Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called "resting state networks"), however they are also present during (and modulated by) the performance of a cognitive task. In this paper we will refer to such networks as temporally coherent networks (TCNs). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this paper we present results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In summary, multiple TCNs are present at rest and during a cognitive task, but are modulated in complex ways.