Wednesday, April 2, 2025
4/2/2025
Predicting Dependency after Traumatic Brain Injury - Project Summary/Abstract Traumatic brain injury (TBI) affects millions of people each year who recover to widely disparate levels of independent function. It is not currently possible to reliably predict who will remain functionally dependent on caregivers. Most patients assessed with existing models are assigned an intermediate likelihood of recovery, remaining in a prognostic ‘grey area’. Accurate prediction is important, because withdrawal of life sustaining therapy based on perceived prognosis is the leading cause of death after TBI. Though advances in brain imaging have enabled precise localization of focal brain injuries, injury location is not incorporated into existing TBI prognostic models. This is because it is not known whether dependency results from injury to specific brain structures or networks. This knowledge gap creates ongoing heterogeneity in clinical practice and limits the development and evaluation of targeted therapeutics. This K23 award addresses this knowledge gap, using multi-modality neuroimaging to identify brain structures, connections and networks that produce dependency when disrupted, and testing whether injury location improves prognostication after TBI. The principal investigator, Dr. Samuel B. Snider, is a Neurocritical Care Neurologist at Brigham and Women’s Hospital (BWH), whose goal is to become a translational neuroscientist using advanced imaging techniques to better understand recovery mechanisms and predict outcomes after acute brain injuries. Dr. Snider has an established early career track record in advanced MRI, and in measuring and predicting TBI outcomes. His preliminary data demonstrate the feasibility of mapping focal traumatic brain injury with CT and MRI at the scale required for this project. Through novel analysis of three existing datasets and one prospectively enrolling TBI study at BWH, Dr. Snider will test whether the locations of hemorrhagic contusions on CT scans (1), axonal injury on diffusion MRI (2a) and functional network disruption on resting state functional MRI (2b) independently improve the prediction of functional dependency after moderate or severe TBI. Using multiple sources to create one of the largest TBI imaging datasets ever assembled, this project will generate novel insights into mechanisms of recovery from brain injury and improve existing prognostic models. The mentored research and structured training in multi-center data harmonization and analysis, resting-state functional MRI, and prospective clinical data collection will provide Dr. Snider with the skills, experience and preliminary data needed to submit an NIH R01 validating the first imaging-based TBI prognostic model. His career development plan utilizes the resources of the world-class Harvard Medical School training environment, bringing together a diverse and multidisciplinary team of mentors and collaborators centered at the BWH. Under the guidance of primary mentor Dr. Michael Fox, co-mentors Drs. Brian Edlow and Nancy Temkin, and advisors Drs. Alexandra Golby, Yogesh Rathi, and Sonia Jain, Dr. Snider will find the support necessary to develop an independent research program translating better prognostic models into better outcomes for patients with brain injuries.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).