  MEG/EEG are non-invasive imaging techniques that record brain activity with
high temporal resolution. However, estimation of brain source currents from
surface recordings requires solving an ill-posed inverse problem. Converging
lines of evidence in neuroscience, from neuronal network models to
resting-state imaging and neurophysiology, suggest that cortical activation is
a distributed spatiotemporal dynamic process, supported by both local and
long-distance neuroanatomic connections. Because spatiotemporal dynamics of
this kind are central to brain physiology, inverse solutions could be improved
by incorporating models of these dynamics. In this article, we present a model
for cortical activity based on nearest-neighbor autoregression that
incorporates local spatiotemporal interactions between distributed sources in a
manner consistent with neurophysiology and neuroanatomy. We develop a dynamic
Maximum a Posteriori Expectation-Maximization (dMAP-EM) source localization
algorithm for estimation of cortical sources and model parameters based on the
Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the
dMAP-EM algorithm to simulated experiments as well as to human experimental
data. Furthermore, we derive expressions to relate our dynamic estimation
formulas to those of standard static models, and show how dynamic methods
optimally assimilate past and future data. Our results establish the
feasibility of spatiotemporal dynamic estimation in large-scale distributed
source spaces with several thousand source locations and hundreds of sensors,
with resulting inverse solutions that provide substantial performance
improvements over static methods.
