Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest

arXiv:2201.00087v5 Announce Type: replace-cross
Abstract: We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We …

arxiv brain cs.lg functional human math.at networks q-bio.nc rest space state type