Cerebral blood flow (CBF) reductions in Alzheimer’s disease (AD) patients and related mouse models have been recognized for decades, but the underlying mechanisms and resulting consequences on AD pathogenesis remain poorly understood. In APP/PS1 and 5xFAD mice we found that an increased number of cortical capillaries had stalled blood flow as compared to wildtype animals, largely due to neutrophils that adhered in capillary segments and blocked blood flow. Administration of antibodies against the neutrophil marker Ly6G reduced the number of stalled capillaries, leading to an immediate increase in CBF and to rapidly improved performance in spatial and working memory tasks. This study identified a novel cellular mechanism that explains the majority of the CBF reduction seen in two mouse models of AD and demonstrated that improving CBF rapidly improved short-term memory function. Restoring cerebral perfusion by preventing neutrophil adhesion may provide a novel strategy for improving cognition in AD patients.

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in: http://oatao. For this purpose, we perform the first simulations of blood flow in an anatomically accurate large human intra-cortical vascular network (~10000 segments), using a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation. This model predicts blood pressure, blood flow and hematocrit distributions, as well as volumes of functional vascular territories, and regional flow at voxel and network scales. First, the influence of the prescribed boundary conditions (BCs) on the baseline flow structure is investigated, highlighting relevant lower-and upper-bound BCs. Independent of these BCs, large heterogeneities of baseline flow from vessel to vessel and from voxel to voxel, are demonstrated. These heterogeneities are controlled by the architecture of the intra-cortical vascular network. In particular, a correlation between the blood flow and the proportion of vascular volume occupied by arterioles or venules, at voxel scale, is highlighted. Then, the extent of venous contamination downstream to the sites of neuronal activation is investigated, demonstrating a linear relationship between the catchment surface of the activated area and the diameter of the intra-cortical draining vein.

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao. a b s t r a c t Considering their extremely complicated and hierarchical structure, a long standing question in vascular physio-pathology is how to characterize blood vessels patterns, including which parameters to use. Another question is how to define a pertinent taxonomy, with applications to normal development and to diagnosis and/or staging of diseases.To address these issues, fractal analysis has been applied by previous investigators to a large variety of healthy or pathologic vascular networks whose fractal dimensions have been sought. A review of the results obtained on healthy vascular networks first shows that no consensus has emerged about whether normal networks must be considered as fractals or not.Based on a review of previous theoretical work on vascular morphogenesis, we argue that these divergences are the signature of a two-step morphogenesis process, where vascular networks form via progressive penetration of arterial and venous quasi-fractal arborescences into a pre-existing homogeneous capillary mesh. Adopting this perspective, we study the multi-scale behavior of generic patterns (model structures constructed as the superposition of homogeneous meshes and quasi-fractal trees) and of healthy intracortical networks in order to determine the artifactual and true components of their multi-scale behavior. We demonstrate that, at least in the brain, healthy vascular structures are a superposition of two components: at low scale, a mesh-like capillary component which becomes homogeneous and space-filling over a cut-off length of order of its characteristic length; at larger scale, quasi-fractal branched (tree-like) structures. Such complex structures are consistent with all previous studies on the multi-scale behavior of vascular structures at different scales, resolving the apparent contradiction about their fractal nature.Consequences regarding the way fractal analysis of vascular networks should be conducted to provide meaningful results are presented. Finally, consequences for vascular morphogenesis or hemodynamics are discussed, as well as implications in case of pathological conditions, such as cancer.

Aging or cerebral diseases may induce architectural modifications in human brain microvascular networks, such as capillary rarefaction. Such modifications limit blood and oxygen supply to the cortex, possibly resulting in energy failure and neuronal death. Modelling is key in understanding how these architectural modifications affect blood flow and mass transfers in such complex networks. However, the huge number of vessels in the human brain—tens of billions—prevents any modelling approach with an explicit architectural representation down to the scale of the capillaries. Here, we introduce a hybrid approach to model blood flow at larger scale in the brain microcirculation, based on its multiscale architecture. The capillary bed, which is a space-filling network, is treated as a porous medium and modelled using a homogenized continuum approach. The larger arteriolar and venular trees, which cannot be homogenized because of their fractal-like nature, are treated as a network of interconnected tubes with a detailed representation of their spatial organization. The main contribution of this work is to devise a proper coupling model at the interface between these two components. This model is based on analytical approximations of the pressure field that capture the strong pressure gradients building up in the capillaries connected to arterioles or venules. We evaluate the accuracy of this model for both very simple architectures with one arteriole and/or one venule and for more complex ones, with anatomically realistic tree-like vessels displaying a large number of coupling sites. We show that the hybrid model is very accurate in describing blood flow at large scales and further yields a significant computational gain by comparison with a classical network approach. It is therefore an important step towards large scale simulations of cerebral blood flow and lays the groundwork for introducing additional levels of complexity in the future.

