Brain Capillary Networks Across Species: A few Simple Organizational Requirements Are Sufficient to Reproduce Both Structure and Function

Abstract

Despite the key role of the capillaries in neurovascular function, a thorough characterization of cerebral capillary network properties is currently lacking. Here, we define a range of metrics (geometrical, topological, flow, mass transfer, and robustness) for quantification of structural differences between brain areas, organs, species, or patient populations and, in parallel, digitally generate synthetic networks that replicate the key organizational features of anatomical networks (isotropy, connectedness, space-filling nature, convexity of tissue domains, characteristic size). To reach these objectives, we first construct a database of the defined metrics for healthy capillary networks obtained from imaging of mouse and human brains. Results show that anatomical networks are topologically equivalent between the two species and that geometrical metrics only differ in scaling. Based on these results, we then devise a method which employs constrained Voronoi diagrams to generate 3D model synthetic cerebral capillary networks that are locally randomized but homogeneous at the network-scale. With appropriate choice of scaling, these networks have equivalent properties to the anatomical data, demonstrated by comparison of the defined metrics. The ability to synthetically replicate cerebral capillary networks opens a broad range of applications, ranging from systematic computational studies of structure-function relationships in healthy capillary networks to detailed analysis of pathological structural degeneration, or even to the development of templates for fabrication of 3D biomimetic vascular networks embedded in tissue-engineered constructs.

Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVis

“…Certain CBF modeling experiments mandate visualizing some vascular properties per segment such as its length, diameter, pressure or flow ( Reichold et al , 2009 ; Smith et al , 2019 ). Therefore, it was essential to implement different coloring schemes allowing the users to select between using a single color to visualize the entire morphology, or using alternating colors to see some structural aspects of the morphology— Figure 4C and E , or even use a high dynamic range colormap to reveal other functional aspects.…”

“…These maps are essential for developing blood flow models to simulate cerebral blood flow (CBF) in realistic vascular networks ( Damseh et al , 2019 ; Reichold et al , 2009 ), analyzing the proximity of cell bodies to vasculature elements ( Blinder et al , 2013 ) and also for classifying vascular components into different types of segments ( Zeng et al , 2017 ). Accurate digital reconstructions of vascular ultrastructure down to capillary level ( Di Giovanna et al , 2018 ) are central to this goal, which requires innovative and rapid software visualization tools to investigate the topology of the vascular network and to model its behavior ( Smith et al , 2019 ). Unfortunately, the existence of convenient domain-specific software frameworks capable of visualizing those vascular models in real time is currently lacking.…”

Susceptibility to capillary plugging can predict brain region specific vessel loss with aging

“…The capillary beds are remarkably similar, too, though the size of the human vasculature is slightly scaled up. Capillaries are slightly longer, wider, and more spaced out in humans than in rodents, which is accompanied by slight differences in red blood cell (RBC) size (Lauwers et al, 2008;Namdee et al, 2015;Smith et al, 2019). However, capillary beds have similar branching properties in each species (Blinder et al, 2013;Smith et al, 2019), and similar density distributions across cortical layers (Schmid et al, 2017a).…”

The need for mathematical modelling of spatial drug distribution within the brain

“…Recently, large-scale anatomical models of brain vascular networks have been developed. There, entire brain vascular networks are constructed based on segmentation of medical images [115–119] or geometric construction [120–124]. These networks consist of a multitude of blood vessel segments connected by nodes, where parameters defining the network (such as blood vessel radius, volume and length) are based on images, experimental data or random distribution.…”