The Ageing Brain: Investigating the Role of Age in Changes to the Human Cerebral Microvasculature With an in silico Model

Abstract

Ageing causes extensive structural changes to the human cerebral microvasculature, which have a significant effect on capillary bed perfusion and oxygen transport. Current models of brain capillary networks in the literature focus on healthy adult brains and do not capture the effects of ageing, which is critical when studying neurodegenerative diseases. This study builds upon a statistically accurate model of the human cerebral microvasculature based on ex-vivo morphological data. This model is adapted for "healthy" ageing using in-vivo measurements from mice at three distinct age groups-young, middle-aged, and old. From this new model, blood and molecular exchange parameters are calculated such as permeability and surface-area-to-volume ratio, and compared across the three age groups. The ability to alter the model vessel-by-vessel is used to create a continuous gradient of ageing. It was found that surface-area-to-volume ratio reduced in old age by 6% and permeability by 24% from middle-age to old age, and variability within the networks also increased with age. The ageing gradient indicated a threshold in the ageing process around 75 years old, after which small changes have an amplified effect on blood flow properties. This gradient enables comparison of studies measuring cerebral properties at discrete points in time. The response of middle aged and old aged capillary beds to micro-emboli showed a lower robustness of the old age capillary bed to vessel occlusion. As the brain ages, there is thus increased vulnerability of the microvasculature-with a "tipping point" beyond which further remodeling of the microvasculature has exaggerated effects on the brain. When developing in-silico models of the brain, age is a very important consideration to accurately assess risk factors for cognitive decline and isolate early biomarkers of microvascular health.

Keywords: capillaries; cerebral blood flow; healthy ageing; micro-emboli; neurodegenaration.

Figure 1

Mouse-human age conversion identified in the literature.

Figure 2

Flowchart mapping route of calculations.

Figure 3

Number density ratios using volume density matching (left) and cerebral blood flow matching (right)—Y = Young; M, Middle-age; O, Old-age.

Figure 4

Visualising the change in vessel parameters in a cubic volume of the ageing brain with a middle-age network (A) and old-age network (B). Blue dashed lines indicate the vessels pruned to achieve an accurate old-age vessel density.

Figure 5

Ratio of pruned network permeability to the unpruned MA base permeability (data points represent the average permeability over 166 networks for each specific number of pruned vessels). Note that the YA, MA, and OA brains all start with the same vessel density—only radius and viscosity is modified for the different age groups. This elucidates the impact of removing individual vessels on permeability in young (left) and old (right) brains.

Figure 6

Permeability distributions for young (top), middle aged (middle), and old (bottom) capillary networks.

Figure 7

Distributions of capillary volume fraction (left) and surface-area-to-volume ratio of the capillaries (right) in young (top), middle aged (middle), and old (bottom) capillary networks.

Figure 8

Percentage permeability drop per year in healthy ageing starting from 45 years old ranging up to 85 years old.

Figure 9

The drops in permeability per vessel blocked in young (A), middle-age (B), and old-age (C) for statistically accurate capillary networks. Lines of best fit are in blue with values of the gradient given in the top right corner of each graph. Each data point shows the permeability fraction at any given vessel blockage fraction for a given network, with 166 networks simulated at each age group.