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Structural and metabolic connectivity are advanced features that facilitate the diagnosis of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Connectivity from a single imaging modality, however, did not show evident discriminative value in predicting MCI-to-AD conversion, possibly because the inter-modal information was not considered when quantifying the relationship between brain regions. Here we introduce a novel approach that extracts connectivity based on both structural and metabolic information to improve AD early diagnosis. Principal component analysis was performed on each imaging modality to extract the key discriminative patterns of each brain region in an independent auxiliary domain composed of AD and normal control (NC) subjects, which were then used to project the two subtypes of MCI to the low-dimensional space. The connectivity between each target brain region and all other regions was quantified via a multi-task regression model using the projected data. The prediction performance was evaluated in 75 stable MCI (sMCI) patients and 51 progressive MCI (pMCI) patients who converted to AD within 3 years. We achieved 79.37% accuracy, with 74.51% sensitivity and 82.67% specificity, in predicting MCI-to-AD progression, superior to other existing algorithms using either structural and metabolic connectivities alone or a combination thereof. Our results demonstrate the effectiveness of multi-modal connectivity, serving as robust biomarker for early AD diagnosis.