 Network measures clustering coefficient and path length were significantly altered across diagnostic groups, with a biphasic u-shape trajectory  . GGMs showed almost no conditional dependencies between modalitieswhen at the same time considering various other regions within the same modalities. However, this approach could be used as a clustering method to derive graph statistics in future studies omitting the need to binarize the network as currently being done for connections based on Pearson correlation .
<clarity>  Network measures clustering coefficient and path length were significantly altered across diagnostic groups, with a biphasic u-shape trajectory  . GGMs showed almost no conditional dependencies between modalitieswhen at the same time considering various other regions within the same modalities. However, this approach could be used as a clustering method to derive graph statistics in future studies omitting the need to binarize the network as currently being done for connections based on Pearson correlation .
 Network measures clustering coefficient and path length were significantly altered across diagnostic groups, with a biphasic u-shape trajectory . GGMs provide a useful methodology to analyze the conditional dependency networks of multimodal neuroimaging data .