  MEG and EEG are noninvasive functional neuroimaging techniques that provide
recordings of brain activity with high temporal resolution, and thus provide a
unique window to study fast time-scale neural dynamics in humans. However, the
accuracy of brain activity estimates resulting from these data is limited
mainly because 1) the number of sensors is much smaller than the number of
sources, and 2) the low sensitivity of the recording device to deep or radially
oriented sources. These factors limit the number of sources that can be
recovered and bias estimates to superficial cortical areas, resulting in the
need to include a priori information about the source activity. The question of
how to specify this information and how it might lead to improved solutions
remains a critical open problem. In this paper we show that the incorporation
of knowledge about the brain's underlying connectivity and spatiotemporal
dynamics could dramatically improve inverse solutions. To do this, we develop
the concept of the \textit{dynamic lead field mapping}, which expresses how
information about source activity at a given time is mapped not only to the
immediate measurement, but to a time series of measurements. With this mapping
we show that the number of source parameters that can be recovered could
increase by up to a factor of ${\sim20}$, and that such improvement is
primarily represented by deep cortical areas. Our result implies that future
developments in MEG/EEG analysis that model spatialtemporal dynamics have the
potential to dramatically increase source resolution.
