Brain Deformation Research Articles

Head Impact Kinematics and Brain Deformation in Paired Opposing Youth Football Players.

Head impact exposure is often quantified using peak resultant kinematics. While kinematics describes the inertial response of the brain to impact, they do not fully capture the dynamic brain response. Strain, a measure of the tissue-level response of the brain, may be a better predictor of injury. In this study, kinematic and strain metrics were compared to contact characteristics in youth football. Players on 2 opposing teams were instrumented with head impact sensors to record impact kinematics. Video was collected to identify contact scenarios involving opposing instrumented players (ie,paired contact scenarios) and code contact characteristics (eg,player role, impact location). A previously validated, high-resolution brain finite element model, the atlas-based brain model, was used to simulate head impacts and calculate strain metrics. Fifty-two paired contact scenarios (n = 105 impacts) were evaluated. Lighter players tended to have greater biomechanical metrics compared to heavier players. Impacts to the top of the helmet were associated with lower strain metrics. Overall, strain was better correlated with rotational kinematics, suggesting these metrics may be better predictors of the tissue-level brain response than linear kinematics. Understanding the effect of contact characteristics on brain strain will inform future efforts to improve sport safety.

Deformation fields: a new source of information to predict brain age

Objective. The modelling of healthy ageing critically requires the identification of methods that detect subtle changes in this process. In the last few years multiple machine learning models have been proposed that learn age patterns from magnetic resonance images. Current standard information sources rely on local volumetric information of brain tissues, namely white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). Information about patterns of brain deformation remains underexplored. In this paper an assessment is performed to understand better the predictive value of the deformation fields. Approach. A shallow approach was used to compare the predictive value of deformation fields with the brain tissues (GM, WM and CSF). Images were compressed into a lower dimension space using Principal Components Analysis and then, a Relevant Vector Regression (RVR) learned the age patterns from the components. A model was trained per modality (deformation fields, GM, WM and CSF) and the performance between the models was compared. To evaluate whether the deformation fields increased the predictive power of GM, a model fusion approach was explored in which the final estimator was an RVR. Each model was validated using a cross-validation approach and was also evaluated on an external dataset. Main results. We found that models trained with deformation patterns have higher predictive value than the ones trained with WM or CSF. Furthermore, deformation fields had a significantly better performance on the test set and also yield the lower difference between the validation and test set. Moreover, the predictions based on the combination of deformation patterns with GM volume yields better results than GM volumetric information alone. Significance. These findings suggest that deformation fields have a higher predictive power than WM and CSF and are robustly invariant across a set of confounding variables. Therefore, deformation fields should be considered in BrainAge models.

Numerical Calculation Method for Brain Shift Based on Hydrostatics and Dynamic FEM

During neurosurgery, brain deformation occurs because of gravity and leakage of the cerebrospinal fluid (CSF), which is referred to as brain shift. Brain shift is a serious problem in neuronavigation because neuronavigation relies on preoperatively taken medical images. This paper presents a brain shift estimation method based on hydrostatics and dynamic FEM, assuming that gravity and leakage of CSF are the main reasons for brain shift. The accuracy of the proposed method was verified via basic experiments conducted using elastic gelatin cubes. In addition, a 3D brain model was created using preoperative medical images of a patient and brain shift estimation simulations were performed. Their accuracy was verified by comparing the simulation results with the actual brain shift during neurosurgery. Assuming that the node in the most anterior position of the frontal lobe and the node in the highest position of the parietal lobe before the brain shift respectively remain in the most anterior position and the highest position even after the brain shift, the corresponding regions before and after the brain shift were searched and the deformations were evaluated. In this error analysis, the maximum estimation error was 4.4 mm. Furthermore, a region of 40 mm × 30 mm in the frontal lobe was chosen as the region of interest (ROI), and the surface errors in the ROI between the intraoperative MRI images and the simulated shifted brain were analyzed. The mean absolute error (MAE) between the surfaces along the z-axis (the direction of gravity) in the ROI was 3.7 mm (maximum absolute error was 8.8 mm). The proposed method was sufficiently simple for computing the brain shift in real-time. The expected contribution of this study toward improving the neuronavigational error and enhancing the safety of neurosurgery will be beneficial for hospitals, especially when the intraoperative MRI cannot be performed.

Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact

Real-time dynamic simulation remains a significant challenge for spatiotemporal data of high dimension and resolution. In this study, we establish a transformer neural network (TNN) originally developed for natural language processing and a separate convolutional neural network (CNN) to estimate five-dimensional (5D) spatiotemporal brain–skull relative displacement resulting from impact (isotropic spatial resolution of 4 mm with temporal resolution of 1 ms). Sequential training is applied to train (N = 5184 samples) the two neural networks for estimating the complete 5D displacement across a temporal duration of 60 ms. We find that TNN slightly but consistently outperforms CNN in accuracy for both displacement and the resulting voxel-wise four-dimensional (4D) maximum principal strain (e.g., root mean squared error (RMSE) of ∼1.0% vs. ∼1.6%, with coefficient of determination, R2 >0.99 vs. >0.98, respectively, and normalized RMSE (NRMSE) at peak displacement of 2%–3%, based on an independent testing dataset; N = 314). Their accuracies are similar for a range of real-world impacts drawn from various published sources (dummy, helmet, football, soccer, and car crash; average RMSE/NRMSE of ∼0.3 mm/∼4%–5% and average R2 of ∼0.98 at peak displacement). Accuracy in strain rate is also illustrated in one case (NRMSE of 7.8% and of 0.91). Sequential training is effective for allowing instantaneous estimation of 5D displacement with high accuracy, although TNN poses a heavier computational burden in training. This work enables efficient characterization of the intrinsically dynamic brain strain in impact critical for downstream multiscale axonal injury model simulation. This is also the first application of TNN in biomechanics and biomedical engineering, which offers important insight into how real-time dynamic simulations can be achieved across diverse engineering fields.

Pose Estimation and Non-Rigid Registration for Augmented Reality During Neurosurgery.

A craniotomy is the removal of a part of the skull to allow surgeons to have access to the brain and treat tumors. When accessing the brain, a tissue deformation occurs and can negatively influence the surgical procedure outcome. In this work, we present a novel Augmented Reality neurosurgical system to superimpose pre-operative 3D meshes derived from MRI onto a view of the brain surface acquired during surgery. Our method uses cortical vessels as main features to drive a rigid then non-rigid 3D/2D registration. We first use a feature extractor network to produce probability maps that are fed to a pose estimator network to infer the 6-DoF rigid pose. Then, to account for brain deformation, we add a non-rigid refinement step formulated as a Shape-from-Template problem using physics-based constraints that helps propagate the deformation to sub-cortical level and update tumor location. We tested our method retrospectively on 6 clinical datasets and obtained low pose error, and showed using synthetic dataset that considerable brain shift compensation and low TRE can be achieved at cortical and sub-cortical levels. The results show that our solution achieved accuracy below the actual clinical errors demonstrating the feasibility of practical use of our system. This work shows that we can provide coherent Augmented Reality visualization of 3D cortical vessels observed through the craniotomy using a single camera view and that cortical vessels provide strong features for performing both rigid and non-rigid registration.

Photoacoustic-MR Image Registration Based on a Co-Sparse Analysis Model to Compensate for Brain Shift.

Brain shift is an important obstacle to the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging to update the image-guided surgery systems. However, due to the innate limitations of the current imaging modalities, accurate brain shift compensation continues to be a challenging task. In this study, the application of intra-operative photoacoustic imaging and registration of the intra-operative photoacoustic with pre-operative MR images are proposed to compensate for brain deformation. Finding a satisfactory registration method is challenging due to the unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for photoacoustic-MR image registration, which can capture the interdependency of the two modalities. The proposed algorithm works based on the minimization of mapping transform via a pair of analysis operators that are learned by the alternating direction method of multipliers. The method was evaluated using an experimental phantom and ex vivo data obtained from a mouse brain. The results of the phantom data show about 63% improvement in target registration error in comparison with the commonly used normalized mutual information method. The results proved that intra-operative photoacoustic images could become a promising tool when the brain shift invalidates pre-operative MRI.

