Documentation:FIB book/The Relationship Between Strain and Brain Injury

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The brain is a critical organ in the body, and injuries to it can have serious repercussions on bodily function and quality of life. A single brain injury can cause physical, cognitive, and emotional symptoms which are often difficult to detect and even more difficult to cure.[1] The effects of brain injuries on the patient, those close to them, and the healthcare system are severe. With public concern surrounding those effects growing, understanding the mechanism of brain injury and defining the conditions that cause internal responses is increasingly important to both injury prevention and intervention.[2] Traumatic brain injury (TBI) contributes to roughly 16% of all hospitalizations as a primary or secondary cause and is responsible for nearly 33% of all injury-related deaths.[3] They can occur in a variety of settings, mainly motor vehicle accidents, but also sports, falls, and blasts (among veteran populations).[3] Unlike many other injuries, the particularly troubling nuance surrounding TBI is the amount that go unreported and undiagnosed, since symptoms can appear mild or not appear at all in the days following the injury.[3]

The brain is primarily composed of water, making it one of the softest biological materials; therefore, it cannot easily compress, and so applying forces to the brain -- such as shearing and strain forces -- leads to deformation.[2] Accordingly, strain to the brain has been shown to be a predictor of concussion and TBI.[4] It is also noteworthy that brain strain is influenced by applied rotational accelerations, intracranial partitioning membranes, and material properties of the brain.[2]

For this reason, brain strain is an important area of focus when examining the biomechanics of brain injury. Presented here is a review of current literature focused on the relationship between applied loading, strain on different regions of the brain, and the cellular mechanisms resulting in brain injury and malfunction, including diffuse axonal injury.

What is Brain Strain?

When the brain experiences an impact, it will rapidly experience deformation caused by applications of pressure and stretch—this can otherwise be described as brain strain. The presence of brain strain will often be associated with the development of traumatic brain injury (TBI) or concussion.[5]

Following the impact, tissue strain begins on a localized surface of the brain, before moving into the white matter and deep grey matter at an increased magnitude.[6] Eventually, strains will accumulate in specific regions of the brain, and can produce varying symptoms accordingly.[6][7] The biomechanics of injuries will also vary depending on whether an individual experiences tensile, compressive, or shear strain. Additionally, strain rate (the rate at which pressure or stretch is applied) plays a role in determining the injury severity in a region of the brain.[7][8]

The mechanism of injury for tensile strain involves stretch deformation and damage to fibers and microvasculature in the brain, while compression leads to injuries resulting from the temporary high pressures which cause blood to escape from capillaries. On the other hand, shear strains are believed to be the result of angular acceleration of the head and complicated deformation patterns, leading to injuries between the grey and white matter of the brain as well as diffuse axonal injury (DAI).[7]

Cellular Mechanisms of Brain Strain Injury

Diffuse Axonal Injury

Diffuse axonal injury (DAI) is just one of the several manifestations of brain injury that can occur after a rapid acceleration imposes a stress and resulting strain on the brain.[9] Rotation and translation of the brain occur, (most commonly during a motor vehicle accident, also during extreme falls or during contact sport) causing shearing of the axons of neurons within the brain.[9][10] This can result in similar symptoms to those of concussion in milder cases. In more severe cases, patients end up in a prolonged, traumatic coma that they often do not fully recover from.[10] It is undoubtedly one of the most severe injuries resulting from the deformation of the brain during a traumatic incident, resulting in death within 6-months following injury in 30.8% of patients studied by a team of researchers at the University of Sao Paulo, Brazil.[9]

Biomechanically speaking, DAI occurs due to different portions of the brain reacting to an applied acceleration differently. At the neuronal level, imbalanced deformation occurs due to some portions of the brain moving at a different speed (i.e. exhibiting more or less strain) than others.[11][12] Further evidence has been uncovered to suggest that unmyelinated axons have a lower threshold to injury than their myelinated counterparts, implying an unequal vulnerability.[11] The white matter lesions that occur in the brain as a result of DAI are particularly troublesome due to the relative difficulty of diagnosis and treatment.[10]

Hematoma

In general, a hematoma occurs when blood collects somewhere in the body outside of a severed or burst blood vessel.[13] The treatment required depends heavily on the location in the body where the hematoma is present.[14] They are particularly problematic in the brain as they are difficult to detect without radiological imaging and difficult to treat, often requiring a surgical decompression procedure to reduce the potentially life-threatening intracranial pressure.[15]

Epidural

CT scan of an epidural hematoma.

