White matter tract-oriented deformation predicts traumatic axonal brain injury and reveals rotational direction-specific vulnerabilities

Sullivan, Sarah; Eucker, Stephanie A.; Gabrieli, David; Bradfield, Connor; Coats, Brittany; Maltese, Matthew R.; Lee, Jong-Ho; Smith, Colin; Margulies, Susan S.

doi:10.1007/s10237-014-0643-z

Cited by 93 publications

(155 citation statements)

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“…Common choices include the maximum strain magnitude from a single element in a particular region, regardless of the time of occurrence or location (i.e., peak strain (Zhang et al 2004; Giordano and Kleiven 2014a)), or a dichotomous variant describing the percentage of tissue volume experiencing large strains (e.g., CSDM (Takhounts et al 2008) or Pop90 (Sullivan et al 2014)). In this study, we evaluated the performances of peak strain, regional average strain, and the dichotomous variants based on either ε p or ε n .…”

Section: Methodsmentioning

confidence: 99%

“…This aligns well with in vitro studies that suggest strain component along axonal longitudinal direction responsible for axonal injury (Cullen and LaPlaca 2006). Initial evidence indicates that WM fiber orientation-dependent strain (termed “fiber strain”, “axonal strain”, or “tract-oriented strain”) improves injury prediction performance relative to its isotropic counterpart, maximum principal strain (Chatelin et al 2011; Wright et al 2013; Giordano and Kleiven 2014a; Sullivan et al 2014; Ji et al 2015). …”

Section: Introductionmentioning

confidence: 99%

“…Thus, a single “training dataset” has been used in previous studies to evaluate injury prediction performance. A separate “testing dataset” is not widely available to enable an independent performance verification, even though this is considered necessary and important (Anderson et al 2007; Sullivan et al 2014). Taken together, these uncertainties and lack of high quality, well-accepted injury data could yield multiple “local minima” in the optimization, which may explain previous conflicting observations based on the same injury dataset.…”

Section: Introductionmentioning

confidence: 99%

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Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Zhao

Cai

et al. 2017

Biomech Model Mechanobiol

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Reliable prediction and diagnosis of concussion is important for its effective clinical management. Previous model- based studies largely employ peak responses from a single element in a pre-selected anatomical region of interest (ROI) and utilize a single training dataset for injury prediction. A more systematic and rigorous approach is necessary to scrutinize the entire white matter (WM) ROIs as well as ROI-constrained neural tracts. To this end, we evaluated injury prediction performances of the 50 deep WM regions using predictor variables based on strains obtained from simulating the 58 reconstructed American National Football League (NFL) head impacts. To objectively evaluate performance, repeated random subsampling was employed to split the impacts into independent training and testing datasets (39 and 19 cases, respectively, with 100 trials). Univariate logistic regressions were conducted based on training datasets to compute the area under the receiver operating characteristic curve (AUC), while accuracy, sensitivity, and specificity were reported based on testing datasets. Two tract-wise injury susceptibilities were identified as the best overall via pair-wise permutation test. They had comparable AUC, accuracy, and sensitivity, with the highest values occurring in SLF (superior longitudinal fasciculus; 0.867–0.879, 84.4–85.2%, and 84.1–84.6%, respectively). Using metrics based on WM fiber strain, the most vulnerable ROIs included genu of corpus callosum, cerebral peduncle, and uncinate fasciculus, while genu and main body of corpus callosum, and SLF were among the most vulnerable tracts. Even for one un-concussed athlete, injury susceptibility of the cingulum (hippocampus) right was elevated. These findings highlight the unique injury discriminatory potentials of computational models, and may provide important insight into how best to incorporate WM structural anisotropy for investigation of brain injury.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Zhao

Cai

et al. 2017

Biomech Model Mechanobiol

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“…In time, with the advances in imaging techniques, axonal injury, which requires excessive regional stretching of axons [19,20], has become one of the leading hypotheses behind the mechanism of concussions. Confirming this hypothesis, strain in the brain and specifically strain in the periventricular region of the brain-with the highest density of axon fibers-have been shown to correlate best with acute concussion and longterm neurological deficits [21][22][23][24]. However, dynamical behavior of the brain during rapid head motions with various amplitudes, durations, and directions, as well as the reason for higher susceptibility of these deep regions of the brain to strain are still largely unknown [22,25].…”

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confidence: 87%

Mechanistic Insights into Human Brain Impact Dynamics through Modal Analysis

et al. 2018

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Although concussion is one of the greatest health challenges today, our physical understanding of the cause of injury is limited. In this Letter, we simulated football head impacts in a finite element model and extracted the most dominant modal behavior of the brain's deformation. We showed that the brain's deformation is most sensitive in low frequency regimes close to 30 Hz, and discovered that for most subconcussive head impacts, the dynamics of brain deformation is dominated by a single global mode. In this Letter, we show the existence of localized modes and multimodal behavior in the brain as a hyperviscoelastic medium. This dynamical phenomenon leads to strain concentration patterns, particularly in deep brain regions, which is consistent with reported concussion pathology. DOI: 10.1103/PhysRevLett.120.138101 Traumatic brain injury (TBI) is a major cause of death and disability in the United States, contributing to about 30% of all injury-related deaths [1,2]. Every year, millions of Americans are diagnosed with TBI [3,4], 80% of which are categorized as mild [2]. Undiagnosed cases, due to either lack of clinical expertise or underreporting, might be twice as high [5][6][7][8]. Given that mild TBI (MTBI), or concussion, has become a serious health concern in society, the burden of understanding and preventing it has become ever more indisputable for clinicians and physicists alike.Efforts to model the brain's physics date back to the 1940s when Holbourn proposed the head as a mechanical system and explored the relation between the input to this system (in the form of head motion) to the output (in the form of relative brain displacement) [9,10]. Kornhauser proposed isodisplacement curves in a second-order springmass system representing relative brain displacement as a measure for classifying injury [11]. Others have also showed that, in different loading regimes, injury could be more sensitive to peak acceleration or maximum change in velocity or a combination of both [12,13]. Since then, many scientists have investigated the brain's response in severe scenarios of TBI with skull flexure [14,15], and more recently in mild scenarios with mostly inertial loading on the brain [16][17][18]. In particular, for helmeted sports, much of previous research has focused on brain deformation while assuming a rigid skull. In time, with the advances in imaging techniques, axonal injury, which requires excessive regional stretching of axons [19,20], has become one of the leading hypotheses behind the mechanism of concussions. Confirming this hypothesis, strain in the brain and specifically strain in the periventricular region of the brain-with the highest density of axon fibers-have been shown to correlate best with acute concussion and longterm neurological deficits [21][22][23][24]. However, dynamical behavior of the brain during rapid head motions with various amplitudes, durations, and directions, as well as the reason for higher susceptibility of these deep regions of the brain to strain are still largely unknow...

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“…34 Using brain growth, myelination, and electrical activity as markers of brain development, the 3-5-day-old piglet brain can be roughly correlated to that of a human infant in the first 2-4 weeks of life. [31][32][33]35 Because of the similarities between the brains of piglets and immature humans, the animal model has been used extensively to investigate mechanisms of pediatric TBI associated with single, nonimpact, rapid head rotations and has successfully identified key attributes of TBI, including the influence of head rotation direction, 14,36,37 changes with developmental age, 16,19,36 changes with post-injury time, 36,38 and behavioral consequences of this injury mechanism. 15,[39][40][41] Injuries reported in these studies include hypoxic-ischemic damage, widespread AI, large unilateral/bilateral subdural and subarachnoid hemorrhage, and ocular hemorrhage.…”

Section: Introductionmentioning

confidence: 99%

Cyclic Head Rotations Produce Modest Brain Injury in Infant Piglets

Coats

Binenbaum

Smith

et al. 2017

Journal of Neurotrauma

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Repetitive back-and-forth head rotation from vigorous shaking is purported to be a central mechanism responsible for diffuse white matter injury, subdural hemorrhage, and retinal hemorrhage in some cases of abusive head trauma (AHT) in young children. Although animal studies have identified mechanisms of traumatic brain injury (TBI) associated with single rapid head acceleration-decelerations at levels experienced in a motor vehicle crash, few experimental studies have investigated TBI from repetitive head rotations. The objective of this study was to systematically investigate the postinjury pathological time-course after cyclic, low-velocity head rotations in the piglet and compare them with single head rotations. Injury metrics were the occurrence and extent of axonal injury (AI), extra-axial hemorrhage (EAH), red cell neuronal/axonal change (RCNAC), and ocular injury (OI). Hyperflexion/extension of the neck were purposefully avoided in the study, resulting in unscaled angular accelerations at the lower end of reported infant surrogate shaking kinematics. All findings were at the mild end of the injury spectrum, with no significant findings at 6 h post-injury. Cyclic head rotations, however, produced modest AI that significantly increased with time post-injury ( p < 0.035) and had significantly greater amounts of RCNAC and EAH than noncyclic head rotations after 24 h post-injury ( p < 0.05). No OI was observed. Future studies should investigate the contributions of additional physiological and mechanical features associated with AHT (e.g., hyperflexion/extension, increased intracranial pressure from crying or thoracic compression, and more than two cyclic episodes) to enhance our understanding of the causality between proposed mechanistic factors and AHT in infants.

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White matter tract-oriented deformation predicts traumatic axonal brain injury and reveals rotational direction-specific vulnerabilities

Cited by 93 publications

References 57 publications

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Mechanistic Insights into Human Brain Impact Dynamics through Modal Analysis

Cyclic Head Rotations Produce Modest Brain Injury in Infant Piglets

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