We address the clinically and scientifically important problem of how to integrate electroencephalography and magnetoencephalography (EMEG) with non-concurrently acquired functional magnetic resonance imaging (fMRI) data for the noninvasive study of large-scale, resting-state brain networks in temporal frequencies ranging from 0.08 Hz to more than 100 Hz. The unified analysis of low frequency hemodynamic signals with spatially ambiguous electromagnetic signals has been a critical barrier to progress in this field, and is addressed by novel methods in this application. These methods exploit the spatial structure of dynamic correlations found in resting fMRI to inform an EMEG inverse technique which, in turn, exploits the highly stochastic nature of ongoing brain activity. Cortical parcellations based on fMRI connectivity will be used to inform two complementary EMEG source estimation methods: A maximum entropy source covariance source estimator (MaxEntCov) provides a globally consistent solution, and a dual vector beamformer (DVB) provides local estimates for possibly- correlated activity in two source regions. MaxEntCov and DVB, which start from opposite perspectives, are combined to optimize model parameters in an iterative process that is designed to provide a convergent solution. After convergence, the estimators may be used to derive various EMEG-based functional connectivity measures. Our computational methods will be verified using quasi-realistic simulations. Research utility will be evaluated using data from an ongoing project to study the process of conversion to the psychosis. Finally, the prototype multimodal resting-state analysis tools will be integrated with commercial EMSE(R) Suite software.