  Population analyses of functional connectivity have provided a rich
understanding of how brain function differs across time, individual, and
cognitive task. An important but challenging task in such population analyses
is the identification of reliable features that describe the function of the
brain, while accounting for individual heterogeneity. Our work is motivated by
two particularly important challenges in this area: first, how can one analyze
functional connectivity data over populations of individuals, and second, how
can one use these analyses to infer group similarities and differences.
Motivated by these challenges, we model population connectivity data as a
multilayer network and develop the multi-node2vec algorithm, an efficient and
scalable embedding method that automatically learns continuous node feature
representations from multilayer networks. We use multi-node2vec to analyze
resting state fMRI scans over a group of 74 healthy individuals and 60 patients
with schizophrenia. We demonstrate how multilayer network embeddings can be
used to visualize, cluster, and classify functional regions of the brain for
these individuals. We furthermore compare the multilayer network embeddings of
the two groups. We identify significant differences between the groups in the
default mode network and salience network - findings that are supported by the
triple network model theory of cognitive organization. Our findings reveal that
multi-node2vec is a powerful and reliable method for analyzing multilayer
networks.