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. In a companion paper (Lorthois et al., Neuroimage, in press), we perform the first simulations of blood flow in an anatomically accurate large human intra-cortical vascular network (~10000 segments), using a 1D non-linear model taking into account the complex rheological properties of blood flow in microcirculation. This model predicts blood pressure, blood flow and hematocrit distributions, volumes of functional vascular territories, regional flow at voxel and network scales, etc. Using the same approach, we study flow reorganizations induced by global arteriolar vasodilations (an isometabolic global increase in cerebral blood flow). For small to moderate global vasodilations, the relationship between changes in volume and changes in flow is in close agreement with Grubb's law, providing a quantitative tool for studying the variations of its exponent with underlying vascular architecture. A significant correlation between blood flow and vascular structure at the voxel scale, practically unchanged with respect to baseline, is demonstrated. Furthermore, the effects of localized arteriolar vasodilations, representative of a local increase in metabolic demand, are analyzed. In particular, localized vasodilations induce flow changes, including vascular steal, in the neighboring arteriolar trunks at small distances (b 300 μm), while their influence in the neighboring veins is much larger (about 1 mm), which provides an estimate of the vascular point spread function. More generally, for the first time, the hemodynamic component of various functional neuroimaging techniques has been isolated from metabolic and neuronal components, and a direct relationship with several known characteristics of the BOLD signal has been demonstrated. IntroductionThere is increasing recognition that, by its structural implication in neuro-vascular coupling, underlying vascular architecture is a key player in hemodynamically based functional imaging techniques (including fMRI and PET) (Harrison et al., 2002;d'Esposito et al., 2003;Logothetis and Wandell, 2004;Lauwers et al., 2008;Weber et al., 2008). In particular, the spatial resolution and specificity of such techniques are bound to the density of blood capillary vasculature and blood flow regulating structures (Harel et al., 2006). This is especially important in the context of high field fMRI Harel et al., 2006), because, despite a dramatic increase in the theoretical spatial resolution obtained by using higher magnetic fields, associated with other hardware advancements, "the spatial extent of the hemodynamic response ultimately will impose a fundamental limitation on the spatial resolution of fMRI modalities" ).However, little is known about the fundamentals of the relationship between vascular structure and hemodynamic changes induced by brain activation. A growing body of evidence indicates that neurons, glia, and cerebral blood vessels, actin...

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.

Despite the development of microfluidics, experimental challenges are considerable for achieving a quantitative study of phase separation, i.e., the non-proportional distribution of Red Blood Cells (RBCs) and suspending fluid, in microfluidic bifurcations with channels smaller than 20 lm. Yet, a basic understanding of phase separation in such small vessels is needed for understanding the coupling between microvascular network architecture and dynamics at larger scale. Here, we present the experimental methodologies and measurement techniques developed for that purpose for RBC concentrations (tube hematocrits) ranging between 2% and 20%. The maximal RBC velocity profile is directly measured by a temporal cross-correlation technique which enables to capture the RBC slip velocity at walls with high resolution, highlighting two different regimes (flat and more blunted ones) as a function of RBC confinement. The tube hematocrit is independently measured by a photometric technique. The RBC and suspending fluid flow rates are then deduced assuming the velocity profile of a Newtonian fluid with no slip at walls for the latter. The accuracy of this combination of techniques is demonstrated by comparison with reference measurements and verification of RBC and suspending fluid mass conservation at individual bifurcations. The present methodologies are much more accurate, with less than 15% relative errors, than the ones used in previous in vivo experiments. Their potential for studying steady state phase separation is demonstrated, highlighting an unexpected decrease of phase separation with increasing hematocrit in symmetrical, but not asymmetrical, bifurcations and providing new reference data in regimes where in vitro results were previously lacking. Published by AIP Publishing. [http://dx