93 research outputs found
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
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
Soliton Dynamics in Computational Anatomy
Computational anatomy (CA) has introduced the idea of anatomical structures
being transformed by geodesic deformations on groups of diffeomorphisms. Among
these geometric structures, landmarks and image outlines in CA are shown to be
singular solutions of a partial differential equation that is called the
geodesic EPDiff equation. A recently discovered momentum map for singular
solutions of EPDiff yields their canonical Hamiltonian formulation, which in
turn provides a complete parameterization of the landmarks by their canonical
positions and momenta. The momentum map provides an isomorphism between
landmarks (and outlines) for images and singular soliton solutions of the
EPDiff equation. This isomorphism suggests a new dynamical paradigm for CA, as
well as new data representation.Comment: published in NeuroImag
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
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 Effect of Negative and Positive Emotionality on Associative Memory: An fMRI Study
In general, emotion is known to enhance memory processes. However, the effect of emotion on associative memory and the underling neural mechanisms remains largely unexplored. In this study, we explored brain activation during an associative memory task that involved the encoding and retrieval of word and face pairs. The word and face pairs consisted of either negative or positive words with neutral faces. Significant hippocampal activation was observed during both encoding and retrieval, regardless of whether the word was negative or positive. Negative and positive emotionality differentially affected the hemodynamic responses to encoding and retrieval in the amygdala, with increased responses during encoding negative word and face pairs. Furthermore, activation of the amygdala during encoding of negative word and neutral face pairs was inversely correlated with subsequent memory retrieval. These findings suggest that activation of the amygdala induced by negative emotion during encoding may disrupt associative memory performance
Local Signal Time-Series during Rest Used for Areal Boundary Mapping in Individual Human Brains
It is widely thought that resting state functional connectivity likely reflects functional interaction among brain areas and that different functional areas interact with different sets of brain areas. A method for mapping areal boundaries has been formulated based on the large-scale spatial characteristics of regional interaction revealed by resting state functional connectivity. In the present study, we present a novel analysis for areal boundary mapping that requires only the signal timecourses within a region of interest, without reference to the information from outside the region. The areal boundaries were generated by the novel analysis and were compared with those generated by the previously-established standard analysis. The boundaries were robust and reproducible across the two analyses, in two regions of interest tested. These results suggest that the information for areal boundaries is readily available inside the region of interest
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