Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation

Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the lack of generalizability caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose brain deformation estimators that integrates unsupervised domain adaptation with a deep neural network to predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With 12,780 simulated head impacts, we performed unsupervised domain adaptation on on-field head impacts from 302 college football (CF) impacts and 457 mixed martial arts (MMA) impacts using domain regularized component analysis (DRCA) and cycle-GAN-based methods. The new model improved the MPS/MPSR estimation accuracy, with the DRCA method significantly outperforming other domain adaptation methods in prediction accuracy (p<0.001): MPS RMSE: 0.027 (CF) and 0.037 (MMA); MPSR RMSE: 7.159 (CF) and 13.022 (MMA). On another two hold-out test sets with 195 college football impacts and 260 boxing impacts, the DRCA model significantly outperformed the baseline model without domain adaptation in MPS and MPSR estimation accuracy (p<0.001). The DRCA domain adaptation reduces the MPS/MPSR estimation error to be well below TBI thresholds, enabling accurate brain deformation estimation to detect TBI in future clinical applications

Substantia Nigra Volume Dissociates Bradykinesia and Rigidity from Tremor in Parkinson’s Disease: A 7 Tesla Imaging Study

Background: In postmortem analysis of late stage Parkinson’s disease (PD) neuronal loss in the substantial nigra (SN) correlates with the antemortem severity of bradykinesia and rigidity, but not tremor. Objective: To investigate the relationship between midbrain nuclei volume as an in vivo biomarker for surviving neurons in mild-to-moderate patients using 7.0 Tesla MRI. Methods: We performed ultra-high resolution quantitative susceptibility mapping (QSM) on the midbrain in 32 PD participants with less than 10 years duration and 8 healthy controls. Following blinded manual segmentation, the individual volumes of the SN, subthalamic nucleus, and red nucleus were measured. We then determined the associations between the midbrain nuclei and clinical metrics (age, disease duration, MDS-UPDRS motor score, and subscores for bradykinesia/rigidity, tremor, and postural instability/gait difficulty). Results: We found that smaller SN correlated with longer disease duration (r = –0.49, p = 0.004), more severe MDS-UPDRS motor score (r = –0.42, p = 0.016), and more severe bradykinesia-rigidity subscore (r = –0.47, p = 0.007), but not tremor or postural instability/gait difficulty subscores. In a hemi-body analysis, bradykinesia-rigidity severity only correlated with SN contralateral to the less-affected hemi-body, and not contralateral to the more-affected hemi-body, possibly reflecting the greatest change in dopamine neuron loss early in disease. Multivariate generalized estimating equation model confirmed that bradykinesia-rigidity severity, age, and disease duration, but not tremor severity, predicted SN volume

Substantia Nigra Volume Dissociates Bradykinesia and Rigidity from Tremor in Parkinson’s Disease: A 7 Tesla Imaging Study

Background: In postmortem analysis of late stage Parkinson’s disease (PD) neuronal loss in the substantial nigra (SN) correlates with the antemortem severity of bradykinesia and rigidity, but not tremor. Objective: To investigate the relationship between midbrain nuclei volume as an in vivo biomarker for surviving neurons in mild-to-moderate patients using 7.0 Tesla MRI. Methods: We performed ultra-high resolution quantitative susceptibility mapping (QSM) on the midbrain in 32 PD participants with less than 10 years duration and 8 healthy controls. Following blinded manual segmentation, the individual volumes of the SN, subthalamic nucleus, and red nucleus were measured. We then determined the associations between the midbrain nuclei and clinical metrics (age, disease duration, MDS-UPDRS motor score, and subscores for bradykinesia/rigidity, tremor, and postural instability/gait difficulty). Results: We found that smaller SN correlated with longer disease duration (r = –0.49, p = 0.004), more severe MDS-UPDRS motor score (r = –0.42, p = 0.016), and more severe bradykinesia-rigidity subscore (r = –0.47, p = 0.007), but not tremor or postural instability/gait difficulty subscores. In a hemi-body analysis, bradykinesia-rigidity severity only correlated with SN contralateral to the less-affected hemi-body, and not contralateral to the more-affected hemi-body, possibly reflecting the greatest change in dopamine neuron loss early in disease. Multivariate generalized estimating equation model confirmed that bradykinesia-rigidity severity, age, and disease duration, but not tremor severity, predicted SN volume

