Recently whole brain dynamic functional network connectivity (dFNC) has become an area of increasing focus in functional magnetic resonance imaging (fMRI) studies. Dynamic functional domain connectivity (dFDC) is a novel information theoretic framework which measures information flow between subsets of the whole brain dFNC. The information shared between domains can potentially shine light on brain disorders. Here we employ this framework to analyze an fMRI dataset containing 163 healthy controls (HCs) and 151 schizophrenia patients (SZs). We measured the entropy within each dFDC and the cross domain mutual information (CDMI) between pairs of dFDC. We performed linear regression on transformed entropy and CDMI to find significant group differences. Results indicate that SZs show significantly higher (transformed) entropy than HCs in subcortical (SC)-SC, default mode (DMN)-SC, cerebellar (CB)auditory (AUD), and CB-attention (ATTN) dFDC. They also demonstrate lower (transformed) CDMI than HCs between SC-visual (VIS) and SC-AUD, AUD-AUD and SC-AUD, AUD-sensorimotor (SM) and AUD-AUD, SM-ATTN and AUD-ATTN, SM-frontal (FRN) and AUD-FRN, VIS-ATTN and SM-ATTN as well as VIS-FRN and SM-FRN dFDC pairs. This implies that different dFDC pairs share lower mutual information in SZs compared to HCs. This in turn corroborates the notion that SZs demonstrate reduced connectivity dynamism in fMRI.