Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

Abstract

Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20–40 rad/s), durations (30–60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports

“…157 Further, the UVA-Axon model has been used to study the functional changes that occur from TBI by evaluating the loss of functional network efficiency from localized regions of high strain. 8 The second CAB-20MSym model 62 was developed based on a template brain image from 20 young, healthy male participants. 130 It was developed to integrate into the Registration-Based Morphing (RBM) technique to automatically generate subjectspecific FE brain models through registration to preserve both external and internal neuroanatomical characteristics.…”

“…1 a). Together with selectively reduced integration and hourglass control, linear hexahedral elements provide sufficient accuracy and efficiency using explicit time integration in LS-Dyna (Ansys, Canonsburg, PA) 54 , 62 , 80 , 99 , 114 , 122 , 135 , 144 , 155 , 166 or ABAQUS (Dassault Systèmes, France) 149 , 172 solvers (Fig. 1 b).…”

“…Constitutive model assumptions and parameters are often periodically updated as new experimental data become available. Both linear viscoelastic 80 , 99 , 114 , 122 , 144 , 166 and nonlinear, visco-hyperelastic 54 , 62 , 99 , 135 , 144 , 149 , 155 , 172 material properties have been commonly used (Fig. 1 c).…”

Subject-Specific Head Model Generation by Mesh Morphing: A Personalization Framework and Its Applications

“…This study addresses the challenge of generating subject-specific head injury models with hexahedrons, especially concerns about mesh morphing, which is an efficient approach for generating subject-specific models. The approach has been used in many biomechanics fields on different organs ( Couteau et al, 2000 ; Castellano-Smith et al, 2001 ; Fernandez et al, 2004 ; Sigal et al, 2008 ; Bucki et al, 2010 ; Bijar et al, 2016 ; Park et al, 2017 ), full-body models ( Davis et al, 2016 ; Beillas and Berthet, 2017 ; Liu et al, 2020 ), as well as for detailed ( Giudice et al, 2020 ; Giudice et al, 2021 ; Li et al, 2021 ; Montanino et al, 2021 ) and simplified brain models ( Hu et al, 2007 ; Ji et al, 2011 ; Ji et al, 2015b ; Wu et al, 2019 ). A typical procedure involves image registration (rigid or affine and followed by nonlinear registrations), from which a displacement field representing the geometrical difference between the subject and baseline model is obtained.…”

“…In particular, deformable image registration-based mesh morphing has been applied to personalize detailed brain models of healthy subjects ( Giudice et al, 2020 ; Giudice et al, 2021 ; Li et al, 2021 ; Montanino et al, 2021 ). However, despite intensive efforts, inter-subject registration between brains with significant anatomical differences is still challenging within neuroimaging field with limited registration accuracy ( Kim et al, 2015 ).…”