Dynamic Topological Data Analysis for Functional Brain Signals

Tananun Songdechakraiwut, Moo K. Chung

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

1 Scopus citations

Abstract

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)
PublisherIEEE
ISBN (Print)9781728174013
DOIs
StatePublished - Jul 31 2020
Externally publishedYes

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