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Cervical lymphatic vessels (cLVs) have been shown to drain solutes and cerebrospinal fluid (CSF) from the brain. However, their hydrodynamical properties have never been evaluated in vivo. Here, we developed two-photon optical imaging with particle tracking in vivo of CSF tracers (2P-OPTIC) in superficial and deep cLVs of mice, characterizing their flow and showing that the major driver is intrinsic pumping by contraction of the lymphatic vessel wall. Moreover, contraction frequency and flow velocity were reduced in aged mice, which coincided with a reduction in smooth muscle actin expression. Slowed flow in aged mice was rescued using topical application of prostaglandin F 2α , a prostanoid that increases smooth muscle contractility, which restored lymphatic function in aged mice and enhanced central nervous system clearance. We show that cLVs are important regulators of CSF drainage and that restoring their function is an effective therapy for improving clearance in aging., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Perivascular spaces (PVSs) carry cerebrospinal fluid (CSF) around the brain, facilitating healthy waste clearance. Measuring those flows in vivo is difficult, and often impossible, because PVSs are small, so accurate modeling is essential for understanding brain clearance. The most important parameter for modeling flow in a PVS is its hydraulic resistance, defined as the ratio of pressure drop to volume flow rate, which depends on its size and shape. In particular, the local resistance per unit length varies along a PVS and depends on variations in the local cross section., Methods: Using segmented, three-dimensional images of pial PVSs in mice, we performed fluid dynamical simulations to calculate the resistance per unit length. We applied extended lubrication theory to elucidate the difference between the calculated resistance and the expected resistance assuming a uniform flow. We tested four different approximation methods, and a novel correction factor to determine how to accurately estimate resistance per unit length with low computational cost. To assess the impact of assuming unidirectional flow, we also considered a circular duct whose cross-sectional area varied sinusoidally along its length., Results: We found that modeling a PVS as a series of short ducts with uniform flow, and numerically solving for the flow in each, yields good resistance estimates at low cost. If the second derivative of area with respect to axial location is less than 2, error is typically less than 15%, and can be reduced further with our correction factor. To make estimates with even lower cost, we found that instead of solving for the resistance numerically, the well-known resistance of a circular duct could be scaled by a shape factor. As long as the aspect ratio of the cross section was less than 0.7, the additional error was less than 10%., Conclusions: Neglecting off-axis velocity components underestimates the average resistance, but the error can be reduced with a simple correction factor. These results could increase the accuracy of future models of brain-wide and local CSF flow, enabling better prediction of clearance, for example, as it varies with age, brain state, and pathological conditions., (© 2024. The Author(s).)

Cerebral oedema is associated with morbidity and mortality after traumatic brain injury (TBI) 1 . Noradrenaline levels are increased after TBI 2-4 , and the amplitude of the increase in noradrenaline predicts both the extent of injury 5 and the likelihood of mortality 6 . Glymphatic impairment is both a feature of and a contributor to brain injury 7,8 , but its relationship with the injury-associated surge in noradrenaline is unclear. Here we report that acute post-traumatic oedema results from a suppression of glymphatic and lymphatic fluid flow that occurs in response to excessive systemic release of noradrenaline. This post-TBI adrenergic storm was associated with reduced contractility of cervical lymphatic vessels, consistent with diminished return of glymphatic and lymphatic fluid to the systemic circulation. Accordingly, pan-adrenergic receptor inhibition normalized central venous pressure and partly restored glymphatic and cervical lymphatic flow in a mouse model of TBI, and these actions led to substantially reduced brain oedema and improved functional outcomes. Furthermore, post-traumatic inhibition of adrenergic signalling boosted lymphatic export of cellular debris from the traumatic lesion, substantially reducing secondary inflammation and accumulation of phosphorylated tau. These observations suggest that targeting the noradrenergic control of central glymphatic flow may offer a therapeutic approach for treating acute TBI., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Flow of cerebrospinal fluid (CSF) through brain perivascular spaces (PVSs) is essential for the clearance of interstitial metabolic waste products whose accumulation and aggregation is a key mechanism of pathogenesis in many diseases. The PVS geometry has important implications for CSF flow as it affects CSF and solute transport rates. Thus, the size and shape of the perivascular spaces are essential parameters for models of CSF transport in the brain and require accurate quantification., Methods: We segmented two-photon images of pial (surface) PVSs and the adjacent arteries and characterized their sizes and shapes of cross sections from 14 PVS segments in 9 mice. Based on the analysis, we propose an idealized model that approximates the cross-sectional size and shape of pial PVSs, closely matching their area ratios and hydraulic resistances., Results: The ratio of PVS-to-vessel area varies widely across the cross sections analyzed. The hydraulic resistance per unit length of the PVS scales with the PVS cross-sectional area, and we found a power-law fit that predicts resistance as a function of the area. Three idealized geometric models were compared to PVSs imaged in vivo, and their accuracy in reproducing hydraulic resistances and PVS-to-vessel area ratios were evaluated. The area ratio was obtained across different cross sections, and we found that the distribution peaks for the original PVS and its closest idealized fit (polynomial fit) were 1.12 and 1.21, respectively. The peak of the hydraulic resistance distribution is [Formula: see text] Pa  s/m[Formula: see text] and [Formula: see text] Pa s/m[Formula: see text] for the segmentation and its closest idealized fit, respectively., Conclusions: PVS hydraulic resistance can be reasonably predicted as a function of the PVS area. The proposed polynomial-based fit most closely captures the shape of the PVS with respect to area ratio and hydraulic resistance. Idealized PVS shapes are convenient for modeling, which can be used to better understand how anatomical variations affect clearance and drug transport., (© 2023. The Author(s).)

