Biology subjects

Kelley, D. H.

Publications and source records attributed to Kelley, D. H..

8 recordsLinked to original sources

bioRxiv2026

Multimodal imaging maps spinal glymphatic transport in health and its disruption after injury

Spinal cord injury (SCI) is an incurable neurological condition in which post-traumatic edema contributes to secondary ischemic damage because the spinal cord is confined within the rigid vertebral canal. However, glymphatic transport in the healthy and injured spinal cord remains poorly characterized. We hypothesized that spinal cerebrospinal (CSF) and glymphatic transport is shaped by anatomy, administration route, and molecular size of the tracer; is disrupted after SCI; and is further impaired by loss of aquaporin-4 (AQP4). CSF tracers were administered by cisterna magna or lumbar intrathecal injection to anesthetized adult C57BL/6 mice. Dynamic contrast-enhanced MRI, ex vivo fluorescence imaging, and histology were used to characterize tracer distribution, influx, and clearance. Constrictions of the spinal subarachnoid space at C3-C7, T6-T12 and L5-S1 spinal segments accelerated contrast-agent dispersion, demonstrating that local anatomy regulates spinal CSF transport. Tracer influx into the cord itself occurred primarily along periarterial pathways.Tracer distribution depended on the administration route: cisterna magna injection preferentially labeled white matter in the upper spinal cord, whereas lumbar injection preferentially labeled grey matter in the lower spinal cord. Gadobutrol and ovalbumin penetrated and dispersed throughout the parenchyma, whereas fibrinogen remained confined to the meninges. Under physiological conditions, intraparenchymal tracers cleared within 24 h. SCI was modeled using a minimally invasive NMDA-induced lesion at T11 that produced focal necrosis while preserving the dura and vertebral structures. Glymphatic transport, lesion volume, and motor recovery were compared between wild-type and AQP4-knockout mice using imaging, the Basso Mouse Scale, and voluntary wheel running. After SCI, intrathecal contrast propagated more rapidly, especially at T7-T8 segments and at the S1 segment, but tissue clearance was profoundly impaired, with tracer retention persisting for up to seven days. AQP4-knockout mice showed greater clearance impairment, larger lesions, and poorer motor recovery than wild-type controls compared at 24 h. These findings define major determinants of spinal glymphatic transport and show that accelerated tracer propagation after SCI does not indicate effective clearance. Instead, SCI produces glymphatic dysfunction that is exacerbated by loss of AQP4 and associated with greater tissue damage and functional impairment. The results extend the glymphatic framework to the spinal cord and identify fluid-clearance pathways as potential therapeutic targets for limiting edema and secondary injury. Future studies should determine whether restoring glymphatic function improves neurological recovery after SCI.

neuroscience↗

bioRxiv2025

MR-AIV reveals in-vivo brain-wide fluid flow with physics-informed AI

The circulation of cerebrospinal and interstitial fluid plays a vital role in clearing metabolic waste from the brain, and its disruption has been linked to neurological disorders. However, directly measuring brain-wide fluid transport--especially in the deep brain--has remained elusive. Here, we introduce magnetic resonance artificial intelligence velocimetry (MR-AIV), a framework featuring a specialized physics-informed architecture and optimization method that reconstructs three-dimensional fluid velocity fields from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MR-AIV unveils brain-wide velocity maps while providing estimates of tissue permeability and pressure fields--quantities inaccessible to other methods. Applied to the brain, MR-AIV reveals a functional landscape of interstitial and perivascular flow, quantitatively distinguishing slow diffusion-driven transport ([~] 0.1 {micro}m/s) from rapid advective flow ([~] 3 {micro}m/s). This approach enables new investigations into brain clearance mechanisms and fluid dynamics in health and disease, with broad potential applications to other porous media systems, from geophysics to tissue mechanics.

neuroscience↗

bioRxiv2024

Inferring in vivo murine cerebrospinal fluid flow using artificial intelligence velocimetry with moving boundaries and uncertainty quantification

