Topological data analysis: a user guide

Wojciech Chacholski

doi:10.1117/12.2594534

1 August 2021 Topological data analysis: a user guide

Wojciech Chacholski

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118040O (2021) https://doi.org/10.1117/12.2594534
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

In the last decade, there has been an unprecedented development in topological data analysis tools to encode geometrical information. These tools have been successful for extracting new information from brain networks with a range of methods such as persistent homology, which provides a framework for obtaining higher order topological features from the brain that go beyond modular structures, and include homological significant aspects such as number of holes and cycles. In this talk I will give an introduction to what these techniques have to offer and present illustrative examples. I will focus particularly on how to extract statistically viable conclusions based on combining topological data analysis methods with subsampling techniques and discuss potential applications of these methods in the context of neurodegenerative diseases. This abstract is part of the symposium: "Diagnosis and Prediction of Neurodegenerative Diseases using Artificial Intelligence and Simulations".

Conference Presentation

Citation Download Citation

Wojciech Chacholski "Topological data analysis: a user guide", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118040O (1 August 2021); https://doi.org/10.1117/12.2594534

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