Patient specific computational modeling of fluid-structure interactions of cerebrospinal fluid for biomarkers in Alzheimer's disease

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Alzheimer's Disease has become a daunting public health concern with devastating effects on the individual and societal level. Early detection and diagnosis of the disease would be incredibly beneficial in the effort to reduce morbidity and mortality of the disease, yet reliable methods of early detection remain frustratingly elusive. Recent work has shown impairment of cerebrospinal fluid (CSF) flow in and around the brain, and how that impairment is linked to brain tissue stiffness, play a vital role in the early development of Alzheimer's Disease pathology. Unfortunately, existing candidates for measuring CSF flow and brain tissue stiffness are either highly invasive or semiquantitative analyses that cannot quantify key mechanical properties of brain tissue deformation and how it relates to the convective transport of AD-related proteins (such as amyloid -peptide and phosphorylated tau) via CSF flow. To overcome these obstacles, we will develop a computational model capable of accurately calculating CSF velocities and the stress-strain status of the brain cortex. Our approach will use state of the art, non-invasive MRI scans in concert with patient-specific computational fluid dynamic (CFD) modeling of CSF and blood flow to determine the physiological conditions of flow in the skull and their interactions with brain tissue. To account for the deformation of brain tissue and the resulting effects on fluid transport, the fluid simulations will be coupled with a structural model of the brain tissue via fluid-structure interaction (FSI) modeling. The results from this technique will allow for the development of novel, noninvasive and quantitative biomarkers such as brain "stiffness" (a surrogate for the hallmark histopathological plaques and tangles), CSF residence time in the subarachnoid space (related to protein-removal rates), and other hydrodynamic factors such as intracranial pressure pulsatility.