Computational modelling of the brain requires accurate representation of the tissues concerned. Mechanical testing has numerous challenges, in particular for low strain rates, like neurosurgery, where redistribution of fluid is biomechanically important. A finite-element (FE) model was generated in FEBio, incorporating a spring element/fluid–structure interaction representation of the pia–arachnoid complex (PAC). The model was loaded to represent gravity in prone and supine positions. Material parameter identification and sensitivity analysis were performed using statistical software, comparing the FE results to human in vivo measurements. Results for the brain Ogden parameters µ, α and k yielded values of 670 Pa, −19 and 148 kPa, supporting values reported in the literature. Values of the order of 1.2 MPa and 7.7 kPa were obtained for stiffness of the pia mater and out-of-plane tensile stiffness of the PAC, respectively. Positional brain shift was found to be non-rigid and largely driven by redistribution of fluid within the tissue. To the best of our knowledge, this is the first study using in vivo human data and gravitational loading in order to estimate the material properties of intracranial tissues. This model could now be applied to reduce the impact of positional brain shift in stereotactic neurosurgery.
Finite-element (FE)-based computational models of the human brain are an increasingly common research tool, with applications ranging from head impact to neurosurgery. Studies considering head impacts are generally concerned with traumatic brain injury (TBI), where a better understanding of the underlying mechanisms is essential for the development of prevention measures [1]. Within neurosurgery, efforts are primarily focused on tumour resection, where loss of cerebrospinal fluid (CSF) and tissue resection are responsible for much of the deformation [2]. Movement and deformation of the intact brain, known as brain shift, is clinically significant in stereotactic neurosurgical procedures such as deep brain stimulation where electrode placement accuracy correlates with patient outcomes [3].
