Journal | Yuzhe Lab

Quantifying Morphology-Related Deviations in Brain Strain Using an Automated Mesh Morphing Method

Fri, 22 May 2026 00:00:00 +0000

Abstract

Finite element head models (FEHMs) have been widely used to study the biomechanics in traumatic brain injury (TBI). Most FEHMs are constructed to reflect the average head shape, which inevitably leads to the omission of individual brain morphology. In this study, an automated mesh morphing method based on radial basis function-thin plate spline (RBF-TPS) with automated landmark extraction and projection was developed. Five representative subject-specific head models and the baseline model were subjected to head kinematics from six datasets covering diverse impact scenarios. Results showed that morphology-related deviations increased with loading severity, reaching up to 0.21 for MPS95 and 0.14 s^-1 for MPSR95. Logistic regression indicated that TBI risk thresholds varied by approximately 19.4% for MPS95 and 11.4% for MPSR95 across representative models. These findings indicate that subject-specific morphology affects strain response beyond size scaling alone, underscoring the importance of incorporating individual morphology into brain injury prediction models.

Local and Global Effects of Inertial Force Components Producing Brain Strain During Head Impacts

Tue, 28 Oct 2025 00:00:00 +0000

Abstract

Traumatic brain injury (TBI) is a brain dysfunction caused by an external mechanical force and is a leading cause of disability worldwide. In traumatic brain injury, the brain strain is driven by inertial force associated with head acceleration. We identified three distinct mechanisms by which inertial forces induce brain strain: the global rotation effect, the global translation effect, and the local force effect. The global rotation and translation effects arise from whole-brain movement relative to the skull, producing brain strain through shearing, pushing, and pulling, respectively. In contrast, the local force effect refers to the strain produced inside the brain by the local force without whole-brain movement. These effects correspond to different inertial force components: Euler force (angular acceleration), linear force (linear acceleration), and centrifugal force (angular velocity). In this study, we applied impact loading by each inertial force component independently to quantify their contributions and clarify the conditions under which Holbourn’s hypothesis applies. We found that 97% of the total MPS in American football impacts was produced by the Euler force. When head kinematics were extended to extreme scenarios such as aviation or high-impact accidents, both linear and centrifugal forces were also capable of producing significant brain strain. Independent kinematic thresholds were estimated, showing that most injurious head impacts consistently exceed angular acceleration thresholds, while corresponding linear accelerations and angular velocities remain below them.

创伤性脑损伤研究的人类头部有限元模型研究进展

Sun, 05 Oct 2025 00:00:00 +0000

Abstract

创伤性脑损伤（traumatic brain injury,TBI）是发病率、患病率最高的神经系统疾病，为全社会带来了巨大的公共卫生负担。深入研究TBI的生物力学原理有助于提升头部防护效果，发展快速评估技术并采取及时干预，从而降低伤情恶化的风险。人类头部有限元模型（finite element head model,FEHM）作为一种数值分析工具，能够模拟头部在受到冲击时的动态响应，包括脑组织的应力应变时空分布、颅内压的变化等，为理解创伤性脑损伤的力学机制提供了重要依据。本文详细总结了国内外主流的人类头部有限元模型的现状与发展，追溯了模型的发展历程，总结了模型的特点并介绍了基于有限元模型的TBI机制研究进展。对相关研究的总结和梳理将有助于开发新型FEHM，并为创伤性脑损伤的风险评估及防护装备的设计提供理论指导和技术支撑。

AI-based identification of head impact locations, speeds, and force based on head kinematics simulations

Fri, 14 Feb 2025 00:00:00 +0000

Abstract

Objective: With the development of wearable sensors, head kinematics data have become widely available. However, key impact information—such as impact direction, speed, and force—which is crucial for helmet development, is still not directly measured. This study presents a deep learning model designed to accurately predict these head impact parameters from head kinematics during helmeted impacts. Methods: Leveraging a dataset of 16,000 simulated helmeted head impacts using the Riddell helmet finite element model, a Long Short-Term Memory (LSTM) network was implemented to process the head kinematics: linear accelerations and angular velocities. Results: In simulations, the model accurately predicted impact direction, speed, and the force profile with R² exceeding 70% for all tasks. Validation on 79 on-field impacts recorded by instrumented mouthguards and videos demonstrated that the model significantly outperformed traditional methods in identifying impact locations, achieving 79.7% accuracy compared to the previous highest of 49.4%. Conclusion: This work highlights the potential of deep learning to enhance helmet design and head impact sensing by recovering key impact parameters from wearable kinematic data. Future work should evaluate model performance across helmet types and sports, and apply transfer learning for broader applicability.

