Biology subjects

Kuchta, M.

Publications and source records attributed to Kuchta, M..

4 recordsLinked to original sources

bioRxiv2025

Advection versus diffusion in brain ventricular transport

Cerebrospinal fluid (CSF) is integral to brain function. CSF provides mechanical support for the brain and helps distribute nutrients, neurotransmitters and metabolites throughout the central nervous system. CSF flow is driven by several processes, including the beating of motile cilia located on the walls of the brain ventricles. Despite the physiological importance of CSF, the underlying mechanisms of CSF flow and solute transport in the brain ventricles remain to be comprehensively resolved. This study analyzes and evaluates specifically the role of motile cilia in CSF flow and transport. We developed finite element methods for modeling flow and transport using the geometry of the zebrafish larval brain ventricles, for which we have detailed knowledge of cilia properties and CSF motion. The computational model is validated by in vivo experiments that monitor transport of a photoconvertible protein secreted in the brain ventricles. Our results show that while cilia contribute to advection of large particles, diffusion plays a significant role in the transport of small solutes. We also demonstrate how cilia location and the geometry of the ventricular system impact solute distribution. Altogether, this work presents a computational framework that can be applied to other ventricular systems, together with new concepts of how molecules are transported within the brain and its ventricles.

neuroscience↗

bioRxiv2025

In-silico molecular enrichment and clearance of the human intracranial space

The mechanisms of intracranial solute transport are fundamental to human brain health, with alterations often linked to disease and functional impairment, and with distinct opportunities for personalized diagnostics and treatment. However, our understanding of these mechanisms and their interplay remains incomplete, in part due to the complexity of integrating insights across scales, between species and from different modalities. Here, we combine mixed-dimensional modelling, multi-modal magnetic resonance images, and high performance computing to construct and explore a high-fidelity in-silico model of human intracranial molecular enrichment. This model predicts the temporo-spatial spreading of a solute within an image-derived geometric representation of the subarachnoid space, ventricular system and brain parenchyma, including networks of surface perivascular spaces (PVSs). Our findings highlight the significant impact of cerebrospinal fluid (CSF) production and intracranial pulsatility on molecular enrichment following intrathecal tracer injection. We demonstrate that low-frequency vasomotion induces moderate CSF flow in surface PVS networks which substantially enhances tracer enrichment, and that impaired enrichment is a direct natural consequence of enlarged PVSs. This openly available technology platform thus provides an opportunity for integrating separate observations on diffusion in neuropil, vascular dynamics, intracranial pulsatility, CSF production, and efflux, and for exploring drug delivery and clearance in the human brain.

neuroscience↗

bioRxiv2022

Sleep cycle-dependent vascular dynamics enhance perivascular cerebrospinal fluid flow and solute transport

Perivascular spaces (PVS) are important highways for fluid and solute transport in the brain enabling efficient waste clearance during sleep. Using two-photon imaging of naturally sleeping mice we demonstrate sleep cycle-dependent PVS dynamics - slow, large-amplitude oscillations in NREM, a reduction in REM and an enlargement upon awakening at the end of a sleep cycle. By biomechanical modeling we demonstrate that these sleep cycle-dependent PVS dynamics drive fluid flow and solute transport.

neuroscience↗

bioRxiv2018

Can the presence of neural probes be neglected in computational modeling of extracellular potentials?

AbstractO_ST_ABSObjectiveC_ST_ABSMechanistic modeling of neurons is an essential component of computational neuroscience that enables scientists to simulate, explain, and explore neural activity. The conventional approach to simulation of extracellular neural recordings first computes transmembrane currents using the cable equation and then sums their contribution to model the extracellular potential. This two-step approach relies on the assumption that the extracellular space is an infinite and homogeneous conductive medium, while measurements are performed using neural probes. The main purpose of this paper is to assess to what extent the presence of the neural probes of varying shape and size impacts the extracellular field and how to correct for them.\n\nApproachWe apply a detailed modeling framework allowing explicit representation of the neuron and the probe to study the effect of the probes and thereby estimate the effect of ignoring it. We use meshes with simplified neurons and different types of probe and compare the extracellular action potentials with and without the probe in the extracellular space. We then compare various solutions to account for the probes presence and introduce an efficient probe correction method to include the probe effect in modeling of extracellular potentials.\n\nMain resultsOur computations show that microwires hardly influence the extracellular electric field and their effect can therefore be ignored. In contrast, Multi-Electrode Arrays (MEAs) significantly affect the extracellular field by magnifying the recorded potential. While MEAs behave similarly to infinite insulated planes, we find that their effect strongly depends on the neuron-probe alignment and probe orientation.\n\nSignificanceIgnoring the probe effect might be deleterious in some applications, such as neural localization and parameterization of neural models from extracellular recordings. Moreover, the presence of the probe can improve the interpretation of extracellular recordings, by providing a more accurate estimation of the extracellular potential generated by neuronal models.

bioengineering↗