Investigating and understanding the biomechanical kinematics and kinetics of human brain axonal fibers during head impact process is crucial to study the mechanisms of Traumatic Axonal Injury (TAI). Such a study may require the explicit incorporation of brain fiber tracts into the host brain in order to distinguish the mechanical states of axonal fibers and brain tissue. Herein we extend our previously developed human head model by using an embedded element method to include fiber tracts reconstructed from diffusion tensor images in a host brain with the purpose of numerically tracking the deformation state of axonal fiber tracts during a head impact simulation. The updated model is validated by comparing its prediction of intracranial pressures with experimental data, followed by a thorough study of the effects of element types used for fiber tracts and the stiffness ratios of fiber to host brain. The validated model is also used to predict and visualize the damaged region of fiber tracts during the head impact process based on different injury criteria. The model is promising in tracking the state of fiber tracts and can add more objective functions such as axonal fiber deformation if used in the future design optimization of head protective equipment such as a football helmet.

Hydrocephalus, characterized by progressive expansion of the cerebrospinal fluid (CSF)-filled ventricles (ventriculomegaly), is the most common reason for brain surgery. \"Communicating\" (i.e., non-obstructive) hydrocephalus is classically attributed to a primary derangement in CSF homeostasis, such as choroid plexus-dependent CSF hypersecretion, impaired cilia-mediated CSF flow currents, or decreased CSF reabsorption via the arachnoid granulations or other pathways. Emerging data suggest abnormal biomechanical properties of the brain parenchyma are an underappreciated driver of ventriculomegaly in multiple forms of communicating hydrocephalus across the lifespan. We discuss recent evidence from human and animal studies that suggests impaired neurodevelopment in congenital hydrocephalus, neurodegeneration in elderly normal pressure hydrocephalus, and, in all age groups, inflammation-related neural injury post-infectious and post-hemorrhagic hydrocephalus, can result in loss of stiffness and viscoelasticity of the brain parenchyma. Abnormal brain biomechanics creates barrier alterations at the brain-CSF interface that pathologically facilitates secondary enlargement of the ventricles, even at normal or low intracranial pressures. This \"brain-centric\" paradigm has implications for the diagnosis, treatment, and study of hydrocephalus from womb to tomb.

Unraveling the intricate relationship between mechanical factors and brain activity is a pivotal endeavor, yet the underlying mechanistic model of signaling pathways in brain mechanotransduction remains enigmatic. To bridge this gap, we introduced an in situ multi-scale platform, through which we delineate comprehensive brain biomechanical traits in white matter (WM), grey-white matter junctions (GW junction), and the pons across human brain tissue from four distinct donors. We investigate the three-dimensional expression patterns of Piezo1, Piezo2, and TMEM150C, while also examining their associated histological features and mechanotransduction signaling networks, particularly focusing on the YAP/β-catenin axis. Our results showed that the biomechanical characteristics (including stiffness, spring term, and equilibrium stress) associated with Piezo1 vary depending on the specific region. Moving beyond Piezo1, our result demonstrated the significant positive correlations between Piezo2 expression and stiffness in the WM. Meanwhile, the expression of Piezo2 and TMEM150C was shown to be correlated to viscoelastic properties in the pons and WM. Given the heterogeneity of brain tissue, we investigated the three-dimensional expression of Piezo1, Piezo2, and TMEM150C. Our results suggested that three mechanosensitive proteins remained consistent across different vertical planes within the tissue sections. Our findings not only establish Piezo1, Piezo2, and TMEM150C as pivotal mechanosensors that regulate the region-specific mechanotransduction activities but also unveil the paradigm connecting brain mechanical properties and mechanotransduction activities and the variations between individuals.

Intracranial compliance (ICC) holds significant potential in neuromonitoring, serving as a diagnostic tool and contributing to the evaluation of treatment outcomes. Despite its comprehensive concept, which allows consideration of changes in both volume and intracranial pressure (ICP), ICC monitoring has not yet established itself as a standard component of medical care, unlike ICP monitoring. This review highlighted that the first challenge is the assessment of ICC values, because of the invasive nature of direct measurement, the time-consuming aspect of non-invasive calculation through computer simulations, and the inability to quantify ICC values in estimation methods. Addressing these challenges is crucial, and the development of a rapid, non-invasive computer simulation method could alleviate obstacles in quantifying ICC. Additionally, this review indicated the second challenge in the clinical application of ICC, which involves the dynamic and time-dependent nature of ICC. This was considered by introducing the concept of time elapsed (TE) in measuring the changes in volume or ICP in the ICC equation (volume change/ICP change). The choice of TE, whether short or long, directly influences the ICC values that must be considered in the clinical application of the ICC. Compensatory responses of the brain exhibit non-monotonic and variable changes in long TE assessments for certain disorders, contrasting with the mono-exponential pattern observed in short TE assessments. Furthermore, the recovery behavior of the brain undergoes changes during the treatment process of various brain disorders when exposed to short and long TE conditions. The review also highlighted differences in ICC values across brain disorders with various strain rates and loading durations on the brain, further emphasizing the dynamic nature of ICC for clinical application. The insight provided in this review may prove valuable to professionals in neurocritical care, neurology, and neurosurgery for standardizing ICC monitoring in practical application related to the diagnosis and evaluation of treatment outcomes in brain disorders.

