Spatio-temporal modeling of neuromagnetic data: I. Multi-source location versus time-course estimation accuracy.

ABSTRACT

Numerical simulations were conducted to examine multi-source spatio-temporal resolution for neuromagnetic field distributions "measured" by a large sensor array (i.e., 135). spatio-temporal field distributions were generated by a series of two-dipole and three-dipole configurations in which source locations, orientations, and temporal dynamics of individual sources were systematically varied to represent classes of cases of interest for neuromagnetic studies. The specific goals of our numerical simulations were to examine multi-source resolution and parameter estimation accuracy as a function of 1) specific multi-source configurations; 2) different time courses, i.e., degree of temporal correlation; 3) measurement noise; 4) spatio-temporal modeling strategy (i.e., sequential fitting of instantaneous field distributions, two-step spatio-temporal modeling); 5) source modeling assumptions associated with model order; and 6) effects of initial modeling assumptions (i.e., starting points for the nonlinear minimization procedure derived by MUltiple SIgnal Classification (MUSIC), sequential instantaneous fitting, and arbitrary selections). The ability to determine the number of active sources by different approaches is compared, and the consequences on the accuracy of estimated solutions for simulated data are discussed. In all cases, model adequacy was assessed using reduced chi-square as a measure of goodness-of-fit. The present simulations demonstrate that location estimation was more robust and accurate compared to the estimation of temporal dynamics of individual sources. Implications for spatio-temporal modeling of neuromagnetic empirical data are suggested.