Hydrocephalus is a clinical condition characterized by abnormalities in the cerebrospinal fluid (CSF) circulation resulting in ventricular dilation. Within limits, the dilation of the ventricles can be reversed by either a shunt placement in the brain or by performing a ventriculostomy surgery, resulting in a relief from the symptoms of hydrocephalus. However, the response of patients to either treatment continues to be poor. Therefore, there is an earnest need to design better therapy protocols for hydrocephalus. An important step in this direction is the development of predictive computational models of the mechanics of hydrocephalic brains. In this paper we present an Immersed Finite Element Method (IFEM) approach to study brain mechanics. IFEM is an emerging computational method that is suitable for analyzing the complex interactions between fluid and deformable structures. We model the brain as a neo-Hookean material that is submerged in the CSF modeled as a Newtonian fluid. We use medical images of a hydrocephalic brain to generate the mesh for the immersed solid. Our preliminary results show that the viscosity of CSF has a significant influence on the deformation of the brain tissue. We believe that our study could play an important role in predicting and ultimately improving the outcome of ventriculostomy.