Biomechanical modelling of normal pressure hydrocephalus

Abstract

This study investigates the mechanics of normal pressure hydrocephalus (NPH) growth using a computational approach. We created a generic 3-D brain mesh of a healthy human brain and modelled the brain parenchyma as single phase and biphasic continuum. In our model, hyperelastic constitutive law and finite deformation theory described deformations within the brain parenchyma. We used a value of 155.77 Pa for the shear modulus ( μ) of the brain parenchyma. Additionally, in our model, contact boundary definitions constrained the brain outer surface inside the skull. We used transmantle pressure difference to load the model. Fully nonlinear, implicit finite element procedures in the time domain were used to obtain the deformations of the ventricles and the brain. To the best of our knowledge, this was the first 3-D, fully nonlinear model investigating NPH growth mechanics. Clinicians generally accept that at most 1 mm of Hg transmantle pressure difference (133.416 Pa) is associated with the condition of NPH. Our computations showed that transmantle pressure difference of 1 mm of Hg (133.416 Pa) did not produce NPH for either single phase or biphasic model of the brain parenchyma. A minimum transmantle pressure difference of 1.764 mm of Hg (235.44 Pa) was required to produce the clinical condition of NPH. This suggested that the hypothesis of a purely mechanical basis for NPH growth needs to be revised. We also showed that under equal transmantle pressure difference load, there were no significant differences between the computed ventricular volumes for biphasic and incompressible/nearly incompressible single phase model of the brain parenchyma. As a result, there was no major advantage gained by using a biphasic model for the brain parenchyma. We propose that for modelling NPH, nearly incompressible single phase model of the brain parenchyma was adequate. Single phase treatment of the brain parenchyma simplified the mathematical description of the NPH model and resulted in significant reduction of computational time.