Elasticnetisdr to Reconstruct Both Sparse Brain Activity and Effective Connectivity

Abstract

Electroencephalography (EEG) distributed source reconstruction methods can be improved by using spatio-temporal constraints. Few methods use structural connectivity (SC), obtained from diffusion MRI, to constrain the EEG source space. In this work, we present a source reconstruction algorithm that uses SC and constrains the source dynamics by a multivariate autoregressive model (MAR) to estimate both the effective connectivity (EC) between brain regions and their activation. To obtain an asymmetric EC, we add a sparse prior to the MAR model. We call this algorithm Elasticnet iterative Source and Dynamics reconstruction (eiSDR). This paper presents our approach and how the proposed model can obtain both brain activation and interactions. Its accuracy is demonstrated using synthetic data and tested with real data for a face recognition task. The results are in phase with other works that used the same data showing that the choice of using a MAR model and some priors on it give relevant results.