Project Description:

Understanding the flow physics of cerebrospinal fluid (CSF) is important for 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 multi-scale computational models. Successful development of the proposed CSF modeling capabilities will provide a framework for collaborative projects with clinicians involved in treatment of CSF-related disorders.

Start Date:

April 2023 

Postdoc Qualifications:

We are looking for a motivated postdoctoral researcher with background in fluid mechanics and computational modeling

Co-Advisors:

Arezoo Ardekani
Professor of Mechanical Engineering
ardekani@purdue.edu
https://engineering.purdue.edu/ComplexFlowLab/

Vitaliy L Rayz
Associate Professor of Biomedical and Mechanical Engineering
vrayz@purdue.edu
https://engineering.purdue.edu/CFML 

Bibliography:

[1] L. E. Bilston, D. F. Fletcher, A. R. Brodbelt, and M. A. Stoodley. Arterial pulsation-driven cerebrospinal fluid flow in the perivascular space: a computational model. Computer Methods in Biomechanics & Biomedical Engineering, 6(4):235–241, 2003.
[2] E. E. Jacobson, D. F. Fletcher, M. K. Morgan, and I. H. Johnston. Computer modelling of the cerebrospinal fluid flow dynamics of aqueduct stenosis. Medical & biological engineering & computing, 37(1): 59–63, 1999.
[3] M. Khani, L. R. Sass, T. Xing, M. Keith Sharp, O. Bal´edent, and B. A. Martin. Anthropomorphic model of intrathecal cerebrospinal fluid dynamics within the spinal subarachnoid space: spinal cord nerve roots increase steady-streaming. Journal of Biomechanical Engineering, 140(8), 2018.
[4] S. Yamada, H Ito, M. Ishikawa, et al., Quantification of Oscillatory Shear Stress from Reciprocating CSF Motion on 4D Flow Imaging. AJNR Am J Neuroradiol. 2021 Mar;42(3):479-486.
[5] M. Matsumae, A. Hirayama, H. Atsumi et al., Velocity and pressure gradients of cerebrospinal fluid assessed with magnetic resonance imaging. J Neurosurg. 2014 Jan;120(1):218-27.