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Abstract

Background

Balance impairments present in approximately 30% of concussion cases. Biomechanical reconstructions model the degree and location of brain tissue strain associated with injury. The objective was to examine the relationship between the magnitude and location of brain tissue strain and balance impairment following a concussion.

Methods

Children one month post-concussion (n = 33) and non-injured children (n = 33) completed two balance conditions while standing on a Wii Balance Board that recorded the centre of pressure during (i) double-leg stance with eyes closed (EC) and (ii) dual-task (DT) combining double-leg stance while completing a cognitive task. Injury reconstructions were performed for 10 of the concussed participants. A 5th percentile Hybrid III headform was used to obtain linear and rotational acceleration time-curves of the head impact. These data were input in the University College Dublin Brain Trauma Model (UCDBTM) to calculate maximum principal strains and cumulative strain damage values at 10% (CSDM-10) and 20% (CSDM-20) for different brain regions. Correlations between balance and reconstruction variables were calculated.

Results

Out of the 10 reconstructed cases, six participants had impaired balance on the EC condition, six had impaired balance on the DT condition and four had impaired balance on both the EC and DT conditions. For maximum principal strain values, correlations with balance variables ranged from −0.0190 to 0.394 for the DT condition and from −0.225 and 0.152 for the EC condition. For CSDM-10 values, correlations with balance variables ranged from 0.280 to 0.386 for the DT condition and from −0.103 to 0.252 for the EC condition. For CSDM-20 values, correlations with balance variables ranged from 0.0629 to 0.289 for the DT condition and from −0.353 to −0.155 for the EC condition.

Conclusions

Although a subset of the concussed participants continued to show balance impairments, no association was established between the presence of balance impairment and the magnitude and/or location of brain tissue strain. Maintaining balance is a complex process integrated into multiple subcortical regions, white matter tracts and cranial nerves, which were not represented in the brain model, and as a result the UCDBTM may not be sensitive to damage in these areas.

Keywords

Concussion centre of pressure biomechanics injury reconstruction postural balance youth

Introduction

Concussions are a growing public health concern among children. Youth report the highest incidence of concussion,¹ and while most concussed children recover within a couple of weeks, 30% of cases experience symptoms that can last up to months or years post-injury.² Balance impairment may affect approximately 30% of individuals who experience a concussion³ and have been shown to persist up to at least one month post-injury in adolescents.^4,5 While balance deficits may occur as the result of a concussive impact, the relationship between how an individual is impacted, the resulting brain tissue deformation and these signs of brain impairment is unknown.

Balance is defined as the ability to maintain one’s centre of mass within the base of support.⁶ The process of maintaining balance is very complex and is dependent on specific structures within the brain, multiple subcortical regions, white matter tracts, cranial nerves and other neural tissue; all these structures may be vulnerable to impairment following an impact to the head. The process of maintaining balance is also inherently unstable. The body continuously produces small deviations from a perfect upright standing position.⁷ In order to correct for these postural deviations, the brainstem integrates sensory information to obtain a sense of the body’s position and orientation in space,⁸ and the cerebellum coordinates the motor outputs that are required to reposition the body within the base of support.⁹

Biomechanically, concussion is often the result of a head impact that causes injurious strains to the brain tissues. The severity of the impact and the magnitude of strain incurred by the brain tissues are influenced by mechanical characteristics such as impact surface, the velocity at which the impact occurred, impact location and angle.^10,11 These characteristics have been shown to influence the head’s dynamic response following the impact in terms of linear and rotational accelerations and the resulting strain on the brain tissues.^12,13 Changes in the dynamics of the impact have also been shown to affect the amount of strain in different regions of the brain,¹⁴ a phenomenon that may be pertinent to the presence of balance impairments from a concussion. Specific structures within the brain, namely the brainstem and the cerebellum, are involved in the process of maintaining balance, and the relationship between impact, strain on the tissues in these regions and outcome measures for concussion is unknown.

The purpose of this exploratory study was to examine the relationship between the magnitude and location of brain tissue strain and resulting balance performance following a concussion. Biomechanical reconstructions of head injuries were performed in a group of concussed children for which a subset was affected with balance impairments to model the degree and location of brain tissue strain. It was hypothesized that the children affected with balance impairments would show greater values of tissue strain in the brainstem and cerebellum compared to the children with normal balance.