Cerebrospinal fluid (CSF) flow is crucial for clearing metabolic waste from the brain, a process whose dysregulation is linked to neurodegenerative diseases like Alzheimers. Traditional approaches like particle tracking velocimetry (PTV) are limited by their reliance on single-plane two-dimensional measurements, which fail to capture the complex dynamics of CSF flow fully. To overcome these limitations, we employ Artificial Intelligence Velocimetry (AIV) to reconstruct three-dimensional velocities, infer pressure and wall shear stress, and quantify flow rates. Given the experimental nature of the data and inherent variability in biological systems, robust uncertainty quantification (UQ) is essential. Towards this end, we have modified the baseline AIV architecture to address aleatoric uncertainty caused by noisy experimental data, enhancing our measurement refinement capabilities. We also implement UQ for the model and epistemic uncertainties arising from the governing equations and network representation. Toward this end, we test multiple governing laws, representation models, and initializations. Our approach not only advances the accuracy of CSF flow quantification but also can be adapted to other applications that use physics-informed machine learning to reconstruct fields from experimental data, providing a versatile tool for inverse problems.

neuroscience↗

bioRxiv2023

Image Analysis Techniques for In Vivo Quantification of Cerebrospinal Fluid Flow

Over the last decade, there has been a tremendously increased interest in understanding the neuro-physiology of cerebrospinal fluid (CSF) flow, which plays a crucial role in clearing metabolic waste from the brain. This growing interest was largely initiated by two significant discoveries: the glymphatic system (a pathway for solute exchange between interstitial fluid deep within the brain and the CSF surrounding the brain) and meningeal lymphatic vessels (lymphatic vessels in the layer of tissue surrounding the brain that drain CSF). These two CSF systems work in unison, and their disruption has been implicated in several neurological disorders including Alzheimers disease, stoke, and traumatic brain injury. Here, we present experimental techniques for in vivo quantification of CSF flow via direct imaging of fluorescent microspheres injected into the CSF. We discuss detailed image processing methods, including registration and masking of stagnant particles, to improve the quality of measurements. We provide guidance for quantifying CSF flow through particle tracking and offer tips for optimizing the process. Additionally, we describe techniques for measuring changes in arterial diameter, which is an hypothesized CSF pumping mechanism. Finally, we outline how these same techniques can be applied to cervical lymphatic vessels, which collect fluid downstream from meningeal lymphatic vessels. We anticipate that these fluid mechanical techniques will prove valuable for future quantitative studies aimed at understanding mechanisms of CSF transport and disruption, as well as for other complex biophysical systems.

neuroscience↗

bioRxiv2021

A network model of glymphatic flow under different experimentally-motivated parametric scenarios

Rapidly growing evidence demonstrates that flow of cerebrospinal fluid (CSF) through perivascular spaces (PVSs) - annular channels surrounding vasculature in the brain - is a critically-important component of neurophysiology. CSF inflow contributes during physiological conditions to clearance of metabolic waste and in pathological situations to edema formation. However, brain-wide imaging methods cannot resolve PVSs, and high-resolution methods cannot access deep tissue or be applied to human subjects, so theoretical models provide essential insight. We model this CSF pathway as a network of hydraulic resistances, built from published parameters. A few parameters have very wide uncertainties, so we focus on the estimated limits of their feasible ranges by analyzing different parametric scenarios. We identify low-resistance PVSs and high-resistance parenchyma (brain tissue) as the scenario that best explains experimental observations. Our results point to the most important parameters that should be measured in future experiments. Extensions of our modeling may help predict stroke severity or lead to neurological disease treatments and drug delivery methods.

neuroscience↗

bioRxiv2020

Bulk flow of cerebrospinal fluid observed in periarterial spaces is not an artifact of injection

