Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive, and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modeled independently: Functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural, and topological information of magnetic resonance imaging (MRI) measurements (structural and resting-state imaging) to patients with schizophrenia ( = 35) and matched healthy individuals ( = 41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole-brain connectivity, including functional, structural, and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity, resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting-state functional MRI, and it can be applied to electro-encephalography, magneto-encephalography, etc.