Abstract

Introduction

Blast-induced traumatic brain injury (bTBI), arising in particular from the exposure to improvised explosive devices, has become a major problem for the armed forces¹ with mounting evidence pointing towards long-term neurodegenerative and neuropsychiatric disorders in veteran populations². However, the link between the blast wave physics and the subsequent biological alterations in the brain remains largely elusive. Post-bTBI pathologies are thought to be mechanically initiated by the early-time propagation of stress waves³, cavitation effects⁴, rapid acceleration of the head resulting from shock wave and blast wind interaction with the body⁵, and secondary and tertiary injuries arising from subsequent impact, acceleration, and penetrating trauma^6,7.

This work aims to provide new insights into the mechanical mechanisms of brain damage resulting from blast exposure by focussing on early-time events of primary blast loading and their correlation to post-injury biochemical and functional impairments. To this end, an in vivo/in silico methodology was developed to assess the consequences of blast on rats. We used a validated mild bTBI model in rats to expose individuals to blast conditions and, then, we performed a set of experimental techniques to characterise the distribution of oxidative stress reaction, a hallmark of secondary injury, across the brain and to relate these results to array of impairments in bTBI rats. The same blast conditions were then simulated in a validated in silico rat model allowing for determining the mechanical variables that govern brain damage resulting in cognitive deficits. After identification of the mechanical injury criteria that best correlate with oxidative stress and cognitive deficits, this approach was leveraged on an in silico human model addressing realistic blast conditions in the military context. The predictive brain damage regions were correlated to cognitive deficits finding a good agreement with impairments reported in the literature. The results have implications for strategies and approaches to diagnosis and treatment of bTBI, as detailed in the discussion. Furthermore, we provide new, validated bTBI in silico models of rat and human subjects capable of testing future innovations in combat head protection from blast and further exploring links between physical injury phenomena and downstream biological processes.

Results

Blast on rat: identification of mechanistic injury criteria in rat

Experimental programme

Previous experimental efforts summarised herein (Fig. 1A–C) were performed using a validated mild bTBI model in rats⁸. Consistent with clinical observations in human mild bTBI, our mild bTBI rats are acutely asymptomatic with unremarkable neuroimaging findings^8,9. Despite the initial subclinical nature of our model, we observed empirical evidence that mild blasts can incite relative displacement of the rat brain with respect to the skull of the order of hundreds of microns to millimetres¹⁰. Consistent with other independent mild bTBI investigations^11,12, we also report acute blood-brain barrier disruption⁸, downstream secondary oxidative stress elevations and neuroinflammatory alterations⁸ with later-onset behavioural and neurophysiologic alterations^9,13.

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Figure 1

Summary of prior and current experimental findings in our rodent bTBI model: (A) T2-weighted MRI images illustrating the anatomy of the rat brain, axial section labelling A–F, and intra-section region labelling (1–9 in correspondence with Fig S1). (B) Spatial summary of significant oxidative stress elevations (red shading) observed via increased acrolein-lysine adducts on Western blot at 24hours post-bTBI. (C) Composite presentation of observed post-bTBI deficits from 24hours to 2 weeks post-injury including safety learning impairments related to orbitofrontal and hippocampal processing (blue X), Parkinsonian protein alterations in the striatum and midbrain (green X), facial and extremity pain (red X), auditory neurophysiological impairments in the auditory cortices, thalami, and inferior colliculi (yellow X). Each deficit was observed in separate cohorts of rats exposed to the same experimental blast exposure conditions used herein. (D) Spatial summary of regions thought to be intact after bTBI (equivalent performance to sham-injured rats) including medial prefrontal cortex and amygdala (blue O), motor cortices (purple O), rostral hippocampal areas (orange O), olfactory bulb and cerebellum (not pictured). Mild bTBI tissue damage and subsequent functional alterations can arise from i) direct mechanical injury to the brain in excess of one or multiple mechanical quantity thresholds and ii) inherent susceptibility of particular brain regions and/or cell types to post-injury secondary processes. The latter can manifest over an extended time scale (weeks-years) as post-injury degeneration unfolds. In the present investigation, we focus on the former and investigate the degree to which observed deficits in our rat bTBI experimental model could potentially be explained by mechanical perturbations incited by primary blast.

