Estimating Effective Connectivity using Brain Partitioning

One of the main outstanding issues in the neuroscience is estimation of effective connectivity in brain networks, which models the causal interactions among neuronal populations. Estimation of effective connectivity embraces two types of the challenges, such as estimation accuracy and computational complexity. In this paper, we consider resting-state (rs) fMRI data serving as an input for a stochastic linear DCM model. The model parameters are estimated through an EM (expectation maximization) iterative procedure. In this work, we propose the alternative scheme for the hyperparameters estimation aiming in reduction of computational burden of the original EM-algorithm. The simulation results demonstrate the viability of the proposed block-reweighting scheme and represents a promising research direction to be further investigated.