Schizophrenia is a psychiatric disorder that has eluded characterization in terms of local abnormalities of brain activity, and is hypothesized to affect the collective, “emergent” working of the brain. Indeed, several recent publications have demonstrated that functional networks in the schizophrenic brain display disrupted topological properties. However, is it possible to explain such abnormalities just by alteration of local activation patterns? This work suggests a negative answer to this question, demonstrating that significant disruption of the topological and spatial structure of functional MRI networks in schizophrenia (a) cannot be explained by a disruption to area-based task-dependent responses, i.e. indeed relates to the emergent properties, (b) is global in nature, affecting most dramatically long-distance correlations, and (c) can be leveraged to achieve high classification accuracy (93%) when discriminating between schizophrenic vs control subjects based just on a single fMRI experiment using a simple auditory task. While the prior work on schizophrenia networks has been primarily focused on discovering statistically significant differences in network properties, this work extends the prior art by exploring the generalization (prediction) ability of network models for schizophrenia, which is not necessarily captured by such significance tests.