Models of neural generators
MEG/EEG forward modeling requires two basic models that are bound to work together in a complementary manner:
- a physical model of neural sources, and
- a model that predicts how these sources generate electromagnetic fields outside the head.
The canonical source model of the net primary intracellular currents within a neural assembly is the electric current dipole. The adequacy of a simple, equivalent current dipole (ECD) model as a building block of cortical current distributions was originally motivated by the shape of the scalp topography of MEG/EEG evoked activity observed. This latter consists essentially of (multiple) so-called ‘dipolar distributions’ of inward/outward magnetic fields and positive/negative electrical potentials. From a historical standpoint, dipole modeling applied to EEG and MEG surface data was a spin-off from the considerable research on quantitative electrocardiography, where dipolar field patterns are also omnipresent, and where the concept of ECD was contributed as early as in the 1960s (Geselowitz, 1964).
However, although cardiac electrophysiology is well captured by a simple ECD model because there is not much questioning about source localization, the temporal dynamics and spatial complexity of brain activity may be more challenging. Alternatives to the ECD model exist in terms of the compact, parametric representation of distributed source currents. They consist either of higher-order source models called multipoles (Jerbi, Mosher, Baillet, & Leahy, 2002, Jerbi et al.., 2004) – also derived from cardiographic research (Karp, Katila, Saarinen, Siltanen, & Varpula, 1980) – or densely-distributed source models (Wang, Williamson, & Kaufman, 1992). In the latter case, a large number of ECD’s are distributed in the entire brain volume or on the cortical surface, thereby forming a dense grid of elementary sites of activity, which intensity distribution is determined from the data.
To understand how these elementary source models generate signals that are measurable using external sensors, further modeling is required for the geometrical and electromagnetic properties of head tissues, and the properties of the sensor array.