First, we aimed to capture the spatial distribution of secondary injury processes across the brain during the peak secondary injury period, 24hours post-injury⁸. To do so, we assessed the spatial distribution of acrolein adducts, a reactive aldehyde species with relatively long biological half-life frequently used by us and others as an indicator of sustained oxidative stress, one of the hallmarks of secondary injury⁸. We expanded our prior analyses⁸ and observed that, in a small minority of rat forebrain regions (8/53 regions), mild bTBI was associated with an increase in acrolein adducts (Fig. 1B). Other regions demonstrated minimal change with respect to sham-injured animals or highly variable measurements. When this distribution was compared to the array of impairments in bTBI rats, a reasonable degree of overlap can be observed (Fig. 1B,D). It is important to note that all prior studies and documented impairments were conducted under the same experimental blast exposure conditions used herein; thus, direct comparisons between current and past findings are appropriate. For example, increased oxidative stress, vis-à-vis acrolein adducts, was observed in the midbrain (Fig. 1B,C, region E8), where we have also observed post-bTBI Parkinsonian protein alterations¹⁴. Similarly, increased oxidative stress in the auditory thalamus and cortex (Fig. 1B,C, regions F4, F6) spatially correlates with our observed post-mild bTBI neurophysiological processing impairments in the same portions of the central auditory system¹³. Impairments in safety learning were noted as well⁹, corresponding to a combined functional alteration of the prefrontal cortex (Fig. 1B,C, regions A4&6) and hippocampus (Fig. 1B,C, regions F4&6), where we also observed an increase in oxidative stress in both regions. Additionally, brain regions related to cognitive deficits but not impacted by an increased oxidative stress (e.g., Fig. 1C, regions B1&3 and D1&3) were all indirectly connected to regions that were (Fig. 1C, regions C1&3). In contrast, regions with intact function/behaviour after bTBI generally did not overlap with areas affected by oxidative stress (Fig. 1B,D). This includes the bilateral motor cortex (Fig. 1B,D, regions B1–3, C2, D2) and amygdala (Fig. 1B,D, regions D7&9), which, consistently with the lack of deficits related to those areas⁹, did not demonstrate significant oxidative stress increases.

Numerical programme

An important unresolved question is the mechanism by which an external head trauma induces the wide-spread patterns of oxidative stress reactions we observed in our rats. How the mechanical impact of bTBI on the brain is modulated by variations in local tissue properties cannot currently be evaluated in vivo. To this end, we aim at assessing correlative relationships between observed brain alterations and a range of mechanical variables by developing a detailed rat finite element head model (FEHM) from various MRI images (Fig. 2A). High-resolution anatomical images were segmented, and appropriate validated constitutive equations were associated to the segmented tissue types (Supplementary Information). In particular, novel constitutive models (Supplementary Information Fig. ^S5) were used for both grey and white matter to capture the differential responses of brain tissues under low- and high-rate loading (Equations (S.1–S.12)). The white matter model also incorporates tract orientation and fractional anisotropy acquired from diffusion-weighted images (Equations (S.7–S.9)). Consistent with in vivo experimental procedures, head fixations were applied in the in silico experiments, thus ensuring that the analysis of wave interactions from primary blast events is made in isolation from secondary and tertiary injuries (Fig. 2B). The model validation was performed against prior brain motion tracking experimental data¹⁰, see Supplementary Information Fig. S6. Using the validated model, numerous injury criteria were then assessed for spatial correlation with post-bTBI impairments (Figs 1 and ​and2)2) a priori linked to mechanical damage of both grey and white matters.

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Figure 2

Numerical model of rat: (A) cut of a full FEHM presenting skin/fat, skull, CSF, grey and white matters; (B) blast loading imposed in numerical simulations; (C) brain injury predictions for shear energy rate criterion in grey matter (C.1) and for axonal stretch energy rate in white matter (C.2).