Functional hyperemia, also known as neurovascular coupling, is a phenomenon that occurs when neural activity increases local cerebral blood flow. Because all biological activity produces metabolic waste, we here sought to investigate the relationship between functional hyperemia and waste clearance via the glymphatic system. The analysis showed that whisker stimulation increased both glymphatic influx and clearance in the mouse somatosensory cortex with a 1.6-fold increase in periarterial cerebrospinal fluid (CSF) influx velocity in the activated hemisphere. Particle tracking velocimetry revealed a direct coupling between arterial dilation/constriction and periarterial CSF flow velocity. Optogenetic manipulation of vascular smooth muscle cells enhanced glymphatic influx in the absence of neural activation. We propose that impedance pumping allows arterial pulsatility to drive CSF in the same direction as blood flow, and we present a simulation that supports this idea. Thus, functional hyperemia boosts not only the supply of metabolites but also the removal of metabolic waste., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Quantifying the flow of cerebrospinal fluid (CSF) is crucial for understanding brain waste clearance and nutrient delivery, as well as edema in pathological conditions such as stroke. However, existing in vivo techniques are limited to sparse velocity measurements in pial perivascular spaces (PVSs) or low-resolution measurements from brain-wide imaging. Additionally, volume flow rate, pressure, and shear stress variation in PVSs are essentially impossible to measure in vivo. Here, we show that artificial intelligence velocimetry (AIV) can integrate sparse velocity measurements with physics-informed neural networks to quantify CSF flow in PVSs. With AIV, we infer three-dimensional (3D), high-resolution velocity, pressure, and shear stress. Validation comes from training with 70% of PTV measurements and demonstrating close agreement with the remaining 30%. A sensitivity analysis on the AIV inputs shows that the uncertainty in AIV inferred quantities due to uncertainties in the PVS boundary locations inherent to in vivo imaging is less than 30%, and the uncertainty from the neural net initialization is less than 1%. In PVSs of N = 4 wild-type mice we find mean flow speed 16.33 ± 11.09 µm/s, volume flow rate 2.22 ± 1.983 × 10 3 µm 3 /s, axial pressure gradient ( - 2.75 ± 2.01)×10 -4 Pa/µm (-2.07 ± 1.51 mmHg/m), and wall shear stress (3.00 ± 1.45)×10 -3 Pa (all mean ± SE). Pressure gradients, flow rates, and resistances agree with prior predictions. AIV infers in vivo PVS flows in remarkable detail, which will improve fluid dynamic models and potentially clarify how CSF flow changes with aging, Alzheimer's disease, and small vessel disease.

In advection-reaction-diffusion systems, the spreading of a reactive scalar can be significantly influenced by the flow field in which it grows. In systems with sharp boundaries between reacted and unreacted regions, motion of the reaction fronts that lie at those boundaries can quantify spreading. Here, we present an algorithm for measuring the velocity of reaction fronts in the presence of flow, expanding previous work on tracking reaction fronts without flow. The algorithm provides localized measurements of front speed and can distinguish its two components: one from chemical dynamics and another from the underlying flow. We validate that the algorithm returns the expected front velocity components in two simulations and then show that in complex experimental flows, the measured front velocity maps fronts from one time step to the next self-consistently. Finally, we observe a variation of the chemical speed with flow speed in a variety of experiments with different time scales and length scales.

We experimentally study spreading of the Belousov-Zhabotinsky reaction behind a bluff body in a laminar flow. Locations of reacted regions (i.e., regions with high product concentration) correlate with a moderate range of Lagrangian stretching and that range is close to the range of optimal stretching previously observed in topologically different flows [T. D. Nevins and D. H. Kelley, Phys. Rev. Lett. 117, 164502 (2016)]. The previous work found optimal stretching in a closed, vortex dominated flow, but this article uses an open flow and only a small area of appreciable vorticity. We hypothesize that optimal stretching is common in advection-reaction-diffusion systems with an excitation threshold, including excitable and bistable systems, and that the optimal range depends on reaction chemistry and not on flow shape or characteristic speed. Our results may also give insight into plankton blooms behind islands in ocean currents.

We present an algorithm for measuring the speed and thickness of reaction fronts, and from those quantities, the diffusivity and the reaction rate of the active chemical species. This front-tracking algorithm provides local measurements suitable for statistics and requires only a sequence of concentration fields. Though our eventual goal is front tracking in advection-reaction-diffusion, here we demonstrate the algorithm in reaction-diffusion. We test the algorithm with validation data in which front speed and thickness are prescribed, as well as simulation results in which diffusivity and reaction rate are prescribed. In all tests, measurements closely match true values. We apply the algorithm to laboratory experiments using the Belousov-Zhabotinsky reaction, producing speed, diffusivity, and reaction rate measurements that are statistically more robust than in prior studies. Finally, we use thickness measurements to quantify the concentration profile of chemical waves in the reaction.