  Functional connections in the brain are frequently represented by weighted
networks, with nodes representing locations in the brain, and edges
representing the strength of connectivity between these locations. One
challenge in analyzing such data is that inference at the individual edge level
is not particularly biologically meaningful; interpretation is more useful at
the level of so-called functional regions, or groups of nodes and connections
between them; this is often called "graph-aware" inference in the neuroimaging
literature. However, pooling over functional regions leads to significant loss
of information and lower accuracy. Another challenge is correlation among edge
weights within a subject, which makes inference based on independence
assumptions unreliable. We address both these challenges with a linear mixed
effects model, which accounts for functional regions and for edge dependence,
while still modeling individual edge weights to avoid loss of information. The
model allows for comparing two populations, such as patients and healthy
controls, both at the functional regions level and at individual edge level,
leading to biologically meaningful interpretations. We fit this model to a
resting state fMRI data on schizophrenics and healthy controls, obtaining
interpretable results consistent with the schizophrenia literature.
