The dynamic response of the human head to side impact was studied by 2-dimensional finite element modeling. Three models were formulated in this study. Model I is an axisymmetric model. It simulated closed shell impact of the human head, and consisted of a single-layered spherical shell filled with an inviscid fluid. The other two models (Model II and III) are plane strain models of a coronal section of the human head. Model II approximated a 50th percentile male head by an outer layer to simulate cranial bone and an inviscid interior core to simulate the intracranial contents. The configuration of Model III is the same as Model II but more detailed anatomical features of the head interior were added, such as, cerebral spinal fluid (CSF); falx cerebri, dura, and tentorium. Linear elastic material properties were assigned to all three models. All three models were loaded by a triangular pulse with a peak pressure of 40 kPa, effectively producing apeak force of 1954 N (440 lb). The purpose of this study was to determine the effects of the membranes and that of the mechanical properties of the skull, brain, and membrane on the dynamic response of the brain during side impact, and to compare the pressure distributions from the plane strain model with the axisymmetric model. A parametric study was conducted on Model II to characterize fully its response to impact under various conditions. It was found that: (a) The membranes affected the dynamic response of the brain significantly, the fundamental frequency of the brain was 72 Hz with membranes and 49 Hz without them: (b) Significant variations in the pressure distribution were obtained as a result of assigning different material properties to the skull and brain; (c) The normal variation of pressure from compression at the pole (impact side) to tension at the antipole (opposite to the impact side) was disrupted by the membrane and a complex distribution of pressure was found.
Investigating the brain regions involved in tDCS-Enhanced category learning using finite element modeling
