Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts
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by
Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
2021
Abstract
Strain and strain rate are effective traumatic brain injury predictors.
Kinematics-based models estimating these metrics suffer from significant
different distributions of both kinematics and the injury metrics across head
impact types. To address this, previous studies focus on the kinematics but not
the injury metrics. We have previously shown the kinematic features vary
largely across head impact types, resulting in different patterns of brain
deformation. This study analyzes the spatial distribution of brain deformation
and applies principal component analysis (PCA) to extract the representative
patterns of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and
MPSXMPSR) in four impact types (simulation, football, mixed martial arts and
car crashes). We apply PCA to decompose the patterns of the injury metrics for
all impacts in each impact type, and investigate the distributions among brain
regions using the first principal component (PC1). Furthermore, we developed a
deep learning head model (DLHM) to predict PC1 and then inverse-transform to
predict for all brain elements. PC1 explained >80% variance on the datasets.
Based on PC1 coefficients, the corpus callosum and midbrain exhibit high
variance on all datasets. We found MPSXMPSR the most sensitive metric on which
the top 5% of severe impacts further deviates from the mean and there is a
higher variance among the severe impacts. Finally, the DLHM reached mean
absolute errors of <0.018 for MPS, <3.7 (1/s) for MPSR and <1.1 (1/s) for
MPSXMPSR, much smaller than the injury thresholds. The brain injury metric in a
dataset can be decomposed into mean components and PC1 with high explained
variance. The brain dynamics decomposition enables better interpretation of the
patterns in brain injury metrics and the sensitivity of brain injury metrics
across impact types. The decomposition also reduces the dimensionality of DLHM.
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