  Subnetwork extraction using community detection methods is commonly used to
study the brain's modular structure. Recent studies indicated that certain
brain regions are known to interact with multiple subnetworks. However, most
existing methods are mainly for non-overlapping subnetwork extraction. In this
paper, we present an approach for overlapping brain subnetwork extraction using
cliques, which we defined as co-activated node groups performing multiple
tasks. We proposed a multisource subnetwork extraction approach based on the
co-activated clique, which (1) uses task co-activation and task connectivity
strength information for clique identification, (2) automatically detects
cliques of different sizes having more neuroscientific justifications, and (3)
shares the subnetwork membership, derived from a fusion of rest and task data,
among the nodes within a clique for overlapping subnetwork extraction. On real
data, compared to the commonly used overlapping community detection techniques,
we showed that our approach improved subnetwork extraction in terms of
group-level and subject-wise reproducibility. We also showed that our
multisource approach identified subnetwork overlaps within brain regions that
matched well with hubs defined using functional and anatomical information,
which enables us to study the interactions between the subnetworks and how hubs
play their role in information flow across different subnetworks. We further
demonstrated that the assignments of interacting/individual nodes using our
approach correspond with the posterior probability derived independently from
our multimodal random walker based approach.
