Professor University of Utah Salt Lake City, Utah, United States
Introduction:: Traumatic brain injury (TBI) claims approximately 1.5 million victims each year and results in a wide range of neurological and neuropsychological impairments. The development of accurate injury risk curves for TBI are critical for (1) developing protective equipment to prevent TBI, (2) establishing safety guidelines and return-to-activity policies, and (3) enabling interdisciplinary empirical-based diagnostic and treatment paradigms for TBI. One of the challenges with developing successful injury risk curves for TBI is the variability in outcome and symptomology despite similar impact history and similar impact severity. The directionality of the impact certainly contributes to this variability, but anatomical variability also exists that can contribute to localized differences in brain deformation magnitude and distribution during head trauma.
Materials and Methods:: I will present our cumulative experimental and computational work characterizing the anatomical and mechanical variability in the porcine and human brain-skull interface using optical coherence tomography, with specific focus on the heterogeneity across the brain, spatial trends, and discuss their influence on brain strain and injury prediction during head trauma.
Results, Conclusions, and Discussions:: Our data indicate that recognizing and representing heterogeneity and structural variability is critical to improving the sensitivity and specificity for predicting traumatic brain injury. Significant localized microstructural variability exists between individuals, but regional averages are similar, suggesting that computational tools adequately capturing generalized heterogeneity in a structure is more important than patient-specific modeling. This assertion, however, does not consider the effect of microstructural variability on progressive damage, which may be a fundamental parameter for predicting individual susceptibility to mild TBI.