Collective sparse symmetric non-negative matrix factorization for identifying overlapping communities in resting-state brain functional networks

Xuan Li, John Q. Gan, Haixian Wang
2018 NeuroImage  
Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable tool to study spontaneous brain activity. Using rs-fMRI, researchers have extensively studied the organization of the brain functional network and found several consistent communities consisting of functionally connected but spatially separated brain regions across subjects. However, increasing evidence in many disciplines has shown that most realistic complex networks have overlapping community structure. Only
more » ... structure. Only recently has the overlapping community structure drawn increasing interest in the domain of brain network studies. Another issue is that the inter-subject variability is often not directly reflected in the process of community detection at the group level. In this paper, we propose a novel method called collective sparse symmetric non-negative matrix factorization (cssNMF) to address these issues. The cssNMF approach identifies the group-level overlapping communities across subjects and in the meantime preserves the information of individual variation in brain functional network organization. To comprehensively validate cssNMF, a simulated fMRI dataset with ground-truth, a real rs-fMRI dataset with two repeated sessions and another different real rs-fMRI dataset have been used for performance comparison in the experiment. Experimental results show that the proposed cssNMF method accurately and stably identifies group-level overlapping communities across subjects as well as individual differences in network organization with neurophysiologically meaningful interpretations. This research extends our understanding of the common underlying community structures and individual differences in community strengths in brain functional network organization. (Haixian Wang) tools for brain imaging data. Moreover, the technology of functional magnetic resonance imaging (fMRI), especially resting state fMRI (rs-fMRI), provides a useful channel to 15 study brain functional networks in depth. The rs-fMRI records the signals of spontaneous brain activities when no particular task is performed. The access to powerful network approaches and rich resources of brain imaging data has largely promoted studies on brain network orga-20 nization (Sporns et al., 2004; Power et al., 2011; Bullmore and Sporns, 2012) . Two primary aspects in understanding brain network organization is the segregation and integration of brain functions. In particular, the functional segregation in brain 25 networks is captured by identifying underlying communities, where a community (or module) consists of highly
doi:10.1016/j.neuroimage.2017.11.003 pmid:29117581 fatcat:3he4fcrsbrfefdyfexte76gi74