Multimodal imaging, the combination of several brain imaging techniques in one subject, provides a wealth of parameters and favours the interpretation of complex models in schizophrenia research. Moreover, new imaging tools allow the investigation of distinct neurotransmitter systems and their modulation by pharmacological intervention. An important feature of multimodal imaging is the possibility to characterize the activation dependencies of different neurotransmitters and provide the experimental tool to test system models of brain function and dysfunction. The combination of measurement techniques with high temporal resolution (e. g. MEG, EEG) and high spatial resolution (e. g. fMRI) facilitate the understanding of local and global systems as well as time characteristics. Moreover, the association of imaging parameters with genetic variations of neurotransmitter systems allows the investigation of neurotransmitter activity and its role in the pathophysiology of schizophrenia. To overcome the limitations of standard statistical methods, new approaches in machine learning have to be adapted to handle multiple parameters obtained from brain imaging and genetic measurements.