Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
This work addresses the challenge of analyzing dynamic brain networks for researchers in neuroscience and genetics, offering an incremental improvement over existing clustering methods.
The paper tackles the problem of estimating state spaces for dynamically changing functional human brain networks at rest by introducing a topological data analysis technique that uses Wasserstein distance to cluster networks into distinct topological states, outperforming k-means clustering. It further investigates the genetic underpinnings of these topological features through a twin study, suggesting that dynamic state changes may hold significant hidden genetic information.
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 further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information. MATLAB code for the method is available at https://github.com/laplcebeltrami/PH-STAT.