Investigation of spectrally coherent resting‐state networks using non‐negative matrix factorization for functional MRI data

Spontaneous fluctuations of the functional magnetic resonance imaging (fMRI) signal during the resting state have been characterized by the dominant spectral components in the low‐frequency (<0.1 Hz) range. Although previous studies have reported the spatial patterns of activity of the resting‐state network (RSN), the frequency‐dependent characteristics (i.e., spectral coherence) of the resting state activity have not been fully investigated. This article describes the novel application of a non‐negative matrix factorization (NMF) algorithm to the decomposition of the magnitude spectra of fMRI time‐series into distinct spectral components. From the fMRI data of healthy volunteers during the resting state, the frequency‐specific components were decomposed into five basis functions. Group analysis revealed five different spatial patterns associated with these basis functions, in which each of the spatial patterns may correspond to a distinct spectrally coherent RSN. The RSN with the lowest center frequency showed a similar spatial pattern to the “default‐mode” network, which involves the medial superior and middle frontal cortex along with the posterior cingulate cortex. On the other hand, RSNs with higher frequencies were observed mainly in several posterior regions of the brain including the precuneus and lingual gyrus. Subsequent Granger causality analysis demonstrated that these posterior regions may function as the “hubs” of the RSNs, whereas the anterior regions, including the medial superior and middle frontal cortex, may be characterized as “peripheries” of the network. Our proposed analysis scheme provides supplemental information on both spectral and temporal characteristics of the RSNs, which might be used in a range of applications, including those involving clinical populations. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 2117–222, 2011