Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability

Abstract

Computational models incorporating anisotropic features of brain tissue have become a valuable tool for studying the occurrence of traumatic brain injury. The tissue deformation in the direction of white matter tracts (axonal strain) was repeatedly shown to be an appropriate mechanical parameter to predict injury. However, when assessing the reliability of axonal strain to predict injury in a population, it is important to consider the predictor sensitivity to the biological inter-subject variability of the human brain. The present study investigated the axonal strain response of 485 white matter subject-specific anisotropic finite element models of the head subjected to the same loading conditions. It was observed that the biological variability affected the orientation of the preferential directions (coefficient of variation of 39.41% for the elevation angle—coefficient of variation of 29.31% for the azimuth angle) and the determination of the mechanical fiber alignment parameter in the model (gray matter volume 55.55–70.75%). The magnitude of the maximum axonal strain showed coefficients of variation of 11.91%. On the contrary, the localization of the maximum axonal strain was consistent: the peak of strain was typically located in a 2 cm3 volume of the brain. For a sport concussive event, the predictor was capable of discerning between non-injurious and concussed populations in several areas of the brain. It was concluded that, despite its sensitivity to biological variability, axonal strain is an appropriate mechanical parameter to predict traumatic brain injury.

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

“…Meanwhile, Kleiven (2007) used the KTH isotropic FE model to examine the strain in different brain regions, including the midbrain, brainstem, and thalamus, and the relationships between different injury predictors. Later, Giordano et al (2017) used the KTH anisotropic FE model to compare the performance of regional maximum axonal strain (rMAS) against rMPS in different brain regions. Zhao et al (2017) extended Giordano and Kleiven's work using rMAS and sampled across all deep white matter regions of interest and neural tracts to determine regional vulnerabilities.…”

“…It was not possible to acquire such information based on our historical dataset. However, others have found that when examining how different brains respond to the same loading conditions, the MPS experienced by the brain had a coefficient of variation of 2.33% (Giordano et al, 2017). In our model, we minimized the error introduced by uncertainty in head kinematics by using the most current kinematic loading conditions (Sanchez et al, 2019).…”

Preliminary evidence from a prospective DTI study suggests a posterior‐to‐anterior pattern of recovery in college athletes with sports‐related concussion

“…The collection of accelerometer data for FEM analyses has progressed from laboratory‐based impact reconstructions to actual helmeted and unhelmeted impacts from subject data (McAllister et al, ; Patton, McIntosh, & Kleiven, ; Viano et al, ; Zhang, Yang, & King, ). Additionally, recent research incorporated WM anisotropy from DTI scans into the FEM (Giordano, Zappalà, & Kleiven, ). Collectively, these studies agree that peak strains and stresses of concussive injury are located near the brainstem in central brain areas such as the midbrain, thalamus, and corpus callosum regardless of the actual head impact location.…”

Embedded finite elements for modeling axonal injury

“…Figure 1 illustrates the white matter fiber tracks in the brain of a healthy subject. The physics and mechanics of axonal fiber tracts is important in various fields including understanding axonal injury [5], [6], predicting the source of electrical signals [7], [8], and learning about brain structure-function relationships [9]- [11]. Recently, we have been using the embedded element approach to understand and predict axonal injury due to impact or blast loading to the head [12]- [15].…”