Cerebrospinal fluid (CSF) flowing through periarterial spaces is integral to the brains mechanism for clearing metabolic waste products. Experiments that track tracer particles injected into the cisterna magna of mouse brains have shown evidence of pulsatile CSF flow in pial periarterial spaces, with a bulk flow in the same direction as blood flow. However, the driving mechanism remains elusive. Several studies have suggested that the bulk flow might be an artifact, driven by the injection itself. Here, we address this hypothesis with new in vivo experiments where tracer particles are injected into the cisterna magna using a dual-syringe system, with simultaneous injection and withdrawal of equal amounts of fluid. This method produces no net increase in CSF volume and no significant increase in intracranial pressure. Yet, particle-tracking reveals flows in the pial periarterial spaces that are completely consistent with the flows observed in earlier experiments with single-syringe injection.

neuroscience↗

bioRxiv2020

Perivascular pumping in the mouse brain: Realistic boundary conditions reconcile theory, simulation, and experiment

Cerebrospinal fluid (CSF) flows through the perivascular spaces (PVSs) surrounding cerebral arteries. Revealing the mechanisms driving that flow could bring improved understanding of brain waste transport and insights for disorders including Alzheimers disease and stroke. In vivo velocity measurements of CSF in surface PVSs in mice have been used to argue that flow is driven primarily by the pulsatile motion of artery walls -- perivascular pumping. However, fluid dynamics theory and simulation have predicted that perivascular pumping produces flows differing from in vivo observations starkly, particularly in the phase and relative amplitude of flow oscillation. Here we show that coupling theoretical and simulated flows to realistic end boundary conditions, using resistance and compliance values measured in mice, results in velocities that match observations closely in phase, relative amplitude of oscillation, and mean flow speed. This new, quantitative agreement among theory, simulation, and in vivo measurement further supports the idea that perivascular pumping is a primary CSF driver in physiological conditions.

neuroscience↗

bioRxiv2019

Hydraulic resistance of perivascular spaces in the brain

Perivascular spaces (PVSs) are annular channels that surround arteries and carry cerebrospinal fluid into the brain, sweeping away metabolic waste. In vivo observations reveal that they are not concentric, circular annuli, however: the outer boundaries are often oblate, and the arteries that form the inner boundaries are often offset from the central axis. Here we show that the observed shapes of PVSs have lower hydraulic resistance than concentric, circular annuli of the same size, and therefore allow faster, more efficient flow of cerebrospinal fluid. To calculate hydraulic resistance, we parameterize PVS cross-sections as circles surrounded by ellipses, varying the radii of the circles, major and minor axes of the ellipses, and two-dimensional eccentricities of the circles with respect to the ellipses. For each shape, we solve the governing Navier-Stokes equation to determine the velocity profile for steady laminar flow and the corresponding hydraulic resistance. We find that the minimum hydraulic resistance (and therefore maximum flow rate) for a given PVS cross-sectional area occurs when the ellipse is elongated and intersects the circle, dividing the PVS into two lobes, as is common around pial arteries. We also find that if both the inner and outer boundaries are circular, the minimum hydraulic resistance occurs when the eccentricity is large, as is common around penetrating arteries. The circular, concentric annuli assumed in recent studies maximize hydraulic resistance and therefore minimize flow. Our parameterization can incorporate more realistic resistances into hydraulic network models of flow of cerebrospinal fluid in the brain.\n\nSignificance StatementRecent experiments show that there is bulk flow of cerebrospinal fluid in brain perivascular spaces (PVSs) in the direction of the blood flow, driven by pulsations of arterial walls due to the heartbeat. Published hydraulic network models have argued against such a flow due to the purported high hydraulic resistance of the system. Here we show that these high resistances are due to misrepresentations of the size and shape of PVSs. We present an adjustable model PVS that can match closely the actual shapes observed in vivo and show that these shapes are often nearly optimal, in the sense of offering the least hydraulic resistance. This optimization may well represent an evolutionary adaptation that maximizes clearance of metabolic waste from the brain.

neuroscience↗