In This Issue
Spring Bridge on Concussion: A National Challenge
April 12, 2016 Volume 46 Issue 1

Emerging Insight from Human and Animal Studies about the Biomechanics of Concussion

Monday, April 18, 2016

Author: Susan S. Margulies

Biomechanics can provide insight into the mechanisms of concussion, including the interrelationships among the forces experienced during impact, head and neck movements, tissue stiffness of the materials that compose the head/neck complex, deformation of structures at the macroscopic and microscopic level, and biological responses to the various forces imposed on the head.

Biological responses in traumatic brain injuries (TBIs) may be immediate or delayed, be structural (torn vessels and axons) or functional (changes in blood flow or neurological status), and differ with maturation. Biomechanical investigations typically include a variety of approaches:

  • direct measurements of loading conditions and responses in humans, animals, and anthropomorphic surrogates (i.e., crash test dummies);
  • visualization of tissue responses to prescribed loads to characterize the responses of complex geometries or composite structures;
  • mechanical property testing of individual components to identify changes with age;
  • computational models to predict how tissues will deform during impact or rapid head rotation; and
  • identification of the timecourse of cell or tissue responses to specified deformations in order to define thresholds associated with various types of injuries.

Sources of Concussion Data for Research

Biomechanics investigators can use human data obtained prospectively (via sensors; Camarillo et al. 2013; Crisco et al. 2010; Daniel et al. 2012; Rowson et al. 2009, 2012) or retrospectively (via crash reconstructions) to help understand what scenarios cause TBIs. Concussions are diagnosed based on symptoms, and most assessments are influenced by the patients’ awareness of or willingness to report their symptoms. But this less reliable form of data gathering skews the dataset and undermines the process of identifying objective biomechanical thresholds associated with concussion using instrumented volunteers. Biomechanical data are occasionally captured by sensors in helmets, patches, and mouthguards but they often report limited information about the rotational head movements associated with concussion.

To obtain kinematic information in more controlled settings, anthropomorphic surrogates (crash test dummies) and laboratory-based studies are used to reenact film and witness accounts of sports-related events in order to estimate the forces of impact and head movements (kinematics). But surrogates cannot be used to predict or measure brain injuries or tissue distortions. Instead, results obtained using surrogates must be correlated with animal studies, autopsy reports, and patient records to infer biological responses to kinematic loading conditions, or with computational models to infer tissue deformations resulting from a head rotation or impact.

Computational models are used to estimate the tissue distortions and stresses that may result from a rapid head motion or head impact, using lifelike tissue stiffness values for children and adults (Cheng et al. 2008). Like surrogates, computational models cannot predict concussion; predicted tissue distortions in response to lifelike loading conditions must be correlated with animal or human data.

Animal models can provide a controlled laboratory setting to investigate the relationships between the risk of concussion and rapid head rotation magnitude and direction, as well as the contributions of age, sex, and previous concussions to biomechanical thresholds for concussion. Animal model–derived biomechanical thresholds are typically for more severe brain injuries than concussion, but animal models do provide insight into how head impacts and sudden head movements produce brain deformations and how such deformations result in a spectrum of brain injuries, from mild to severe TBI.

Human Studies of Concussion Biomechanics

Quantifying the relationship between biomechanical input and clinical outcome is critical to the advancement of concussion prevention principles, including the assessment of injury risk, the design of protective equipment such as helmets, and the development of training and policies intended to limit exposure to head impacts and injury risk.

Measuring Injury Risk and Impact

The most common approach to quantifying the link between biomechanical input and concussion is through injury risk curves (figure 1), which describe injury probability given a specific mechanical input—for example, concussion risk given a particular head acceleration. Pellman, Rowson, Duma, and colleagues (Pellman et al. 2003; Rowson and Duma 2011) have used football head impact data to describe the relationship between linear acceleration and concussion risk (Rowson and Duma 2013) and between rotational acceleration and concussion risk (Rowson et al. 2012).

Figure 1

Head impact sensors have been widely used to understand the link between the biomechanics of head impact and clinical outcomes of concussion in humans (Brainard et al. 2012; Crisco et al. 2010; Mihalik et al. 2007; Rowson et al. 2009; Wilcox et al. 2015). These sensors—attached to a helmet, headband, skullcap, or mouthguard, or directly attached to the athlete’s head (Bartsch et al. 2014; Hernandez et al. 2015; King et al. 2015)—consist of accelerometers, and in some cases gyroscopes, to estimate the magnitude of linear and rotational acceleration experienced by the athlete during head impact.

Recent studies, however, have quantified errors in risk curves associated with significant sensor inaccuracy (Allison et al. 2014, 2015; Funk et al. 2012; Jadischke et al. 2013), underreporting of concussion (estimated at 53 percent; McCrea et al. 2004), and incorrect clinical diagnosis (Elliott et al. 2015). Decreasing sources of error will be important for improving the accuracy of injury risk estimates.

Challenges in Diagnosis and Assessment

Improvements in concussion reporting and diagnosis are essential to define injury risk curves for concussion. Concussion diagnosis remains largely an inexact clinical determination, using subjective assessments and symptom self-reports (IOM 2014; Master et al. 2014) of neurocognitive effects (van Kampen et al. 2006), vestibular balance (Corwin et al. 2015; Guskiewicz 2011), oculomotor/visual systems (Master et al. 2015), and sleep (Towns et al. 2015).

Current clinical assessments—the Sport Concussion Assessment Tool 3 (SCAT3) (Guskiewicz et al. 2013), Vestibulo-Oculomotor Screen (Mucha et al. 2014), King-Devick Test (Galetta et al. 2013), computerized neurocognitive testing such as the Immediate Post Concussion Assessment and Cognitive Testing (ImPACT) (van Kampen et al. 2006), and self-report of symptoms like the Post-Concussion Symptom Scale (Chen et al. 2007)—have components that are subjective and dependent on the effort of the injured individual or influenced by repeated testing effects (Resch et al. 2013). Moreover, because concussion may be diagnosed by a variety of individuals—parents, coaches, trainers, primary care, emergency medicine, or urgent care clinicians (Leong et al. 2014; Taylor et al. 2015)—it is important to develop robust, accessible, and validated metrics for use.

Future research should target the validation of objective, graded, effort-independent neurologic system assessments (such as vestibular balance, eye tracking, visual function, and sleep) for concussion to enable timely and accurate diagnosis across a wide age spectrum. These quantitative involuntary metrics can also be used to guide clinical diagnosis and management of concussion and inform evidence-based decisions about athletes’ return to sport.

Animal Studies of Concussion Biomechanics

Because human data and computational models have limitations, researchers use experimental substitutes such as animals, tissues, and isolated cells to create controllable settings with similar predisposing conditions and reproducible mechanical loads.

Extensive Utility of Animal Studies

Animal models are useful for measuring physiological responses, neuropathology, and neurofunctional changes at prescribed time-points after injury. As a surrogate for humans, the animal models most commonly used to study brain injury are mice and rats, but ovine, porcine, and nonhuman primate models have also been used (Browne et al. 2011; Durham et al. 2000; Finnie et al. 2012; Gennarelli et al. 1981, 1982; Viano et al. 2012). Because reports indicate that rodents have limited similarity to human genomic and proteomic responses, injury timecourses, and grey and white brain matter distribution (Duhaime 2006; Seok et al. 2013), there may be challenges in applying what is learned about injury in the rodent brain to humans (Wall and Shani 2008). Animal models are nonetheless a valuable tool for understanding how head impacts and sudden head movements translate to short- and long-term biological responses, and how environment and agents can exacerbate or mitigate these responses.