Differences between two maximal principal strain rate calculation schemes in traumatic brain analysis with in-vivo and in-silico datasets

Thu, 05 Dec 2024 00:00:00 +0000

Abstract

Brain deformation caused by a head impact leads to traumatic brain injury (TBI). The maximum principal strain (MPS) was used to measure the extent of brain deformation and predict injury, and recent evidence has indicated that incorporating the maximum principal strain rate (MPSR) and the product of MPS and MPSR, denoted as MPS × SR, enhances the accuracy of TBI prediction. However, ambiguities have arisen about the calculation of MPSR. Two schemes have been utilized: one is to use the time derivative of MPS (MPSR1), and another is to use the first eigenvalue of the strain rate tensor (MPSR2). To quantify the discrepancies between these two methodologies, we compared them across eight in-vivo and one in-silico head impact datasets and found that 95MPSR1 was slightly larger than 95MPSR2 and 95MPS × SR1 was 4.85% larger than 95MPS × SR2 on average. Across every element in all head impacts, the average MPSR1 was 12.73% smaller than MPSR2, and MPS × SR1 was 11.95% smaller than MPSR2. Logistic regression models trained to predict TBI showed no significant difference in predictability between the two schemes. The consequence of misuse of MPSR and MPS × SR thresholds was also examined, showing false decision rates around 1%, suggesting that the two methodologies are not significantly different in detecting TBI.

Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings

Tue, 01 Oct 2024 00:00:00 +0000

Abstract

S.I. : Concussions II Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings NICHOLAS J. C ECCHI ,1 ASHLYN A. C ALLAN,1 LANDON P. W ATSON,1 YUZHE LIU,1 XIANGHAO ZHAN,1 RAMANAND V. V EGESNA,2 COLLIN PANG,1 ENORA LE FLAO,1 GERALD A. G RANT,3,4,5 MICHAEL M. Z EINEH,6 and D AVID B. C AMARILLO1,3,7 1Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; 2Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA; 3Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; 4Department of Neurology, Stanford University, Stanford, CA 94305, USA; 5Department of Neurosurgery, Duke University, Durham, NC 27710, USA; 6Department of Radiology, Stanford University, Stanford, CA 94305, USA; and 7Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA (Received 29 November 2022; accepted 8 February 2023) Associate Editor Stefan M. Du

AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction

Mon, 29 Apr 2024 00:00:00 +0000

Abstract

Wearable devices used for measuring head impact kinematics are inherently noisy due to imperfect coupling with the human body. This study developed one-dimensional convolutional neural network (1D-CNN) models to denoise tri-axial linear acceleration and angular velocity signals recorded from instrumented mouthguards. Using 163 dummy head impacts for training, validation was performed at three levels: kinematics, brain injury criteria, and tissue-level strain and strain rate. Denoising reduced pointwise RMSE by 36% and peak absolute error by 56%, while six brain injury criteria errors decreased by 82% on average. Maximum principal strain and strain-rate errors were reduced by 35% and 69%, respectively. Blind testing on 118 college football impacts and 413 PMHS impacts demonstrated similar improvements. The 1D-CNN approach provides an effective method for reducing measurement noise in mouthguard-derived head kinematics and supports future real-world TBI monitoring.

A wearable hydraulic shock absorber with efficient energy dissipation

Sat, 10 Feb 2024 00:00:00 +0000

Abstract

Advances in shock absorber technology are often translated to wearable personal protective equipment to protect humans from impact-related injuries. In this study, the authors leveraged the energy dissipation of fluid flow using soft structures to prototype a novel wearable hydraulic shock absorber, the Soft Hydraulic Shock. The device achieved an efficient energy absorption ratio of 100% across a range of impact loading conditions and maintained stable energy dissipation across a wide temperature range. Finite element analyses further explored its behavior under different design parameters and impact loadings. When implemented into a full helmet system, the Soft Hydraulic Shock significantly mitigated brain injury risk, demonstrating the promise of wearable hydraulic shock absorbers for protective equipment.

Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types

Mon, 15 Jan 2024 00:00:00 +0000

Abstract

Objective: The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM. Methods: To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). Results: The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than 7 s⁻¹ in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models. Conclusion: The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate. Significance: This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.

Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks

Fri, 05 Jan 2024 00:00:00 +0000

Abstract

Machine-learning head models (MLHMs) have shown promise in estimating traumatic brain injury (TBI)-related brain deformation from head kinematics, but overfitting to simulated impacts and performance degradation under distribution shift hinder clinical deployment. This study introduces an adaptive MLHM combining unsupervised domain adaptation and generative adversarial networks to address dataset distributional differences across simulated impacts, college football, mixed martial arts, and boxing datasets. Using domain regularized component analysis (DRCA) and cycle-GAN-based adaptation, the proposed models markedly improved maximum principal strain (MPS) and strain-rate (MPSR) prediction accuracy. The DRCA-based approach achieved the best performance with MPS mean absolute errors of 0.017 (college football) and 0.020 (MMA), and MPSR MAE of 4.09 s⁻¹ and 6.61 s⁻¹, outperforming baseline MLHMs across multiple hold-out test sets. These results demonstrate that unsupervised domain adaptation effectively reduces cross-domain error and moves MLHMs toward clinically reliable TBI detection.

Finite element evaluation of an American football helmet featuring liquid shock absorbers for protecting against concussive and subconcussive head impacts

Fri, 09 Jun 2023 00:00:00 +0000

Abstract

This study develops and evaluates a finite element (FE) model of an American football helmet incorporating 21 liquid shock absorbers distributed throughout the shell. Using an anthropomorphic test headform and impactor FE setup, the helmet was tested under a protocol representative of National Football League concussive impacts at multiple locations and velocities, as well as lower-velocity subconcussive impacts. Head kinematics were used to compute the Head Acceleration Response Metric (HARM) and brain strain from an FE head model. The liquid helmet achieved the lowest HARM values in most conditions and yielded substantial reductions in both HARM and brain strain compared to four existing helmet designs, demonstrating the promise of liquid shock absorbers for improved helmet safety performance.

Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics

Mon, 13 Mar 2023 00:00:00 +0000

Abstract

This study investigates the spectral characteristics of head kinematics across different types of head impacts using a machine-learning-based classification framework. A random forest classifier utilizing spectral densities of linear acceleration and angular velocity was trained on 3262 impacts from lab reconstruction, American football, mixed martial arts, and car crash data. The classifier achieved a median accuracy of 96% across 1000 random train-test partitions. Spectral characteristics varied systematically across impact types, with mixed martial arts impacts showing higher high-frequency spectral densities relative to low-frequency regions. Type-specific nearest-neighbor regression models demonstrated improved performance over baseline models, suggesting that subtype-based modeling enhances strain prediction. These findings enable better understanding of impact-type-specific kinematic signatures and offer tools for evaluating impact-simulation systems and augmenting on-field datasets.

Brain strain rate response: Addressing computational ambiguity and experimental data for model validation

Sun, 01 Jan 2023 00:00:00 +0000

Abstract

Traumatic brain injury (TBI) is an alarming global public health issue with high morbidity and mortality rates. Although the causal link between external insults and consequent brain injury remains largely elusive, both strain and strain rate are generally recognized as crucial factors for TBI onsets. With respect to the flourishment of strain-based investigation, ambiguity and inconsistency are noted in the scheme for strain rate calculation within the TBI research community. Furthermore, there is no experimental data that can be used to validate the strain rate responses of finite element (FE) models of the human brain. The current work presented a theoretical clarification of two commonly used strain rate computational schemes: the strain rate was either calculated as the time derivative of strain or derived from the rate of deformation tensor. To further substantiate the theoretical disparity, these two schemes were respectively implemented to estimate the strain rate responses from a previous-published cadaveric experiment and an FE head model secondary to a concussive impact. The results clearly showed scheme-dependent responses, both in the experimentally determined principal strain rate and model-derived principal and tract-oriented strain rates. The results highlight that cross-scheme comparison of strain rate responses is inappropriate, and the utilized strain rate computational scheme needs to be reported in future studies. The newly calculated experimental strain rate curves in the supplementary material can be used for strain rate validation of FE head models. Statement of significance: - Delineates a theoretical clarification of two algorithms for strain rate computation. - Highlights the strain rate responses directly depends on the computational schemes. - Presents experimental strain rate curves, serving as references for strain rate validation of finite element head models. 1.