Whole-brain MR-registered cryo-imaging of a porcine-human glioma model to compare contrast agent biodistributions.

As rapidly accelerating technology, fluorescence guided surgery (FGS) has the potential to place molecular information directly into the surgeon's field of view by imaging administered fluorescent contrast agents in real time, circumnavigating pre-operative MR registration challenges with brain deformation. The most successful implementation of FGS is 5-ALA-PpIX guided glioma resection which has been linked to improved patient outcomes. While FGS may offer direct in-field guidance, fluorescent contrast agent distributions are not as familiar to the surgical community as Gd-MRI uptake, and may provide discordant information from previous Gd-MRI guidance. Thus, a method to assess and validate consistency between fluorescence-labeled tumor regions and Gd-enhanced tumor regions could aid in understanding the correlation between optical agent fluorescence and Gd-enhancement. Herein, we present an approach for comparing whole-brain fluorescence biodistributions with Gd-enhancement patterns on a voxel-by-voxel basis using co-registered fluorescent cryo-volumes and Gd-MRI volumes. In this initial study, a porcine-human glioma xenograft model was administered 5-ALA-PpIX, imaged with MRI, and euthanized 22 hours following 5-ALA administration. Following euthanization, the extracted brain was imaged with the cryo-macrotome system. After image processing steps and non-rigid, point-based registration, the fluorescence cryo-volume and Gd-MRI volume were compared for similarity metrics including: image similarity, tumor shape similarity, and classification similarity. This study serves as a proof-of-principle in validating our screening approach for quantitatively comparing 3D biodistributions between optical agents and Gd-based agents.

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy.

Increased intracranial pressure is the source of most critical symptoms in patients with glioma, and often the main cause of death. Clinical interventions could benefit from non-invasive estimates of the pressure distribution in the patient's parenchyma provided by computational models. However, existing glioma models do not simulate the pressure distribution and they rely on a large number of model parameters, which complicates their calibration from available patient data. Here we present a novel model for glioma growth, pressure distribution and corresponding brain deformation. The distinct feature of our approach is that the pressure is directly derived from tumour dynamics and patient-specific anatomy, providing non-invasive insights into the patient's state. The model predictions allow estimation of critical conditions such as intracranial hypertension, brain midline shift or neurological and cognitive impairments. A diffuse-domain formalism is employed to allow for efficient numerical implementation of the model in the patient-specific brain anatomy. The model is tested on synthetic and clinical cases. To facilitate clinical deployment, a high-performance computing implementation of the model has been publicly released.

Numerical Simulation of Concussive-Generated Cortical Spreading Depolarization to Optimize DC-EEG Electrode Spacing for Noninvasive Visual Detection.

