The brain exhibits temporally coherent networks (TCNs) involving numerous cortical and sub-cortical regions both during the rest state and during the performance of cognitive tasks. TCNs represent the interactions between different brain areas, and understanding such networks may facilitate electroencephalography (EEG) source estimation. We propose a new method for examining TCNs using scalp EEG in conjunction with data obtained by functional magnetic resonance imaging (fMRI). In this approach, termed NEtwork based SOurce Imaging (NESOI), multiple TCNs derived from fMRI with independent component analysis (ICA) are used as the covariance priors of the EEG source reconstruction using Parametric Empirical Bayesian (PEB). In contrast to previous applications of PEB in EEG source imaging with smoothness or sparseness priors, TCNs play a fundamental role among the priors used by NESOI. NESOI achieves an efficient integration of the high temporal resolution EEG and TCN derived from the high spatial resolution fMRI. Using synthetic and real data, we directly compared the performance of NESOI with other distributed source inversion methods, with and without the use of fMRI priors. Our results indicated that NESOI is a potentially useful approach for EEG source imaging.