Translational models of mild traumatic brain injury tissue biomechanics

Thu, 01 Dec 2022 00:00:00 +0000

Abstract

T raumatic brain injury (TBI) is a global health concern. Mild TBI (mTBI) which accounts for the majority of TBI cases, is hard to detect since often the imaging is normal but can still cause brain damage and long-term sequelae. Physiologically, acute primary damage to the brain is thought to be caused by tissue deformation from the inertial movement of the brain after rapid head rotation. Respecting tissue biomechanics, animal models are often used to understand the pathophysiology of mTBI. We have reviewed the literature focusing on connecting biome- chanics with mTBI pathologies at the tissue scale using neuroimaging, neurobehavioral tests, and pathologies across species, particularly studies using strain and strain rate. These studies have found strain and strain rate predict mTBI pathol- ogy and strain is generalizable across species, including small animals, large animals, and humans. We propose that re- searchers can leverage tissue-level strain and strain rate to bridge biomechanics and mTBI pathology . Addresses 1 Department of Bioengineering, Stanford University, Stanford, CA, USA 2 Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of T echnology and Emory University, Atlanta, GA, USA 3 Department of Radiology, Stanford University, Stanford, CA, USA 4 Department of Neurosurgery , Duke University, Durham, NC, USA

Physics-Informed Machine Learning Improves Detection of Head Impacts

Tue, 01 Nov 2022 00:00:00 +0000

Abstract

Concussions Physics-Informed Machine Learning Improves Detection of Head Impacts SAMUEL J. R AYMOND ,1 NICHOLAS J. C ECCHI,1 HOSSEIN VAHID ALIZADEH,1 ASHLYN A. C ALLAN,1 ELI RICE,2 YUZHE LIU,1 ZHOU ZHOU,1 MICHAEL ZEINEH,3 and D AVID B. C AMARILLO1,4,5 1Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; 2Stanford Center for Clinical Research, Stanford University, Stanford, CA 94305, USA; 3Department of Radiology, Stanford University, Stanford, CA 94305, USA; 4Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; and 5Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA (Received 31 August 2021; accepted 1 January 2022) Associate Editor Stefan M. Duma oversaw the review of this article. Abstract-In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Monitoring player impacts is vitally importan

Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts

Wed, 03 Aug 2022 00:00:00 +0000

Abstract

Building on previous findings that no single global linear model can describe the relationship between brain strain and kinematic features across diverse head impact types, this study examines whether piecewise multivariate linearity exists. Using K-means clustering, 3161 impacts from simulations, college football, mixed martial arts, and car crashes were partitioned into data-driven clusters. Within clusters, cumulative strain damage measure (CSDM, threshold 0.15) was regressed on kinematic features. K-means-based partitioning significantly improved regression accuracy compared to models without partitioning or those based solely on impact type. Additional analyses showed that partitioning by maximum angular acceleration at 4706 rad/s² yielded particularly strong piecewise linearity. These results support the existence of piecewise multivariate linear relationships and suggest improved strategies for rapid CSDM prediction.

Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis

Tue, 29 Mar 2022 00:00:00 +0000

Abstract

This study applies principal component analysis (PCA) to investigate spatial co-variation patterns of traumatic brain injury metrics, including maximum principal strain (MPS), MPS rate (MPSR), and their product, across four types of head impacts: simulations, football, mixed martial arts, and car crashes. PCA was used to decompose element-wise injury metrics into principal components, with the first principal component (PC1) explaining over 80% of the variance in all datasets. High PC1 coefficients were consistently observed in regions such as the corpus callosum and midbrain. A PCA-based machine learning head model (PCA-MLHM) was then developed to predict PC1 and inverse-transform to reconstruct whole-brain injury metrics. Compared with the previous MLHM, PCA-MLHM achieved similar MPS estimation accuracy while reducing model parameters by 74%, improving interpretability and computational efficiency.