Pigs are a popular large animal model used for assessing motor, cognitive, and behavioral responses after traumatic brain injury, stroke, and cardiac arrest (Gieling et al. 2011; Jiwa et al. 2010; Lind et al. 2007; Sullivan et al. 2013a,b; Wang et al. 2012). A sensitive and specific battery of behavioral, motor, memory, learning, and cognitive assessments developed for piglets have revealed the timecourse after traumatic brain injury and the importance of the direction of head rotation for head injury responses (Friess et al. 2007, 2009; Naim et al. 2010; Sullivan et al. 2013a,b). And recently developed objective assessments in the piglet show the feasibility of translating nonverbal assessments used in human studies, including balance, activity/rest, and serum biomarkers, to piglets (Costine et al. 2012; Diaz-Arrastia et al. 2013; Egea-Guerrero et al. 2012; Kilbaugh et al. 2015; Kochanek et al. 2013; Okonkwo et al. 2013).

Effects of Velocity Change and Rotation

Researchers have determined that, with or without a helmet, when the head contacts a stationary or moving object there is a rapid change in velocity and a possible deformation of the skull. Skull deformation may produce a local contusion or hemorrhage if the deformations of the tissues exceed their injury thresholds. When the properties of the contact surfaces are softer or allow sliding or deformation, the rate of velocity change is lower. Similarly, if there is no head contact but only body contact, the deceleration of the moving head is usually lower than when the head is contacted directly.

After the initial rapid change in velocity caused by impact to the head or body, the motion of the head is influenced by the location of the initial point of contact and the interaction between the head, neck, and body. There are three possible types of responses to head contact. First, if the contact is directed through the center of mass of the brain (centroid), there may be linear motion and no rotation of the head. Animal studies have shown that these purely linear motions produce little brain motion or distortion and no concussion (Hardy et al. 2001; Ommaya and Gennarelli 1974; Ommaya and Hirsch 1971; Ommaya et al. 1966).

However, most often the contact force is not directed through the centroid of the brain, and in the second type of response to contact the head may rotate without a linear motion (e.g., shaking the head “no”). The rotational motion produces a distortion of the brain’s neural and vascular structures in the skull because the brain is softer than and loosely coupled to the skull.

Third, and more commonly, a head impact produces both linear acceleration and rotation of the head. Internal structures of the head, such as the falx cerebri and tentorium, influence how the brain moves in the skull and may cause local brain regions to have very high deformations only in certain directions of head rotation. For example, sagittal and coronal rotations may produce more severe injuries in primates at lower accelerations and velocities (Gennarelli et al. 1982).

Moreover, animal and human studies have shown a general trend that higher rotational velocities and accelerations—rather than linear accelerations—can cause larger diffuse brain deformations and worse diffuse brain injuries (Gennarelli et al. 2003; Kimpara and Iwamoto 2012; Ommaya and Hirsch 1971), and that head injuries depend on the direction of head motion as well as on the magnitude of rotational kinematics (Eucker et al. 2011; Gennarelli et al. 1981; Ommaya and Gennarelli 1974; Sullivan et al. 2013a, 2015). Animal studies have indicated that it is important to limit the duration of exposure to acceleration, as concussions occur when that duration is increased (Ommaya 1966; Ommaya et al. 1966).

Research in New Tools and Technologies to Increase Understanding

Ongoing studies are identifying the causal relationship between head rotational acceleration magnitude and direction and head injury outcomes, and the influence of age, sex, and previous exposures to head injury. But further research is needed to understand the biomechanics of concussion and define thresholds for rotational accelerations associated with concussion across the age spectrum.

Emerging research in objective, involuntary neurofunctional metrics and biomarkers can bridge the gap between human and animal research, and provide important insight into the biomechanics of concussion, to provide a rational foundation for injury prevention, safety equipment design, rules of play, treatments and interventions.


Allison MA, Kang YS, Bolte JH 4th, Maltese MR, Arbogast KB. 2014. Validation of a helmet-based system to measure head impact biomechanics in ice hockey. Medicine and Science in Sports and Exercise 46(1):115–123.

Allison MA, Kang YS, Maltese MR, Bolte JH 4th, Arbogast KB. 2015. Measurement of Hybrid III head impact kinematics using an accelerometer and gyroscope system in ice hockey helmets. Annals of Biomedical Engineering 43(8):1896–1906.

