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

TL;DR

This study applies topological data analysis to compare pulmonary arterial network structures in control and hypertensive mice, revealing topological differences associated with pulmonary hypertension that could aid diagnosis.

Contribution

It introduces a novel framework using persistent homology to analyze arterial network topology in pulmonary hypertension, incorporating pruning algorithms for network normalization.

Findings

Hypertensive trees exhibit greater depth.

Directional complexities correlate with branch number.

Pruning affects spatial statistics and complexity measures.

Abstract

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.