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. 2013 Aug 21;79(4):798-813.
doi: 10.1016/j.neuron.2013.07.035.

Evidence for Hubs in Human Functional Brain Networks

Free PMC article

Evidence for Hubs in Human Functional Brain Networks

Jonathan D Power et al. Neuron. .
Free PMC article


Hubs integrate and distribute information in powerful ways due to the number and positioning of their contacts in a network. Several resting-state functional connectivity MRI reports have implicated regions of the default mode system as brain hubs; we demonstrate that previous degree-based approaches to hub identification may have identified portions of large brain systems rather than critical nodes of brain networks. We utilize two methods to identify hub-like brain regions: (1) finding network nodes that participate in multiple subnetworks of the brain, and (2) finding spatial locations in which several systems are represented within a small volume. These methods converge on a distributed set of regions that differ from previous reports on hubs. This work identifies regions that support multiple systems, leading to spatially constrained predictions about brain function that may be tested in terms of lesions, evoked responses, and dynamic patterns of activity.


Figure 1
Figure 1. Degree is a problematic measure of node importance in Pearson correlation networks
A) A computer network with 3 communities is shown. Degree identifies uniquely important nodes in the graph and there is a weak relationship between degree and community size. B) A block model corresponding to a birdsong correlation network. Three flocks are present, each singing a song uncorrelated with the other flock. C) As in (B) except that blocks are now allowed to correlate. This could correspond to a situation where there was similarity in the birdsong of different flocks, or where flocks sang the same song for limited periods of time. D) As in (C) except that the perfect correlations within blocks are relaxed to imperfect correlations. This could correspond to individual imperfections in birdsong, or individual birds switching songs occasionally.
Figure 2
Figure 2. Degree is influenced by community size in RSFC graphs
A) The RSFC correlation matrix of a 264-node graph in 120 young adults. Communities over a range of thresholds are shown as colors in the second panel. The number of nodes in the communities and node strengths at every threshold are shown in the third and fourth panels. A linear fit of node strength to community size is plotted for the 2, 4, 6, and 8% edge density analyses. Small dots indicate individual datapoints, large dots indicate average values in a community. Fits excluded communities with fewer than 5 nodes. The threshold range used corresponds to that used in (Power et al., 2011) and spans thresholds where many communities are present (higher edge densities such as 20% or 15% yield coarse structure with 2 or 4 communities) down to thresholds where the graph begins to fragment due to edge removal. B) Communities were identified in a voxelwise graph formed in the same subjects. For the 5% edge density analysis, for every voxel, the size of its community and its strength is shown on a brain surface. The default mode system is the largest community and contains the voxels with highest degree. Linear fits of node strength to community size at several thresholds are shown. Fits excluded communities with less than 250 nodes. These thresholds correspond to those used in (Power et al., 2011). C) R2 of linear fits of node strength to community size at several thresholds in each network (thresholds are reported in terms of edge density and the threshold used on the correlation matrix to produce the desired edge density).
Figure 3
Figure 3. Degree is influenced by community size in Pearson correlation networks
The table lists the properties of 19 real-world networks, 5 of which are correlation networks (red text). For each network Infomap was used to identify communities, and the the r and R2 values for linear fits of community size vs. node strength are shown. The bar graph plots the R2 values. The plots at right depict several of the linear fits (depicted networks have squares in the first column). For the correlation networks, several thresholds were analyzed (the edge densities from Figure 2 for the RSFC graphs, and r > 0, 0.2, 0.4, 0.6, and 0.8 for the 3 other correlation networks). For correlation networks, the top numbers are for the lowest threshold and the bottom numbers (in parentheses) are for the highest threshold, conveying the range of values the networks displayed; the R2 values are the mean over all analyses. As in Figure 2, fits for all graphs excluded communities with fewer than 5 nodes (and fewer than 250 nodes for the voxelwise graph). Reported graph properties reflect the properties of the nodes qualifying for the fits (small communities excluded). See Figure S1 for further details and graph properties.
Figure 4
Figure 4. Degree-based hubs are weak and provincial in RSFC graphs but not in other real-world graphs
A) A model network depicting how (Guimerà and Nunes Amaral, 2005) define node roles. B) Node role plots for several real-world networks. C) Node roles were calculated in the areal RSFC graph for each threshold in the 10-2% threshold range. Only a single hub ROI was found (this is true across all positive thresholds: 44%-1% edge density). This node, in the precuneus, is a provincial hub (the black sphere). One other node immediately anterior to this node approaches but does not meet hub classification criteria (the gray sphere). D) Node roles were calculated in the voxelwise RSFC graph for each threshold in the 5-1% threshold range. Node roles at 5% edge density are plotted; this plot is typical of the other thresholds. The surfaces show locations of voxels that, across thresholds, are identified as hubs in at least 3 of 5 analyses. These voxels are provincial hubs located in the precuneus.
Figure 5
Figure 5. Volume-based models of brain organization may distort information processing properties of the brain
A) Assume a spatially embedded economic system in which California (CA) is a hub of interstate commerce and Alaska (AK), Washington (WA), and Rhode Island (RI) play more peripheral roles. A graph in which nodes represent states correctly identifies CA as a hub. However, if states are represented by their areas (e.g., nodes of square miles), Alaska dominates the graph structure and is identified as the seat of hubs in the network simply by being the largest physical entity in the system. B) The parallels to RSFC are straightforward: areas contain voxels in proportion to their volume, and nodes within larger areas (and by extension members of larger systems) will tend be identified as hubs by degree simply because they are part of a large physical entity. Self-connections are allowed in the state graphs to emulate how voxels can and will correlate strongly to other voxels within the same area or system.
Figure 6
Figure 6. Putative hubs in the areal network identied by high participation coefficients
A) A graph with several communities (yellow, green, pink) illustrates the meaning of participation coefficient. B) Surface and spring-embedded plots of communities in the areal graph at 5% edge density, with nodes colored by participation coefficient at right. C) Summed participation coefficients across thresholds. See Figures S2–S3 for replicability over sub-cohorts and the robustness of these calculations to data smoothing and global signal regression.
Figure 7
Figure 7. Articulation points: brain locations that are densely populated by functional systems
A) Communities in the 1% edge density analysis is shown; colors represent communities. All communities with fewer than 125 voxels are colored white (and are treated as a single community in community density calculations). B) Community density is calculated as the number of unique communities present within some distance of a source node (here, within 8 mm of a source voxel, in the 1% edge density analysis). C) Summed community density. See Figures S4–S5 for analyses of the influence of subcortical and contralateral tissue on these calculations, replicability in sub-cohorts, and stability over the parameter spaces of thresholds and sampling radii. The data in this figure and subsequent figures are derived from single-hemisphere community analyses of all voxels within the AAL atlas, followed by community density calculation excluding subcortical structures.
Figure 8
Figure 8. Areal participation coefficients plotted over community density, with consensus communities for reference
A) Overlaid data from Figures 6 and 7. The correlation between the two measures is r = 0.57 (calculated in ROIs where at least 10/19 voxels were defined in the community density analysis, 245/264 ROIs). B) The consensus community assignments from (Power et al., 2011) are provided as a reference to illustrate the communities present near areas of high community density. Positions and MNI coordinates for peaks in community density are shown. See Table S2 for ROI locations and summed measures. See Figure S6–S8 for flat-map illustrations of the same data, similar findings in a separate cohort, and plots of the interdependence of participation coefficient, community density, and degree.

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