The mechanical role of the skull-brain interface is critical to the pathology of concussion and traumatic brain injury (TBI) and may evolve with age. Here we characterize the skull-brain interface in juvenile, female Yucatan mini-pigs from 3 to 6 months old using techniques from magnetic resonance elastography (MRE). The displacements of the skull and brain were measured by a motion-sensitive MR imaging sequence during low-amplitude harmonic motion of the head. Each animal was scanned four times at 1-month intervals. Harmonic motion at 100 Hz was excited by three different configurations of a jaw actuator in order to vary the direction of loading. Rigid-body linear motions of the brain and skull were similar, although brain rotations were consistently smaller than corresponding skull rotations. Relative displacements between the brain and skull were estimated for voxels on the surface of the brain. Amplitudes of relative displacements between skull and brain were 1-3 μm, approximately 25-50% of corresponding skull displacements. Maps of relative displacement showed variations by anatomical region, and the normal component of relative displacement was consistently 25-50% of the tangential component. These results illuminate the mechanics of the skull-brain interface in a gyrencephalic animal model relevant to human brain injury and development.

Hydrocephalus is classically considered to be a disorder of altered cerebrospinal fluid (CSF) circulation, leading to the dilation of cerebral ventricles. Here, we report a clinical case of a patient who presented with fetal-onset hydrocephalus with diffusely reduced cortical and white matter volumes resulting from a genetic mutation in L1CAM, a well-known hydrocephalus disease gene involved in neuronal cell adhesion and axon development. After CSF was drained from the ventricle intraoperatively, the patient\'s cortical mantle collapsed and exhibited a \"floppy\" appearance on neuroimaging, suggesting an inability of the hydrocephalic brain to maintain its structural integrity. The case provides clinical support for altered brain biomechanical properties in human hydrocephalus and adds to the emerging hypothesis that altered brain development with secondary impact on brain structural stability may contribute to ventricular enlargement in some subsets of hydrocephalus.

OBJECTIVE: Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as \"instrumented\" or \"smart\" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion.
METHODS: We have built a brain modeling platform using several of Amazon\'s Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline.
RESULTS: The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D.
CONCLUSIONS: We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.

Understanding the mechanical behaviors and properties of brain tissue are crucial to study the mechanisms of traumatic brain injury (TBI). Such injury may be associated with high rate loading conditions and the large deformation of brain tissue. Thus, constitutive models that consider the rate dependent large deformation of brain tissue and its possible damage initiation and evolution may help uncover the related mechanisms of TBI. Motivated from this, in this paper we present a fully three-dimensional large strain viscohyperelastic-damage model with the purpose of reproducing the experimentally observed rate sensitive elastic and damage-induced stress softening behaviors of brain tissue. The parameters of the proposed model can be identified using the experimental data from simple monotonic tests such as uniaxial tension, compression and simple shear. The proposed model is validated by comparing its prediction with experimental data. Good agreement between predictive results and experimental data is achieved indicating the potential of the proposed model in characterizing the mechanical behaviors of brain tissue considering rate dependence and damage effect.

Traumatic brain injury (TBI) is a major health concern affecting both military and civilian populations. Despite notable advances in TBI research in recent years, there remains a significant gap in linking the impulsive loadings from a blast or a blunt impact to the clinical injury patterns observed in TBI. Synthetic head models or phantoms can be used to establish this link as they can be constructed with geometry, anatomy, and material properties that match the human brain, and can be used as an alternative to animal models. This study presents one such phantom called the Anthropomorphic Neurologic Gyrencephalic Unified Standard (ANGUS) phantom, which is an idealized gyrencephalic brain phantom composed of polyacrylamide gel. Here we mechanically characterized the ANGUS phantom using tagged magnetic resonance imaging (MRI) and magnetic resonance elastography (MRE), and then compared the outcomes to data obtained in healthy volunteers. The direct comparison between the phantom\'s response and the data from a cohort of in vivo human subjects demonstrate that the ANGUS phantom may be an appropriate model for bulk tissue response and gyral dynamics of the human brain under small amplitude linear impulses. However, the phantom\'s response differs from that of the in vivo human brain under rotational impacts, suggesting avenues for future improvements to the phantom.

Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes.