MUSIC seeded multi-dipole MEG modeling using the Constrained Start Spatio-Temporal modeling procedure.

Abstract

The Constrained Start Spatio-Temporal modeling program (CSST) is an objective multi-dipole, multi-start MEG/EEG analysis procedure that randomly selects from 100 to 100,000 initial dipole configurations, and runs a nonlinear simplex search on each of these configurations employing a reduced Chi-square statistic as the minimization criterion, to obtain a set of dipole configurations that best fit the data [Ranken, 2002]. A parallel version of CSST is implemented in IDL and MPI, making CSST usable on a single computer, or on a Linux cluster. We have now developed a multi-resolution version of MUSIC [Mosher, 1992] [Mosher, 1998] that provides an 80% or more reduction in the number of forward calculations needed to obtain results comparable to a 160,000 point MUSIC scan, on a 2 mm grid that defines a brain volume. The multi-resolution MUSIC scan provides an improved set of initial dipole estimates for the CSST analysis. In preliminary tests on real and simulated MEG data, with model orders ranging between 5 and 7 dipoles, the best performance improvements were obtained by mixing in 1 to 3 dipole locations randomly drawn from the best MUSIC locations, with randomly selected locations from the brain volume to complete the selected model order. We have also developed an improved method for sampling the brain volume for initial configurations. These improvements have led to a 75% reduction in the number of starting configurations required to obtain 5-10 best solutions with equal or lower reduced Chi-square values, when compared to the best solutions from the previous version of CSST.