An epidural hematoma is the collection of blood within the space between the dura mater (i.e. the tough membrane that encompasses the brain) and the inside lining of the skull. It is often a direct result of head acceleration from an impact, occurring in approximately 10% of hospitalized TBI cases. The majority of cases occur as a result of bleeding from the arteries in the aforementioned region of the brain, with the remainder being caused by venous bleeding.[16] The bleeding occurs rapidly due to the high-pressure of these blood vessels, progressing and worsening symptoms quickly.[17] Epidural hematomas are most often diagnosed through a computer tomogram (CT) scan or magnetic resonance imaging (MRI) following the injurious event. Without emergency surgery to alleviate the building in the brain, permanent damage or death are the most likely occurrences.[16]

Subdural

From a biomechanical perspective, a subdural hematoma is similar to a epidural hematoma. They both occur as a result of rotational acceleration, and by extension, strain within the brain. However, subdural hematomas occur between the dura mater and the arachnoid mater, one level deeper than an epidural hematoma. Subdural hematomas as a result of brain injury can be acute, with symptoms appearing almost immediately post-injury, or subacute, with symptoms appearing over hours or days.[18] Symptoms for both an epidural and subdural hematoma include but are not limited to severe, relentless headaches, nausea and vomiting, dizziness, unilateral weakness, or drowsiness. For the most part, the protocol for diagnosis and treatment of both subdural and epidural hematomas is identical.[15]

Contusion

TBI manifests as a cerebral contusion in 35% of severe cases, often progressing in the hours, days, and weeks following the traumatic event. The level of contusion can be measured through radiological imaging and determines the rate of progression and/or healing. It is dependent on the amount of rotational acceleration and strain experienced by the brain during a traumatic event, as well as an individual’s concussion history.[19] Other demographics can influence contusion progression, such as patient age, sex, and past medical history (e.g. hypertension, smoking habits, etc.).[20] Due to the direction of head acceleration in most injurious scenarios, contusions usually occur in the frontal and temporal lobes of the brain.[21] During more severe impacts, they can occur in multiple locations, increasing the likelihood of progression drastically.[22] The expansion of a cerebral contusion serves as a biomarker for TBI development so any medical interventions to alleviate it likely will not address the root cause and therefore will not assist in concussion recovery directly.[19]

Location of Impact & Clinical Injury Symptoms

It is important to note the different causes and outcomes of strain in different areas of the brain, such as the corpus callosum, the thalamus, and the basal ganglia. Since each region of the brain has different functions, symptoms of injury may change depending on the strain applied to each area. For example, the corpus callosum is in charge of communication between the right and left hemispheres, therefore, corpus callosum injury is related to abnormal interhemispheric function and motor impairment.[23] Strain-induced injury to the corpus callosum may present differently than an injury to the thalamus, which plays a major role in sensory and motor function.[24]

This relationship is demonstrated in one study[6] which made use of a human head finite element (FE) model that had been validated against cadaveric data to analyze injury data from reconstructions of automotive crashes. With this FE model, they were able to relate localized tissue strain to symptoms of brain injury experienced by occupants of the automotive crashes.

In this study, two loading conditions were applied: rotational acceleration to the center of gravity of the FE model in the sagittal (about y-axis), and coronal (about x-axis) planes. While the magnitude of induced strain did not vary significantly between the two conditions, different strain distributions were observed. For coronal rotation, the highest strains occurred in the thalamus, midbrain, caudate, hippocampus and temporal lobe regions. For sagittal rotation, the hippocampus, corpus callosum and cortex at inter-hemispheric fissure experienced the highest strains.

When comparing the localizations of strain predicted by the FE model with the clinical symptoms presented by occupants in the automotive crashes, there was a notable correlation. For example, predictions for increased strain in the fornix, midbrain and corpus callosum were associated with symptoms such as memory and cognitive impairments, loss of consciousness and increased intervals for full recovery.

