DIpoles on Networks as miNimal modEls of bRain MicrOvascular dySfuncTion – INNERMOST

Blood microcirculation supplies neurons with oxygen and clears their neurotoxic waste through a dense capillary network connected to larger tree-like vessels. This microvascular architecture results in highly heterogeneous blood flow and travel time distributions, whose consequences on brain pathophysiology begin to be uncovered. In this context, the INNERMOST partners bridged together their expertise on the physics of transport in disordered media and on brain microcirculation to provide a novel modelling framework describing the dynamics of blood flow and solute transport in brain microvascular networks, based on the theoretical resolution of the transport dynamics of dipole flow on random networks (Goirand et al. Nat Comm. 2021). This model captures the emergence of anomalous transport driving the appearance of critical regions, hypoxic or with high concentrations of metabolic wastes centrally involved in Alzheimer’s Disease.

While opening unprecedented opportunities to establish the first quantitative relationships between network structure and brain microvascular dysfunction, the “dipole on network” approach opens a range of new questions, that INNERMOST will explore. Using highly-resolved intracortical blood flow and transport simulations in anatomically-realistic and model networks, we will investigate the coupling between anomalous transport and non-linear red blood cell flow and reactive processes (WP1). We will then establish how the network structures macroscopically control oxygen delivery and waste clearance (WP2), demonstrating the pathological consequences of structural alterations. We will then address whether dynamic adaptations of vessel diameters induced by neuronal activity or physiological stress may mitigate the detrimental effect of anomalous transport (WP3). INNERMOST will thus open a new avenue for physics-based brain transport models, providing a new framework for understanding the role of microvascular mechanisms in brain pathology.