Dynamic functional connectivity (dFC) analysis aims at understanding how interactions across the brain resting-state networks (RSNs) evolve over time. Here, we introduce a novel methodological framework operating at the level of RSN activity time courses. Through the use of coupled hidden Markov models (CHMMs), we model cross-network couplings, i.e. the ability of one RSN to influence state transitions of the others. Because such modulatory influences are not expected across all possible pairs of RSNs, we combine this modeling strategy with ℓ1 regularisation to derive a sparse set of cross-network modulatory coefficients. As a validation of this framework, we first demonstrate the ability of the sparse CHMM approach to disentangle intrinsic state transition probabilities from external modulatory influences on an artificially generated dataset. We then perform preliminary analyses on a real resting-state dataset, using RSN activity time courses derived from a state-of-the-art deconvolution technique as inputs to our framework, and shed light on several significant cross-network couplings across major RSNs.