Spontaneous activations in resting-state fMRI have been shown to corroborate recurrent intrinsic functional networks. Recent studies have explored integration of brain function in terms of spatially overlapping networks. We have proposed a method to recover not only spatially but also temporally overlapping networks, which we named innovation-driven coactivation patterns (iCAPs). These networks are driven by the sparse innovation signals recovered from Total Activation (TA), a spatiotemporal regularization framework for fMRI deconvolution. The fMRI data is processed with TA, which uses the inverse of the hemodynamic response function-as a linear differential operator-combined with the derivative in the regularization with e(1)-norm. As a result, sparse innovation signals are reconstructed as the deconvolved fMRI time series. Temporal clustering of innovation signals lead to iCAPs. In this work, we investigate the reproducible iCAPs in individuals with relapsing-remitting multiple sclerosis and healthy volunteers.