Effective reaction time (<i>RT*</i>) depending on task and stimulus conditions.

<p><i>RT*</i> was longer for the shape than color task, incongruent than congruent stimuli, and West compared to East signs. Error bars represent standard errors.</p

Mean effective reaction time and <i>SD</i> (in ms) for different conditions.

<p>Mean effective reaction time and <i>SD</i> (in ms) for different conditions.</p

Illustration of the predictive power of the analytical predictions of FC in the deterministic SER model.

<p>Example of coactivation (FC) for a randomly selected pair of nodes (first column), correlation (second column) and mean (signed) difference (third column) between simulated and predicted FCs. The plots are a function of the number of nodes initially in the E state and the analytical predictions (magenta, red, green and blue 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>, 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>, respectively, black codes for the simulated FC). The last column represents the scatter plot of the relation between FC2 (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006084#pcbi.1006084.e016" target="_blank">Eq 6</a>) and simulated FC. Each row represents a typical network topology, that is, random (first row), scale-free (middle row) and modular (last row).</p

Ampelmännchen stimuli.

<p>A) Congruent condition: go/stop signs from East (upper panel) and West Germany (lower panel); left figures signaling “go”; right figures “stop”; B) Incongruent condition: go/stop signs with altered colors; C) Examples for control condition: color circles with same area size (number of pixels) of color as corresponding signs.</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

Examples of FC patterns in the deterministic SER model.

<p>(Left) State space of all possible initial conditions. (Right) Exemplar structural connectivity (first column), and associated patterns of coactivation (next columns) for different initial conditions as represented by colored disks. Each row represents a typical network topology, specifically, random (first row), scale-free (middle row) and modular (last row).</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

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

Features of spatial and functional segregation and integration of the primate connectome revealed by trade-off between wiring cost and efficiency

<div><p>The primate connectome, possessing a characteristic global topology and specific regional connectivity profiles, is well organized to support both segregated and integrated brain function. However, the organization mechanisms shaping the characteristic connectivity and its relationship to functional requirements remain unclear. The primate brain connectome is shaped by metabolic economy as well as functional values. Here, we explored the influence of two competing factors and additional advanced functional requirements on the primate connectome employing an optimal trade-off model between neural wiring cost and the representative functional requirement of processing efficiency. Moreover, we compared this model with a generative model combining spatial distance and topological similarity, with the objective of statistically reproducing multiple topological features of the network. The primate connectome indeed displays a cost-efficiency trade-off and that up to 67% of the connections were recovered by optimal combination of the two basic factors of wiring economy and processing efficiency, clearly higher than the proportion of connections (56%) explained by the generative model. While not explicitly aimed for, the trade-off model captured several key topological features of the real connectome as the generative model, yet better explained the connectivity of most regions. The majority of the remaining 33% of connections unexplained by the best trade-off model were long-distance links, which are concentrated on few cortical areas, termed long-distance connectors (LDCs). The LDCs are mainly non-hubs, but form a densely connected group overlapping on spatially segregated functional modalities. LDCs are crucial for both functional segregation and integration across different scales. These organization features revealed by the optimization analysis provide evidence that the demands of advanced functional segregation and integration among spatially distributed regions may play a significant role in shaping the cortical connectome, in addition to the basic cost-efficiency trade-off. These findings also shed light on inherent vulnerabilities of brain networks in diseases.</p></div

Modularity of original Macaque network and reconstructed networks.

<p>(A) Layout placement of 103 areas and the connections of the Macaque cortical network. (B) The two modules of the real network (open and filled circles) are compared to the two spatial clusters (blue and red). The corresponding modularity is . (C) As in (A), but for a reconstructed network at . The blue and red colors of the nodes represent the two spatial clusters. (D) The same as (B), but the modules are from the reconstructed network in (C), with . (E) Mismatch between the module partitions of the reconstructed and real networks. The mismatching areas are indicated by the pentagrams. (F) shows the fraction of mismatching areas between reconstructed and original networks in module partition, with respect to . The result did not include where the reconstructed networks did not show strong modularity. Blue bars are for the mismatching areas appearing in more than of all 50 realizations at each , while white bars are for the mismatching areas appearing at least once in the 50 realizations at the given . The dashed line represents the mismatching rate between the real networks and shuffled modules (see section “Matching between partitions” in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002937#s4" target="_blank"><i>Materials and Methods</i></a>).</p