A set of literature-established and novel injury criteria was analysed for both grey and white matters: pressure, von Mises stress, equivalent strain, volumetric and shear energy rates. Note that the energy (as opposed to energy rate) sometime used in the literature as an injury criterion was not considered as a valid criterion as it can a priori reach high values in non-injuring, low frequency, low stress, long period loading. In the white matter, two complementary criteria were considered to account for mechanical anisotropy: axonal stretch and energy rate from axonal stretch (see Supplementary Information for further details). A summary showing the accuracy of each different criterion at predicting the damaged (Fig. 1C) and non-damaged (Fig. 1D) brain regions observed experimentally is presented in Table 1. The criterion best correlated with injured grey matter regions is the shear energy rate (damage threshold $\approx$100 MJ/m³s) at a matching accuracy of 72%. In the white matter, axonal deformation energy rate (damage threshold $\approx$1.5 MJ/m³s) was found to be the best criterion in terms of injury correlation, albeit with a relatively poor matching accuracy (56%). For all tested injury criteria, the predictive value was lower for white matter than grey matter. This finding supports indications that white matter deficits in the rat may be more likely to result from long-term secondary degenerative mechanisms mediated by glial and infiltrating peripheral inflammatory cells¹⁵. These predictions are supported by experimental observations in our own experimental mild bTBI model, after which these simulations were devised: i) unchanged post-TBI signal conduction speed between multiple relay stations ranging from the auditory nerve to the cortex¹³, suggesting intact myelination; and ii) intact resting-state functional connectivity between remote regions of a network involved in safety learning despite safety learning impairments⁹, suggesting that inter-regional projections were unharmed while intra-regional microstructural features suffered damage.

Table 1

Matching accuracy of the mechanical injury criteria based on the correlation of numerical results with experiments to predict damaged and non-damaged regions of brain.

Criterion	Matching accuracy (%)
	Grey matter	White matter
Pressure stress	56	44
von Mises stress	39	33
Equivalent strain	56	44
Volumetric energy rate	56	22
Shear energy rate	72	44
Axonal stretch	—	33
Axonal stretch energy rate	—	56

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Blast on human

The methodology developed for the rat FEHM was applied to the study of primary blast early-time wave interactions in the human head by identifying potential overlaps between areas injured in bTBI simulations and areas where damage is known to result in cognitive impairments in humans following bTBI³. The approach consists in leveraging the overall microstructural composition that gives rise to relative mechanical behaviours in white and grey matter¹⁶, and establishing inter-species mechanistic thresholds that correlate with cognitive damage at the tissue level irrespectively of the shape or size of tissue mesoscopic structures. For this purpose, the validated FEHM recently developed by Garcia-Gonzalez et al.¹⁷ was refined and human MRI Diffusion Tensor Imaging (DTI) information was incorporated to estimate the regional fibre orientation of white matter (Figs 3A and S4). The human head model was subjected in silico to four blast injury scenarios: frontal and lateral blast incident orientations at two blast intensities each: i) detonation of 2.3kg charge of C-4 at a 2.3m standoff distance (360kPa magnitude blast) and ii) detonation of 3kg charge of Octol explosive at 2–3m standoff distance (1.3MPa magnitude blast). Both scenarios are within the marginal limits for threshold lung damage but predicted to be survivable¹⁸. The lower intensity C-4 charge conditions did not breach the best correlated injury criteria thresholds obtained from the rat model (shear energy rate criterion for grey matter and axonal deformation energy rate for white matter), suggesting that brain injury from primary blast would not occur under these conditions. This is in agreement with previous observations¹⁹ suggesting that no serious cerebral contusions are expected under 379kPa. Under the more intense Octol charge conditions, however, numerical results predicted primary blast-induced injury in numerous brain regions (Fig. 3B,C).

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Figure 3

Numerical model of human head: (A) methodology based on the combination of axonal anisotropy from DTI (A.1), brain topology from MRI (A.2) and material properties for each head constituent (A.3) for the development of the full FEHM (here with fractional anisotropy heat map) (A.4); (B,C) brain injury predictions under lateral/frontal blast for shear energy rate criterion in grey matter (B.1/C.1) and for axonal deformation energy rate in white matter (B.2/C.2). The brain slices are presented from right to left.

The lateral-incident blast simulations predict acute grey matter damage in the ipsilateral cerebellar cortex (lobule III/IV) and inferior/middle temporal gyrus, bilateral thalami, and contralateral hippocampus, see Fig. 3B.​.1.1. Predicted white matter disruptions (Fig. 3B.​.2)2) are localised to the corpus callosum and corticospinal tract, with additional involvement of the cerebellar peduncles, anterior thalamic radiations, lateral and dorsal segments of the superior longitudinal fasciculus pathways and sagittal stratum. Despite the much lower degree of confidence of the proposed white mater injury criterion in the rat, the overall pattern of predicted tissue injury correlates well with many regions known to be vulnerable to blast, namely the thalamus²⁰, hippocampus and cerebellum²¹, as well as the white matter fibres of the cerebellar peduncles and internal capsule²². Disruption of additional fibre pathways varies across blast trauma survivors^23,24 and, according to our results, may at least partially reflect differential effects of blast injury depending on head orientation and blast intensity. Indeed, the frontal-incident blast wave simulations (Fig. 3C) induce focal injuries in a different distribution than is observed for the lateral-incident blast. While a frontal blast is also predicted to injure the thalamus and corpus callosum, other brain areas differ in terms of injury severity. The observed multi-focal injury distribution patterns with dependence on blast incident orientation is consistent with prior reports³. Previous investigations implemented stress, strain, or pressure-based injury criteria and predicted primarily coup-contrecoup injury patterns^3,25. However, our predictions based on shear energy rate (grey matter) and axonal energy deformation rate (white matter) suggest a largely periventricular injury distribution focussed around the centre of the brain. Note that our results showed shear concentration in different brain regions under blast exposure. Contrary conclusions have been suggested for this type of loading⁷. This difference comes from the consideration of different components within the brain (grey/white matter and ventricles) that results in shear accumulation on the material boundaries. These observations are in agreement with the work of Taylor and Ford³, where focal shear concentrations in grey-white matter junctions for early-time wave interactions were found.

Discussion

The pattern of injury in bTBI is likely to determine the ensuing cognitive and psychiatric problems suffered by injured humans and seen in experimental models. The ability to predict such patterns is, therefore, a crucially important component of bTBI models, especially those designed to test the likely benefit of head protection. Our human FEHM predicted that periventricular brain regions, particularly the thalamus and corpus callosum, may be vulnerable to early-time events during blast exposure in an orientation-independent manner. Abnormalities in the thalamus are a consistent feature of TBI, likely due to its dense cortical interconnections. PET studies suggest that thalamic abnormalities represent long-term markers of TBI, in particular thalamic neuroinflammation that, interestingly, co-localises with axonal damage²⁶. Severity of thalamic injury has been proposed as a potential predictor of various clinical and neurocognitive outcomes following TBI²⁰. In addition, the corpus callosum has long been hypothesised as a vulnerable substrate for mechanical disruption in TBI and has been observed to be compromised in both rodent²⁷ and human²⁸ bTBI. Notably, DTI reveals significant, localised fractional anisotropy reductions in the corpus callosum in veterans with a history of blast-exposure compared to those without²⁸, while other brain regions remain ostensibly unaffected. Additionally, it is well recognised in TBI among other conditions that the precise focus of injury often only partially explains patient symptoms. Instead, damage to the brain’s major white matter connection pathways has been consistently linked to post-injury performance declines on cognitive measures including executive function and associative memory²⁹. These deleterious effects of white matter disruption are thought to arise from disruption to widely distributed functional networks³⁰. Both the thalamus and corpus callosum are positioned centrally with widespread, cross-brain connectivity which, if damaged, could have profound implications on normal brain function. Resultant dysfunction from white matter damage to these structures could manifest in numerous ways, ranging from discrete neurological deficits to network-scale dysfunction. This could appear clinically with changes as simple as altered sensory processing secondary to discrete lesions of white matter tracts (e.g., a lesion exclusively affecting thalamic relay afferents to the auditory cortex). The contribution of distinct patterns of cortical and white matter damage to emotional processing in TBI has received less attention. However, recent data indicate that damage to white matter connections, especially in frontal lobe pathways, differentiates TBI patients with and without mood disorders³¹. The impact of tract-specific injury may, however, be more nuanced, as further evidence indicates that both white matter and functional MRI measures of cortical synchronisation independently predict post-traumatic stress disorder severity³². Together, these emerging findings lend support to the possibility that blast-induced damage to inter-regional axonal projections would impair coordination of synchronised activity across brain regions within a given network, resulting in functional impairments.

