Dynamic Topological Data Analysis for Functional Brain Signals

Tananun Songdechakraiwut, Moo K. Chung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


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.
Original languageEnglish (US)
Title of host publication2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)
ISBN (Print)9781728174013
StatePublished - Jul 31 2020
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank Hernando Ombao of KAUST, Yuan Wang of University of South Carolina, Yasu Wang of Ohio University, Taniguchi Masanobu of Waseda University and other participants of the KAUST workshop on TDA in January 2020 for valuable discussions on the Rips filtration and dynamic-TDA.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


Dive into the research topics of 'Dynamic Topological Data Analysis for Functional Brain Signals'. Together they form a unique fingerprint.

Cite this