Hi Aiden,

The adjacency matrix is always defined by thresholding the original ROI-to-ROI correlation coefficient matrix, but the details of this thresholding operation vary depending on the choices in the 'Network edges (adjacency matrix threshold)' options in the 'graph theory results explorer' gui.

If you choose 'correlation coefficient' there, then the adjacency matrix is formed by selecting the edges with correlation coefficient values above the threshold value that you choose (e.g. if you choose a value of 0.5 then two ROIs are connected if their bivariate correlation is above 0.5). If you use 'z-score' then the correlation-coefficient values are first transformed to z-scores (i.e. normalized to have zero mean and variance one, separately for each subject), and the adjacency matrix is formed by selecting the edges with z-scores above the chosen threshold value (e.g. if you choose a value of 1 then two ROIs are connected if their correlation coefficients are one standard deviation above the mean -for a given subject). Last, if you choose 'cost' (the default setting), then the adjacency matrix is formed by selecting a fixed percentile (the chosen threshold value) of the edges in each network (those with the largest correlation coefficient values, separately for each subject). Note that this results in graphs for each subject that have the same 'cost' (e.g. if you choose a value of 0.15 then each subject graph will have a fixed cost of 0.15, meaning that 15% of all possible edges are present)

Let me know if this clarifies

Best
Alfonso

Originally posted by Aiden Arnold: