Altered brain laterality is frequently reported in morphological brain studies of individuals with schizophrenia. However, these studies utilize voxel/vertex-wise univariate methods which may not be optimal for examining brain laterality. We introduce a novel multivariate approach to estimate covarying lateralized networks. In our approach, lateralized grey matter maps were computed by subtracting volumetric data one hemisphere from the other, and analyzed via independent component analysis (ICA), followed by testing loading parameters from components identifying covariation within laterality networks. Results display significant relationships with temporal lobe and cerebellar laterality and negative symptoms of schizophrenia that warrant further exploration with multimodal analyses.