As a result, this study concluded that developing a localized strain criteria could be capable of addressing specific symptoms and severity of concussion injury.[6]

Biomechanical Experiments

Direction of Impact

Biomechanical experiments have been completed to identify a relationship between impact location and resultant brain structure injury. Tiernan and Byrne concluded that lateral, frontal, and rear impacts involving rotational acceleration cause different strains in varied brain regions.[25] The midbrain experienced 47% more strain during a frontal impact in comparison to a lateral impact, and 37% more strain in comparison to a rear impact.[25] The brain stem also experienced the highest maximum principal strain (MPS) during frontal impact tests.[25] The corpus callosum experiences the highest MPS during lateral impacts, and the thalamus experiences approximately consistent MPS during frontal, lateral, and rear impact tests.[25]

These impact-strain experiments are performed using the hybrid III ATD in order to perform finite element simulation of impact.[25] Biomechanical variables used to simulate brain impact include maximum principal strain, cumulative strain damage measure (CSDM) value, and average strain.[26] The CDSM20 value was calculated for different brain regions, which represents the percentage of elements for which strain is higher than 0.2.[26] The CDSM20 is a significant measure as it is a potential threshold for concussion.[27] The results of this biomechanics study for CDSM20 of different brain regions from varied directions of impact are as follows: corpus callosum= 0.39 (lateral bending), 0.28 (axial rotation), basal ganglia= 0.11 (lateral bending), 0.31 (axial rotation), and thalamus= 0.25 (lateral bending), 0.16 (axial rotation).[26] These strain damage results are important for traumatic brain injury research in order to identify the mechanism of injury for treatment as well as future prevention such as car safetying and helmet improvement.

Finite Element Modelling

Computational models like the finite element (FE) head models are a reliable way to determine how strains, strain rate, and biomechanical forces can be predicted on different tissues.[28] There are many well-validated FE models to represent an average adult head, which include head components such as the scalp, skull, brain, meninges, cerebral spinal fluid, bridging veins, white and grey matter and the ventricles.[6] The properties of the models are determined based on data from cadaveric and animal testing. These FE models can be used to investigate the response of the brain to strain in an attempt to determine injury thresholds and the correlation with DAI.[6]

It is important to mention the time-consuming nature of the FE method and the need to have biomechanics expertise performing the simulations. Those factors create limitations in the usage of FE by medical professionals in the diagnosis of brain strain injury.[29]

in vivo Testing

Animal models that study mouse brains greatly help traumatic brain injury research.

Early on, injury biomechanics experiments have been done on cadavers as they are the most biofidelic human surrogate for testing. However, the usage of these surrogates is limited as their injury responses are restricted and an adequate threshold is difficult to determine; for a long time, skull fractures were generally used as “the” threshold, since it correlates with brain injury.[30] Amid the early 60’s, a single accelerometer was anchored in the cadaver’s skull during experiments. However, those injury criteria are rudimentary as they don't account for three-dimension linear acceleration. This was the case up until the early 70’s, when research teams decided to use two biaxial accelerometers and calculate rotational acceleration of the head, which deepens the understanding behind brain injury.[30] It is important to mention that while most types of head injury pathology are not demonstrated by cadavers, their experimental data can be applied to computational models where brain pressures and strains can be calculated and compared to injuries from animal models. [30] Rowson and their team explain that overall, even if the cadavers have the accurate weight and anatomy in respect to living humans, they are not consistent, nor are their impact responses. Volunteers are often used in experiences as they are more helpful than cadavers and dummies, but the experimentational settings are limited as they shouldn’t injure the volunteer. For a more ethical method, brain strain data and levels of acceleration are collected from athletes’ collisions on the field, through sensors embedded in headbands, mouthguards, helmets, and earpieces. [30]

Animal models, especially mouse’s brains, help traumatic brain injury research, since researchers get the capacity to isolate injury mechanisms and follow the progression of their injuries using neuropathological and neurobehavioral metrics. [31] However, the anatomical differences between a human and a mouse skull creates significant obstacles in replicating human traumatic brain injuries. For instance, the size and shape of the mouse’s skull, the gyral complexity and white to gray matter will generate brain injuries in different areas compared to brain injuries in humans. [32] There’s also a difficulty to correlate the behavioural response in mice and humans.[31]

ATD Testing

Originally, the Hybrid III dummy was crafted to be used in automobile data collection and was modelled to have a biofidelic response. The headform was then updated for helmet impact experimentations to depict a more lifelike head shape.[33] The idea of using ATDs to assess strain-based metrics for brain injuries is very appealing; however, the current lack of a clear strain-based metric means using ATDs is often unfeasible. In general, investigating brain strain and DAI is impossible with ATD’s, as MRIs are used to assess the presence of DAI in the head after head injury.[34]