Methods

Participants

Concussion group

Participants with acute concussion (less than 48 h) were recruited from the emergency department (ED) as part of a large, prospective multicentre study.¹⁵ As part of the larger study, participants were diagnosed with a concussion by a physician based on the criteria from the Zurich consensus statement.¹⁶ Thirty-three children between 9 and 17 years of age (mean age = 14.2 ± 1.5, 12 males and 21 females) with a diagnosed concussion also completed a balance testing protocol as part of an a priori-planned substudy.⁵ Only participants whose concussion resulted from a direct blow to the head were included in this reconstruction study.

Control group

Thirty-three non-injured children between the same ages (mean age = 15.0 ± 1.5, 9 males and 24 females) also completed the balance testing protocol as part of the previous study.⁵ These participants reported no concussive symptoms and had not suffered any head trauma within the last year. In the context of the current study, the control group data were used to establish a threshold for normal balance to determine if the concussed participants had normal or impaired balance.

Balance testing

Protocol

Prior to participating in this study, all participants and their parents provided written informed consent. This study was approved by the institution’s ethics research board. Participants with concussion completed the balance testing protocol at approximately one month post-injury (mean number of days = 32 ± 3). Participants in the control group completed the protocol once.

Comprehensive methods are provided in a previously published paper.⁵ Participants completed three different balance conditions while standing on a Nintendo Wii Balance Board (WBB) that recorded the movement of the centre of pressure (COP) under their feet. Participants completed 2 min trials while standing on the WBB with their feet shoulder width apart with eyes open and then with eyes closed (EC). Participants also completed a dual-task (DT) condition consisting of standing shoulder width apart on the WBB while simultaneously completing a Stroop-Colour word test. The Stroop-Colour word test was presented on a board placed in front of the participant and displayed 20 rows of five words consisting of a random series of the words ‘red,’ ‘blue,’ ‘yellow’ and ‘green’ written in an incongruent ink colour. Participants were asked to name the colour of the ink that each word was printed in.

Material and data processing for WBB data

The WBB raw pressure data were recorded to a laptop computer via a Bluetooth device at a sampling frequency of 30 Hz. A custom Matlab (The Mathworks Inc., Natick, MA, USA) script was used to transform raw pressure data to COP values in the anterior/posterior and lateral directions. The COP data were filtered using a second order low-pass Butterworth filter with a cut-off frequency of 12 Hz. The COP 95% ellipse was calculated for both the EC and DT conditions for each participant.

Balance status concussion group

Participants with concussion were identified as having either impaired or normal balance on both the EC and DT conditions. For both conditions, participants were identified as having impaired balance if they showed a value of at least two standard deviations above the control group’s mean for the 95% ellipse. For both conditions, participants were identified as having normal balance if they showed a value within two standard deviations from the control group’s mean for the 95% ellipse.

Injury reconstruction

Impact characteristics

A detailed description of the events surrounding each participant’s head injury was obtained during their visit to the ED through use of a standardized mechanism of injury report form^15,17 and during a telephone follow-up. Nine of the 10 cases included in this study were falls. The following information was needed to proceed with laboratory reconstructions to ensure the most accurate representation of the injury: height of the subject, falling height, age, gender, impacting surface, the impact location on the head and the body part that made the first contact with the ground and general description of the event and the environment where the event occurred. The impact location was quantified by using the grid shown in Figure 1.

Figure 1.

Grid used to quantify the impact location.

To determine falling head contact velocity, the information recorded was used as input for a Mathematical Dynamic Models (MADYMO) simulation.¹⁸ To conduct this analysis, a simulation was conducted using anthropometric ellipsoid dummy models that were closely matched to the dimensions of the subject. This dummy was placed in the virtual environment that closely matched that of the witness reports and was allowed to fall in a way similar to that of the subject. The simulation would then allow the ellipsoid dummy to fall with the kinematics and restrictions of a human fall and produce a measurable output upon contact with the impact surface. In the case of this research the head impact velocity was the output. For the one collision case, additional details surrounding the event were necessary such as the sport being played (in this case soccer); if the subject and striking player were immobile, jogging, or running at the time of the event; and what body part made contact with the head. In this case, the impact velocity was determined using the literature describing running speed for a teenage soccer player.¹⁹ These methods have been used for brain injury reconstruction for paediatric and adult cases and were found to be in close agreement with anatomical and animal research.^17,20

