Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury

Abstract

With the growing rate of traumatic brain injury (TBI), there is an increasing interest in validated tools to predict and prevent brain injuries. Finite element models (FEM) are valuable tools to estimate tissue responses, predict probability of TBI, and guide the development of safety equipment. In this study, we developed and validated an anisotropic pig brain multi-scale FEM by explicitly embedding the axonal tract structures and utilized the model to simulate experimental TBI in piglets undergoing dynamic head rotations. Binary logistic regression, survival analysis with Weibull distribution, and receiver operating characteristic curve analysis, coupled with repeated k-fold cross-validation technique, were used to examine 12 FEM-derived metrics related to axonal/brain tissue strain and strain rate for predicting the presence or absence of traumatic axonal injury (TAI). All 12 metrics performed well in predicting of TAI with prediction accuracy rate of 73-90%. The axonal-based metrics outperformed their rival brain tissue-based metrics in predicting TAI. The best predictors of TAI were maximum axonal strain times strain rate (MASxSR) and its corresponding optimal fraction-based metric (AF-MASxSR 7.5) that represents the fraction of axonal fibers exceeding MASxSR of 7.5 s −1. The thresholds compare favorably with tissue tolerances found in in-vitro/in-vivo measurements in the literature. In addition, the damaged volume fractions (DVF) predicted using the axonal-based metrics, especially MASxSR (DVF = 0.05-4.5%), were closer to the actual DVF obtained from histopathology (AIV = 0.02-1.65%) in comparison with the DVF predicted using the brain-related metrics (DVF = 0.11-41.2%). The methods and the results from this study can be used to improve model prediction of TBI in humans.

Using Serum Amino Acids to Predict Traumatic Brain Injury: A Systematic Approach to Utilize Multiple Biomarkers

“…The collection of fresh and unfixed brain and measurements of AA levels in the brain tissue were only performed for the Validation Dataset (n = 9 including 5 RNR TBIs and 4 shams). Both of the injury models used in this study have previously showed to cause mitochondrial dysfunction [6,7] and brain damage such as brain contusion, intracranial hemorrhage, and/or diffuse axonal damage as determined by MRI imaging analysis and pathology analysis [32,[53][54][55]. However, the animals typically recovered quickly, reached the baseline physiology within 1-5 h post injury.…”

“…This approach may cause overfitting, dependency of the model to the development dataset, and biased assessment of the prediction performance, especially in multivariate analysis with small size datasets. Repeated random training-testing splitting and repeated k-fold cross-validation techniques, in which data are repeatedly split into independent and non-overlapping development and validation datasets, can be used instead of "all in" approach to prevent overfitting and provide more realistic assessment of the accuracy performance of the biomarker models in diagnosing and predicting the outcomes of new patient(s)/subject(s) [32]. Repeated k-fold cross-validation has superiority over repeated random training-testing splitting because the number of times that each data point is used in the training and testing datasets is the same for all data points.…”

“…The regression model which resulted in the lowest CAIC represents the best fit model with optimal variables. In addition to using the best subset selection technique, the multivariate logistic regression analysis was combined with repeated k-fold cross validation (CV) technique to reduce possible overfitting and dependency of the optimal fitted model to the Development Dataset and thus, possible bias in the selection of the AAs for the optimal multi-AA regression biomarker model [32]. Therefore, instead of using all 25 uninjured samples and 25 injured serum samples as the Development Dataset in a single analysis to find the optimal multi-AAs, the data was repeatedly split into k (k = 5) equal-sized sub samples, and the process of finding the best set of AAs was repeated for 25 iterations (5 repetitions of 5-fold CV).…”

“…An RNR TBI model, as shown in Figure 1, utilizing a well-characterized rotational acceleration device [1,7,32,53,55] was used to produce a single rapid sagittal head rotation causing purely inertial diffuse axonal injury. This injury mechanism produces unconsciousness, sustained cognitive dysfunction, decreased CBF, bilateral diffuse axonal and hemorrhagic injury.…”

“…This injury mechanism produces unconsciousness, sustained cognitive dysfunction, decreased CBF, bilateral diffuse axonal and hemorrhagic injury. As described previously [1,7,32,53,55], while the animal is under anesthesia, the head is secured to the inertial injury apparatus by a snout clamp. Immediately prior to injury, anesthesia, supplemental oxygen, and mechanical ventilation were withdrawn, so the piglet could breathe spontaneously on room air.…”