Model-predicted injuries to brain regions outside the thalamus and corpus callosum vary with exposure conditions, consistent with the wide range of post-bTBI clinical presentations and known variability in fibre tract involvement among bTBI survivors^23,24. These injuries may also manifest as isolated neurological deficits or disruption of network-scale coordinated activity, per our discussion above. Persistent cognitive deficits following head trauma most often involve memory, attentional set-shifting and processing speed³³. Predicted damage to the vulnerable fibre tracts was shown to significantly predict variance in executive functions and processing speed in TBI patients³⁴. Additionally, disruption of hippocampal networks offers a recognised mechanism for deficits in learning and memory. Recently, dysfunction of the anterior hippocampus, implicated in emotional memories³⁵, has been linked directly with severity of post-traumatic stress disorder in veterans³⁶. Focal damage to the anterior hippocampus observed in our lateral blast model, and in previous human postmortem samples², therefore, offers a candidate substrate for both memory and chronic emotional disorders prevalent after TBI²². Predicted damage to both the anterior cerebellum, containing projections of the sensorimotor system³⁷, and corticospinal fibres in the immediate aftermath of blast wave exposure likely account for occasional, usually transient²¹, motor deficits noted in murine and non-human primate³⁸ blast trauma models.

An important question arising from this work is whether brain injuries resulting from blast events differ significantly from ‘conventional’ non-blast TBI incited by impact-acceleration events. This work points towards the conclusion that blast and impact-acceleration injuries do mechanistically differ at multiple scales because of three key factors: injury location, time scale of injury, and co-occurrence. The most common blast injury locations were at material boundaries (brain-CSF or grey-white matter, consistent with independent predictions^39,40) near the centre of the brain, whereas conventional TBI classically presents in coup-contrecoup, diffuse axonal injury, and rigid-boundary-adjacent distributions⁴¹. Conventional TBI injury distributions have historically been quite well-predicted by traditional stress and strain injury criteria⁴², which performed poorly in predicting injury in our bTBI model in comparison to shear and axonal stretch energy rates. Prior to this work, it was unknown whether experimental blast-induced mechanical injury profiles bear any relationship with preclinical ‘outcomes’ – ranging from biochemistry to behaviour – which we now suggest is the case. Broadly, this means that even in the absence of longer time-scale impact-acceleration injuries, high-rate early time (order of microseconds) mechanical injuries from blast loading can cascade into relevant physical damage to brain tissue which correlates with experimental endpoints. It is well established that CNS injuries are loading rate- and magnitude-dependent^43,44. It is a logical extension that blast-type loading, which occurs over a much shorter time scale compared to conventional (impact) TBI (microseconds vs. milliseconds) and induces loading at higher strain rates, should thus differ in the resultant injury profile to cells and tissue. We have recently predicted some mechanical effects of such high-rate blast injuries at the cellular⁴⁵ and molecular level⁴⁶, but supporting experimental data is lacking in the current body of bTBI literature. It is important to note that blast and conventional TBI often (though not exclusively) occur together, and as such may be difficult to distinguish clinically in many cases due to limitations of current diagnostic tools. Despite this, mindfulness of differential injury mechanisms between blast and conventional TBI, particularly with respect to injury locations and loading rates, can guide future efforts toward mechanobiological understanding as well as diagnostic and therapeutic innovations.

