  Functional subnetwork extraction is commonly used to explore the brain's
modular structure. However, reliable subnetwork extraction from functional
magnetic resonance imaging (fMRI) data remains challenging due to the
pronounced noise in neuroimaging data. In this paper, we proposed a high order
relation informed approach based on hypergraph to combine the information from
multi-task data and resting state data to improve subnetwork extraction. Our
assumption is that task data can be beneficial for the subnetwork extraction
process, since the repeatedly activated nodes involved in diverse tasks might
be the canonical network components which comprise pre-existing repertoires of
resting state subnetworks. Our proposed high order relation informed subnetwork
extraction based on a strength information embedded hypergraph, (1) facilitates
the multisource integration for subnetwork extraction, (2) utilizes information
on relationships and changes between the nodes across different tasks, and (3)
enables the study on higher order relations among brain network nodes. On real
data, we demonstrated that fusing task activation, task-induced connectivity
and resting state functional connectivity based on hypergraphs improves
subnetwork extraction compared to employing a single source from either rest or
task data in terms of subnetwork modularity measure, inter-subject
reproducibility, along with more biologically meaningful subnetwork
assignments.
