Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.13.335356v1?rss=1 Authors: Yokoyama, H., Kitajo, K. Abstract: Recent neuroscience studies suggest that flexible changes in functional brain networks are associated with cognitive functions. Therefore, the technique that detects changes in dynamical brain structures, which is called "dynamic functional connectivity (DFC) analysis", has become important for the clarification of the crucial roles of functional brain networks. Conventional methods analyze DFC applying static indices based on the correlation between each pair of time-series data in the different brain areas to estimate network couplings. However, correlation-based indices lead to incorrect conclusions contaminated by spurious correlations between time-series data. These spurious correlation issues of network analysis could be reduced by performing the analysis assuming data structures based on a relevant model. Therefore, we propose a novel approach that combines the following two methods: (1) model-based network estimation assuming a dynamical system for time evolution, and (2) sequential estimation of model parameters based on Bayesian inference. We, thus, assumed that the model parameters reflect dynamical structures of functional brain networks. Moreover, by introducing model parameter values prior to the Bayesian inference, the network changes can be quantified based on the comparison between prior and posterior distributions of model parameters. In this comparison, we used the Kullback-Leibler (KL) divergence as an index for such changes. To validate our method, we applied it to numerical data and electroencephalographic (EEG) data. As a result, we confirmed that the KL divergence increased only when changes in dynamical structures occurred. Our proposed method successfully estimated both network couplings and change points of dynamic structures in the numerical and EEG data. The results suggest that our proposed method is useful in revealing the neural basis of dynamic functional networks. Copy rights belong to original authors. Visit the link for more info