Recent functional magnetic resonance imaging (fMRI) studies have found distinctive functional connectivity in the schizophrenia brain. However, most of the studies focused on the correlation value to define the functional connectivity for BOLD fluctuations between brain regions, which resulted in the limited understanding to the network properties of altered functional connectivity in the schizophrenia brain. Here I characterized the distinctiveness of BOLD connectivity pattern in the schizophrenia brain relative to healthy brain with various similarity measures using time-frequency domain analysis, while participants performing the working memory task in the MRI scanner. To assess the distinctiveness of the BOLD connectivity in the schizophrenia, recognition performances of pattern classifier machine trained by each similarity measure were compared. Interestingly, classifier machine trained by time-lagging patterns of low-frequency fluctuation (LFF) produced highest classifying accuracy than the machines trained by other measures. Also, classifier machine trained by coherence pattern in LFF band also made better performance than the machine trained by correlation-based connectivity pattern. These results indicate that characteristics of altered functional network in the schizophrenia brain can hardly defined with single aspect of relationship across the multiple brain regions.