Biology subjects

Boster, K. A. S.

Publications and source records attributed to Boster, K. A. S..

2 recordsLinked to original sources

bioRxiv2025

MR-AIV reveals in-vivo brain-wide fluid flow with physics-informed AI

The circulation of cerebrospinal and interstitial fluid plays a vital role in clearing metabolic waste from the brain, and its disruption has been linked to neurological disorders. However, directly measuring brain-wide fluid transport--especially in the deep brain--has remained elusive. Here, we introduce magnetic resonance artificial intelligence velocimetry (MR-AIV), a framework featuring a specialized physics-informed architecture and optimization method that reconstructs three-dimensional fluid velocity fields from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MR-AIV unveils brain-wide velocity maps while providing estimates of tissue permeability and pressure fields--quantities inaccessible to other methods. Applied to the brain, MR-AIV reveals a functional landscape of interstitial and perivascular flow, quantitatively distinguishing slow diffusion-driven transport ([~] 0.1 {micro}m/s) from rapid advective flow ([~] 3 {micro}m/s). This approach enables new investigations into brain clearance mechanisms and fluid dynamics in health and disease, with broad potential applications to other porous media systems, from geophysics to tissue mechanics.

neuroscience↗

bioRxiv2021

A network model of glymphatic flow under different experimentally-motivated parametric scenarios

Rapidly growing evidence demonstrates that flow of cerebrospinal fluid (CSF) through perivascular spaces (PVSs) - annular channels surrounding vasculature in the brain - is a critically-important component of neurophysiology. CSF inflow contributes during physiological conditions to clearance of metabolic waste and in pathological situations to edema formation. However, brain-wide imaging methods cannot resolve PVSs, and high-resolution methods cannot access deep tissue or be applied to human subjects, so theoretical models provide essential insight. We model this CSF pathway as a network of hydraulic resistances, built from published parameters. A few parameters have very wide uncertainties, so we focus on the estimated limits of their feasible ranges by analyzing different parametric scenarios. We identify low-resistance PVSs and high-resistance parenchyma (brain tissue) as the scenario that best explains experimental observations. Our results point to the most important parameters that should be measured in future experiments. Extensions of our modeling may help predict stroke severity or lead to neurological disease treatments and drug delivery methods.

neuroscience↗