Joint estimation of source dynamics and interactions from MEG data

Current techniques to estimate directed functional connectivity from magnetoencephalography (MEG) signals involve two sequential steps: (a) estimation of the sources and their amplitude time series from the MEG data and (b) estimation of directed interactions between the source time series. However, such a sequential approach is not optimal as it leads to spurious connectivity due to spatial leakage. Here, we present an algorithm to jointly estimate the source and connectivity parameters using Bayesian filtering. We refer to this new algorithm as JEDI-MEG (Joint Estimation of source Dynamics and Interactions from MEG data). By formulating a state-space model for the locations and amplitudes of a given number of sources, we show that estimation of their connections can be reduced to a system identification problem. Using simulated MEG data, we show that the joint approach provides a more accurate reconstruction of connectivity parameters than the conventional two-step approach. Using real MEG responses to visually presented faces in 16 subjects, we also demonstrate that our method gives source and connectivity estimates that are both physiologically plausible and largely consistent across subjects. In conclusion, the proposed joint estimation approach outperforms the traditional two-step approach in determining functional connectivity in MEG data.

Functional connectivity is currently estimated from electromagnetic brain signals such as magnetoencephalography (MEG) in two consecutive steps: First, the inverse problem is solved to estimate the locations and temporal dynamics of brain sources. Second, connectivity metrics are computed between these estimated sources. This approach suffers from the limitation that the information provided by the connectivity structure is not exploited in the estimation of source activity and vice versa. Here, we present a novel algorithm, utilizing Bayesian filtering, to jointly estimate the source and connectivity parameters to overcome this limitation. Compared with state-of-the-art two-step approaches, our method provides a more accurate reconstruction of the connectivity parameters, which we demonstrate using a standard connectivity benchmark simulation and an electrocorticography-based simulation of MEG data. We also applied our method to real MEG responses (open-access dataset) to visually presented faces in 16 subjects, and the results show that our approach provides source and connectivity estimates that are both physiologically plausible and largely consistent across subjects. Overall, this work contributes to methodological advances in estimating functional connectivity from MEG data.