Fitting a single equivalent current dipole model to MEG data with exhaustive search optimization is a simple, practical and very robust method given the speed of modern computers

Abstract

Abstract Fitting an equivalent current dipole (ECD) to magnetoencephalography (MEG) data is a widely used method for analyzing MEG data, especially in clinical applications. However, as is well known, the iterative fitting algorithms routinely employed can converge on a local minima far from the best fit. While a host of fitting algorithms have been proposed to remedy this problem, the simple exhaustive search optimization algorithm has not been considered in the literature — possibly because it is assumed to be too slow for routine use. Taking advantage of the speed of modern computers, it is demonstrated that using exhaustive search to fit the parameters of a single ECD to 151 MEG channels yields both robust and reproducible fits within reasonable computation times. On a high end personal computer, with a 2 mm grid spacing over the head, exhaustive search can fit 450 ECD's per hour with only an additional 5 min spent on calculating the lead fields. As the algorithm is fully automatic, it is very useful when the goal is to detect ECD's in a large dataset. Also, an interpreter can be provided with parameters from the ECD that fits for all latencies after stimulus, allowing a more informed decision of which latency to use for further analysis.