Bartsch A, Samorezov S, Benzel E, Miele V, Brett D. 2014. Validation of an “intelligent mouthguard” single event head impact dosimeter. Stapp Car Crash Journal 58:1–27.

Brainard LL, Beckwith JG, Chu JJ, Crisco JJ, McAllister TW, Duhaime AC, Maerlender AC, Greenwald RM. 2012. Gender differences in head impacts sustained by collegiate ice hockey players. Medicine and Science in Sports and Exercise 44(2):297–304.

Browne KD, Chen XH, Meaney DF, Smith DH. 2011. Mild traumatic brain injury and diffuse axonal injury in swine. Journal of Neurotrauma 28(8):1747–1755.

Camarillo DB, Shull PB, Mattson J, Shultz R, Garza D. 2013. An instrumented mouthguard for measuring linear and angular head impact kinematics in American football. Annals of Biomedical Engineering 41(9):1939–1949.

Chen JK, Johnston KM, Collie A, McCrory P, Ptito A. 2007. A validation of the post-concussion symptom scale in the assessment of complex concussion using cognitive testing and functional MRI. Journal of Neurology, Neurosurgery, and Psychiatry 78(11):1231–1238.

Cheng S, Clarke EC, Bilston LE. 2008. Rheological properties of the tissues of the central nervous system: A review. Medical Engineering and Physics 30(10):1318–1337.

Corwin DJ, Wiebe DJ, Zonfrillo MR, Grady MF, Robinson RL, Goodman AM, Master CL. 2015. Vestibular deficits following youth concussion. Journal of Pediatrics 166(5):1221–1225.

Costine BA, Quebeda-Clerkin PB, Dodge CP, Harris BT, Hillier SC, Duhaime AC. 2012. Neuron-specific enolase, but not S100B or myelin basic protein, increases in peripheral blood corresponding to lesion volume after cortical impact in piglets. Journal of Neurotrauma 29(17):2689–2695.

Crisco JJ, Fiore R, Beckwith JG, Chu JJ, Brolinson PG, Duma S, McAllister TW, Duhaime AC, Greenwald RM. 2010. Frequency and location of head impact exposures in individual collegiate football players. Journal of Athletic Training 45(6):549–559.

Daniel RW, Rowson S, Duma SM. 2012. Head impact exposure in youth football. Annals of Biomedical Engineering 40(4):976–991.

Diaz-Arrastia R, Wang KK, Papa L, Sorani MD, Yue JK, Puccio AM, McMahon PJ, Inoue T, Yuh EL, Lingsma H, Maas AI, and 5 others. 2013. Acute biomarkers of traumatic brain injury: Relationship between plasma levels of ubiquitin C-terminal hydrolase-L1 (UCH-L1) and glial fibrillary acidic protein (GFAP). Journal of Neurotrauma 31(1):19–25.

Duhaime AC. 2006. Large animal models of traumatic injury to the immature brain. Developmental Neuroscience 28(4–5):380–387.

Durham SR, Raghupathi R, Helfaer MA, Marwaha S, Duhaime AC. 2000. Age-related differences in acute physiologic response to focal traumatic brain injury in piglets. Pediatric Neurosurgery 33(2):76–82.

Egea-Guerrero JJ, Revuelto-Rey J, Murillo-Cabezas F, Muñoz-Sánchez MA, Vilches-Arenas A, Sánchez-Linares P, Domínguez-Roldán JM, León-Carrión J. 2012. Accuracy of the S100b protein as a marker of brain damage in traumatic brain injury. Brain Injury 26(1):76–82.

Elliott MR, Margulies SS, Maltese MR, Arbogast KB. 2015. Accounting for sampling variability, injury under-reporting, and sensor error in concussion injury risk curves. Journal of Biomechanics 48(12):3059–3065.

Eucker SA, Smith C, Ralston J, Friess SH, Margulies SS. 2011. Physiological and histopathological responses following closed rotational head injury depend on direction of head motion. Experimental Neurology 227(1):79–88.

