Bayesian Multi--Dipole Modeling of a Single Topography in MEG by Adaptive Sequential Monte Carlo Samplers

TL;DR

This paper introduces a Bayesian method using adaptive sequential Monte Carlo samplers to estimate neural current dipoles from MEG topographies, effectively determining their number and parameters in a static inverse problem.

Contribution

It presents a novel Bayesian approach with adaptive sampling for static MEG inverse problems, estimating both the number and parameters of neural dipoles simultaneously.

Findings

Accurate estimation of up to four dipoles in synthetic data.

Effective application to real somatosensory evoked fields data.

Comparison with other methods shows improved performance.

Abstract

In the present paper, we develop a novel Bayesian approach to the problem of estimating neural currents in the brain from a fixed distribution of magnetic field (called \emph{topography}), measured by magnetoencephalography. Differently from recent studies that describe inversion techniques, such as spatio-temporal regularization/filtering, in which neural dynamics always plays a role, we face here a purely static inverse problem. Neural currents are modelled as an unknown number of current dipoles, whose state space is described in terms of a variable--dimension model. Within the resulting Bayesian framework, we set up a sequential Monte Carlo sampler to explore the posterior distribution. An adaptation technique is employed in order to effectively balance the computational cost and the quality of the sample approximation. Then, both the number and the parameters of the unknown current dipoles are simultaneously estimated. The performance of the method is assessed by means of synthetic data, generated by source configurations containing up to four dipoles. Eventually, we describe the results obtained by analyzing data from a real experiment, involving somatosensory evoked fields, and compare them to those provided by three other methods.