Inertial sensors are commonly used to measure human head motion.(R1–3) Some sensors have been tested with dummy or cadaver experiments with mixed results, and methods to evaluate sensors in vivo are lacking. Here we present an in vivo(R3–10) method using high speed video to test teeth-mounted (mouthguard), soft tissue-mounted (skin patch), and headgear-mounted (skull cap) sensors during 6–13g(R1–20) sagittal soccer head impacts. Sensor coupling to the skull (R1–3) was quantified by displacement from an ear-canal reference. Mouthguard displacements were within video measurement error (<1mm), while the skin patch and skull cap displaced up to 4mm and 13mm from the ear-canal reference, respectively. We used the mouthguard, which had the least displacement from skull (R1–5), as the reference to assess 6-degree-of-freedom skin patch and skull cap measurements. Linear and rotational acceleration magnitudes were over-predicted by both the skin patch (with 120% NRMS error for amag, 290% for αmag(R1–6)) and the skull cap (320% NRMS error for amag, 500% for αmag(R1–6)). Such over-predictions were largely due to out-of-plane motion. To model sensor error, we found that in-plane skin patch acceleration peaks in the anterior-posterior direction could be modeled by an underdamped viscoelastic system. In summary, the mouthguard showed tighter skull coupling than the other sensor mounting approaches(R1–7). Furthermore, the in vivo methods presented are valuable for investigating skull acceleration sensor technologies.

Accumulation of head impacts may contribute to acute and long-term brain trauma. Wearable sensors can measure impact exposure, yet current sensors do not have validated impact detection methods for accurate exposure monitoring. Here we demonstrate a head impact detection method that can be implemented on a wearable sensor for detecting field football head impacts. Our method incorporates a support vector machine classifier that uses biomechanical features from the time domain and frequency domain, as well as model predictions of head-neck motions. The classifier was trained and validated using instrumented mouthguard data from collegiate football games and practices, with ground truth data labels established from video review. We found that low frequency power spectral density and wavelet transform features (10~30 Hz) were the best performing features. From forward feature selection, fewer than ten features optimized classifier performance, achieving 87.2% sensitivity and 93.2% precision in cross-validation on the collegiate dataset (n = 387), and over 90% sensitivity and precision on an independent youth dataset (n = 32). Accurate head impact detection is essential for studying and monitoring head impact exposure on the field, and the approach in the current paper may help to improve impact detection performance on wearable sensors.

Phase-based aMRI might be used in the future for quantitative analysis of minute changes in brain motion and may reveal subtle physiological variations of the brain as a result of pathology using processing of the fundamental harmonic or by selectively varying temporal harmonics. Preliminary data shows the potential of phase-based aMRI to qualitatively assess abnormal biomechanics in Chiari I malformation.

Alterations in brain rheology are increasingly recognized as a diagnostic marker for various neurological conditions. Magnetic resonance elastography now allows us to assess brain rheology repeatably, reproducibly, and non-invasively in vivo. Recent elastography studies suggest that brain stiffness decreases one percent per year during normal aging, and is significantly reduced in Alzheimer's disease and multiple sclerosis. While existing studies successfully compare brain stiffnesses across different populations, they fail to provide insight into changes within the same brain. Here we characterize rheological alterations in one and the same brain under extreme metabolic changes: alive and dead. Strikingly, the storage and loss moduli of the cerebrum increased by 26% and 60% within only three minutes post mortem and continued to increase by 40% and 103% within 45 minutes. Immediate post mortem stiffening displayed pronounced regional variations; it was largest in the corpus callosum and smallest in the brainstem. We postulate that post mortem stiffening is a manifestation of alterations in polarization, oxidation, perfusion, and metabolism immediately after death. Our results suggest that the stiffness of our brain-unlike any other organ-is a dynamic property that is highly sensitive to the metabolic environment. Our findings emphasize the importance of characterizing brain tissue in vivo and question the relevance of ex vivo brain tissue testing as a whole. Knowing the true stiffness of the living brain has important consequences in diagnosing neurological conditions, planning neurosurgical procedures, and modeling the brain's response to high impact loading.

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...