Intercorrelations of glucose metabolic rates between brain regions: application to healthy males in a state of reduced sensory input.

We use a correlational analysis of regional metabolic rates to characterize relations among different brain regions. Starting with rates of local glucose metabolism (rCMRglc) obtained by positron emission tomography using [18F]fluorodeoxyglucose, we propose that the strength of the association is proportional to the magnitude of the correlation coefficient. Partial correlation coefficients, controlling for whole brain glucose metabolism, are used in the analysis. We also introduce a graphical technique to display simultaneously all the correlations, allowing us to examine patterns of relations among them. The method was applied to 40 very healthy males under conditions of reduced auditory and visual inputs (the "resting state"). Dividing the brain into 59 regions, and keeping only those partial correlation coefficients significant to p less than 0.01, we found the following: (a) All regions were significantly correlated with their contralateral homologues. For the most part, the largest partial correlation coefficients were between homologous brain regions. (b) Generally, the pattern of significant correlations between any two lobes in the left hemisphere did not differ statistically from the corresponding pattern in the right hemisphere. (c) Strong correlations were observed between primary somatosensory areas and premotor association areas. Correlations between these association areas and primary visual and auditory regions were not statistically significant. (d) Significant correlations between inferior occipital and temporal areas were found. Metabolic rates in the superior part of the occipital lobe were not correlated significantly with metabolic rates in regions of the temporal lobe, nor with metabolism in the parietal lobe. (e) As a whole, there were numerous correlations among frontal and parietal lobe regions, on the one hand, and among temporal and occipital lobe regions, on the other, but few statistically significant correlations between these two domains. We relate our results to various aspects of known brain anatomy, physiology, and cognitive functioning.