Reconstruction procedure

The laboratory reconstruction of the brain injury event used computational, physical and finite element models. For the falls, once the impact velocity was determined from MADYMO the Hybrid III headform was attached to the monorail drop rig and dropped from a height to attain the appropriate speed on impact. The headform was positioned to impact the same location reported on the standardized mechanism of injury report form. The impacted surface (anvil) was also of the same composition as the surface that the head impacted during the event. For the collision, a pendulum impactor was used to simulate the event by impacting the headform at the impact location described in the standardized mechanism of injury report form. Three impacts were conducted for each injury case. From these impacts (falls and the collision), the linear and rotational acceleration times histories were recorded and then used for analysis of strain in the brain tissues using the University College Dublin Brain Trauma Model (UCDBTM).²¹

Equipment

Monorail

The monorail was a 4.7 m long vertical single rail to which a drop carriage was affixed via ball bushings. The headform was attached to the drop carriage by an unbiased neckform. The drop carriage was released by a pneumatic piston after which the drop carriage with the head and neckform attached would slide down and impact the anvil at the base. The anvil at the base of the monorail was comprised of concrete with a steel top with bolt holes to allow different surfaces to be fixed to it. The impact velocity was measured within 0.02 m of the impact via a photoelectric timegate.

Pendulum

The pendulum was a four wire cylinder that was suspended from the ceiling. This pendulum was drawn back using a winch attached to a distal wall and released via an electromagnet. Upon release it would travel until it impacted the headform that was fixed on an impact table. This table allowed for the placement of the headform in 6 degrees of freedom so that the appropriate impact location could be contacted for the reconstruction. The mass of the impactor was 9.6 kg and determined from literature describing the effective mass of impact.²² The impact velocity was determined 0.02 m prior to impact by Photron High Speed Camera (HSI Inc.).

Headform, neckform and data acquisition

A Hybrid III 5th headform was used to conduct the physical reconstructions as this size headform closely matched the head size of the population. The headform was outfitted with a 3–2-2–2 accelerometer array for the measurement of linear and rotational acceleration in 6 degrees of freedom.²³ The accelerometers used were nine Endevco 7264 C-2KTZ-2–300 sensors (Irvine, CA, USA). The unbiased neckform was a series of alternating symmetrical aluminium and rubber butyl discs with the same dimensions as a Hybrid III neckform, but without the biased response that the Hybrid III would provide upon impact.²⁴ The data were collected at 20 kHz, with a CFC 180 filter using DTS TDAS software (Seal Beach, CA, USA).

Finite element model

To determine the maximum principal strain (MPS) values for the reconstructions of the concussion cases, the UCDBTM was used. This finite element model had geometry that was determined from medical imaging of the head of a male cadaver that included scalp, skull, pia, falx, tentorium, cerebrospinal fluid (CSF), grey and white matter, cerebellum and the brainstem.¹² In total, the UCDBTM was comprised of approximately 26,000 hexahedral elements. The simulations were conducted using ABAQUS explicit software (Dassault Systèmes, MA, USA).

The material properties of the model²¹ were developed from tissue sample and anatomical testing. The tissues of the brain were modelled using a linearly viscoelastic model combined with large deformation theory. The brain tissue behaviour was characterized as viscoelastic in shear with a deviatoric stress rate dependent on the shear relaxation modulus.²¹ The compression of the brain tissue was defined as elastic. The shear characteristic of the viscoelastic brain was expressed

G (t) = G_{\infty} + (G_{0} - G_{\infty}) e^{- β t}

(1)

with G_∞ representing the long-term shear modulus, G₀ the short-term modulus and β the decay factor. The brain skull interaction was defined as sliding with no separation between the pia and the CSF, with the CSF modelled with a bulk modulus of water with a low shear modulus.²¹ The sliding interface had a coefficient of friction of 0.2.²⁵

Validation of the model was accomplished by comparing the UCDBTM’s responses to cadaveric research.^26,27 Further brain injury comparisons were conducted and achieved good agreement with the magnitudes of strain and stress in the literature for the target injury types.^18,20,28

There are newer and more complex finite element models than the UCDBTM that have been developed and have been used for brain injury research.^29–31 While not as complex as these models, the UCDBTM has been used to process the largest dataset of brain injured, and non-injured cases, and may provide a better reference than these other models with which to interpret the magnitudes of response for the current research.^{11,17,20,32,33}

