Effect of modeling subject-specific cortical folds on brain injury risk prediction under blunt impact loading

Computational head models are essential tools for predicting the risk of mild traumatic brain injury (mTBI). However, computational models vary in the level of anatomical details, most notably the cortical folds. This study aims to determine the effect of modeling cortical folds on mTBI risk assessment. We compared gyrencephalic (with cortical folds) and lissencephalic (without cortical folds) finite element (FE) head models of 18 subjects aged 9-18 years, subjected to a rotational head acceleration of 10 krad/s $_{}^2$ (10 ms duration) about each principal head axis. We analyzed the effect of cortical folds on different tissue-level mTBI injury metrics, including maximum principal strain (MPS95), maximum principal strain rate (MPSR95), and cumulative strain damage measure (CSDM15). The inclusion of cortical folds consistently yielded higher injury metrics across all individuals and rotational directions, with a bias (mean ± std. dev. relative to maximum lissencephalic values) of $21.7\pm 9.1\%$ in MPS95, $17.1\pm 7.6\%$ in MPSR95, and $14.4\pm 11.3\%$ in CSDM15. Differences in the spatial strain distribution were also found between the models, with the DICE similarity coefficient ranging between $0.07-0.43$ and $0.42-0.70$ for the peak MPS and CSDM15, respectively. Increases in peak injury metrics (up to $\sim$ 50%) were found for brain regions such as the corpus callosum, cerebellum, and brain stem. This study finds that the inclusion of cortical folds significantly alters the pattern of deformation in the brain and results in a prediction of higher mTBI risk.