Scaled subprofile model of principal component analysis (SSM-PCA) is a multivariate statistical method, widely used in positron emission tomography (PET). Recently, SSM-PCA has been applied to resting-state functional MRI (RS-fMRI). However, the intra- and inter-scanner reliability of SSM-PCA in RS-fMRI is not investigated systematically yet. Results from eyes-open (EO) and eyes-closed (EC) dataset demonstrate that both the intra- and inter-scanner reliability is excellent for EO and EC related covariance pattern (EOEC-pattern) and fair to good for EOEC-pattern’s expression. Moreover, SSM-PCA and conventional T-test are complementary for neuroimaging researches. This study illustrates the great potential of SSM-PCA for further applications in RS-fMRI.