Figure 1.

A series of post-processing steps were performed on rs-fc data in order to reduce dimensionality and generate a single summary value. 298 seed-based functional regions-of-interest (ROI) were identified and Pearson correlations between each were calculated and Z transformed for normality. These ROIs were organized into thirteen resting state networks (RSNs), with ROIs of unknown function classified as “unassigned”. Correlations within each of the thirteen RSNs were averaged to obtain a 13 × 13 matrix for each individual. We then performed PCA on the intra-network connections for each individual, that is, the diagonal of each individual’s rs-fc matrix. We calculated the dot product of the eigenvector corresponding to the first principal component and the intra-network connections on an individual-by-individual basis to obtain a single summary value referred to as “AD global rs-fc signature”. Certain networks have been previously shown to be affected by AD. Post hoc analyses using the DMN x DMN intra-network composite and the MEM x MEM intra-network composite were therefore performed. These intra-network composites are labeled for clarity here.