Dynamic Topological Data Analysis for Functional Brain Signals

We propose a novel dynamic topological data analysis (TDA) framework that builds persistent homology over a time series of 3D functional brain images. The proposed method encodes the time series as a time-ordered sequence of Vietoris-Rips complexes and their corresponding barcodes in studying dynami...

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Bibliographic Details
Published in2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) pp. 1 - 4
Main Authors Songdechakraiwut, Tananun, Chung, Moo K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2020
Subjects
barcodes
Data analysis
Functional magnetic resonance imaging
persistent homology
Pipelines
resting-state fMRI
Three-dimensional displays
time series
Time series analysis
Topological data analysis
Topology
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Summary:We propose a novel dynamic topological data analysis (TDA) framework that builds persistent homology over a time series of 3D functional brain images. The proposed method encodes the time series as a time-ordered sequence of Vietoris-Rips complexes and their corresponding barcodes in studying dynamically changing topological patterns. The method is applied to the resting-state functional magnetic resonance imaging (fMRI) of the human brain. We demonstrate that the dynamic-TDA can capture the topological patterns that are consistently observed across different time points in the resting-state fMRI.
DOI:10.1109/ISBIWorkshops50223.2020.9153431