Network-driven anomalous transport is a fundamental component of brain microvascular dysfunction

The brain's microvascular network is crucial for delivering oxygen and nutrients, and removing waste products. Impaired microvascular function, characterized by abnormal vessel architecture and reduced blood flow, contributes to age-related cognitive decline and diseases like Alzheimer's Disease (AD). This impairment affects both oxygen delivery and the clearance of neurotoxic substances such as amyloid-β. The complex architecture of the microvasculature, with its tree-like arterioles and venules connected to a dense capillary network, creates significant spatial heterogeneity in blood flow and transit times. Even under normal conditions, some vessels exhibit low flow rates, approaching the hypoxic threshold, making them vulnerable to pathological stress. Current models, such as the Capillary Transit Time Heterogeneity (CTH) model, are phenomenological, relying on empirical travel time distributions and lacking a quantitative link to the underlying network architecture. This limitation hinders the understanding of how changes in vessel structure affect transport dynamics and the development of critical regions with impaired oxygen supply or waste clearance. The study aims to bridge this gap by investigating the relationship between network architecture, flow distributions, and transport dynamics in the brain microcirculation, particularly focusing on the emergence of critical regions under normal and hypoperfused conditions.

Existing literature highlights the importance of the brain's microvascular network in maintaining neuronal function by supplying oxygen and nutrients while removing metabolic waste. Studies have shown that impaired blood flow and abnormal vessel architectures are associated with age-related cognitive decline and neurodegenerative diseases. The complex network structure leads to heterogeneous blood flow and transit times, even under normal conditions. The CTH model is mentioned as an existing approach to understanding the impact of transit time heterogeneity, but its limitation in relating transport dynamics to the underlying network structure is emphasized. The authors point out that in vivo measurements are limited in temporal resolution, hindering the accurate calibration and predictive power of existing models, especially for longer time scales associated with the development of critical regions.

The research uses highly-resolved simulations of blood flow in anatomically realistic microvascular networks, validated against in vivo measurements. The simulations incorporate non-linear blood rheology and red blood cell repartition at vessel bifurcations. The analysis focuses on a mouse cortical microvessel network with approximately 15,000 vessels. The probability density functions (PDFs) of blood flow rates and vessel transit times are analyzed, revealing power-law scaling behaviors above a characteristic flow rate and time. Particle tracking simulations are employed to study network-scale transport properties, including trajectory lengths and travel times. The researchers develop a Continuous-Time Random Walk (CTRW) theory to capture the observed anomalous transport dynamics. This theory considers the broad distribution of trajectory lengths and transit times within the network. The model accounts for the dipole-like flow patterns in the large flow regime and random network topology for small flow rates. Oxygen and amyloid-β transport simulations are performed, incorporating first-order kinetics for oxygen consumption and amyloid-β production. The simulations are used to investigate the development of critical vessels exhibiting low oxygen concentrations or high amyloid-β levels under varying degrees of hypoperfusion.

The study reveals power-law scaling in the PDFs of blood flow rates and vessel transit times, indicating anomalous transport. The PDF of trajectory lengths also exhibits power-law scaling with an exponential cutoff. The average travel time scales linearly with trajectory length in the shallow network and follows a power-law scaling in the deep capillary network. A mean-field transport model, neglecting random fluctuations but capturing the dipole-driven trajectory length distribution, accurately predicts the travel time distribution for oxygen. A CTRW model, incorporating both the trajectory length distribution and the heterogeneity of vessel transit times, provides an accurate prediction of the travel time distribution for amyloid-β. Simulations of oxygen distribution show that the minimum oxygen concentration along trajectories decreases with trajectory length, consistent with experimental observations. The study demonstrates that anomalous transport leads to the early appearance of hypoxic regions under mild hypoperfusion conditions. The impact of anomalous transport is more pronounced for amyloid-β due to its low diffusivity. The probability of critical vessels with inefficient amyloid-β clearance is significantly larger than predicted by current models, emphasizing the importance of anomalous transport in disease development.

The findings address the research question by providing a physics-based understanding of how microvascular architecture shapes blood travel time distributions and leads to the emergence of critical regions. The results highlight the importance of considering anomalous transport dynamics when modeling transport in brain microvascular networks. The developed CTRW model offers an improved alternative to current empirical models, particularly for predicting long-time transport behaviors. The study's implications are significant for understanding the role of microvascular dysfunction in the development of brain diseases, such as AD. The non-linear relationship between hypoperfusion and the emergence of hypoxic and amyloid-β-rich regions suggests that even mild hypoperfusion could play a crucial role in disease onset and progression. The location of critical regions, deterministically located in regions fed by long trajectories, potentially explains why direct associations between capillaries with abnormal flow and amyloid deposition are not consistently found experimentally.

This study unveils the fundamental mechanisms of network-driven anomalous transport in brain microcirculation, demonstrating its significance in the development of critical regions with impaired oxygen delivery and amyloid-β clearance. The developed CTRW model provides accurate predictions of travel time distributions, surpassing the capabilities of current empirical models. The findings emphasize the impact of even mild hypoperfusion in initiating the amyloid cascade and highlight the need for future research to explore larger simulation domains and more complex transport dynamics, bridging the gap between computational modeling and clinical imaging data interpretation.

The study utilizes simplified first-order kinetic models for oxygen consumption and amyloid-β clearance, neglecting the complex interplay of multiple isoforms and their interactions. The simulations focus on mouse cortical microvascular networks, and the generalizability to other brain regions and species requires further investigation. The model's accuracy depends on the accuracy of the underlying anatomical networks used for simulation. The effects of neurovascular coupling and cerebral autoregulation are not explicitly modeled, which could influence the results.