Online Publication Date2020-07-31
Print Publication Date2020-04
Permanent link to this recordhttp://hdl.handle.net/10754/665250
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AbstractWe 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.
CitationSongdechakraiwut, T., & Chung, M. K. (2020). Dynamic Topological Data Analysis for Functional Brain Signals. 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops). doi:10.1109/isbiworkshops50223.2020.9153431
SponsorsWe 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.
Conference/Event name17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020