(248b) A Comprehensive Model of Intracranial Dynamics of the Human Brain

The objective of this work is to develop a mathematical model that describes the intracranial dynamics of the circulatory system in the human brain, based on the first principles of fluid dynamics and linear elasticity. The circulatory system of the human brain comprises of blood and cerebrospinal fluid (CSF) in interaction with the solid brain parenchyma (grey & white matter), forming a dynamic fluid-solid interaction system. This model will be able to describe the flow and pressure fields of blood and CSF, as well as the deformation of the parenchyma as a function of time in the cardiac cycle. Although the flow of CSF is linked to the pulsations of the cerebral vasculature, there exists currently no first principles model capable of explaining the causal relationship of blood flow in the brain with the CSF flow as well as the relationship between the intracranial pressure, the CSF pulse pressure and the blood pressure. It is also difficult to explain why the unidirectional flow of blood causes a flow reversal of CSF in the ventricles. In order to understand the interactions of blood flow and CSF flow with the deformation of the parenchyma consistently, it is necessary to describe the fluid as well as the solid forces in the system simultaneously. Therefore, for the first time we propose to quantify the stresses and strains in the parenchyma together with the blood and CSF flow and pressure profiles using a holistic approach.

The proposed model will quantify the flow fields, fluid displacements and exchange of water content in the parenchyma and the deformations of parenchyma as well as the cerebral ventricles. This model will distinguish itself from earlier works that were based mainly on very high level abstractions such as simplified compartmental or electric circuit models. In order to validate our model we will also present experimental evidence from data obtained by cine phase contrast Magnetic Resonance Imaging (MRI). This will demonstrate the ability of the model to predict very accurately the three dimensional flow field of the CSF flow in the brain. The model also has the advantage of predicting the pressure gradients in the brain which cannot be measured directly from the MRI data. This provides the physician with information about the intracranial pressure states which are important in diagnosing and treating pathological situations such as Hydrocephalus. This model is an important step towards understanding the intracranial dynamics completely, without any abstractions and with only a small set of physical parameters. The extension of this work to develop a three-dimensional patient specific model from imaging techniques will provide the physicians with a valuable tool in diagnosis and treatment of cerebral disorders.