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subjects diagnosed with mild cognitive impairment (MCI). PET volumes were registered to a customized MRI template in MNI stereotaxic space, and standardized uptake value ratio (SUVR) images were generated using Biospective’s fully-automated PIANO TM image processing software. The amyloid burden for each subject was determined from a composite region-of-interest (ROI) on [18F]florbetapir images, and subjects were categorized into Amyloid-Low (Ab L) and Amyloid-High (Ab H) groups. We generated a set of hierarchical likelihood ratio tests to assess betweengroups differences in metabolic connectivity patterns arising from (1) alterations in seed-based correlations and (2) alterations in seed-based covariances and variances with stable seed-based correlations. Results: We observed statistically significant differences in metabolic correlations between the Ab L and Ab H groups for multiple cortical seeds regions, including the angular gyrus and the inferior temporal gyrus. The seed-based covariance analysis identified connectivity patterns in particular brain regions (e.g. precuneus) that were not detected by classical seed-based correlation analysis. Conclusions: We have introduced a new, multivariate metabolic connectivity analysis technique to examine disruptions of the cortical correlation architecture as a function of b-amyloid burden. The novel approach employed in this study may be generalized to other connectivity measures, such as functional connectivity derived from BOLD fMRI, and may provide unique insights into disease-related alterations of the connectome.