Research Update

1. Research Update Ian Gopal Gould UIC – LPPD Dr. Andreas Linninger April 21, 2011

2. + controller - sensor Cost involved with hydrocephalus treatment (Patwardhan, 2005) Pressure [Pa] EE IE Vasculature-brain tissue interaction and cerebrospinal fluid flow in the brain(Multi-scale modeling of biomechanical interactions in the brain) Brian J Sweetman, Ian G Gould, and Andreas A. LinningerLaboratory for Product and Process Design, Department of Bioengineering, UIC UIC Student Research Forum, Chicago IL, April 19, 2011 Motivation Process Flow Diagram Objectives • To answer fundamental questions about brain mechanics • Can cerebrospinal fluid (CSF) flow be quantified using first principles of fluid mechanics? • Do blood flow dynamics cause pulsatile CSF motion? V I II IV III Investigate the effects of pulsatile flow through tissue-embedded vasculature and subsequent deformation of surrounding fluid space Dynamically quantify the deformation of brain tissue and the resulting pressure and velocity gradient of CSF in the subarachnoid space and ventricular system Advance our understanding of fluid dynamics within the central nervous system using first principles of mathematics. We couple blood flow, porous brain tissue deformation, and pulsatile CSF flow Medical Image Grid Generation Vasculature Integration Grid Deformation Computational Results Address the need for better treatments of brain diseases • Many brain diseases result from abnormal CSF dynamics • Knowledge of CSF dynamics will improve cerebral pharmacokinetic models • Quantifying CSF-brain interaction will lead to improved medical device design I: MRI details brain architecture of tissue and fluid spaces II: Discrete MR images converted to unstructured mesh III: Generated vasculature structure integrated with tissue mesh IV: Pulsatile flow through vasculature deforms mesh dynamically V: Dynamic pressure and velocity fluids computed in fluid domain CSF Fluid Dynamics Vasculature-Brain Interaction Hydrocephalus Treatment Tissue Volumetric Stain Model Insights for Sensor Development Mesh Space Conservation Brain Tissue Displacement • The model predicts very small pressure gradients in both normal and diseased brains • A volume sensor is therefore needed to detect accumulation of CSF in patients • Our model quantifies these volumetric changes; helps optimize sensitivity of the sensor Embedded Vascular Tree Blood Pressure Waveform Continuity Momentum - 4 [mm] Blood Flow Field Tissue Displacement (Basati, 2010) Conclusions Acknowledgements NSF CBET 0756154-Interstitial dynamics of the poroelastic brain and cerebral vasculature in humans • Cerebral vasculature distensibility and brain tissue motion lead to pulsatile CSF flow • Quantification of CSF flow field allows prediction of therapeutic drug transport in the brain

4. Biomechanics of Hydrocephalus: A New Theoretical Model - Nagashimi (1987) • FEM Method, 2-D model, mechanical behavior of a deformed porous medium containing a viscous fluid • Parenchyma boundary conditions - FSI • Extracellular fluid determined - Neumann • Predicts qualitative CSF edema and enlarged ventricles • Results compared against patient MRI • Heightened intracranial pressure lagged behind the detection of periventricular lucency (PVL) • Question – how closely linked is PVL (common in pre-mature infants) to heightened intracranial pressure?

5. A Two-Dimensional, Finite Element Analysis of Vasogenic Brain Edema – Nagashimi (1990) • Dynamic, FEM, continuous porous media, deformable mesh (318 elements) • Bulk transport between vasculature and ECF determined by modified Starling • BBB damage simulated by reducing σ 100x • Lump term for bulk flux • L different for gray and white matter • “Threshold of Clearance,” minimal edema necessary before significant absorption into CSF • Model compared against experimental data in cats, model predicted much higher absorption of fluid into capillaries • Due to assumptions of constant osmolarity of ECF, lump term for species transport • Hook’s Law, Darcy’s Law, linear time-invariant elastic and conductivity constants

6. Thanks