The Presence of the Temporal Horn Exacerbates the Vulnerability of Hippocampus During Head Impacts

Tue, 22 Mar 2022 00:00:00 +0000

Abstract

The Presence of the Temporal Horn Exacerbates the Vulnerability of Hippocampus During Head Impacts Zhou Zhou 1,2*†, Xiaogai Li 2†, August G. Domel 1, Emily L. Dennis 3,4, Marios Georgiadis 4, Yuzhe Liu 1, Samuel J. Raymond 1, Gerald Grant 5,6, Svein Kleiven 2‡, David Camarillo 1,5,7‡ and Michael Zeineh 4*‡ 1Department of Bioengineering, Stanford University, Stanford, CA, United States, 2Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden, 3TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States, 4Department of Radiology, Stanford University, Stanford, CA, United States, 5Department of Neurosurgery, Stanford University, Stanford, CA, United States, 6Department of Neurology, Stanford University, Stanford, CA, United States, 7Department of Mechanical Engineering, Stanford University, Stanford, CA, United States Hippocampal injury is common in traumatic brain injury (TBI) patients, but the underlying pathogenesis rema

Toward a Comprehensive Delineation of White Matter Tract-Related Deformation

Wed, 01 Dec 2021 00:00:00 +0000

Abstract

Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal defor- mation along white matter (WM) fiber tracts (i.e., tract-oriented strain) recently has been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal defor- mation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury pre- dictability of these three newly proposed strain peaks along with the previously used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an ad- vanced mechanobiological understanding and enhanced computational predictability of brain injury.

Identifying Factors Associated with Head Impact Kinematics and Brain Strain in High School American Football via Instrumented Mouthguards

Fri, 13 Aug 2021 00:00:00 +0000

Abstract

This study investigates head impact kinematics and brain strain in high school American football athletes using an improved instrumented mouthguard (MiG2.0). Across 888 athlete exposures and 602 verified impacts, peak linear acceleration, angular velocity, angular acceleration, and 95th percentile maximum principal strain were quantified. Forward-direction impacts produced significantly higher kinematic magnitudes and brain strain than lateral or rearward impacts, and skill-position athletes experienced greater impact severity than line-position players. No significant differences were found for concussion history, helmet model, or team level. These results offer novel insight into real-world head impact exposure and resulting brain strain in high school athletes.

Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football

Tue, 06 Jul 2021 00:00:00 +0000

Abstract

Wearable devices have been shown to effectively measure head movement during impacts in sports such as American football. The device collects kinematic signals within a predefined time window when triggered by an impact, which are then used by finite element (FE) head models to compute brain strain and strain rate. To identify the minimum sufficient time window for FE analysis, 118 video-confirmed on-field football impacts recorded by the Stanford Instrumented Mouthguard were analyzed. Simulations using truncated kinematics were compared with the original full window. Considering individual differences in brain geometry, six representative brain models were included. Larger brains required longer time windows, but a pre-trigger of 40 ms and post-trigger of 70 ms yielded strain and strain-rate calculations not significantly different from the original 200 ms window. A total duration of approximately 110 ms is recommended for accurate modeling of football impacts.

Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation

Thu, 10 Jun 2021 00:00:00 +0000

Abstract

This study investigates the predictive power of various rotational kinematic factors for traumatic brain injury by analyzing their relationships with 95% maximum principal strain (MPS95). Using datasets from laboratory tests, American football, MMA, NHTSA crash tests, and NASCAR events, the study evaluates derivative orders, directions, and polynomial powers of rotational velocity and acceleration. Through regression models and statistical interpretation—including zero-order correlation, structure coefficients, commonality analysis, and dominance analysis—the study identifies angular acceleration, magnitude, and first-power features as the most predictive across most datasets, with notable exceptions in MMA and NASCAR impacts. The results highlight that predictive kinematic factors vary substantially across impact types.

The Relationship Between Brain Injury Criteria and Brain Strain Across Different Types of Head Impacts Can Be Different

Thu, 06 May 2021 00:00:00 +0000

Abstract

This study evaluates how brain injury criteria (BIC) relate to brain strain across diverse types of head impacts including sports collisions and automotive crash tests. Using linear regression models, the study analyzes 95% maximum principal strain, regional strain metrics, and cumulative strain damage across 18 BIC. Results demonstrate significantly different relationships between BIC and brain strain across datasets, showing that a given BIC value can correspond to different strain levels depending on impact type. These findings highlight the limitations of applying a BIC developed from one impact type to another and emphasize the need for context-specific injury risk estimation.

