Toy example.

<p>Result of clustering for BayesCorr, BayesCov and Bic. The values on the <i>y</i> axis correspond to the log₁₀ Bayes factor in favor of the global clustering obtained at each step compared to a model where all variables are independent (step 0 of hierarchical clustering). The dotted lines correspond to clustering steps that were not performed with the automatic stopping rule.</p

Simulation study.

<p>Computational time (top), adjusted Rand index (middle) and proportion of correct classifications (bottom) for <i>D</i> = 6 (left) and <i>D</i> = 10 (right).</p

Real data—consensus clustering.

<p>A consensus clustering was generated based on the average adjacency matrices across all subjects (Panel <b>a</b>). The (weighted) adjacency matrix associated with the consensus clustering is represented along with a volumetric brain parcellation (Panel <b>b</b>). The weights in the adjacency matrix were added to establish a visual correspondence with the volumetric representation. Note that the brain regions have been order based on the hierarchical clustering generated with WardAbs.</p

Toy example.

<p>Result of clustering. Algorithms in the top row clustered <i>X</i>₄ at the last step, while it was clustered at the before the last step for algorithms in the bottom row. Algorithms in the left column clustered <i>X</i>₆ with {<i>X</i>₃, <i>X</i>₅}, while <i>X</i>₆ was clustered with {<i>X</i>₁, <i>X</i>₂} for the algorithms in the right column. Parts in grey correspond to clustering steps that were not performed by BayesCovAuto or BayesCorrAuto in (G1), or Bic in (G3).</p

Toy example.

<p>Result of spectral clustering with increasing number of clusters.</p

Potential triangle motifs surrounding a pair of nodes.

<p>The first row represents the first level of the hierarchy of triangles, with triangles adjacent to at least one node of the pair, while the second row represents the second level with triangles non-adjacent to the pair, but adjacent to at least one common neighbor. The third row represents the third level, where triangles are non-adjacent to the pair and to the set of their common neighbors, but adjacent to at least one neighbor of one node of the pair. Red nodes represent the pair considered, red (resp. blue) dashed edges represent optional edges (resp. potential motifs not considered in the current formalism).</p

Prediction of the FC patterns in the deterministic SER model for toy examples.

<p>(A) Toy structural connectivity network with a pair of nodes in red and their common neighbors in green (top), and associated patterns of coactivation as a function of the initial percentage of excited nodes (bottom). Colors code for the different predictions (magenta, red and green for the prediction from SC, TO—<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006084#pcbi.1006084.e001" target="_blank">Eq 1</a> and FC1—<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006084#pcbi.1006084.e006" target="_blank">Eq 5</a>, respectively, black codes for the simulated FC). (B) Same as (A) with additional links (in blue) added randomly in the original graph.</p

Predictive power of the analytical predictions of FC over the full space of initial conditions in the deterministic SER model.

<p>Columns code for the different potential predictors (SC, TO—<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006084#pcbi.1006084.e001" target="_blank">Eq 1</a>, FC1—<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006084#pcbi.1006084.e006" target="_blank">Eq 5</a> and FC2—<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006084#pcbi.1006084.e016" target="_blank">Eq 6</a>, from left to right) and rows represent typical network topology. For each panel, the upper (resp. lower) triangular parts of the matrices represent the correlation (resp. mean difference) between the simulated FC and its predictor.</p

Dynamic FC.

<p>Bootstrap SVD extracted a reproducible linear space of dimension one for all window sizes but the shortest, where no reproducible linear space was found. <b>(a)</b> Part of variance explained by the first dimension as a function of the size of the time window used to compute dynamic FC. <b>(b)</b> First dimension of dynamic FC as a function of the first dimension of cFC. <b>(c)</b> First dimension of dynamic FC as a function of the first dimension of empirical SC.</p

Real resting-state fMRI data—computational cost.

<p>* For nonhierarchical methods, we summed the times used to perform clustering at each scale.</p><p>Time required by each method to cluster one dataset.</p