Caulking the Leakage Effect in MEEG Source Connectivity Analysis

TL;DR

This paper introduces BC-VARETA, a Bayesian method that jointly estimates neural source activity and connectivity in MEEG data, effectively reducing leakage effects and outperforming existing inverse solvers.

Contribution

It proposes a novel Bayesian framework for source activity and connectivity estimation that mitigates leakage, with rigorous leakage quantification measures and superior performance.

Findings

BC-VARETA significantly reduces leakage effects.

The new measures quantify connectivity leakage accurately.

BC-VARETA outperforms state-of-the-art inverse solvers by orders of magnitude.

Abstract

Simplistic estimation of neural connectivity in MEEG sensor space is impossible due to volume conduction. The only viable alternative is to carry out connectivity estimation in source space. Among the neuroscience community this is claimed to be impossible or misleading due to Leakage: linear mixing of the reconstructed sources. To address this problematic we propose a novel solution method that caulks the Leakage in MEEG source activity and connectivity estimates: BC-VARETA. It is based on a joint estimation of source activity and connectivity in the frequency domain representation of MEEG time series. To achieve this, we go beyond current methods that assume a fixed gaussian graphical model for source connectivity. In contrast we estimate this graphical model in a Bayesian framework by placing priors on it, which allows for highly optimized computations of the connectivity, via a new procedure based on the local quadratic approximation under quite general prior models. A further contribution of this paper is the rigorous definition of leakage via the Spatial Dispersion Measure and Earth Movers Distance based on the geodesic distances over the cortical manifold. Both measures are extended for the first time to quantify Connectivity Leakage by defining them on the cartesian product of cortical manifolds. Using these measures, we show that BC-VARETA outperforms most state of the art inverse solvers by several orders of magnitude.