Topological Analysis of Mouse Brain Vasculature via 3D Light-sheet Microscopy Images

TL;DR

This paper introduces a topological data analysis approach to characterize mouse brain vasculature in 3D light-sheet microscopy images, providing insights into structural differences related to health conditions.

Contribution

It applies topological data analysis to vascular networks, capturing structural features beyond traditional geometric measures, and compares these features across different populations.

Findings

Topological features effectively distinguish between normal and obese mouse vasculature.

Topological analysis reveals structural differences not captured by geometric features.

Method demonstrates robustness across multi-scale vascular structures.

Abstract

Vascular networks play a crucial role in understanding brain functionalities. Brain integrity and function, neuronal activity and plasticity, which are crucial for learning, are actively modulated by their local environments, specifically vascular networks. With recent developments in high-resolution 3D light-sheet microscopy imaging together with tissue processing techniques, it becomes feasible to obtain and examine large-scale brain vasculature in mice. To establish a structural foundation for functional study, however, we need advanced image analysis and structural modeling methods. Existing works use geometric features such as thickness, tortuosity, etc. However, geometric features cannot fully capture structural characteristics such as the richness of branches, connectivity, etc. In this paper, we study the morphology of brain vasculature through a topological lens. We extract topological features based on the theory of topological data analysis. Comparing of these robust and multi-scale topological structural features across different brain anatomical structures and between normal and obese populations sheds light on their promising future in studying neurological diseases.