  The resolution matrix is a mathematical tool for analyzing inverse problems
such as computational imaging systems. When treating network connectivity
estimation as an inverse problem, the resolution matrix describes the degree to
which network nodes and edges can be resolved. This is useful both for
quantifying robustness of the network estimate, as well as identifying
correlated activity. In this report we analyze the resolution matrix for
functional MRI data from the Human Connectome project. We find that common
metrics of the resolution metric can be used to identify networked activity,
though with a new twist on the relationship between default mode network and
the frontoparietal attention network.
