Dynamic Topological Data Analysis for Functional Brain Signals

Tananun Songdechakraiwut; Moo K. Chung

doi:10.1109/isbiworkshops50223.2020.9153431

Dynamic Topological Data Analysis for Functional Brain Signals

Tananun Songdechakraiwut, Moo K. Chung

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 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 language	English (US)
Title of host publication	2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)
Publisher	IEEE
ISBN (Print)	9781728174013
DOIs	https://doi.org/10.1109/isbiworkshops50223.2020.9153431
State	Published - Jul 31 2020
Externally published	Yes

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.

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10.1109/isbiworkshops50223.2020.9153431

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title = "Dynamic Topological Data Analysis for Functional Brain Signals",

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.",

author = "Tananun Songdechakraiwut and Chung, {Moo K.}",

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.",

year = "2020",

month = jul,

day = "31",

doi = "10.1109/isbiworkshops50223.2020.9153431",

language = "English (US)",

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publisher = "IEEE",

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AU - Songdechakraiwut, Tananun

AU - Chung, Moo K.

N1 - 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.

PY - 2020/7/31

Y1 - 2020/7/31

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/665250

UR - https://ieeexplore.ieee.org/document/9153431/

UR - http://www.scopus.com/inward/record.url?scp=85090403885&partnerID=8YFLogxK

U2 - 10.1109/isbiworkshops50223.2020.9153431

DO - 10.1109/isbiworkshops50223.2020.9153431

M3 - Conference contribution

SN - 9781728174013

BT - 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)

PB - IEEE

ER -

Dynamic Topological Data Analysis for Functional Brain Signals

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