Finnie JW, Blumbergs PC, Manavis J, Turner RJ, Helps S, Vink R, Byard RW, Chidlow G, Sandoz B, Dutschke J, Anderson RW. 2012. Neuropathological changes in a lamb model of non-accidental head injury (the shaken baby syndrome). Journal of Clinical Neuroscience: Official Journal of the Neurosurgical Society of Australasia 19(8):1159–1164.

Friess SH, Ichord RN, Owens K, Ralston J, Rizol R, Overall KL, Smith C, Helfaer MA, Margulies SS. 2007. Neurobehavioral functional deficits following closed head injury in the neonatal pig. Experimental Neurology 204(1):234–243.

Friess SH, Ichord RN, Ralston J, Ryall K, Helfaer MA, Smith C, Margulies SS. 2009. Repeated traumatic brain injury affects composite cognitive function in piglets. Journal of Neurotrauma 26(7):1111–1121.

Funk JR, Rowson S, Daniel RW, Duma SM. 2012. Validation of concussion risk curves for collegiate football players derived from HITS data. Annals of Biomedical Engineering 40(1):79–89.

Galetta MS, Galetta KM, McCrossin J, Wilson JA, Moster S, Galetta SL, Balcer LJ, Dorshimer GW, Master CL. 2013. Saccades and memory: Baseline associations of the King-Devick and SCAT2 SAC tests in professional ice hockey players. Journal of the Neurological Sciences 328(1–2):28–31.

Gennarelli TA, Adams JH, Graham DI. 1981. Acceleration induced head injury in the monkey. I. The model, its mechanical and physiological correlates. Acta Neuropathologica Supplementum 7:23–25.

Gennarelli TA, Thibault LE, Adams JH, Graham DI, Thompson CJ, Marcincin RP. 1982. Diffuse axonal injury and traumatic coma in the primate. Annals of Neurology 12(6):564–574.

Gennarelli TA, Pintar FA, Yoganandan N. 2003. Biomechanical tolerances for diffuse brain injury and a hypothesis for genotypic variability in response to trauma. Annual Proceedings of the Association for the Advancement of Automotive Medicine 47:624.

Gieling ET, Nordquist RE, van der Staay FJ. 2011. Assessing learning and memory in pigs. Animal Cognition 14(2):151–173.

Guskiewicz KM. 2011. Balance assessment in the management of sport-related concussion. Clinical Sports Medicine 30(1):89–102, ix.

Guskiewicz KM, Register-Mihalik J, McCrory P, McCrea M, Johnston K, Makdissi M, Dvorak J, Davis G, Meeuwisse W. 2013. Evidence-based approach to revising the SCAT2: Introducing the SCAT3. British Journal of Sports Medicine 47(5):289–293.

Hardy WN, Foster CD, Mason MJ, Yang KH, King AI, Tashman S. 2001. Investigation of head injury mechanisms using neutral density technology and high-speed biplanar x-ray. Stapp Car Crash Journal 45:337–368.

Hernandez F, Wu LC, Yip MC, Laksari K, Hoffman AR, Lopez JR, Grant GA, Kleiven S, Camarillo DB. 2015. Six degree-of-freedom measurements of human mild traumatic brain injury. Annals of Biomedical Engineering 43(8):1918–1934.

IOM [Institute of Medicine]. 2014. Sports-Related Concussions in Youth: Improving the Science, Changing the Culture. Washington: National Academies Press.

Jadischke R, Viano DC, Dau N, King AI, McCarthy J. 2013. On the accuracy of the Head Impact Telemetry (HIT) System used in football helmets. Journal of Biomechanics 46(13):2310–2315.

Jiwa NS, Garrard P, Hainsworth AH. 2010. Experimental models of vascular dementia and vascular cognitive impairment: A systematic review. Journal of Neurochemistry 115(4):814–828.