Injury Criteria and Tolerances

Head Injury Criteria (HIC) is a well-established injury criteria which evolved from the Wayne State concussion curve of acceleration, which identified the maximum linear acceleration the head can withstand in a period of time.[35] This criterion is widely used in automotive safety testing, however it is not an accurate predictor of brain strain injury since it only takes into consideration the translational acceleration of the skull and does not consider the specific behaviours of the brain inside the skull.[36][37]

The Brain Injury Criteria (BrIC) is a rotational injury criterion which was developed as a stress and strain based criteria for various types of brain injury as a complement to HIC.[37] BrIC was established using the following methods: 2 different mathematical models of the human head (SIMon and GHBMC), scaled animal injury for physical brain injury criteria, and multiple versions of Anthropomorphic Test Device (ATD) test data to determine Brain Injury Criteria.[36] The animal injury data used took into account diffuse axonal injury, which is an injury caused by brain strain which can be a predictor of TBI.[36] BrIC is more accurate for the behaviours of the human brain since SIMon approximates brain elements such as cerebrospinal fluid, intracranial pressures, and deformation of the skull which were derived from cadaver and animal research.[37]

Currently, there is no widely-used injury tolerance measure of brain strain specifically. However, many models have been developed which use measures of stress and strain to estimate brain injury risk and distribution.[37] A study by Knowles et al. aims to predict cumulative and maximum brain strain measures using CSDM-15 and MPS as mechanical measures which represent brain tissue deformation. These measures are modeled with SIMon using injury data from animal models and college football impact collection data to determine a correlation between CSDM and the probability of diffuse axonal injury. Limitations of this specific study include the use of only one head model (SIMon) and only one type of ATD (HybridIII). As well, any testing which involves the head and brain has limitations due to a lack of human injury data to validate criteria.[36]

Injury Prevention Devices

Brain strain-focused studies have modelled helmets with face shields (such as football helmets) to evaluate their effectiveness at injury prevention.

Although there exists many devices for head protection (ie. helmets, airbags, mouthguards), there is limited research which evaluates the brain strain response of impact while wearing such protective devices, especially since current helmet regulations usually only consider the peak linear acceleration.[38][35] An experiment by Bian et al. used a finite element model to calculate brain strain during impact on head models both wearing a helmet and unhelmeted. The cumulative strain damage measure (CDSM15) was used to quantify brain strain distribution as well as peak intracranial pressure of the models were identified during impact to determine the protectiveness of the helmet. The strain distribution of several different impact locations were determined, such as frontal, lateral, rear, and facemask. The results of the study showed that for each impact, the CSDM15 and peak pressure values were reduced in models wearing the helmet.[38]

A strength of this study is that it provides an in-depth analysis of brain strain while wearing an injury prevention device (helmet) and compares it to strain in a model not wearing the device. The paper took into account all components of the helmet, including the facemask, which is neglected in regulatory helmet testing. The data collected through these impact tests are valuable in order to make improvements to existing helmets to further prevent injury from brain strain. A weakness of this study is that the neck muscles used in the model were passive and not active, and therefore did not contract during impact. The test model could have been more biofidelic if the muscles were active and tensed during impact, like how most humans would tense. As well, the head and helmet models used for the FE analysis were not validated for use together.

Limitations when it comes to many injury prevention devices is that brain strain still continues to cause injury since brain strain is not reduced to 0 during impact, even while wearing/using such devices. In some cases in this paper, strain distribution of the helmeted model was only reduced by 18%, which may not be adequate to prevent brain injury.[38] Finally, a limitation which pertains to all injury prevention devices is that there is no definite brain strain injury tolerance, therefore there is no indication how much brain strain must be reduced in order to have little risk of brain injury upon impact.

