Figure 5. Individual clustering stage.
Step A1 Graph formation. An individual graph was constructed, consisting of M cortical voxels and (M2 -M)/2 edges connecting all voxel pairs. The weight w(i,j) of edge e(i,j) connecting voxel i and voxel j was computed as the correlation between the filtered time-series of voxel i and voxel j, reflecting the level of functional connectivity between the two voxels. Step A2 Clustering. Prior to the clustering, a cut-off threshold of 0.4 was applied to reduce the complexity of the graph and lower the computational load, setting all weights to zero that did not reach this threshold. Normalized cut clustering was used to partition the graph in a fixed number of 20 networks, grouping voxels that showed a high level of functional connectivity into networks, resulting in an individual clustermap.