Kilbaugh TJ, Lvova M, Karlsson M, Zhang Z, Leipzig J, Wallace DC, Margulies SS. 2015. Peripheral blood mitochondrial DNA as a biomarker of cerebral mitochondrial dysfunction following traumatic brain injury in a porcine model. PLoS One 10(6):e0130927.

Kimpara H, Iwamoto M. 2012. Mild traumatic brain injury predictors based on angular accelerations during impacts. Annals of Biomedical Engineering 40(1):114–126.

King D, Hume PA, Brughelli M, Gissane C. 2015. Instrumented mouthguard acceleration analyses for head impacts in amateur rugby union players over a season of matches. American Journal of Sports Medicine 43(3):614–624.

Kochanek PM, Berger RP, Fink EL, Au AK, Bayir H, Bell MJ, Dixon CE, Clark RS. 2013. The potential for bio-mediators and biomarkers in pediatric traumatic brain injury and neurocritical care. Frontiers in Neurology 4:40.

Leong DF, Balcer LJ, Galetta SL, Liu Z, Master CL. 2014. The King-Devick test as a concussion screening tool administered by sports parents. Journal of Sports Medicine and Physical Fitness 54(1):70–77.

Lind NM, Moustgaard A, Jelsing J, Vajta G, Cumming P, Hansen AK. 2007. The use of pigs in neuroscience: Modeling brain disorders. Neuroscience and Biobehavioral Reviews 31(5):728–751.

Master CL, Balcer L, Collins M. 2014. In the clinic: Concussion. Annals of Internal Medicine 160(3):ITC2–1.

Master CL, Scheiman M, Gallaway M, Goodman A, Robinson RL, Master SR, Grady MF. 2015. Vision diagnoses are common after concussion in adolescents. Clinical Pediatrics, Epub ahead of print.

McCrea M, Hammeke T, Olsen G, Leo P, Guskiewicz K. 2004. Unreported concussion in high school football players: Implications for prevention. Clinical Journal of Sport Medicine 14(1):13–17.

Mihalik JP, Bell DR, Marshall SW, Guskiewicz KM. 2007. Measurement of head impacts in collegiate football players: An investigation of positional and event-type differences. Neurosurgery 61(6):1229–1235; discussion 1235.

Mucha A, Collins MW, Elbin RJ, Furman JM, Troutman-Enseki C, DeWolf RM, Marchetti G, Kontos AP. 2014. A brief Vestibular/Ocular Motor Screening (VOMS) assessment to evaluate concussions: Preliminary findings. American Journal of Sports Medicine 42(10):2479–2486.

Naim MY, Friess S, Smith C, Ralston J, Ryall K, Helfaer MA, Margulies SS. 2010. Folic acid enhances early functional recovery in a piglet model of pediatric head injury. Developmental Neuroscience 32(5–6):466–479.

Okonkwo DO, Yue JK, Puccio AM, Panczykowski DM, Inoue T, McMahon PJ, Sorani MD, Yuh EL, Lingsma HF, Maas AI, Valadka AB, Manley GT, and TRACK-TBI Investigators. 2013. GFAP-BDP as an acute diagnostic marker in traumatic brain injury: Results from the Prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study. Journal of Neurotrauma 30(17):1490–1497.

Ommaya AK. 1966. Trauma to the nervous system. Annals of the Royal College of Surgeons of England 39(6):317–347.

Ommaya AK, Gennarelli TA. 1974. Cerebral concussion and traumatic unconsciousness: Correlation of experimental and clinical observations of blunt head injuries. Brain: A Journal of Neurology 97(4):633–654.

Ommaya AK, Hirsch AE. 1971. Tolerances for cerebral concussion from head impact and whiplash in primates. Journal of Biomechanics 4(1):13–21.

Ommaya AK, Hirsch AE, Flamm ES, Mahone RH. 1966. Cerebral concussion in the monkey: An experimental model. Science 153(3732):211–212.

Pellman EJ, Viano DC, Tucker AM, Casson IR, Waeckerle JF. 2003. Concussion in professional football: Reconstruction of game impacts and injuries. Neurosurgery 53(4):799–812; discussion 812–814.