Gaps in the Research and Future Suggestions

After a traumatic brain incident, the head abbreviated injury score, the injury severity score, and the Glasgow Coma Scale are used as measures of assessment of injury severity. The Glasgow Coma Scale, a physiological measure of injury severity based on clinical examination, is limited in its assessment and is not made to represent the differences in patients’ situations.[39] The injury severity score and the head abbreviated injury score are both anatomical assessments of injury severity, based on CT scans or post mortem findings.[40] Even though these assessments are widely used, very little studies have been made to confirm the values’ accuracy and the impact of external variables on the scores.[39]

As with much research in the domain of brain injury, there exist many notable gaps. One common issue is among brain injury criteria: while many are designed based on explicit head impacts scenarios, many of them are not used in situations reflecting their particular development background, leading to inconsistencies in the metrics.[41] Additionally, the exact placement and extent of DIA that is caused by brain strain following a traumatic event are assumed, but the association between the biomechanical forces and the damage is not known.[42] More than that, more research should be conducted to establish the strength of associations between brain regions and transformations in mental functions following an injury, and to determine which biomechanical forces most affect those brain regions.[43] Furthermore, most of the studies surrounding brain tissue deformities have been done on small animal models, which are not easily adapted to human conditions, or ATDs, who are very restricted in their biofidelic response during an impact and provide very little injury data.[43] For this reason, neuroprotective treatments following traumatic brain injuries do not exist and head gear is not engineered correctly. There is also a gap in quantification and visualization of the nervous effects of brain injuries.[43] This signifies that currently, conventional neuroimaging tools are not effective in the understanding of the effects of brain strain. Other studies have been done using finite element modeling to simulate biomechanical head responses and measure brain strain in different situations. However, it is difficult to attest to the accuracy of the results since the finite element models are not made to represent true anatomic and physiologic details of the head.[43] Since there exists an overall lack of understanding between head kinematics and brain strain, this creates a demand for biomechanical experiments that can help to identify this relationship.[26]

Future study on the topic of brain strain injury prevention should be to identify a correlation between head kinematics and brain strain in order to produce a validated brain injury criteria for TBI research. As well, prevention devices should be improved upon in order to further reduce the strain acting on the brain during impact. In the context of the helmet, many different aspects can be improved upon such as outer shell maximum energy absorption and the optimization of inner foam material.[38] Other future research should focus on developing more accurate and less computationally expensive finite element models of injuries to the head, as it would allow for a better understanding of brain strain and corresponding injuries.

