The Presence of the Temporal Horn Exacerbates the Vulnerability of Hippocampus During Head Impacts

Abstract

Hippocampal injury is common in traumatic brain injury (TBI) patients, but the underlying pathogenesis remains elusive. In this study, we hypothesize that the presence of the adjacent fluid-containing temporal horn exacerbates the biomechanical vulnerability of the hippocampus. Two finite element models of the human head were used to investigate this hypothesis, one with and one without the temporal horn, and both including a detailed hippocampal subfield delineation. A fluid-structure interaction coupling approach was used to simulate the brain-ventricle interface, in which the intraventricular cerebrospinal fluid was represented by an arbitrary Lagrangian-Eulerian multi-material formation to account for its fluid behavior. By comparing the response of these two models under identical loadings, the model that included the temporal horn predicted increased magnitudes of strain and strain rate in the hippocampus with respect to its counterpart without the temporal horn. This specifically affected cornu ammonis (CA) 1 (CA1), CA2/3, hippocampal tail, subiculum, and the adjacent amygdala and ventral diencephalon. These computational results suggest that the presence of the temporal horn exacerbate the vulnerability of the hippocampus, highlighting the mechanobiological dependency of the hippocampus on the temporal horn.

Brain strain rate response: addressing computational ambiguity and experimental data for model validation

“…The time-accumulated peaks of and were determined and labelled as and , respectively. Scheme 2 has been previously used to extract the principal strain rate and/or the tract-oriented strain rates in the brain [23, 25, 26, 41-44] and other biological tissues (e.g., leg muscles [45-48], tongue [49, 50], myocardium [51-53]).…”

“…The time-accumulated peaks of and were determined and labelled as and , respectively. Scheme 2 has been previously used to extract the strain rate in the brain (Reese et al, 2002; Zhou et al, 2022) and other biological tissues (e.g., leg muscles (Malis et al, 2020; Mazzoli et al, 2018; Sinha et al, 2020; Sinha et al, 2018), myocardium (Dou et al, 2003; Robson and Constable, 1996; Wedeen, 1992)).…”

“…In the computational works by Zhou, et al [25] and Carlsen, et al [26], the strain rate was derived from the strain-rate tensor, while King, et al [27] instead calculated the strain rate by differentiating the FEderived maximum principal strain vs. time curves. Concerningly, little or limited knowledge exists on how this disparity in computational schemes affects the resultant strain rate values.…”

“…In two experimental paradigms, Rashid, et al [23] determined the strain rate from the symmetric part of the velocity gradient in the simple shear experiments of brain tissue, while LaPlaca and Thibault [24] quantified strain rate as the time derivative of strain in an in vitro TBI system. In the computational works by Zhou, et al [25] and Carlsen, et al [26], the strain rate was derived from the strain-rate tensor, while King, et al [27] instead calculated the strain rate by differentiating the FE-derived maximum principal strain vs. time curves. Concerningly, little or limited knowledge exists on how this disparity in computational schemes affects the resultant strain rate values.…”

Fiber orientation downsampling compromises the computation of white matter tract-related deformation

“…Such FE models with high-level structural details often possess fine meshes in order to appropriately resolve the geometrical complexities. For instance, two recent brain models with conforming hexahedral meshes that captured the morphological heterogeneity of the cerebral cortex (i.e., gyri and sulci) and lateral ventricles exhibited a mean mesh size of 0.85 mm with up to 1.2 million elements for the brain (Li et al, 2021; Zhou et al, 2022; Zhou et al, 2019b). For head models with smoothed brain surfaces, the number of brain elements varied from 5 k to 202 k with mesh resolutions of 1.8 – 6 mm (Chatelin et al, 2011; Kleiven and von Holst, 2002; Mao et al, 2013; Zhao and Ji, 2019a; Zhou et al, 2016).…”