate relationship of all predictor and outcome variables simultaneously as a network diagram, where similar individuals are clustered into nodes, and clusters that share one or more individuals are joined by an edge (Fig. 1c)
TDA-based data-driven analyses revealed a set of important findings, some of which dramatically confirmed existing ‘hunches’ in the published literature, whereas others represented novel findings that were not previously identified, even in legacy data sets

Summary

Introduction

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. 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. The present study applies a data-analytic approach, topological data analysis (TDA)[12], for improved discovery of fundamental syndromic injury patterns and assessment of precision therapeutic targeting from preclinical drug trials in SCI and TBI. Application of TDA to preclinical therapeutic trials revealed irreproducible efficacy of methylprednisolone (MP) and minocycline treatment between cervical and thoracic SCI, yet uncovered the novel discovery that perioperative hypertension predicts worse neurological recovery following thoracic SCI

Methods

Results

Conclusion