Cortical spreading depolarization (SD) is a propagating depolarization wave of neurons and glial cells in the cerebral gray matter. SD occurs in all forms of severe acute brain injury, as documented by using invasive detection methods. Based on many experimental studies of mechanical brain deformation and concussion, the occurrence of SDs in human concussion has often been hypothesized. However, this hypothesis cannot be confirmed in humans, as SDs can only be detected with invasive detection methods that would require either a craniotomy or a burr hole to be performed on athletes. Typical electroencephalography electrodes, placed on the scalp, can help detect the possible presence of SD but have not been able to accurately and reliably identify SDs. To explore the possibility of a noninvasive method to resolve this hurdle, we developed a finite element numerical model that simulates scalp voltage changes that are induced by a brain surface SD. We then compared our simulation results with retrospectively evaluated data in patients with aneurysmal subarachnoid hemorrhage from Drenckhahn et al. (Brain 135:853, 2012). The ratio of peak scalp to simulated peak cortical voltage, Vscalp/Vcortex, was 0.0735, whereas the ratio from the retrospectively evaluated data was 0.0316 (0.0221, 0.0527) (median [1st quartile, 3rd quartile], n = 161, p < 0.001, one sample Wilcoxon signed-rank test). These differing values provide validation because their differences can be attributed to differences in shape between concussive SDs and aneurysmal subarachnoid hemorrhage SDs, as well as the inherent limitations in human study voltage measurements. This simulated scalp surface potential was used to design a virtual scalp detection array. Error analysis and visual reconstruction showed that 1cm is the optimal electrode spacing to visually identify the propagating scalp voltage from a cortical SD. Electrode spacings of 2cm and above produce distorted images and high errors in the reconstructed image. Our analysis suggests that concussive (and other) SDs can be detected from the scalp, which could confirm SD occurrence in human concussion, provide concussion diagnosis on the basis of an underlying physiological mechanism, and lead to noninvasive SD detection in the setting of severe acute brain injury.

Comprehensive Analysis of Brain Volume in REM Sleep Behavior Disorder with Mild Cognitive Impairment.

Rapid-eye-movement sleep behavior disorder (RBD) is a major risk factor for Parkinson's disease and dementia with Lewy bodies. More than a third of RBD patients have mild cognitive impairment (MCI), but their specific structural brain alterations remain poorly understood. This study aimed to investigate the local deformation and volume of gray and white matter tissue underlying MCI in RBD. Fifty-two idiopathic RBD patients, including 17 with MCI (33%), underwent polysomnography, neuropsychological, neurological, and magnetic resonance imaging assessments. MCI diagnosis was based on a subjective complaint, cognitive impairment on the neuropsychological battery, and preserved daily functioning. Forty-one controls were also included. Deformation-based morphometry (DBM), voxel-based morphometry (VBM), and regional volume analyses of the corpus callosum and cholinergic basal forebrain were performed. Multiple regression models were also computed using anatomical, cognitive (composite z scores), and motor parameters. Globally, patients with MCI displayed a widespread pattern of local deformation and volume atrophy in the cortical (bilateral insula, cingulate cortex, precuneus, frontal, temporal and occipital regions, right angular gyrus, and mid-posterior segment of the corpus callosum) and subcortical (brainstem, corona radiata, basal ganglia, thalamus, amygdala, and right hippocampus) regions compared to patients without MCI (DBM) or controls (DBM and VBM). Moreover, brain deformation (DBM) in patients were associated with lower performance in attention and executive functions, visuospatial abilities, and higher motor symptoms severity. The present study identified novel brain structural alterations in RBD patients with MCI which correlated with poorer cognitive performance. These results are consistent with those reported in patients with synucleinopathies-related cognitive impairment.

Foundations of Brain Image Segmentation: Pearls and Pitfalls in Segmenting Intracranial Blood on Computed Tomography Images.

Not only the time-dependent varying of signal intensity (i.e. haematoma evolution) characteristics of the intracranial blood in computed tomography images, but also the fluctuating image quality, the distortions introduced after medical interventions, and the brain deformations and intensity profile variations due to underlying pathologies make the segmentation of intracranial blood a challenging task. In addition to describing various challenges with blood segmentation, this chapter also reviews the following: (1) the general concept of segmentation-explaining why a proper segmentation is a critical step when creating machine learning algorithms for image detection purposes, (2) the different segmentation types and how different medical conditions and technical issues can further complicate this task, (3) how to choose a proper software to facilitate the segmentation task, and (4) useful tips that may be applied before launching a similar segmentation project.

