A0475
Title: Topological state-space estimation of dynamically changing functional human brain networks
Authors: Moo K Chung - University of Wisconsin-Madison (United States) [presenting]
Abstract: A new data-driven topological approach is presented for estimating state spaces in dynamically changing functional brain networks of humans. The approach penalizes the topological distance between networks and clusters, dynamically changing brain networks into topologically distinct states. The method considers the temporal dimension of the data through the Wasserstein distance between networks. The method is shown to outperform the widely used k-means clustering often used in estimating the state space in brain networks. The method is applied to accurately determine the state spaces of dynamically changing functional brain networks. Subsequently, the question of if the overall topology of brain networks is a heritable feature using the twin study design is addressed.