Relationships between microstructural physical damage, corresponding mechanical thresholds for cognitive, emotional and neuromotor deficits, and subsequent recovery or degeneration within injured areas offer important avenues for further research. The proposed approach allows for the prediction of cognitive deficits in rats submitted to early-time blast effects with an indication that white matter damage mainly arises from secondary effects in the rat. Our human predictions are aligned with literature findings of both white and grey matter damage. The difference between rats and humans in white matter early-time injury might be linked to a different colocation of reached mechanical thresholds and molecules associated with neuroinflammatory processes, and/or might be explained by the structural differences between lissencephalic and gyrencephalic brains⁴⁷. TBI pathophysiology is known to exist on a continuum initiated by primary (physical) injury consisting of disruption to cellular structures, that then progresses via secondary (biochemical) mechanisms which culminate in neuronal dysfunction, degeneration, and ultimately clinical deficits⁴⁸. Widely reported secondary injury mechanisms include oxidative stress, excitotoxicity, and neuroinflammation⁴⁸. Interceding mechanisms linking primary and secondary injury mechanisms remain, however, poorly understood. Mechanosensitive proteins are putative mediators of the transition from primary to secondary injury processes. Transient receptor potential (TRP), two-pore domain potassium (TREK, TRAAK), and aquaporin (AQP) channel families all represent mechanosensitive protein targets for future research. Each group contains member proteins which are highly expressed in the brain, specialised for sensation of and response to mechanical perturbations, and have been linked to downstream neuroinflammatory processes^49–51. Future work targeting the effects of modulating these proteins on post-TBI secondary injury processes and outcomes should be explored to fully dissociate early-time physical damage from secondary injuries.

Beyond future research into pathophysiological mechanisms of bTBI, the findings herein have implications for both preventative and therapeutic innovations.

Preventative innovation in protective devices

In the military context, prevention is multifactorial, influenced by personnel training, pre-operative military intelligence, combat tactics and protective technology. To date, improvements in helmet design have reduced the number of penetrating injuries, but their effect on outcomes following bTBI have been less pronounced. In part, this results from decreases in wearability when adjustments are made to helmets with the aim of protecting against blast. For example, increased padding adds additional weight, whilst visors capable of dissipating blast wave energy can restrict both head movement and visibility. Further helmet design, using novel materials and shapes, is urgently needed. However, a suitable model of bTBI is crucial to the design process. To that end, the work presented here has the potential to provide significant advances in the future development of bTBI protective equipment. By identifying the mechanical variables governing damage in the rat brain, transferring them to the human brain model, and predicting injury in those brain regions known to be damaged by bTBI, our approach enables the guided design of new protective devices against blast. It does so through: i) the study of the relationship between the protective device mechanical properties and the resulting bTBI protection; ii) a complete study of the protective capability against bTBI of final prototypes according to the new cognition-based mechanistic criteria presented. We present here an analysis of i) and provide the basis, tools and methodology for further investigation in terms of ii).

To analyse the dependences of bTBI protective capability on the material properties of protective devices, a simplified FEHM was designed as a combination of concentric spheres representing: skull, CSF, brain and ventricles. The homogenised brain tissue was assumed isotropic for this purpose using the grey matter constitutive model developed herein, which incorporates the shear energy rate criterion. An additional layer was incorporated representing a protective shield on the head. The protective role played by the shield relies on its ability to influence three aspects of the transmitted stress wave: a reduction in the stress amplitude; impulse mitigation; and a change in the stress wave shape⁵². Therefore, the optimal design of protective devices must take into account these three characteristics of the stress wave transmitted to the head and, subsequently, to the brain tissue. The reduction in the stress amplitude can be controlled through the acoustic impedance of the shield material $\sqrt{{\mathrm{E}}_{\mathrm{s}}{\mathrm{\rho }}_{\mathrm{s}}}$, where ${\mathrm{E}}_{\mathrm{s}}$ is the Young’s modulus of the shield and ${\mathrm{\rho }}_{\mathrm{s}}$ is the density of the shield (see Supplementary Information for further details). Impulse mitigation can be reached by increasing the shield mass and/or by additional consideration of inelastic deformation in the shield (affecting ${\mathrm{E}}_{\mathrm{s}}$). Here, we focus particularly on the design of shields involving different structural materials resulting in regional variations of the wave speed and acoustic impedances. The variations in wave speed induce changes in the shape of the stress wave propagated within the brain, which we aim at leveraging for increased protective efficacy of the shield. To this end, with the aim of decreasing the large accumulation of stress, and eventually the increase of shear energy rate, around the ventricles in the centre of the head, the shape of the stress wave transmitted to the brain can be modulated by introducing a wave speed gradient along the shield by controlling $\sqrt{{\mathrm{E}}_{\mathrm{s}}/{\mathrm{\rho }}_{\mathrm{s}}}$ (see Fig. 4B). Note that a variation in wave speed implies the variation of the acoustic impedance, the transmitted impulse or both.

