Associate Professor Weill Institute for Neurosciences, University of California, San Francisco (UCSF) San Francisco, California
Abstract: Neurotrauma involves changes to the cellular, molecular, and tissue integrity of the nervous system, leading to loss of motor control, sensory and autonomic dysfunction, cognitive problems, and behavior/emotional impairment. The acute biological response to spinal cord injury (SCI) and traumatic brain injury (TBI), for example, is a systemic process that involves, among other pathologies, complex inflammatory and immunomodulatory cascades. The complexity of SCI and TBI and collinearity of symptoms limit reproducibility of findings across laboratories and translation of new treatments from the bench to the bedside. The SCI and TBI research field has a big-data problem; there are too many variables, metrics, symptoms, and consequences associated with the trauma. To identify a single mechanistic target or a set of prognostic biomarkers that generalize across the full heterogeneity of people with SCI and TBI is challenging.
Modern analytics, machine learning, and contemporary data science tools have the potential to enable researchers to query and extract patterns from complex data to form hypothesis, make new discoveries, and set realistic rehabilitation goals. To use these tools, large volumes of data are needed, requiring sample sizes exceeding those typically collected within a single laboratory. To realize the potential of data science, a major barrier to overcome is the cultural skepticism to data sharing.
In this Plenary, Dr. Adam Ferguson will share his innovative work at the interface of data science and neurotrauma. The VISION-SCI, a community data repository, hosts subject-level data from over 4000 rodents with SCI. The Open Data Commons for SCI (odc-sci.org), a cloud-based data infrastructure currently hosting 155 datasets from 44 laboratories, focuses on building a scalable, structured data sharing platform that enables users to upload, query, and download pre-clinical data. Also, the Open Data Commons for TBI (odc-tbi.org), designed to accelerate progress in pre-clinical TBI research through data sharing and re-using, currently has 39 datasets from 16 laboratories. These data portals help harmonize and democratizes data, and grant users access to large volumes of data that are otherwise inaccessible. Examples will also be given regarding how these community data repositories could promote reproducibility across laboratories, hasten new discoveries in SCI and TBI with a data-driven approach, and improve the care of people with SCI and TBI.
Dr. Adam Ferguson will also discuss his clinical research, a multicenter prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) and SCI (TRACK-SCI), and TBI Endpoints Development (TED) Initiative.
In summary, collectively, Dr. Adam Ferguson has extensive experiences working with big data representing multiple species tracked on over 20,000 biological/clinical variables, genomics, and high-resolution imaging. The ACRM audience, clinically-oriented researchers of many disciplines and career stages, would benefit greatly from hearing his work in leveraging data science to advance discoveries in SCI, TBI, and beyond.
Abbreviated Description: In this Plenary, Dr. Adam Ferguson will share his innovative work at the interface of data science and neurotrauma, including VISION-SCI, a community data repository, that hosts subject-level data from over 4000 rodents with SCI and Open Data Commons for SCI (odc-sci.org) and TBI (odc-tbi.org), a cloud-based data infrastructure designed to accelerate progress in pre-clinical SCI and TBI research through data sharing and re-using. His current work in large-scale clinical projects will also be discussed, including a multicenter prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) and SCI (TRACK-SCI) studies, and TBI Endpoints Development (TED) Initiative.
Describe the complex and heterogenous nature of neurotrauma and associated big-data challenges in research and new discoveries
Describe the potential of harnessing big data to drive reproducibility and translation in neurotrauma research
Discuss the utility of leveraging data science in advancing translational research in neurotrauma and its clinical implications