Generalized DCM models for pre-filtering compensation

Estimation of brain effective connectivity (EC) faces several issues, among which are computational complexity and estimation accuracy. Here, we focus on increasing estimation accuracy, in particular addressing the bias introduced by the pre-processing pipeline employed on resting-state (rs) fMRI signals, which includes temporal band-pass prefiltering. In this paper, we propose an alternative model (so-called "Filtered rs-DCM") that adapts a stochastic linear rs-DCM model to the filtered input. Filtered rs-DCM is built by augmenting the neuronal rs-DCM with the filter model used in the pre-processing step. The parameter estimation is performed with EM-algorithm with a sparsity inducing prior. The simulation experiments run on band-pass filtered synthetic datasets demonstrate that this approach outperforms classical DCM models in terms of EC estimation accuracy.