Development and validation of an atlas-based finite element brain model

Abstract

Traumatic brain injury is a leading cause of disability and injury-related death. To enhance our ability to prevent such injuries, brain response can be studied using validated finite element (FE) models. In the current study, a high-resolution, anatomically accurate FE model was developed from the International Consortium for Brain Mapping brain atlas. Due to wide variation in published brain material parameters, optimal brain properties were identified using a technique called Latin hypercube sampling, which optimized material properties against three experimental cadaver tests to achieve ideal biomechanics. Additionally, falx pretension and thickness were varied in a lateral impact variation. The atlas-based brain model (ABM) was subjected to the boundary conditions from three high-rate experimental cadaver tests with different material parameter combinations. Local displacements, determined experimentally using neutral density targets, were compared to displacements predicted by the ABM at the same locations. Error between the observed and predicted displacements was quantified using CORrelation and Analysis (CORA), an objective signal rating method that evaluates the correlation of two curves. An average CORA score was computed for each variation and maximized to identify the optimal combination of parameters. The strongest relationships between CORA and material parameters were observed for the shear parameters. Using properties obtained through the described multiobjective optimization, the ABM was validated in three impact configurations and shows good agreement with experimental data. The final model developed in this study consists of optimized brain material properties and was validated in three cadaver impacts against local brain displacement data.

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

“…Another study correlated simulated strain patterns from a generic model with injury findings from individual neuroimages for three reconstructed bicycle accidents, where model and images differed in size and shape (Fahlstedt et al 2015). An image-atlas-based model represented “averaged” neuroimages but not specific individuals (Miller et al 2016). Further work is necessary to understand the implications of using generic vs. individualized model and/or neuroimages in injury characterization; however, this is beyond the scope of this study.…”

“…Sophisticated head models continue to emerge with more anatomical details (Mao et al 2013), representing subject-specific anatomies (Ji et al 2015), and characterizing anisotropic material properties of the white matter (WM) (Sahoo et al 2014; Giordano and Kleiven 2014b). Lately, there are also efforts to integrate information from neuroimages (Fahlstedt et al 2015; Miller et al 2016), e.g., WM structural anisotropy (Wright and Ramesh 2012; Garimella and Kraft 2016), into biomechanical modeling for injury analysis. This aligns well with in vitro studies that suggest strain component along axonal longitudinal direction responsible for axonal injury (Cullen and LaPlaca 2006).…”

A computational study of invariant I5 in a nearly incompressible transversely isotropic model for white matter

“…Applications of the MITI model to white matter showed that this model was capable of properly capturing tissue behaviors, especially for shear in the large-strain regime. Future studies include applications of the model to biaxial experiments of cardiovascular tissues (Sacks, 2000; Sacks, 1999) and indentation/compression tests (Namani et al, 2012; Rashid et al, 2013), and incorporating the white matter anisotropy to improve injury predictions in realistic human head FE models (Giordano and Kleiven, 2014; Miller et al, 2016; Zhang et al, 2004; Zhao et al, 2016). …”

“…However, recent analytical and experimental studies (Destrade et al, 2013; Feng et al, 2013) have shown that at least two anisotropic invariants, I 4 and the fifth invariant, I 5 , are needed to fully characterize the transverse isotropy. To understand the behavior of white matter for large deformations in injury-level loading conditions, finite element (FE) simulations are used (Bayly et al, 2005; Iwata et al, 2004; Ji et al, 2015; Miller et al, 2016; Zhang et al, 2004). However, most of the FE implementations of hyperelasticity are based on a single anisotropic invariant, I 4 , which contains only the fiber stretch information (Holzapfel, 2000; Lu and Zhang, 2005; Swedberg et al, 2014), with the corresponding anisotropic component described in a quadratic form (Ning et al, 2006) or in an exponential form (Chatelin et al, 2012; Gasser et al, 2006).…”

Validation performance comparison for finite element models of the human brain

“…This study examined the following six brain FE models: the atlas-based brain model (ABM) (Miller et al 2016), Simulated Injury Monitor (SIMon) (Takhounts et al 2003), Global Human Body Models Consortium (GHBMC) head model (Mao et al 2013), Total Human Model for Safety (THUMS) head model (Kimpara et al 2006), Kungliga Tekniska Högskolan (KTH) model (Kleiven and von Holst 2002, Kleiven 2007), and the Dartmouth Head Injury Model (DHIM) (Ji et al 2015). These six models were chosen based on model access and availability of validation results.…”