arXiv subjects

Mette S Olufsen

Publications and source records attributed to Mette S Olufsen.

4 recordsLinked to original sources

arXiv2026

Multiscale hemodynamics model for the pulmonary arteries, arterioles, capillaries, venules and veins

This study presents the first mathematical model of pulsatile hemodynamics that encompasses the complete pulmonary circulation, explicitly linking the large arteries, arterioles, capillaries, venules, and large veins. To overcome the limitations of previous models that exclude explicit capillary dynamics, we incorporate a one-dimensional structured-tree model of the pulmonary arteries and veins with a dynamic capillary sheet model. This approach establishes a recursive method for coupling the capillary sheets to the structured trees, connecting arterioles and venules in a ladder-like architecture. To evaluate the impact of incorporating this capillary structure, we compare simulated hemodynamics in a healthy control subject and a pulmonary hypertension (PH) patient. Results illustrate that including capillaries in the model significantly alters hemodynamic predictions by introducing downstream damping. In the healthy control subject, the inclusion of the capillary network attenuates pulsatile energy, yielding the expected steady venous pressure and flow profiles, whereas omitting the capillaries results in an unphysiological high pulsatility transmitting into the venous system. The structural impact of the capillaries is even more pronounced in the PH patient, where explicitly modeling the capillary bed corrects an over-prediction in peak systolic pressure in the main pulmonary artery. Furthermore, unlike the healthy control subject, the remodeled PH microvasculature fails to completely isolate the venous system from arterial pulsations. Finally, we employ parametric sensitivity analysis to investigate how specific biomechanical factors drive vascular remodeling, demonstrating the framework's capability to quantify disease progression and severity.

q-bio.TO↗

arXiv2024

Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images

Patient-specific computational modeling is a popular, non-invasive method to answer medical questions. Medical images are used to extract geometric domains necessary to create these models, providing a predictive tool for clinicians. However, in vivo imaging is subject to uncertainty, impacting vessel dimensions essential to the mathematical modeling process. While there are numerous programs available to provide information about vessel length, radii, and position, there is currently no exact way to determine and calibrate these features. This raises the question, if we are building patient-specific models based on uncertain measurements, how accurate are the geometries we extract and how can we best represent a patient's vasculature? In this study, we develop a novel framework to determine vessel dimensions using change points. We explore the impact of uncertainty in the network extraction process on hemodynamics by varying vessel dimensions and segmenting the same images multiple times. Our analyses reveal that image segmentation, network size, and minor changes in radius and length have significant impacts on pressure and flow dynamics in rapidly branching structures and tapering vessels. Accordingly, we conclude that it is critical to understand how uncertainty in network geometry propagates to fluid dynamics, especially in clinical applications.

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arXiv2023

A Topological Data Analysis Study on Murine Pulmonary Arterial Trees with Pulmonary Hypertension

Pulmonary hypertension (PH), defined by a mean pulmonary arterial blood pressure above 20 mmHg, is a cardiovascular disease impacting the pulmonary vasculature. PH is accompanied by vascular remodeling, wherein vessels become stiffer, large vessels dilate, and smaller vessels constrict. Some types of PH, including hypoxia-induced PH (HPH), lead to microvascular rarefaction. The goal of this study is to analyze the change in pulmonary arterial network morphometry in the presence of HPH. To do so, we use novel methods from topological data analysis (TDA), employing persistent homology to quantify arterial network morphometry for control and hypertensive mice. These methods are used to characterize arterial trees extracted from micro-computed tomography (micro-CT) images. To compare results between control and hypertensive animals, we normalize generated networks using three pruning algorithms. This proof-of-concept study shows that the pruning methods effects the spatial tree statistics and complexities of the trees. Results show that HPH trees have higher depth and that the directional complexities correlate with branch number, except for trees pruned by vessel radius, where the left and anterior complexity are lower compared to control trees. While more data is required to make a conclusion about the overall effect of HPH on network topology, this study provides a framework for analyzing the topology of biological networks and is a step towards the extraction of relevant information for diagnosing and detecting HPH.

q-bio.QM↗

arXiv2016

Modeling the differentiation of A- and C-type baroreceptor firing patterns

The baroreceptor neurons serve as the primary transducers of blood pressure for the autonomic nervous system and are thus critical in enabling the body to respond effectively to changes in blood pressure. These neurons can be separated into two types (A and C) based on the myelination of their axons and their distinct firing patterns elicited in response to specific pressure stimuli. This study has developed a comprehensive model of the afferent baroreceptor discharge built on physiological knowledge of arterial wall mechanics, firing rate responses to controlled pressure stimuli, and ion channel dynamics within the baroreceptor neurons. With this model, we were able to predict firing rates observed in previously published experiments in both A- and C-type neurons. These results were obtained by adjusting model parameters determining the maximal ion-channel conductances. The observed variation in the model parameters are hypothesized to correspond to physiological differences between A- and C-type neurons. In agreement with published experimental observations, our simulations suggest that a twofold lower potassium conductance in C-type neurons is responsible for the observed sustained basal firing, whereas a tenfold higher mechanosensitive conductance is responsible for the greater firing rate observed in A-type neurons. A better understanding of the difference between the two neuron types can potentially be used to gain more insight into the underlying pathophysiology facilitating development of targeted interventions improving baroreflex function in diseased individuals, e.g. in patients with autonomic failure, a syndrome that is difficult to diagnose in terms of its pathophysiology.

q-bio.NC↗