Scaling of the UCDBTM

Currently there is no agreed scientific consensus as to the proper representation of brain tissue for a youth population for a finite element model of the head and brain, with many researchers suggesting the material characteristics being within those of adults.^34–36 As a result, the UCDBTM was scaled to represent the youth head geometry for this research, with the size matched to the anthropometrics of the population that was part of this research. This scaling reduced the size of the UCDBTM to 95% of the original size, which was based on MRI brain size data for this population.³⁷ The fit of the model was concentrated on the anterior–posterior and inferior superior axes (within one standard deviation).¹⁷

Biomechanical dependent variables

The biomechanical variables used to determine damage to the brain tissues were MPS and cumulative strain damage measure (CSDM; 10% and 20%). The MPS was used as it has been identified through research to be correlated to the mechanisms of concussive injury.^12,38 The CSDM is a measure that can be used to determine the amount of elements of the model that has incurred strain above a certain amount, in this case 10% and 20% strain.^29,39 The 10% strain is commonly used in biomechanical analyses of impact-induced injury and has been found to have some predictive capacity in terms of concussive and non-concussive events.^29,40 The CSDM set to 20% is a measure that examines how much of the brain tissues passes 20% strain, which has been identified as the amount of deformation that may be associated with structural changes to the grey and white matter.³⁸

Statistical analysis

Pearson correlations were calculated using Microsoft Excel between the reconstruction variables including MPS, CSDM_10% and CSDM_20% values for each brain region and the 95% ellipse of the COP measured during both the EC and DT conditions. Correlations between 0 and 0.39 were considered weak, correlations between 0.4 and 0.59 were considered moderate, correlations between 0.6 and 0.79 were considered strong and correlations between 0.8 and 1.0 were considered very strong.

Results

Head injury reconstructions were completed for 10 concussed participants that completed the balance testing protocol (mean age = 12.8 ± 1.2, six males and four females) between 28 and 40 days post-injury (mean number of days = 31.9 ± 1.98). Participant demographics and their corresponding injury characteristics are summarized in Table 1. Out of the10 participants for which reconstructions were completed, six were identified as having impaired balance on the EC condition, and six were identified as having impaired balance on the DT condition.

Table 1.

Participant characteristics for injury reconstructions.

Case no.	Age	Gender	Mechanism of injury	Type of surface/object	Balance status Eyes closed	Balance status Dual-task
1	15	Female	Fall	Grass	Impaired	Impaired
2	13	Male	Fall	Concrete	Impaired	Normal
3	13	Female	Fall	Ice with helmet	Normal	Normal
4	14	Female	Fall	Concrete	Impaired	Normal
5	12	Female	Fall	Steel	Normal	Impaired
6	12	Male	Fall	Branch	Normal	Impaired
7	9	Male	Fall	Sand	Impaired	Impaired
8	16	Male	Collision	Shoulder	Normal	Normal
9	13	Male	Fall	Ice with helmet	Impaired	Impaired
10	11	Male	Fall	Ice with helmet	Impaired	Impaired

Mean MPS, CSDM_10% and CSDM_20% values for each brain region are presented in Table 2. Correlations between the 95% ellipse measured during the EC condition and the MPS, CSDM_10% and CSDM_20% values are presented in Table 3. Correlations between the 95% ellipse measured during the DT condition and the MPS, CSDM_10% and CSDM_20% values are also presented in Table 3.

Table 2.

MPS, CSDM_10% and CSDM_20% values for different brain regions.

Brain regions	MPS	CSDM-10	CSDM-20
Frontal lobe	0.351 ± 0.213	39.9 ± 29.3	6.72 ± 8.15
Temporal lobe	0.400 ± 0.159	47.1 ± 28.6	8.72 ± 8.81
Occipital lobe	0.299 ± 0.180	46.6 ± 35.7	7.57 ± 12.7
Parietal lobe	0.271 ± 0.177	37.8 ± 31.9	3.88 ± 6.90
Brainstem	0.796 ± 0.403
Cerebellum	0.497 ± 0.252

Data are presented as means + standard deviations.

MPS: maximum principal strain; CSDM10_%: cumulative strain damage values at 10%; CSDM20_%: cumulative strain damage values at 20%.

Table 3.

Correlations between 95% ellipse measured during the eyes closed condition and MPS, CSDM_10% and CSDM_20% values for different brain regions (left side) and correlations between 95% ellipse measured during the dual-tsk condition and MPS, CSDM_10% and CSDM_20% values for different brain regions (right side).

