Cognition based bTBI mechanistic criteria; a tool for preventive and therapeutic innovations

Abstract

Blast-induced traumatic brain injury has been associated with neurodegenerative and neuropsychiatric disorders. To date, although damage due to oxidative stress appears to be important, the specific mechanistic causes of such disorders remain elusive. Here, to determine the mechanical variables governing the tissue damage eventually cascading into cognitive deficits, we performed a study on the mechanics of rat brain under blast conditions. To this end, experiments were carried out to analyse and correlate post-injury oxidative stress distribution with cognitive deficits on a live rat exposed to blast. A computational model of the rat head was developed from imaging data and validated against in vivo brain displacement measurements. The blast event was reconstructed in silico to provide mechanistic thresholds that best correlate with cognitive damage at the regional neuronal tissue level, irrespectively of the shape or size of the brain tissue types. This approach was leveraged on a human head model where the prediction of cognitive deficits was shown to correlate with literature findings. The mechanistic insights from this work were finally used to propose a novel protective device design roadmap and potential avenues for therapeutic innovations against blast traumatic brain injury.

Acrolein-mediated alpha-synuclein pathology involvement in the early post-injury pathogenesis of mild blast-induced Parkinsonian neurodegeneration

“…Specifically, we have noted conspicuous microvascular damage, neuroinflammation, and increased oxidative stress in the hours and days following injury (Walls et al, 2016). Furthermore, we have discovered a significant elevation of acrolein, an α,β-unsaturated aldehyde that is both a product and catalyst of lipid peroxidation and stimulator of oxidative stress and inflammation (Garcia-Gonzalez et al, 2018;Walls et al, 2016). Finally, we have demonstrated that acrolein plays a key role in the pathogenesis of PD in a 6-OHDA injection model, highlighting the need for additional mechanistic investigations (Ambaw et al, 2018).…”

“…Furthermore, dopaminergic neurons in the SN are known to be more susceptible to oxidative processes than dopaminergic neurons found elsewhere in the brain (Burns et al, 1983;German et al, 1988;Hung and Lee, 1998;Wang and Michaelis, 2010;Waters et al, 1987). Differential mechanical injury profiles between brain regions could also contribute, wherein the SN experiences more injurious loading conditions than the STR, as our lab's computational biomechanics work in this mb-TBI model has suggested (Garcia-Gonzalez et al, 2018). Regardless of the underlying mechanism, these data suggest differential localization and regulation of the TH protein between different brain loci, in a similar fashion to the α-syn protein, after injury.…”

“…During TBI, including those from blast exposure, the brain immediately undergoes primary physical damage to neurons, glial cells, and microvasculature. Primary injury induces secondary cellular and biochemical processes including oxidative stress, lipid peroxidation, abnormal protein aggregation, inflammation, and neuronal death (Acosta et al, 2015;Garcia-Gonzalez et al, 2018;Glover et al, 2012;Walls et al, 2016;Werner and Engelhard, 2007;Yu et al, 2009). Secondary injuries initiate on the order of minutes after injury and, even after mild injuries, can continue for months (Cernak et al, 2011;DePalma et al, 2005;Desmoulin and Dionne, 2009;Miller, 2012;Rosenfeld et al, 2013;Walls et al, 2016).…”

A Machine Learning Enhanced Mechanistic Simulation Framework for Functional Deficit Prediction in TBI

“…The FEHM is adapted from a previous version proposed earlier by Garcia-Gonzalez et al ( 2018b ), where it was validated for cranial impacts. It accounts for the gray matter, white matter with axonal anisotropy captured from diffusion tensor imaging (DTI), cerebrospinal fluid (CSF), skull, falx, scalp, and ventricles and consists of 2,354,594 tetrahedral elements.…”

“…However, as local mechanical disturbances in the brain can lead to time-dependent systemic biological and multiphysics responses, these models are intrinsically unable to mechanistically predict functional alterations or cognitive deficits. Barring a few exceptions (Garcia-Gonzalez et al, 2018b ), very little work has focussed on correlating functional deficits, tissue damage, and mechanical features in a fully validated framework, e.g., with clinical or animal data. Even then, in most of the cases, the high cost (both in man-hour and computational) to develop, run, and analyze the underlying FEHM remains extremely impractical and not fit for direct clinical use.…”