Investigation of smoking related features in spatio-spectral domain on resting-state fMRI data using nonnegative matrix factorization

Smoking is a typical exemplar in drug addiction researches for the wide range of tobacco users. The presented study uses functional magnetic resonance imaging (fMRI) to explore the resting-state (RS) neural mechanisms associated with smoking deprivation. We propose a novel analysis approach which applies nonnegative matrix factorization (NMF) algorithm on spatially concatenated two group datasets to investigate smoking related RS features in spatio-spectral domain. The NMF algorithm decomposed the magnitude spectra of fMRI time series into distinct frequency-specific basis functions and corresponding nonnegative spatial maps. After a two sample T-test on the z-scored spatial maps between groups, representative feature regions such as insula, anterior cingulate cortex and precuneus were found to be associated with smoking. These regions were consistent with that revealed by previous literatures studying in spatio-temporal domain. It indicates that our proposed analysis method provides another option for exploring neural mechanism differences between two groups, which might be used in a range of applications.