Rapid Estimation of Entire Brain Strain Using Deep Learning Models

Wed, 14 Apr 2021 00:00:00 +0000

Abstract

This study proposes a deep learning head model for rapid estimation of whole-brain strain during head impacts. A five-layer deep neural network with feature engineering was trained on 2511 head impacts obtained from finite element simulations and on-field measurements in college football and mixed martial arts. The model predicts the maximum principal strain (Green–Lagrange) for all brain elements in less than 0.001 seconds, achieving an average RMSE of 0.022 with a standard deviation of 0.001 across repeated training runs. This approach enables fast, accurate estimation of brain deformation and supports potential clinical use in real-time traumatic brain injury detection.

A new open-access platform for measuring and sharing mTBI data

Mon, 05 Apr 2021 00:00:00 +0000

Abstract

1 Vol.:(0123456789)Scientific Reports | (2021) 11:7501 | https://doi.org/10.1038/s41598-021-87085-2 www.nature.com/scientificreports A new open‑access platform for measuring and sharing mTBI data August G. Domel1,12, Samuel J. Raymond1,12*, Chiara Giordano1,12, Yuzhe Liu1, Seyed Abdolmajid Yousefsani1, Michael Fanton2, Nicholas J. Cecchi1, Olga Vovk3, Ileana Pirozzi1, Ali Kight1, Brett Avery4, Athanasia Boumis4, Tyler Fetters3, Simran Jandu4, William M. Mehring4, Sam Monga5,6, Nicole Mouchawar7, India Rangel4, Eli Rice4, Pritha Roy4, Sohrab Sami4, Heer Singh4, Lyndia Wu1,8, Calvin Kuo2,9, Michael Zeineh7, Gerald Grant10,11 & David B. Camarillo1,2,11 Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high‑risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. H

Emergency ventilator for COVID-19

Wed, 30 Dec 2020 00:00:00 +0000

Abstract

RESEA RCH ARTICL E A low-cost, highly functional, emergency use ventilator for the COVID-19 crisis Samuel J. Raymond ID 1 *, Sam Baker 2 , Yuzhe Liu 1 , Mauricio J. Bustamante 3 , Brett Ley 4 , Michael J. Horzewski 5 , David B. Camarillo 1,5,6,7☯ , David N. Cornfield 8☯ 1 Department of Bioenginee ring, Stanford University , Stanford, CA, United States of America, 2 Department of Comp arative Medicine, Stanford University , Stanford, CA, United States of America, 3 Department of Electrica l Engineeri ng and Computer Science, UC Berkeley, CA, United States of America, 4 Kaiser Pulmon ology and Critical Care, Fontana, CA, United States of America, 5 O2U Inc., Stanfor d, CA, United States of America, 6 Department of Mechanic al Engineering, Stanford University, Stanfor d, CA, United States of America, 7 Department of Neurosur gery, Stanfor d University, Stanford , CA, United States of America, 8 Department of Pediat rics-Pulm onary Medicine, Stanford University , Stanford, CA, United State

Concussion and the severity of head impacts in mixed martial arts

Tue, 01 Dec 2020 00:00:00 +0000

Abstract

Concern about the consequences of head impacts in US football has motivated researchers to investigate and develop instrumentation to measure the severity of these impacts. However, the severity of head impacts in unhelmeted sports is largely unknown as miniaturised sensor technology has only recently made it possible to measure these impacts in vivo. The objective of this study was to measure the linear and angular head accelerations in impacts in mixed martial arts, and correlate these with concussive injuries. Thirteen mixed martial arts fighters were fitted with the Stanford instrumented mouthguard (MiG2.0) participated in this study. The mouthguard recorded linear acceleration and angular velocity in 6 degrees of freedom. Angular acceleration was calculated by differentiation. All events were video recorded, time stamped and reported impacts confirmed. A total of 451 verified head impacts above 10g were recorded during 19 sparring events (n = 298) and 11 competitive events (n = 153). The average resultant linear acceleration was 38.0624.3g while the average resultant angular acceleration was 256761739 rad/s2. The competitive bouts resulted in five concussions being diagnosed by a medical doctor. The average resultant acceleration (of the impact with the highest angular acceleration) in these bouts was 86.7618.7g and 756163438 rad/s2. The average maximum Head Impact Power was 20.6kW in the case of concussion and 7.15kW for the uninjured athletes. In conclusion, the study recorded novel data for sub-concussive and concussive impacts. Events that resulted in a concussion had an average maximum angular acceleration that was 24.7% higher and an average maximum Head Impact Power that was 189% higher than events where there was no injury. The findings are significant in understanding the human tolerance to short-duration, high linear and angular accelerations.

Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts

Fri, 18 Sep 2020 00:00:00 +0000

Abstract

Instrumented mouthguards are widely used to measure head kinematics due to the rigid coupling of upper dentition and skull. This study validates and compares five commonly used instrumented mouthguards using pneumatic impacts delivered to a Hybrid III headform. Results show all devices accurately measure peak angular acceleration, angular velocity, and brain injury criteria values. Mouthguards with sufficiently long sampling windows also provide accurate inputs for a convolutional neural network–based brain model to compute brain strain. Measurement accuracy varies with impact location but is largely insensitive to impact velocity.

Dynamic Blood-Brain Barrier Regulation in Mild Traumatic Brain Injury

Wed, 01 Jan 2020 00:00:00 +0000

Abstract

Whereas the diagnosis of moderate and severe traumatic brain injury (TBI) is readily visible on current medical imaging paradigms (magnetic resonance imaging [MRI] and computed tomography [CT] scanning), a far greater challenge is associated with the diagnosis and subsequent management of mild TBI (mTBI), especially concussion which, by definition, is characterized by a normal CT. To investigate whether the integrity of the blood-brain barrier (BBB) is altered in a high- risk population for concussions, we studied professional mixed martial arts (MMA) fighters and adolescent rugby players. Additionally, we performed the linear regression between the BBB disruption defined by increased gadolinium contrast extravasation on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on MRI and multiple biomechanical parameters indicating the severity of impacts recorded using instrumented mouthguards in professional MMA fighters. MMA fighters were examined pre-fight for a baseline and again within 120 h post-competitive fight, whereas rugby players were examined pre-season and again post-season or post-match in a subset of cases. DCE-MRI, serological analysis of BBB biomarkers, and an analysis of instrumented mouthguard data, was performed. Here, we provide pilot data that demonstrate disruption of the BBB in both professional MMA fighters and rugby players, dependent on the level of exposure. Our data suggest that biomechanical forces in professional MMA and adolescent rugby can lead to BBB disruption. These changes on imaging may serve as a biomarker of exposure of the brain to repetitive subconcussive forces and mTBI.

A theoretical model of the shrinking metal tubes

Fri, 15 Jun 2018 00:00:00 +0000

Abstract

This study proposes the first theoretical model for shrinking metal tubes. According to the relation between the actual die radius and a critical value determined by tube geometry and conical angle, three deformation modes are identified. Experimental data and numerical simulations across a wide parameter range validate the model. Predictions of compressional force, reduced radius, and equivalent plastic strain agree with FEM results for die angles ≤40° and radius–thickness ratio ≥10. Influences of friction, dynamic effects, unsteady deformation stages, and conical angles are analyzed. Comparisons with expansion tubes reveal that shrinking exhibits higher energy absorption at the same deformation ratio, and an optimization is provided to maximize SEA.

A theoretical model of the inversion tube over a conical die

Sat, 03 Feb 2018 00:00:00 +0000

Abstract

As an important impact energy absorber and a method to produce double-wall tubes, the inversion of metal tubes over a conical die is studied. Inspired by FEM-derived deformation profiles, a theoretical model is proposed in which energy is dissipated through meridional bending and compression, and circumferential expansion. FEM simulations over a wide parameter range validate the model, showing accurate predictions for compressional force and final circular radius. Energy dissipation mechanisms along the tube axis are analyzed, and deformation in the thickness direction is identified as the major source of deviation. Effects of die radius, friction, and dynamic factors are also discussed.

