For each value of K, we compared the cluster solutions generated for Group 1 and Group

2 using a metric developed for assessing the similarity of cluster assignments: variation of information (VI; Meila, 2007). We repeated the entire process 100 times, each time generating two new groups of 18 participants. We determined the optimal K (or range of K ) by computing the mean VI across the 100 permuted groups, for each K, and selecting the non-trivial (i.e. K > 2) solution that showed the lowest mean VI. The mean VI across solutions also allowed us to determine which of the two algorithms (spectral or hierarchical) produced the most consistent solution. The results of the above-described analysis suggested that the spectral clustering algorithm produced

more consistent clustering solutions (associated selleck chemical with the lowest mean VI) across the permuted groups, relative to the hierarchical clustering algorithm (see Results). Accordingly, we used the spectral clustering algorithm for the remaining analyses. To further discern the optimal K, we calculated a modified silhouette value for each value of K, for cluster solutions produced when the spectral clustering algorithm was applied to each individual’s η2 matrix. The silhouette is a standard metric, which provides, for each point (in our case, voxel), a measure of how similar it is to other points within the same cluster, vs. how similar it is to points in other clusters. In the following equation, S(i) is the silhouette value for a single voxel, ηwi corresponds to the mean of the η2 values describing the similarity between voxel i and voxels within the selleckchem same cluster, and ηbi corresponds to the K−1 means of the η2 values describing the similarity between voxel i and voxels in other clusters: Instead of estimating a voxel-wise S, we estimated a modified cluster-wise silhouette value in order to provide a summary measure of the similarity of points within a cluster,

relative to the similarity between clusters: In the equation for , ηwk corresponds to the mean η2 value describing the similarity between all voxels within cluster k (), while ηbk corresponds to the K−1 mean η2 values describing the similarity between all pairings Sitaxentan of voxels within cluster k ( ) and voxels within other clusters (): To compute the mean modified silhouette, we first applied the spectral clustering algorithm to each participant’s η2 matrix, to identify cluster solutions for the range K = 2 : 12. We then performed the calculations described above, to compute the modified silhouette for each value of K and for each participant. We then plotted the mean and standard deviation, across participants. During data preprocessing, we applied a 6-mm FWHM Gaussian spatial smoothing filter. To assess whether smoothing affects cluster assignment, we repeated the analyses and η2 matrix generation without spatial smoothing.