Dynamic Graphical Models with Variable Selection for Effective Connectivity

This paper proposes a novel approach that combines dynamic linear models applied to graph data and variable selection through spike-and-slab priors. The new class of models, called Dynamic Graphical Variable Selection, is used to infer effective connectivity in certain brain regions allowing both connectivity weights and graphical structure to vary over time. One advantage of our method is that as the graphical structure is estimated inferentially, the computational cost is reduced. That way our methodology can accomodate high-dimentional data, such as large networks observed through long periods of time. We illustrate our methodology via numerical experiments with simulated and synthetic data, and then applied to a fNIRS real data. The obtained results showed that the static version of our model is competitive against previous methodologies and demands a lower computational cost. Our model is more flexible than the previous methodologies by allowing graphical structure to vary over time.