Network response of brain microvasculature to neuronal stimulation

Abstract

Neurovascular coupling (NVC), or the adjustment of blood flow in response to local increases in neuronal activity is a hallmark of healthy brain function, and the physiological foundation for functional magnetic resonance imaging (fMRI). However, it remains only partly understood due to the high complexity of the structure and function of the cerebrovascular network. Here we set out to understand NVC at the network level, i.e. map cerebrovascular network reactivity to activation of neighbouring neurons within a 500×500×500 μm3 cortical volume (∼30 high-resolution 3-nL fMRI voxels). Using 3D two-photon fluorescence microscopy data, we quantified blood volume and flow changes in the brain vessels in response to spatially targeted optogenetic activation of cortical pyramidal neurons. We registered the vessels in a series of image stacks acquired before and after stimulations and applied a deep learning pipeline to segment the microvascular network from each time frame acquired. We then performed image analysis to extract the microvascular graphs, and graph analysis to identify the branch order of each vessel in the network, enabling the stratification of vessels by their branch order, designating branches 1–3 as precapillary arterioles and branches 4+ as capillaries. Forty-five percent of all vessels showed significant calibre changes; with 85 % of responses being dilations. The largest absolute CBV change was in the capillaries; the smallest, in the venules. Capillary CBV change was also the largest fraction of the total CBV change, but normalized to the baseline volume, arterioles and precapillary arterioles showed the biggest relative CBV change. From linescans along arteriole-venule microvascular paths, we measured red blood cell velocities and hematocrit, allowing for estimation of pressure and local resistance along these paths. While diameter changes following neuronal activation gradually declined along the paths; the pressure drops from arterioles to venules increased despite decreasing resistance: blood flow thus increased more than local resistance decreases would predict. By leveraging functional volumetric imaging and high throughput deep learning-based analysis, our study revealed distinct hemodynamic responses across the vessel types comprising the microvascular network. Our findings underscore the need for large, dense sampling of brain vessels for characterization of neurovascular coupling at the network level in health and disease.