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. 2023 Apr 10;25(2):541–558. doi: 10.1093/biostatistics/kxad007

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Fig. 1. — The SICERS pipeline: (a) define brain regions as nodes and connectivity metrics between each pair of nodes as edges; (b) and (c) calculate the connectivity matrix for each single subject in a study, where each off-diagonal element in the matrix represents the connectivity strength between two nodes, then identify differential connectivity patterns between clinical groups; (d) plot the edge-wise statistical inference, where each off-diagonal element is a negatively logarithmically transformed -value (e.g., two-sample-test -value per edge between clinical groups and a hotter point in the heatmap suggests larger group-wise difference); (e) reveal the disease-related subnetwork detected by SICERS; (f) show the corresponding 3D (3D) brain image. Note that (e) was obtained by reordering the nodes in (d) by listing the detected subnetwork first (i.e., these two graphs are isomorphic).

Inline graphic — The SICERS pipeline: (a) define brain regions as nodes and connectivity metrics between each pair of nodes as edges; (b) and (c) calculate the connectivity matrix for each single subject in a study, where each off-diagonal element in the matrix represents the connectivity strength between two nodes, then identify differential connectivity patterns between clinical groups; (d) plot the edge-wise statistical inference, where each off-diagonal element is a negatively logarithmically transformed -value (e.g., two-sample-test -value per edge between clinical groups and a hotter point in the heatmap suggests larger group-wise difference); (e) reveal the disease-related subnetwork detected by SICERS; (f) show the corresponding 3D (3D) brain image. Note that (e) was obtained by reordering the nodes in (d) by listing the detected subnetwork first (i.e., these two graphs are isomorphic).