AUTHOR=Mériaux Sébastien , Conti Allegra , Larrat Benoît TITLE=Assessing Diffusion in the Extra-Cellular Space of Brain Tissue by Dynamic MRI Mapping of Contrast Agent Concentrations JOURNAL=Frontiers in Physics VOLUME=6 YEAR=2018 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2018.00038 DOI=10.3389/fphy.2018.00038 ISSN=2296-424X ABSTRACT=

The characterization of extracellular space (ECS) architecture represents valuable information for the understanding of transport mechanisms occurring in brain parenchyma. ECS tortuosity reflects the hindrance imposed by cell membranes to molecular diffusion. Numerous strategies have been proposed to measure the diffusion through ECS and to estimate its tortuosity. The first method implies the perfusion for several hours of a radiotracer which effective diffusion coefficient D* is determined after post mortem processing. The most well-established techniques are real-time iontophoresis that measures the concentration of a specific ion at known distance from its release point, and integrative optical imaging that relies on acquiring microscopy images of macromolecules labeled with fluorophore. After presenting these methods, we focus on a recent Magnetic Resonance Imaging (MRI)-based technique that consists in acquiring concentration maps of a contrast agent diffusing within ECS. Thanks to MRI properties, molecular diffusion and tortuosity can be estimated in 3D for deep brain regions. To further discuss the reliability of this technique, we point out the influence of the delivery method on the estimation of D*. We compare the value of D* for a contrast agent intracerebrally injected, with its value when the agent is delivered to the brain after an ultrasound-induced blood-brain barrier (BBB) permeabilization. Several studies have already shown that tortuosity may be modified in pathological conditions. Therefore, we believe that MRI-based techniques could be useful in a clinical context for characterizing the diffusion properties of pathological ECS and thus predicting the drug biodistribution into the targeted area.