Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Abstract

Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

Multi-tract multi-symptom relationships in pediatric concussion

“…Multi-tract relationships may be driven by the metabolic demands imposed by the network structure of the brain, which is known to predict the course of several brain diseases, 42 by biomechanical constraints imposed by the skull and other structures exposing certain areas to more shearing strain, 43 or by both factors simultaneously. 44 These possibilities need to be tested further.…”

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

“…The injury risk curves that express the likelihood of sustaining CC depending on the different FE-derived injury predictors were obtained by performing univariate logistic regression, a technique that has been broadly used to determine injury tolerances based on experimental data and predict the outcome of TBI (Shreiber et al, 1997;Takhounts et al, 2003;Kleiven, 2007;Steyerberg et al, 2008;Cai et al, 2018;Anderson et al, 2020;Hajiaghamemar et al, 2020). We selected a logistic regression classifying method due to the binary nature (CC or no CC) of the outcome of the CCI data, obtained by means of the post-injury MR scans and histology assessments (De Kegel et al, 2021).…”

“…The accuracy of the injury metric-specific prediction of CC likelihood was evaluated using a leave-one-out cross-validation (LOOCV). We selected a LOOCV framework because of its low bias and the limited size of our dataset (14 samples) (Beleites et al, 2005;Cai et al, 2018;Anderson et al, 2020). In order to perform an objective comparison between the performances of the univariate logistic regression classifiers, we computed the LOOCV accuracy, sensitivity, and specificity.…”

A Review of Validation Methods for the Intracranial Response of FEHM to Blunt Impacts

“…Overall, the sonomicrometry technique allows the relative displacements to be presented along all three anatomical axes, where biplanar X-ray techniques are limited to monitoring two directions. This work has been applied to investigations into the advancement and application of the GHBMC head model [50,51].…”