Evaluation of the head protection effectiveness of cyclist helmets using full-scale computational biomechanics modelling of cycling accidents

Introduction: Cycling is a popular choice for urban transportation. Helmets are important and the most popular means of head protection for cyclists. However, a debate about the effectiveness of helmets in protecting a cyclist’s head from injury continues. Method: We employed computational biomechanics methods to analyze the head protection effectiveness of nine off-the-shelf-helmets for two typical impact scenarios that occur in cycling accidents: cyclist’s head impacting a kerb (kerb-impact) and cyclist skidding (skidding impact) on the road surface. We conducted drop tests for all nine analyzed helmets, and used the test data for validation of the corresponding helmet finite element (FE) models created in this study. The validated helmet models were then used in the full-scale computer simulations (FE analysis for the skull, brain and helmet, and multibody dynamics for the remaining segments of the cyclist’s body) of the cycling accidents for cyclists wearing a helmet and without a helmet. Results: The results indicate that helmets can reduce both the peak linear acceleration of the cyclist head center of gravity (COG) and the risk of cyclist skull fracture. However, higher rotational acceleration of the head COG was predicted for cyclists wearing helmets. The results obtained using the injury criteria that rely on the brain deformations (maximum shear strain MPS and cumulative strain damage measure CSDM) suggest that helmets may offer protection in all the analyzed cyclist impact scenarios. However, the predicted level of protection varies for different helmets and impact scenarios with appreciable variations in the predictions obtained using different injury criteria. Reduction in the maximum principal strain (MPS0.98) for helmeted cyclists was predicted for both impact scenarios. In contrast, wearing the helmet reduced the CSDM only for the skidding impact scenario. For the kerb-impact scenario, no clear influence of the helmet on the predicted CSDM was observed.

Morphological Study of the Effects of Caffeine Beverages Can Cause Birth Defects on Swiss White Mice Embryos

The current study was conducted to identify the effects of different doses of Arabic coffee, black tea and Coca-Cola syrup on the possibility of birth defects in Swiss white mice embryos. (25) pregnant female mice were used in the study, divided into five experimental groups, including the control group dosed with distilled water, and four experimental groups were dosed with concentrations (4000-2500-2000-1000) mg/kg body weight for Arabic coffee and black tea and (2,3,4,6 ml/kg) for Coca-Cola syrup; once a day, from the seventh day until the eighteenth day of pregnancy. The results of the current study showed cases of abortion and fetal death at varying degrees and many abnormalities in white mouse embryos due to the different concentrations of the beverages used were characterized by the appearance of elongated and enlarged embryos. The elongation and deformation of the fetus, And the embryos mutated. Small embryos folded like a ball and curved in the shape of letter C, and the severity of the deformities increased by the appearance of mutated embryos resembling the body of a fish and deformed embryos similar to a water mermaid and not fully developed and the appearance of the head in the shape of a blurred triangle, as well as forward with a pointed end resembling a bird's beak, and brain deformation through depression in the posterior cranial region of the skull, and for the first time a new deformation is a cerebral Meningomyelocele in the form of a cystic tumor in the posterior brain region.

Computer simulation of tumour resection-induced brain deformation by a meshless approach.

Tumour resection requires precise planning and navigation to maximise tumour removal while simultaneously protecting nearby healthy tissues. Neurosurgeons need to know the location of the remaining tumour after partial tumour removal before continuing with the resection. Our approach to the problem uses biomechanical modelling and computer simulation to compute the brain deformations after the tumour is resected. In this study, we use meshless Total Lagrangian explicit dynamics as the solver. The problem geometry is extracted from the patient-specific magnetic resonance imaging (MRI) data and includes the parenchyma, tumour, cerebrospinal fluid and skull. The appropriate non-linear material formulation is used. Loading is performed by imposing intra-operative conditions of gravity and reaction forces between the tumour and surrounding healthy parenchyma tissues. A finite frictionless sliding contact is enforced between the skull (rigid) and parenchyma. The meshless simulation results are compared to intra-operative MRI sections. We also calculate Hausdorff distances between the computed deformed surfaces (ventricles and tumour cavities) and surfaces observed intra-operatively. Over 80% of points on the ventricle surface and 95% of points on the tumour cavity surface were successfully registered (results within the limits of two times the original in-plane resolution of the intra-operative image). Computed results demonstrate the potential for our method in estimating the tissue deformation and tumour boundary during the resection.

Amniotic Band Sequence Syndrome – A Rare Disorder

Amniotic bands sequence is a inherited diseases distinguished by craniofacial, body wall, and limb oddities that may be in relation with foetal-placental fibrous bands. Its pervasiveness has been delineated to range from 0.19 to 8.1 per 10 000 births [1]. It is a customary state prospectively alike with a variety of different confinements’ disability. The deformities arise in the wake of the pompous bodily part have formed usually in early evolution. The required cause of amniotic disruption complex is undisclosed .The three most usual decorative design are constriction ring syndrome signalized by one or more limbs being high-flown; the limb-body- wall complex; and amniotic band syndrome characterized by abnormalities of the head and face (craniofacial abnormalities), inadequacies of the brain and genuine deformation of the appendages [2].&#x0D; The condition in few cases might be considered prior to nativity (antepartum), unlikely to change upon the sequels of explicit detailed imaging practices, like fetal USG, which may give away the feature inadequacies [3].&#x0D; Here we present a case of pregnant woman admitted in our Obstetrics and gynecological department with her ultrasonography report indicative of amniotic band and further discussed about antenatal diagnosis, diagnostic and therapeutic approach, prognosis and genetic counselling of amniotic disruption complex.

Head Impact Modeling to Support a Rotational Combat Helmet Drop Test.

The Advanced Combat Helmet (ACH) military specification (mil-spec) provides blunt impact acceleration criteria that must be met before use by the U.S. warfighter. The specification, which requires a helmeted magnesium Department of Transportation (DOT) headform to be dropped onto a steel hemispherical target, results in a translational headform impact response. Relative to translations, rotations of the head generate higher brain tissue strains. Excessive strain has been implicated as a mechanical stimulus leading to traumatic brain injury (TBI). We hypothesized that the linear constrained drop test method of the ACH specification underreports the potential for TBI. To establish a baseline of translational acceleration time histories, we conducted linear constrained drop tests based on the ACH specification and then performed simulations of the same to verify agreement between experiment and simulation. We then produced a high-fidelity human head digital twin and verified that biological tissue responses matched experimental results. Next, we altered the ACH experimental configuration to use a helmeted Hybrid III headform, a freefall cradle, and an inclined anvil target. This new, modified configuration allowed both a translational and a rotational headform response. We applied this experimental rotation response to the skull of our human digital twin and compared brain deformation relative to the translational baseline. The modified configuration produced brain strains that were 4.3 times the brain strains from the linear constrained configuration. We provide a scientific basis to motivate revision of the ACH mil-spec to include a rotational component, which would enhance the test's relevance to TBI arising from severe head impacts.

Segmentation of brain tumour in 3D Intraoperative Ultrasound imaging.

Intraoperative ultrasound (iUS), using a navigation system and preoperative magnetic resonance imaging (pMRI), supports the surgeon intraoperatively in identifying tumour margins. Therefore, visual tumour enhancement can be supported by efficient segmentation methods. A semi-automatic and two registration-based segmentation methods are evaluated to extract brain tumours from 3D-iUS data. The registration-based methods estimated the brain deformation after craniotomy based on pMRI and 3D-iUS data. Both approaches use the normalised gradient field and linear correlation of linear combinations metrics. Proposed methods were evaluated on 66 B-mode and contrast-mode 3D-iUS data with metastasis and glioblastoma. The semi-automatic segmentation achieved superior results with dice similarity index (DSI) values between [85.34, 86.79]% and contour mean distance values between [1.05, 1.11]mm for both modalities and tumour classes. Better segmentation results were obtained for metastasis detection than glioblastoma, preferring 3D-intraoperative B-mode over 3D-intraoperative contrast-mode.

A direct Jacobian total Lagrangian explicit dynamics finite element algorithm for real‐time simulation of hyperelastic materials

AbstractThis article presents a novel direct Jacobian total Lagrangian explicit dynamics (DJ‐TLED) finite element algorithm for real‐time nonlinear mechanics simulation. The nodal force contributions are expressed using only the Jacobian operator, instead of the deformation gradient tensor and finite deformation tensor, for fewer computational operations at run‐time. Owing to this proposed Jacobian formulation, novel expressions are developed for strain invariants and constant components, which are also based on the Jacobian operator. Results show that the proposed DJ‐TLED consumed between 0.70× and 0.88× CPU solution times compared to state‐of‐the‐art TLED and achieved up to 121.72× and 94.26× speed improvements in tetrahedral and hexahedral meshes, respectively, using GPU acceleration. Compared to TLED, the most notable difference is that the notions of stress and strain are not explicitly visible in the proposed DJ‐TLED but embedded implicitly in the formulation of nodal forces. Such a force formulation can be beneficial for fast deformation computation and can be particularly useful if the displacement field is of primary interest, which is demonstrated using a neurosurgical simulation of brain deformations for image‐guided neurosurgery. The present work contributes towards a comprehensive DJ‐TLED algorithm concerning isotropic and anisotropic hyperelastic constitutive models and GPU implementation.

Orientation of neurites influences severity of mechanically induced tau pathology

Chronic traumatic encephalopathy is a neurodegenerative disease associated with repeated traumatic brain injury (TBI). Chronic traumatic encephalopathy is a tauopathy, in which cognitive decline is accompanied by the accumulation of neurofibrillary tangles of the protein tau in patients’ brains. We recently found that mechanical force alone can induce tau mislocalization to dendritic spines and loss of synaptic function in in vitro neuronal cultures with random cell organization. However, in the brain, neurons are highly aligned, so here we aimed to determine how neuronal organization influences early-stage tauopathy caused by mechanical injury. Using microfabricated cell culture constructs to control the growth of neurites and an in vitro simulated TBI device to apply controlled mechanical deformation, we found that neuronal orientation with respect to the direction of a uniaxial high-strain-rate stretch injury influences the degree of tau pathology in injured neurons. We found that a mechanical stretch applied parallel to the neurite alignment induces greater mislocalization of tau proteins to dendritic spines than does a stretch with the same strain applied perpendicular to the neurites. Synaptic function, characterized by the amplitude of miniature excitatory postsynaptic currents, was similarly decreased in neurons with neurites aligned parallel to stretch, whereas in neurons aligned perpendicular to stretch, it had little to no functional loss. Experimental injury parameters (strain, strain rate, direction of stretch) were combined with a standard viscoelastic solid model to show that in our in vitro model, neurite work density during stretch correlates with tau mislocalization. These findings suggest that in a TBI, the magnitude of brain deformation is not wholly predictive of neurodegenerative consequences of TBI but that deformation relative to local neuronal architecture and the neurite mechanical energy during injury are better metrics for predicting trauma-induced tauopathy.

Position-Based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery

Many tasks in robot-assisted surgery require planning and controlling manipulators' motions that interact with highly deformable objects. This study proposes a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion for pre-operative path planning and intra-operative guidance in keyhole surgical procedures. It maximizes the probability of success by accounting for uncertainty in deformation models, noisy sensing, and unpredictable actuation. The PBD deformation parameters were initialized on a parallelepiped-shaped simulated phantom to obtain a reasonable starting guess for the brain white matter. They were calibrated by comparing the obtained displacements with deformation data for catheter insertion in a composite hydrogel phantom. Knowing the gray matter brain structures' different behaviors, the parameters were fine-tuned to obtain a generalized human brain model. The brain structures' average displacement was compared with values in the literature. The simulator's numerical model uses a novel approach with respect to the literature, and it has proved to be a close match with real brain deformations through validation using recorded deformation data of in-vivo animal trials with a mean mismatch of 4.73$\pm$2.15%. The stability, accuracy, and real-time performance make this model suitable for creating a dynamic environment for KN path planning, pre-operative path planning, and intra-operative guidance.