	Eyes closed condition			Dual-task condition
Brain region	MPS	CSDM-10	CSDM-20	MPS	CSDM-10	CSDM-20
Frontal lobe	−0.171	0.124	−0.289	0.174	0.321	0.163
Temporal lobe	0.152	0.252	−0.155	0.394	0.386	0.0911
Occipital lobe	−0.143	0.0527	−0.248	0.131	0.280	0.0629
Parietal lobe	−0.192	−0.103	−0.353	0.158	0.314	0.289
Brainstem	−0.225			−0.0190
Cerebellum	−0.181			0.0476

MPS: maximum principal strain; CSDM_10%: cumulative strain damage values at 10%; CSDM_20%: cumulative strain damage values at 20%.

Correlations between the 95% ellipse measured during the EC condition were weak for both the cerebellum (Figure 2(a)) and brainstem (Figure 2(b)) and for all other brain regions (Figure 3). Correlations between the 95% ellipse measured during the DT condition and the MPS values were also weak for both the cerebellum (Figure 2(a)) and the brainstem (Figure 2(b)) as well as for all other brain regions (Figure 3). These correlations demonstrate a lack of relationship between the amount of postural sway and the magnitude of the reconstruction variables.

Figure 2.

Association between maximum principal strain values and the 95% ellipse measured during the eyes closed and dual-task conditions are shown for each participant for the cerebellum (a) and brainstem (b). Dashed line represents threshold for normal balance for the eyes closed condition, and solid line represents threshold for normal balance for the dual-task condition. Normal balance is defined as two standard deviations within the control group’s mean.

Figure 3.

Association between 95% ellipse measured during the eyes closed and dual-task conditions and MPS values are shown for each participant for different brain regions: frontal lobe (a), temporal lobe (b), parietal lobe (c) and occipital lobe (d). Dashed line represents threshold for normal balance for the eyes closed condition, and solid line represents threshold for normal balance for the dual-task condition. Normal balance is defined as two standard deviations within the control group’s mean.

The correlations between the CSDM_10% values and the 95% ellipse measured during both the EC and DT conditions (Figure 4) and the correlations between the CSDM_20% values and the 95% ellipse measured during both balance conditions (Figure 5) were also weak demonstrating again that there is no association between the balance measures and the reconstruction variables.

Figure 4.

Association between 95% ellipse measured during the eyes closed and dual-task conditions and CSDM_10% values are shown for each participant for different brain regions: frontal lobe (a), temporal lobe (b), parietal lobe (c) and occipital lobe (d). Dashed line represents threshold for normal balance for the eyes closed condition, and solid line represents threshold for normal balance for the dual-task condition. Normal balance is defined as two standard deviations within the control group’s mean.

Figure 5.

Association between 95% ellipse measured during the eyes closed and dual-task balance conditions and CSDM_20% values are shown for each participant for different brain regions: frontal lobe (a), temporal lobe (b), parietal lobe (c) and occipital lobe (d). Dashed line represents threshold for normal balance for the eyes closed condition, and solid line represents threshold for normal balance for the dual-task condition. Normal balance is defined as two standard deviations within the control group’s mean.

Discussion

Association between biomechanics and balance measures

The goal of this study was to examine the relationship between the location and magnitude of brain tissue strain and balance performance at one month post-concussion in a group of concussed children. The correlations between the balance variables and the reconstruction variables were low for all brain regions including the brainstem and the cerebellum demonstrating that there is little relationship between the location and amount of brain tissue strain and resulting balance performance. Considering that higher strain in the brain tissues has been postulated to result in more severe injury, these results suggest that the current methods and metrics of head injury reconstructions may not be sensitive to balance performance at one month following a concussion.

While this research identified that the biomechanics of impact do not predict long-term balance performance, there has been research in the past linking impact severity and magnitudes of strain in the brain to severity and duration of symptoms in animal models.^41,42 This research found results from 0.27 to 0.40 MPS in the cerebrum and higher in the cerebellum and brainstem. In terms of concussive injury, these magnitudes are well within the range of risk of concussion and structural damage to the brain tissues.^29,38,43 In animal and tissue models, these magnitudes of strain have also been identified to result in pathophysiological cascades as well as cellular damage.^44,45 As these biomechanical measures are from the moment of impact, there may be physiological processes associated with recovery from impact that may occlude any relationships. This lack of significant findings in the current study may be related to this time lapse between the biomechanics of the impact and the resulting measures. The impact biomechanics are related to the immediate structural effect of the event and may not reflect the subsequent pathophysiological cascades that occur over a one month period. Balance testing closer to the event may result in a different outcome in terms of the relationships between these variables.