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Figure 4

(A) Proportion of brain region reaching the shear energy rate threshold of 100 MJ/m³s (TBI) for different acoustic impedance ratios between shield components ($\sqrt{{\mathrm{E}}_2{\rho }_2}/\sqrt{{\mathrm{E}}_1{\rho }_1}$) and wave speed ratio between shield components ($\sqrt{{\mathrm{E}}_2/{\rho }_2}/\sqrt{{\mathrm{E}}_1/{\rho }_1}$). The mechanical properties of polycarbonate (used in visors of blast protective helmets) are assumed for ${\mathrm{E}}_1$ and ${\mathrm{\rho }}_1$. (B) Schematic pressure wave patterns within brain tissue depending on the shield material composition (assuming constant impulse mitigation and acoustic impedance). (C) Wave speed ratio vs. acoustic impedance ratio map for polycarbonate visor helmet materials ➁ selection (➀ is polycarbonate).

In this study, the acoustic impedance and the variation of the stress wave shape were analysed. As a preliminary design for the shield, two regions with different material properties are considered. The central region (① in Fig. 4B, with a ratio of 1/3 between the central region diameter and the total shield diameter) is chosen to be polycarbonate, a material commonly used in the visor of blast protective helmets⁵³. The Young’s modulus and density of region ② were varied accordingly to analyse their influence on the transmitted stress wave. The different cases were simulated using the finite element solver Abaqus/Explicit. A VUMAT subroutine was used to define the brain behaviour as homogenised and isotropic, incorporating the shear energy rate damage variable. The threshold obtained from the rat simulations (100 MJ/m³s) was used to compare the different cases in terms of damage patterns. The blast loading imposed was that used in the human simulations resulting in bTBI. The simulations show that the extent of damage in the brain region strongly depends on the shield’s mechanical properties. In this regard, a reduction in the wave speed (lower wave speed ratios in Fig. 4A) or an increase of acoustic impedance in the shield (higher acoustic impedance ratios in Fig. 4A) both result in an improvement in terms of blast protection. The influence of the acoustic impedance ratio and the wave speed ratio between shield regions, and the interplay between both on the effectiveness of the protective device is presented in Figs 4A and C. In Fig. 4C, the regions covered by polycarbonate-based shields for different materials accompanying the polymer are overlapped. The results for the specific geometry employed show optimal solutions for shields that combine polycarbonate visor with metals or foams shells. The most obvious missing considerations are i) the weight and ii) the possibility to multilayer the shield, both leading to the consideration of additional dimensions in the optimisation process.

Overall, the numerical tools and the methodology developed herein provide the basis for further optimisation of protective devices to avoid bTBI. Efforts toward innovative protective technology should be experimentally tested and validated. Such advancements, while beneficial and necessary, are unlikely to completely obviate the need for additional TBI intervention.

Methods

Electronic supplementary material

Acknowledgements

Author Contributions

R.S. and A.J. designed research (R.S., the experiments and A.J., the simulations). A.J. invented the protective mechanisms. N.S.R., G.A, M.C.-H. and J.T. participated in collecting experimental data. S.S. provided the human DTI and MRI. D.G.-G. and N.S.R. built the rat and human models. D.G.-G. developed and implemented the material models, performed the simulations and the analyses of the protective device. N.V. and D.J. provided the analyses of the human and protective device simulations, respectively. D.G.-G., N.S.R. and A.J. wrote the manuscripts. N.V. and D.J. contributed to the manuscript. All authors revised the manuscript.

Notes

Footnotes

Contributor Information

Riyi Shi, Email: ude.eudrup@iyir.

Antoine Jérusalem, Email: [email protected].

References