A Guide to Delineate the Logic of Neurovascular Signaling in the Brain

The neurovascular system may be viewed as a distributed nervous system within the brain. It transforms local neuronal activity into a change in the tone of smooth muscle that lines the walls of arterioles and microvessels. We review the current state of neurovascular coupling, with an emphasis on signaling molecules that convey information from neurons to neighboring vessels. At the level of neocortex, this coupling is mediated by: (i) a likely direct interaction with inhibitory neurons, (ii) indirect interaction, via astrocytes, with excitatory neurons, and (iii) fiber tracts from subcortical layers. Substantial evidence shows that control involves competition between signals that promote vasoconstriction versus vasodilation. Consistent with this picture is evidence that, under certain circumstances, increased neuronal activity can lead to vasoconstriction rather than vasodilation. This confounds naïve interpretations of functional brain images. We discuss experimental approaches to detect signaling molecules in vivo with the goal of formulating an empirical basis for the observed logic of neurovascular control

Pericyte degeneration leads to neurovascular uncoupling and limits oxygen supply to brain

Pericytes are perivascular mural cells of brain capillaries. They are positioned centrally in the neurovascular unit between endothelial cells, astrocytes and neurons. This position allows them to regulate key neurovascular functions of the brain. The role of pericytes in the regulation of cerebral blood flow (CBF) and neurovascular coupling remains, however, under debate. Using loss-of-function pericyte-deficient mice, here we show that pericyte degeneration diminishes global and individual capillary CBF responses to neuronal stimuli, resulting in neurovascular uncoupling, reduced oxygen supply to the brain and metabolic stress. Neurovascular deficits lead over time to impaired neuronal excitability and neurodegenerative changes. Thus, pericyte degeneration as seen in neurological disorders such as Alzheimer's disease may contribute to neurovascular dysfunction and neurodegeneration associated with human disease.R01 AG023084 - NIA NIH HHS; P50 AG005142 - NIA NIH HHS; R01 AG039452 - NIA NIH HHS; R24 NS092986 - NINDS NIH HHS; R01 EB000790 - NIBIB NIH HHS; R01 NS034467 - NINDS NIH HHS; R01 NS091230 - NINDS NIH HHS; R01 NS100459 - NINDS NIH HHS; P01 NS055104 - NINDS NIH HHS; P01 AG052350 - NIA NIH HHS; R01 MH111359 - NIMH NIH HHSAccepted manuscrip

Two-Photon Imaging of Cortical Surface Microvessels Reveals a Robust Redistribution in Blood Flow after Vascular Occlusion

A highly interconnected network of arterioles overlies mammalian cortex to route blood to the cortical mantle. Here we test if this angioarchitecture can ensure that the supply of blood is redistributed after vascular occlusion. We use rodent parietal cortex as a model system and image the flow of red blood cells in individual microvessels. Changes in flow are quantified in response to photothrombotic occlusions to individual pial arterioles as well as to physical occlusions of the middle cerebral artery (MCA), the primary source of blood to this network. We observe that perfusion is rapidly reestablished at the first branch downstream from a photothrombotic occlusion through a reversal in flow in one vessel. More distal downstream arterioles also show reversals in flow. Further, occlusion of the MCA leads to reversals in flow through approximately half of the downstream but distant arterioles. Thus the cortical arteriolar network supports collateral flow that may mitigate the effects of vessel obstruction, as may occur secondary to neurovascular pathology

Depth-dependent flow and pressure characteristics in cortical microvascular networks

A better knowledge of the flow and pressure distribution in realistic microvascular networks is needed for improving our understanding of neurovascular coupling mechanisms and the related measurement techniques. Here, numerical simulations with discrete tracking of red blood cells (RBCs) are performed in three realistic microvascular networks from the mouse cerebral cortex. Our analysis is based on trajectories of individual RBCs and focuses on layer-specific flow phenomena until a cortical depth of 1 mm. The individual RBC trajectories reveal that in the capillary bed RBCs preferentially move in plane. Hence, the capillary flow field shows laminar patterns and a layer-specific analysis is valid. We demonstrate that for RBCs entering the capillary bed close to the cortical surface (< 400 μm) the largest pressure drop takes place in the capillaries (37%), while for deeper regions arterioles are responsible for 61% of the total pressure drop. Further flow characteristics, such as capillary transit time or RBC velocity, also vary significantly over cortical depth. Comparison of purely topological characteristics with flow-based ones shows that a combined interpretation of topology and flow is indispensable. Our results provide evidence that it is crucial to consider layer-specific differences for all investigations related to the flow and pressure distribution in the cortical vasculature. These findings support the hypothesis that for an efficient oxygen up-regulation at least two regulation mechanisms must be playing hand in hand, namely cerebral blood flow increase and microvascular flow homogenization. However, the contribution of both regulation mechanisms to oxygen up-regulation likely varies over depth

Pre-processing of the microvascular networks and the approach to assign boundary conditions.

<p>(A) Histogram-based upscaling approach for microvascular network 1. The grey and the black histogram show the original and the final diameter distribution, respectively. The mean, the standard deviation (std), the maximum value (max) and the minimum value (min) of all capillary diameters are stated in grey and black for the original and the final capillary diameter distribution, respectively. The red curve is the goal beta distribution. (B) Summary of the pressure measurements in the pial vasculature available in literature and the fit we used to assign the pressure boundary conditions at the pial arterioles. At the pial venules we uniformly prescribed a pressure of 10 mmHg. Data from: Harper [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005392#pcbi.1005392.ref007" target="_blank">7</a>], Werber [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005392#pcbi.1005392.ref008" target="_blank">8</a>], Hudetz [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005392#pcbi.1005392.ref044" target="_blank">44</a>], Shapiro [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005392#pcbi.1005392.ref009" target="_blank">9</a>]. (C) Schematic illustration of the steps of the hierarchical boundary condition approach. On the left the three different components of the full compound network are shown. The red and green spheres in the realistic implant represent the pial and capillary in- and outflows, respectively.</p

Number of available unique paths from DA+A to V+AV for the five ALs averaged over the RBC trajectories from 3 MVNs.

<p>Number of available unique paths from DA+A to V+AV for the five ALs averaged over the RBC trajectories from 3 MVNs.</p

Pearson’s correlation coefficient for the relative end point frequencies and five trajectory characteristics averaged over 3 MVNs.

<p>Pearson’s correlation coefficient for the relative end point frequencies and five trajectory characteristics averaged over 3 MVNs.</p

Validation of the simulation results with literature data.

<p>Validation of the simulation results with literature data.</p