References

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  2. ↑ 2.0 2.1 2.2 Meaney, David F. (10 November 2021). "Biomechanics of Concussion". Clinics in Sports Medicine. Elsevier (published 01 January 2011). 30 (1): 19–31. Check date values in: |publication-date= (help)
  3. ↑ 3.0 3.1 3.2 Gabler, Lee F.; Crandall, Jeff R.; Panzer, Matthew B. (10 November 2021). "Development of a Metric for Predicting Brain Strain Responses Using Head Kinematics". Annals of Biomedical Engineering. Springer Nature (published 28 March 2018). 46: 972–985.
  4. ↑ Gabler, Lee F.; Crandall, Jeff R.; Panzer, Matthew B. (10 November 2021). "Development of a Second-Order System for Rapid Estimation of Maximum Brain Strain". Annals of Biomedical Engineering. Springer Nature (published 04 December 2018). 47: 1971–1981. Check date values in: |publication-date= (help)
  5. ↑ Bian, Kewei; Mao, Haojie (2020). "Mechanisms and variances of rotation-induced brain injury: a parametric investigation between head kinematics and brain strain". Biomechanics and Modeling in Mechanobiology. 19: 2323–2341.
  6. ↑ 6.0 6.1 6.2 6.3 6.4 6.5 Zhang, Liying; Yang, King; Gennarelli, Thomas (2008). "Mathematical Modelling of Cerebral Concussion: Correlations of Regional Brain Strain with Clinical Symptoms" (PDF). PROCEEDINGS OF IRCOBI 2008 (INTERNATIONAL RESEARCH COUNCIL ON THE BIOMECHANICS OF INJURY) CONFERENCE.
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  8. ↑ Hosseini-Farid, Mohammad; Amiri-Tehrani-Zadeh, MaryamSadat; Ramzanpour, Mohammadreza; Ziejewski, Mariusz; Karami, Ghodrat (2020). "The Strain Rates in the Brain, Brainstem, Dura, and Skull under Dynamic Loadings". Mathematical and Computational Applications. 25(2): 21.
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  13. ↑ "Hematoma". MedicineNet. 17 March 2021. Retrieved 03 December 2021. Check date values in: |access-date= (help)
  14. ↑ "Intracranial Hematoma". Mayo Clinic. 13 June 2020. Retrieved 03 December 2021. Check date values in: |access-date= (help)
  15. ↑ 15.0 15.1 "Subdural Hematoma". Cleveland Clinic. 04 May 2020. Retrieved 03 December 2021. Check date values in: |access-date=, |date= (help)
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  19. ↑ 19.0 19.1 Adatia, Krishma; Newcombe, Virginia F. J.; Menon, David K. (27 May 2020). "Contusion Progression Following Traumatic Brain Injury: A Review of Clinical and Radiological Predictors, and Influence on Outcome". Neurocritical Care. 34: 312–324 – via Springer Nature.
  20. ↑ Wan, Xueyan; Fan, Ting; Wang, Sheng; Zhang, Suojun; Liu, Shengwen; Yang, Hongkuan; Shu, Kai; Lei, Ting (09 December 2016). "Progressive hemorrhagic injury in patients with traumatic intracerebral hemorrhage: characteristics, risk factors and impact on management". Acta Neurochir. 159 (2): 227–235. Check date values in: |date= (help)
  21. ↑ Yadav, Yad R; Basoor, Abhijeet; Jain, Gaurav; Nelson, Adam (December 2006). "Expanding traumatic intracerebral contusion/hematoma". Neurol India. 54 (4): 377–381.
  22. ↑ Rehman, Lal; Afzal, Ali; Aziz, Hafiza Fatima; Akbar, Sana; Abbas, Asad; Rizvi, Raza (June 2019). "Radiological Parameters to Predict Hemorrhagic Progression of Traumatic Contusional Brain Injury". J Neurosci Rural Pract. 10 (2): 212–217.
  23. ↑ Chamard, Emilie; Lefebvre, Genevieve; Lassonde, Maryse; Theoret, Hugo (10 November 2021). "Long-Term Abnormalities in the Corpus Callosum of Female Concussed Athletes". Journal of neurotrauma. Mary Ann Liebert, Inc. (published 21 June 2016). 33 (13): 1220–1226.
  24. ↑ Pessoa, Luiz (10 November 2021). "Understanding brain networks and brain organization". Physics of Life Reviews. Elsevier (published 18 April 2014). 11 (3): 400–435.
  25. ↑ 25.0 25.1 25.2 25.3 25.4 Tiernan, Stephen; Byrne, Gary (10 November 2021). "The effect of impact location on brain strain". Brain Injury. Taylor & Francis Online (published 19 January 2019). 33 (4): 427–434.
  26. ↑ 26.0 26.1 26.2 26.3 Bian, Kewei; Mao, Haojie (10 November 2021). "Mechanisms and variances of rotation-induced brain injury: a parametric investigation between head kinematics and brain strain". Biomechanics and modeling in mechanobiology. Springer Nature (published 24 May 2020). 19 (6): 2323–2341.
  27. ↑ Svein, Kleiven (10 November 2021). "Predictors for traumatic brain injuries evaluated through accident reconstructions". Stapp Car Crash J. 51: 81–114. Check date values in: |year=, |year= / |date= mismatch (help)
