  We take an image science perspective on the problem of determining brain
network connectivity given functional activity. But adapting the concept of
image resolution to this problem, we provide a new perspective on network
partitioning for individual brain parcellation. The typical goal here is to
determine densely-interconnected subnetworks within a larger network by
choosing the best edges to cut. We instead define these subnetworks as
resolution cells, where highly-correlated activity within the cells makes edge
weights difficult to determine from the data. Subdividing the resolution
estimates into disjoint resolution cells via clustering yields a new variation,
and new perspective, on spectral clustering. This provides insight and
strategies for open questions such as the selection of model order and the
optimal choice of preprocessing steps for functional imaging data. The approach
is demonstrated using functional imaging data, where we find the proposed
approach produces parcellations which are more predictive across multiple scans
versus conventional methods, as well as versus alternative forms of spectral
clustering.
