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Activity Number: 438 - Statistical Methods for Topological Data Analysis
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #310931
Title: Persistent Topological Descriptors for Functional Brain Network
Author(s): Hyunnam Ryu* and Nicole Lazar
Companies: University of Georgia and University of Georgia
Keywords: Topological Data Analysis; Persistent Homology; fMRI; Brain Connectivity

We compare the topological features of functional brain networks. In general, functional brain networks are dealt with in an elementwise manner based on the connectivity matrix as part of network data analysis. This tends to ignore the higher-order topology of the network, which can have significant implications. In recent studies, researchers have been interested in topological data analysis. Persistent homology is known to be useful for studying dynamic topological invariants hidden in complex data obtained from topological space. Analysis using persistent homology not only captures topological features that could be overlooked in the network data analysis but also addresses threshold selection problems commonly found in network data analysis. We use persistent homology to compare the topological features of brain networks. We construct a brain network from the fMRI time series BOLD signal and calculate the persistent homology through the weighted brain network. Also, we compare the summarized topological features of different subject groups by calculating the persistence landscape.

Authors who are presenting talks have a * after their name.

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