| Date |
2023-08-11 ~ 2023-08-11 |
Time |
16:00:00 ~ 18:00 |
| Speaker |
Moo Kyung Chung |
Affiliation |
University of Wisconsin-Madison |
| Place |
Math Bldg 404 & Online streaming (Zoom) |
Streaming link |
ID : 688 896 1076 /
PW : 54321 |
| Topic |
Topological State-Space Estimation of Dynamically Changing Functional Brain Networks |
| Contents |
We introduce a novel data-driven topological approach for the estimation of state spaces in dynamically changing functional brain networks of humans. The approach penalizes the topological distance between networks, clustering them into topologically distinct states. The method incorporates the temporal aspect of the data by using the Wasserstein distance, probabilistic version of optimal transport, between networks at different time points. The approach has shown to outperform the widely used k-means clustering, which is commonly applied in the state space estimation in brain networks. The method enables the precise determination of the state spaces in brain networks from resting-state functional magnetic resonance imaging (rs-fMRI). We also address the intriguing question of whether the overall topology of brain networks is genetically heritable through a twin brain imaging study. The talk is based on arXiv:2201.00087 |