Incurring high economic cost due to medical imaging modalities, there is a need for a low-cost, on site, diagnostic screening tool for the early detection of Traumatic Brain Injury (TBI). We hypothesize that patients with TBI will exhibit temporal and spatial gradient dynamics in the thermal signature on the surface of the skin, and that these dynamics reflect the inflammatory process. Hence, we implemented far-infrared (FIR) thermography using a blunt TBI rat model to analyze changes in the external, surface temperature gradient as an indication of internal inflammation. Results show a consistent increase in average surface temperature after 0.5 days of recovery post-impact. The trend in average surface temperature decreases after 1 day of recovery with a continual decline observed after a 4-day recovery. After 7 days of recovery, the average surface temperature begins to increase with a substantial surge seen 14 days post-impact. The trend appears to correlate well with the inflammatory process.

In the current study, mechanoporation-related neuronal injury as a result of mechanical loading has been studied using a multiscale approach. Injurious mechanical loads to the head induce strains in the brain tissue at the macroscale. As each length scale has its own unique morphology and heterogeneities, the strains have been scaled down from the macroscale brain tissue to the nanoscale neuronal components that result in mechanoporation of the neuronal membrane, while relevant neuronal membrane mechanoporation-related damage criteria have been scaled up to the macroscale. To achieve this, first, damage evolution equations has been developed and calibrated to molecular dynamics simulations of a representative neuronal membrane at the nanoscale. These damage evolution equations are strain rate and strain state dependent. The resulting damage evolution model has been combined with Nernst-Planck diffusion equations to analytically compare to intracellular ion concentration disruption through mechanical loading of in vitro neuron cell culture and found to agree well. Then, these damage evolution equations have been scaled up to the microscale for dynamic simulations of 3-dimensional reconstructed neurons of similar mechanical loads. It was found that the neuronal orientation significantly affects average damage accumulation on the neuron, while the morphology of neurons, for a given neuron type, had little effect on the average damage accumulation. At the mesoscale, finite element simulations of geometrical complexities of sulci and gyri, and the structural complexities of the gray and white matter and CSF on stress localization were studied. It was found that the brain convolutions, sulci, and gyri, along with the effects of impedance mismatch between the cerebrospinal fluid (CSF) and brain tissue localized shear stresses, at the depths of the sulcus end (near field effects) and in-between sulci (far field effects), that correlated well with the regions of tau protein accumulation that is observed in chronic traumatic encephalopathy (CTE). Further, sulcus length and orientation, with respect to impending stress waves, had a significant impact on the magnitude of stress localization in the brain tissue. Lastly, gray-white matter differentiation, pia matter, and brain-CSF interface interaction properties had minimal impact of the shear stress localization trends observed in these simulations.

Due to traumatic brain injury (TBI), numerous studies have focused on comprehensively determining the mechanical properties of the brain. This study examined the strain rate dependence of porcine brain under compression, and the microstructural damage was quantified using a confocal microscope and graphical user interface (GUI). The selected strain rates were 0.10 s-1, 0.025 s-1, and 0.00625 s-1 while the strain levels targeted for confocal imaging were 15%, 30%, and 40%. This study also characterized the stress-state dependence at a strain rate and strain level of 0.10 s-1 and 40%, respectively, under compression, tension, and shear. Strain rate dependency data exhibited viscoelastic behavior, and the analysis parameters correlated with increasing strain rate and strain level. Stress-state dependency data demonstrated distinct nonlinear behavior, and disparities were observed in the analysis parameters between different testing modes. Finite element procedures can implement this supplementary data for devising more realistic models.

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