While no relationship was identified in this present study between the biomechanics and balance measures, they both identify that an injurious event had occurred. Therefore, the lack of relationship may also be related to what each form of analysis represents. The biomechanical analyses are specific to brain tissue responses and may not be precise enough to target the areas within the brain that are involved in maintaining balance. Key structures such as the brainstem and cerebellum do play important roles in the process of maintaining balance, yet distinct areas within these structures, as opposed to the structures as a whole, accomplish specific functions to maintain balance.⁴⁶ Obtaining measures of brain tissue strain within a cortical lobe or within the brainstem or cerebellum as a whole may not be precise enough to identify a relationship between the magnitude of brain tissue strain and balance impairments.

The process of maintaining balance is also dependent on structures located inside and outside of the brain. The vestibular system is strongly involved in the process of maintaining balance and consists of the inner ear, the vestibular nerve, the cerebellum and brainstem, the thalamic relay pathways and the extensive cortical projections.^47,48 The peripheral component inside the inner ear includes two types of receptors that sense linear and rotational accelerations of the head, and it has been suggested that damage to these receptors can lead to balance impairments in some individuals who have experienced a concussion.³ Therefore, the lack of association identified between the amount of brain tissue strain and balance performance in this study could be due in part to some participants experiencing balance impairments due to damage located outside of the brain. Screening for damage to these receptors by measuring the vestibulo-ocular reflex and with the visual vertical test and/or vestibular evoked myogenic potentials should be done in future studies in order to rule out this possibility.⁴⁹ The lack of association between the amount of brain tissue strain and balance performance could also be due to the distributed nature of the vestibular cortical projections within the brain.⁵⁰ These cortical projections may not be represented with adequate resolution in the brain model to permit determination of cause and effect. More sensitive methods may be needed to identify the cortical impairments associated with balance impairments in concussion.

Limitations

A limitation of this study involves the small sample size. The limited number of participants included in this study may have influenced the correlations between the balance measures and the measures of brain tissue strain. Although weak correlations were identified between the majority of the balance and reconstruction variables, stronger correlations may emerge with a larger sample size. In addition, participants in future studies should also be screened for other conditions that may affect balance that are not related to damage within the brain such as benign paroxysmal positional vertigo and vestibular migraine. A final limitation is that the Hybrid III reconstruction parameters were based upon subject descriptions and eyewitness reports. While this process has been used in the past for biomechanical reconstructions of brain injury,^20,51 it is prone to recall errors of those individuals involved. As a result, the reconstruction may not be as precise as if there were video of the event available. The use of subject descriptions of the event may have affected the accuracy of the biomechanical results. The finite element model is a representation of the structure and material properties of a human head and brain system. As a result, the magnitudes of response reported by the model are dependent upon the assumptions in material characteristics and boundary conditions that were used in its construction.

Conclusion

In this exploratory study, it was demonstrated that values of brain tissue strain are not associated with balance performance at one month post-concussion in a group of children. The values obtained from biomechanical reconstruction of head injuries may be useful in understanding the conditions that lead to concussion, but may not offer any predictive value in terms of balance performance following a concussion. This may be related to the complex nature of balance impairments and to some impairments resulting from injury to regions outside of the brain. This study should be repeated with a larger sample size in order to confirm these results.

Footnotes

Acknowledgements

We would like to thank the parents and children for their participation. We would like to thank all undergraduate student volunteers for their help with data collection.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project is a sub-study of a larger prospective concussion study funded by a Canadian Institutes of Health Research (CIHR) Operating Grant (MOP: #126197); a CIHR-Ontario Neurotrauma Foundation Mild Traumatic Brain Injury Team Grant (TM1: # 127047); and a CIHR planning grant (MRP: #119829).

ORCID iD

Coralie Rochefort

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Brain tissue strain and balance impairments in children following a concussion: An exploratory study

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

Participants

Concussion group

Control group

Balance testing

Protocol

Material and data processing for WBB data

Balance status concussion group

Injury reconstruction

Impact characteristics

Reconstruction procedure

Equipment

Monorail

Pendulum

Headform, neckform and data acquisition

Finite element model

Scaling of the UCDBTM

Biomechanical dependent variables

Statistical analysis

Results

Discussion

Association between biomechanics and balance measures

Limitations

Conclusion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

ORCID iD

References