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

Protective headgear effects measured in the laboratory may not always translate to the field. In this study, we evaluated the impact attenuation capabilities of a commercially available padded helmet shell cover in the laboratory and field. In the laboratory, we evaluated the efficacy of the padded helmet shell cover in attenuating impact magnitude across six impact locations and three impact velocities when equipped to three different helmet models. In a preliminary on-field investigation, we used instrumented mouthguards to monitor head impact magnitude in collegiate linebackers during practice sessions while not wearing the padded helmet shell covers (i.e., bare helmets) for one season and whilst wearing the padded helmet shell covers for another season. The addition of the padded helmet shell cover was effective in attenuating the magnitude of angular head accelerations and two brain injury risk metrics (DAMAGE, HARM) across most laboratory impact conditions, but did not significantly attenuate linear head accelerations for all helmets. Overall, HARM values were reduced in laboratory impact tests by an average of 25% at 3.5 m/s (range: 9.7 - 39.6%), 18% at 5.5 m/s (range: -5.5 - 40.5%), and 10% at 7.4 m/s (range: -6.0 - 31.0%). However, on the field, no significant differences in any measure of head impact magnitude were observed between the bare helmet impacts and padded helmet impacts. Further laboratory tests were conducted to evaluate the ability of the padded helmet shell cover to maintain its performance after exposure to repeated, successive impacts and across a range of temperatures. This research provides a detailed assessment of padded helmet shell covers and supports the continuation of in vivo helmet research to validate laboratory testing results.Comment: 49 references, 8 figure

Classification of head impacts based on the spectral density of measurable kinematics

Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are less accurate across the variety of impacts that patients may undergo. We investigated the spectral characteristics of different head impact types with kinematics classification. Data was analyzed from 3,262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football), reaching a median accuracy of 96% over 1,000 random partitions of training and test sets. To test the classifier on data from different measurement devices, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards with the classifier reaching over 96% accuracy. The most important features in the classification included both low-frequency and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low-frequency and high-frequency ranges (e.g., the spectral densities of MMA impacts were higher in high-frequency range than in the low-frequency range). Finally, with the classifier, type-specific, nearest-neighbor regression models were built for 95th percentile maximum principal strain, 95th percentile maximum principal strain in corpus callosum, and cumulative strain damage (15th percentile). This showed a generally higher R2-value than baseline models. The classifier enables a better understanding of the impact kinematics in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation. Key words: traumatic brain injury, head impacts, classification, impact kinematicsComment: 16 pages, 5 figure

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

Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95\% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude, and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics in three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts

The ENIGMA sports injury working group - an international collaboration to further our understanding of sport-related brain injury

Sport-related brain injury is very common, and the potential long-term effects include a wide range of neurological and psychiatric symptoms, and potentially neurodegeneration. Around the globe, researchers are conducting neuroimaging studies on primarily homogenous samples of athletes. However, neuroimaging studies are expensive and time consuming, and thus current findings from studies of sport-related brain injury are often limited by small sample sizes. Further, current studies apply a variety of neuroimaging techniques and analysis tools which limit comparability among studies. The ENIGMA Sports Injury working group aims to provide a platform for data sharing and collaborative data analysis thereby leveraging existing data and expertise. By harmonizing data from a large number of studies from around the globe, we will work towards reproducibility of previously published findings and towards addressing important research questions with regard to diagnosis, prognosis, and efficacy of treatment for sport-related brain injury. Moreover, the ENIGMA Sports Injury working group is committed to providing recommendations for future prospective data acquisition to enhance data quality and scientific rigor

Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment