A computer simulation model was developed to investigate the use of interregional correlations of cerebral metabolic rates to analyze functional interactions in the brain. The model generates simulated metabolic data for individual brain regions in a specified number of subjects, where there are defined functional couplings amongst the regions. Random numbers provide the variability seen in measured metabolic data. Correlational analysis is performed on these simulated data sets. The parameters of the model can be chosen so that simulated and actual metabolic data are very similar. The model demonstrates that the change in the correlation coefficient between normalized metabolic data in two brain regions is related to the change in the strength of the functional association between the two regions. The model also is used to explore the relations between patterns of correlations and the underlying sets of functional couplings. The results indicate that correlational analysis provides more information about regional involvement in neural systems than does region-by-region comparisons of absolute metabolic rates.