PROJECT SUMMARY: Trauma to the spinal cord and brain (neurotrauma) together impact over 2.5 million people per year in the US, with economic costs of $80 billion in healthcare and loss-of-productivity. Yet precise pathophysiological processes impacting recovery remain poorly understood. This lack of knowledge limits the reliability of therapeutic development in animal models and limits translation across species and into humans. Part of the problem is that neurotrauma is intrinsically complex, involving heterogeneous damage to the central nervous system (CNS), the most complex organ system in the body. This results in a multifarious CNS syndrome spanning across heterogeneous data sources and multiple scales of analysis. Multi-scale heterogeneity makes spinal cord injury (SCI) and traumatic brain injury (TBI) difficult to understand using traditional analytical approaches that focus on a single endpoint for testing therapeutic efficacy. Single endpoint-testing provides a narrow window into the complex system of changes that describe the holistic syndromes of SCI and TBI. In this sense, complex neurotrauma is fundamentally a problem that requires big- data analytics to evaluate reproducibility in basic discovery and cross-species translation. For the proposed TOP-VISION cooperative agreement we will: 1) integrate preclinical neurotrauma data on a large-scale; 2) develop novel applications of cutting-edge multidimensional analytics to make sense of complex neurotrauma data; and 3) validate bio-functional patterns in targeted big-data-to-bench experiments in multi-PI single center (UG3 phase), and multicenter (UH3 phase) studies. The goal of the proposed project is to develop an integrated workflow for preclinical discovery, reproducibility testing, and translational discovery both within and across neurotrauma types. Our team is well-positioned to execute this project given that with prior NIH funding we built one of the largest multicenter, multispecies repositories of neurotrauma data to-date, housing detailed multidimensional outcome data on nearly N=5000 preclinical subjects and over 20,000 curated variables. We will leverage these existing data resources and apply recent innovations from data science to render complex multidimensional endpoint data into robust syndromic patterns that can be visualized and explored by researchers and clinicians for discovery, hypothesis-generation and ultimately translational outcome testing.