Resch J, Driscoll A, McCaffrey N, Brown C, Ferrara MS, Macciocchi S, Baumgartner T, Walpert K. 2013. ImPact test-retest reliability: Reliably unreliable? Journal of Athletic Training 48(4):506–511.

Rowson S, Duma SM. 2011. Development of the STAR evaluation system for football helmets: Integrating player head impact exposure and risk of concussion. Annals of Biomedical Engineering 39(8):2130–2140.

Rowson S, Duma SM. 2013. Brain injury prediction: Assessing the combined probability of concussion using linear and rotational head acceleration. Annals of Biomedical Engineering 41(5):873–882.

Rowson S, Brolinson G, Goforth M, Dietter D, Duma S. 2009. Linear and angular head acceleration measurements in collegiate football. Journal of Biomechanical Engineering 131(6):06101.

Rowson S, Duma SM, Beckwith JG, Chu JJ, Greenwald RM, Crisco JJ, Brolinson PG, Duhaime AC, McAllister TW, Maerlender AC. 2012. Rotational head kinematics in football impacts: An injury risk function for concussion. Annals of Biomedical Engineering 40(1):1–13.

Seok J, Warren HS, Cuenca AG, Mindrinos MN, Baker HV, Xu W, Richards DR, McDonald-Smith GP, Gao H, Hennessy L, and 30 others. 2013. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proceedings of the National Academy of Sciences 110(9):3507–3512.

Sullivan S, Friess SH, Ralston J, Smith C, Propert KJ, Rapp PE, Margulies SS. 2013a. Behavioral deficits and axonal injury persistence after rotational head injury are direction dependent. Journal of Neurotrauma 30(7):538–545.

Sullivan S, Friess SH, Ralston J, Smith C, Propert KJ, Rapp PE, Margulies SS. 2013b. Improved behavior, motor, and cognition assessments in neonatal piglets. Journal of Neurotrauma 30(20):1770–1779.

Sullivan S, Eucker SA, Gabrieli D, Bradfield C, Coats B, Maltese MR, Lee J, Smith C, Margulies SS. 2015. White matter tract-oriented deformation predicts traumatic axonal brain injury and reveals rotational direction-specific vulnerabilities. Biomechanics and Modeling in Mechanobiology 14(4):877–896.

Taylor AM, Nigrovic LE, Saillant ML, Trudell EK, Proctor MR, Modest JR, Vernacchio L. 2015. Trends in ambulatory care for children with concussion and minor head injury from Eastern Massachusetts between 2007 and 2013. Journal of Pediatrics 167(3):738–744.

Towns SJ, Silva MA, Belanger HG. 2015. Subjective sleep quality and postconcussion symptoms following mild traumatic brain injury. Brain Injury 29(11):1337–1341.

van Kampen DA, Lovell MR, Pardini JE, Collins MW, Fu FH. 2006. The “value added” of neurocognitive testing after sports-related concussion. American Journal of Sports Medicine 34(10):1630–1635.

Viano DC, Hamberger A, Bolouri H, Saljo A. 2012. Evaluation of three animal models for concussion and serious brain injury. Annals of Biomedical Engineering 40(1):213–226.

Wall RJ, Shani M. 2008. Are animal models as good as we think? Theriogenology 69(1):2–9.

Wang S, Wang S, Li C. 2012. Infusion of 4°C normal saline can improve the neurological outcome in a porcine model of cardiac arrest. Journal of Trauma and Acute Care Surgery 72(5):1213–1219; discussion 1219.

Wilcox BJ, Beckwith JG, Greenwald RM, Raukar NP, Chu JJ, McAllister TW, Flashman LA, Maerlender AC, Duhaime AC, Crisco JJ. 2015. Biomechanics of head impacts associated with diagnosed concussion in female collegiate ice hockey players. Journal of Biomechanics 48(10):2201–2204.

About the Author:Susan S. Margulies is George H. Stephen-son Professor of Bioengineer-ing, Department of Bio-engi-neering, University of Pennsylvania.