Before revision:  Network measures clustering coefficient, path length and small-world coefficient were significantly altered across diagnostic groups, with a biphasic u-shape trajectory , i. e. increased small-world coefficient in early MCI , intermediate values in late MCI, and decreased values in ADpatients compared to controls. In contrast, no group differences were found for clustering coefficient and small-world coefficient when estimating conditional dependency networkson single imaging modalities. GGMs provide a useful methodology to analyze the conditional dependency networks of multimodal neuroimaging data .
After revision:  Network measures clustering coefficient, path length and small-world coefficient were significantly altered across diagnostic groups, with a biphasic u-shape trajectory . GGMs removed shared variance among multimodal measures of regional brain alterations in MCI and AD, and yielded sparser matrices compared to Pearson correlation networks. Therefore, GGMs may be used as alternative to thresholding-approaches typically applied to correlation networks to obtain the most informative relations between variables .