An improved two-arcs deformational theoretical model of the expansion tubes

Thu, 24 Aug 2017 00:00:00 +0000

Abstract

In this study, the theoretical model of the expansion metal tubes reported in [1] is improved by considering the die radius r_die. After introducing the critical die radius r_die determined by the tube radius–thickness ratio and conical die angle, the tube expansion is classified into three deformation modes. Detailed theoretical derivations are provided to extend the applicability of the model. Compared with experiment and FEM results, the model accurately predicts the steady compressional force for tube radius–thickness ratio ≥ 20 and die angle ≤ 40°. The expanded tube radius is also accurately predicted, which is important in metal forming. Finally, the energy absorption capability of expansion tubes is optimized by including material properties and friction effects.

A study of woodpecker's pecking process and the impact response of its brain

Fri, 26 May 2017 00:00:00 +0000

Abstract

Head impact injuries always cause severe diseases and deaths of human. In contrast, woodpeckers are able to withstand fierce impact during pecking without brain damage. In this study, a trunk-like piezoelectric force sensor was used to measure pecking impulse, and the corresponding pecking process was recorded by a high-speed camera. The woodpecker head was scanned by micro-computed tomography and the pecking process was simulated by the material point method. The simulated impact impulse matches the experimental result, and the energy transmission and brain impact responses are discussed. Head Injury Criterion and Average Resultant Acceleration for woodpeckers are proposed by scaling analyses to measure the possibility of brain damage, and the influence of higher pecking velocities and the rhamphotheca layer in the beak is analyzed.

Analysis of the energy absorption properties for tubular structure under axial compression of different failure models

Sat, 01 Oct 2016 00:00:00 +0000

Abstract

Tubular structures such as circular tubes and square tubes under axial compression are widely used as structural components in engineering applications. Considering differences in tube geometries, boundary conditions and material properties, this review classifies tube failure under axial compression into five mechanisms: progressive buckling, global buckling, inversion, expansion and splitting. Theoretical, experimental and numerical studies on these failure modes are reviewed, and the mechanical responses and energy absorption properties are compared and discussed.

A theoretical study of the expansion metal tubes

Tue, 17 May 2016 00:00:00 +0000

Abstract

A theoretical model is proposed to analyze the expansion of metal tubes over a conical mandrel die, based on energy conservation. Unlike previous momentum-based analyses, this model considers that the external compressive work is dissipated through circumferential expansion, meridional bending, and friction during steady-state deformation. Predictions are provided for steady compression force and expanded tube radius for rigid–perfectly-plastic materials, with and without strain hardening. Comparisons with experimental data and finite element simulations show that the model accurately predicts steady compression force and expanded radius. These results highlight the advantages of expansion tubes—long stroke, stable reaction force, and low sensitivity to inclined loading—making them suitable for energy absorption applications.

How does a woodpecker work? An impact dynamics approach

Tue, 12 May 2015 00:00:00 +0000

Abstract

To understand how a woodpecker is able to accelerate its head to such a high velocity in a short amount of time, a multi-rigid-segment model of a woodpecker’s body is established in this study. Based on skeletal specimens and several pecking videos, the parameters of a three-degree-of-freedom model are determined. The high head velocity is found to result from a whipping effect influenced by muscle torque and tendon stiffness. By comparing hinged-rod and rigid-rod responses, three dynamic modes are identified, with Mode II generating the highest free-end velocity. The model is further generalized to a multi-hinge system, showing that free-end velocity increases with the number of hinges. The effects of damping and mass distribution are also discussed.

Response of Woodpecker’s Head during Pecking Process Simulated by Material Point Method

Wed, 22 Apr 2015 00:00:00 +0000

Abstract

Prevention of brain injury in woodpeckers under high deceleration during the pecking process has long been a biomechanical question. Although several hypotheses have been proposed, the functional role of the hyoid bone has not been fully elucidated. In this work, a material point method (MPM) model of the woodpecker head is developed based on micro-CT images to study the relationship between head impact response and hyoid bone structure. The maximum shear stress in the brainstem (SSS) is used as the injury indicator. The motion of the first cervical vertebra is found to be the main cause of brainstem shear stress. The study shows that the hyoid bone reduces SSS, increases head rigidity, and suppresses post-impact oscillations of internal structures. The mechanical mechanism is discussed and the influence of muscle properties is examined.