State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing

TL;DR

This paper introduces a dynamic state-space model for solving the MEG inverse problem, incorporating temporal and spatial correlations to improve neural source localization accuracy using high performance computing.

Contribution

It presents a novel state-space approach that models neural dynamics and spatial-temporal correlations, advancing beyond static regularization methods like MNE.

Findings

Enhanced source localization accuracy demonstrated

Efficient computation enabled by high performance computing

Incorporation of temporal dynamics improves model stability

Abstract

Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem. One well-known technique designed to address this imbalance is the minimum norm estimator (MNE). This approach imposes an $L^2$ regularization constraint that serves to stabilize and condition the source parameter estimates. However, these classes of regularizer are static in time and do not consider the temporal constraints inherent to the biophysics of the MEG experiment. In this paper we propose a dynamic state-space model that accounts for both spatial and temporal correlations within and across candidate intracortical sources. In our model, the observation model is derived from the steady-state solution to Maxwell's equations while the latent model representing neural dynamics is given by a random walk process.