  28. ↑ Donat, Cornelius; Yanez Lopez, Maria; Sastre, Magdalena; Baxan, Nicoleta; Goldfinger, Marc; Seeamber, Reneira; Müller, Franziska; Davies, Polly; Hellyer, Peter (2021). "From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury". Brain. 144(1): 70–91.
  29. ↑ Zhan, Xianghao; Liu, Yuzhe; Raymond, Samuel; Alizadeh, Hossein Vahid; Domel, August; Gevaert, Olivier; Zeineh, Michael; Grant, Gerald; Camarillo, David (2020). "Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion". arXiv:2010.08527 – via Cornell University.
  30. ↑ 30.0 30.1 30.2 30.3 Rowson, Bethany; Tyson, Abigail; Rowson, Steven; Duma, Stefan (2018). Measuring head impacts: accelerometers and other sensors. Handbook of Clinical Neurology: Elsevier. pp. 235–243.
  31. ↑ 31.0 31.1 Zhang, Yi Ping; Cai, Jun; Shields, Lisa B. E.; Liu, Naikui; Xu, Xiao-Ming; Shields, Christopher B. (05 February 2014). "Traumatic Brain Injury Using Mouse Models". Translational Stroke Research: 454–471. Check date values in: |date= (help)
  32. ↑ Xiong, Ye; Mahmood, Asim; Chopp, Michael (18 January 2013). "Animal models of traumatic brain injury". Nature Reviews Neuroscience: 128–142.
  33. ↑ Rowson, Bethany; Tyson, Abigail; Rowson, Steven; Duma, Stefan. "Chapter 23 - Measuring head impacts: accelerometers and other sensors". Handbook of Clinical Neurology. 158: 235–243.
  34. ↑ Gabler, Lee; Crandall, Jeff; Panzer, Matthew (2018). "Development of a Metric for Predicting Brain Strain Responses Using Head Kinematics". Annals of Biomedical Engineering. 46: 972–985.
  35. ↑ 35.0 35.1 Hulme, A; Mills, N.J; Gilchrist (1995). "Industrial head injuries and the performance of helmets" (PDF). School of Metallurgy and Materials, University of Birmingham. Unknown parameter |first name 3= ignored (help)
  36. ↑ 36.0 36.1 36.2 36.3 Takhounts, Erik; Craig, Matthew (2013). "Development of Brain Injury Criteria (BrIC)". Stapp Car Crash Journal. 57: 243–266. Unknown parameter |first name 3= ignored (help); Unknown parameter |first name 4= ignored (help); Unknown parameter |last name 4= ignored (help); Unknown parameter |last name 3= ignored (help)
  37. ↑ 37.0 37.1 37.2 37.3 Knowles, Brooklynn; Dennison, Christopher (2017). "Predicting Cumulative and Maximum Brain Strain Measures From HybridIII Head Kinematics: A Combined Laboratory Study and Post-Hoc Regression Analysis". Annals of biomedical engineering: 2146–2158.
  38. ↑ 38.0 38.1 38.2 38.3 Bian, Kewei (2019). "Predicting and preventing traumatic brain injury: A novel computational approach". Electronic Thesis and Dissertation Repository.
  39. ↑ 39.0 39.1 Demetriades, Demetrios; Kuncir, Eric; Murray, James; Velmahos, George C; Rhee, Peter; Chan, Linda (10 November 2021). "Mortality prediction of head abbreviated injury score and Glasgow Coma Scale: analysis of 7,764 head injuries". Journal of the American College of Surgeons. Elsevier Inc. (published 01 August 2004). 199 (2): 216–222. Check date values in: |publication-date= (help)
  40. ↑ Foreman, Brandon P.; Caesar, Ruth R.; Parks, Jennifer; Madden, Christopher; Gentilello, Larry M.; Shafi, Shahid; Carlile, Mary C.; harper, Caryn R.; Diaz-Arrastia, Ramon R. (10 November 2021). "Usefulness of the Abbreviated Injury Score and the Injury Severity Score in Comparison to the Glasgow Coma Scale in Predicting Outcome After Traumatic Brain Injury". Journal of Trauma. Wolters Kluwer Health, Inc. (published 01 April 2007). 62 (4): 946–950. Check date values in: |publication-date= (help)
  41. ↑ Zhan, Xianghao; Li, Yiheng; Domel, August G; Alizadeh, Hossein Vahid; Raymond, Samuel J; Ruan, Jesse; Barbat, Saeed; Tiernan, Stephen; Gevaert, Olivier (10 November 2021). "The relationship between brain injury criteria and brain strain across different types of head impacts can be different". Journal of the Royal Society Interface. The Royal Society (published 02 June 2021). 18 (179). Check date values in: |publication-date= (help)
  42. ↑ Cornelius K, Donat; Lopez, Maria Yanez; Sastre, Magdalena; Baxan, Nicoleta; Goldfinger, Marc; Seeamber, Reneira; Muller, Franziska; Davies, Polly; Hellyer, Peter (10 November 2021). "From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury". Brain. Oxford University Press (published 17 January 2021). 144 (1): 70–91.
  43. ↑ 43.0 43.1 43.2 43.3 McAllister, Thomas W; Ford, James C; Ji, Songbai; Beckwith, Jonathan G; Flashman, Laura A; Paulsen, Keith; Greenwald, Richard (10 November 2021). "Maximum Principal Strain and Strain Rate Associated with Concussion Diagnosis Correlates with Changes in Corpus Callosum White Matter Indices". Annals of Biomedical Engineering. Springer Nature (published January 2012). 40 (1): 127–140.
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