Multi-scale computational modeling of cerebrospinal fluid flow and transport
|Interdisciplinary Areas:||Engineering-Medicine, Integrated Neuroscience and Engineering
Understanding the flow physics of cerebrospinal fluid (CSF) is important for the treatment of various brain disorders. Reductions in CSF flow are associated with the accumulation of toxic protein aggregates that are linked to Alzheimer’s disease. Abnormal CSF flow is also linked to normal pressure hydrocephalus, resulting in excessive accumulation of CSF in the ventricles, which can damage surrounding brain tissue. Despite its crucial role in the brain’s health and function, the affiliated CSF-flow dynamic is still poorly quantified and understood. Computational fluid dynamics (CFD) tools can use patient-specific data to provide high-resolution information on CSF flow in ventricular, subarachnoid, and perivascular space [1–3]. Alternatively, CSF flow in cerebral ventricles can be measured with MRI (4D flow MRI); however, the limited resolution of this imaging modality makes CSF measurements particularly challenging [4 – 5]. The present project focuses on developing a high-fidelity computational tool to elucidate the mechanisms of CSF flow and transport in cerebral ventricles and perivascular space. MRI measurements of CSF and blood flow and structural anatomies will be used to inform subject-specific multiscale computational models. Successful development of the proposed CSF modeling capabilities will provide a framework for collaborative projects with clinicians involved in treating CSF-related disorders.
Experience in computational fluid dynamics and numerical modeling.
University Faculty Scholar
Professor, School of Mechanical Engineering
Associate Professor and Associate Head of Academic Programs,
Weldon School of Biomedical Engineering
Associate Professor, Mechanical Engineering (courtesy appointment)
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