Stability-driven non-negative matrix factorization-based approach for extracting dynamic network from resting-state EEG

Human behavior in health and disease is associated with dynamic functional networks, which are characterized as hierarchical ensembles with segregated spatial architecture and corresponding temporal architecture. How best to extract dynamical functional network structure and coefficient time series from neural data remains an open problem. We developed a new method, which called stability-driven non-negative matrix factorization (staNMF), that combines non-negative matrix factorization with a novel data-driven model selection criterion to decompose dynamic functional networks into sets of superposed subnetworks. To evaluate our method, we first use simulated noisy connectivity networks to demonstrate that staNMF is able to find the correct number of subnetworks, obtain the connectivity matrix of each subnetwork, and recover the corresponding activation coefficient time series. We then apply staNMF to a resting state EEG dataset recorded from 41 five-year-old children with autism spectrum disorder (ASD) and 44 age-matched typically-developing (TD) children. StaNMF extracted several robust and biologically interpretable subnetworks for which significant differences between ASD and TD could be found. Specifically, the strength of the activation coefficient time series of one identified local connections subnetwork was higher in ASD children than in TD. Several subnetworks that had interhemispheric, long-range connections had lower energy of activation coefficient time series in ASD compared to TD. In conclusion, the analyses on simulated and real EEG data demonstrate that staNMF can be used to characterize dynamic functional networks, making it a promising tool for investigating disorders using resting-state EEG.