Functional network organization of the human brain

Abstract

Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a "processing" system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex.

Copyright © 2011 Elsevier Inc. All rights reserved.

Figures

Figure 1. Areal subgraph structure is highly similar across cohorts and subgraph structure is similar between areal and modified voxelwise graphs

Top left: a spring embedded layout of the areal graph at 4% tie density visualizing the graph and the basis for subgraphs. Top right: for both cohorts, plots are shown of the areal assignments into subgraphs (colors) at tie densities from 10% down to 2% in 1% steps. ROI ordering is identical, and all subgraphs with fewer than 4 members are colored white. The standard measure of subgraph similarity, normalized mutual information, between node assignments of the cohorts at identical tie densities ranged from 0.86-0.92, indicating highly similar patterns across cohorts (1 = identical assignments, 0 = no information shared between assignments). Bottom: subgraphs from three thresholds are shown for the areal (spheres) and modified voxelwise graphs (surfaces). Note the similarity of subgraph assignments beteween networks, despite the great difference in network size and cortical coverage, even in different subjects (main vs replication cohorts). All areal subgraphs with fewer than 4 members are colored white, and all modified voxelwise subgraphs with fewer than 100 voxels are colored white. Areal networks are shown at 10%, 3%, and 2% tie density (r > 0.16, 0.30, and 0.33), and modified voxelwise networks are shown at 5%, 2%, and 0.5% tie density (r > 0.16, 0.23, and 0.31).

Figure 2. Many modified voxelwise subgraphs replicate across cohorts and even within single subjects

Select subgraphs from the modified voxelwise analysis are presented from a dorsal view for both cohorts and for an additional single subject. Cohort subgraphs are taken from the 2% tie density analysis and subgraphs in the individual are taken from a 0.5% tie density analysis. The overall NMI between cohort assignments at this threshold was 0.71, and NMI values between subgraphs from different cohorts are shown in the matrix to the right. Additional views of this data and replications of subgraphs from other thresholds are found in Figure S3.

Figure 3. Graph definition dictates fidelity to functional brain organization

At left, the task-defined locations of four established functional systems. The next three columns display, for the main cohort, the single subgraph that best corresponds to each functional system under the four graph definitions. Circles are placed around small portions of subgraphs that might otherwise be overlooked (there are small green regions within green circles). Data from a single threshold tailored to each graph are shown. The threshold was the next-to-highest threshold that each graph can achieve before the graph becomes severely fragmented (defined by the giant component containing fewer than 50% of the nodes in the graph). Tailored thresholds were 3% for the areal graph, 5% for the AAL-based graph, and 2% for both voxel-based graphs. Correspondence between these functional systems and subgraphs is good for the areal and modified voxelwise graphs, intermediate for the voxelwise graph, and poor for the AAL-based graph. Note especially the correspondence between areal (spheres) and modified voxelwise (surface) subgraphs, despite great differences in network size (N = 264 vs N = 40,100). See Figure S4 and S5 for more comprehensive and quantitative presentations of subgraph assignments. Images in the left column are modified from (Corbetta et al., 2008; Corbetta and Shulman, 2002; Dosenbach et al., 2007; Shulman et al., 1997).

Figure 4. Subgraph identities

Left: Visual (blue), auditory (pink), and hand (cyan) and face (orange) sensory-somatomotor (SSM) subgraphs are shown for the areal network at 2% (spheres) and the modified voxelwise network at 0.5% tie density (surface). Below, the mean correlations in the main cohort between auditory processing (pink, MNI: −38 −33 17) and hand (cyan, −40 −19 54) and face (orange, −49 −11 35) regions are shown. Auditory-face correlations are significantly higher than auditory-hand correlations in both cohorts (p < 0.001, two-sample two-tail t-test). Bottom, slices from the 4% tie density modified voxelwise analysis, with labels on relevant thalamic nuclei (numbers are z coordinates). Middle: Two cingulo-opercular subgraphs shown from the 3% areal (spheres) and 2% tie density modified voxelwise analysis (surface). Middle, published ROIs (cingulo-opercular task control: (Dosenbach et al., 2007); salience: (Seeley et al., 2007)) or modified voxelwise subgraphs, with an overlaid heat map of on-cue meta-analysis activation. On-cue activity localizes to the purple subgraph. Bottom, very strong fc-Mapping gradients are displayed separating the black and purple subgraphs, indicating that they possess distinct rs-fcMRI signals. Right: At top, three unknown subgraphs from the 0.5% tie density modified voxelwise analyses are shown. The salmon subgraph (gray in all other figures, here salmon for contrast) is reproduced with a 2% areal subgraph overlaid as spheres, and the strongest activations from the memory retrieval meta-analysis are shown below. The light blue subgraph is also reproduced and the coordinates of a putative functional system from (Nelson et al., 2010a) are overlaid as tan spheres.

Figure 5. The “task positive system” consists of multiple subgraphs, including dorsal attention, fronto-parietal task control, and cingulo-opercular task control systems

At left, the “task+ system” of (Fox et al., 2005). At right, three subgraphs from the 0.5% tie density modified voxelwise analysis. The “task+ system” is composed of at least three subgraphs, corresponding to the fronto-parietal task control, cingulo-opercular task control, and dorsal attention systems.

Figure 6. Default, visual, and somatosensory-motor systems are well-integrated on local scales but are relatively isolated in relation to other functional systems

At top, the subgraphs, local efficiencies, and participation coefficients for all nodes in the areal network over a range of thresholds are shown. The local efficiency of each node indicates the extent to which a node is embedded in a richly connected local environment. High (hot color) values indicate a richly connected local environment. The participation coefficient of each node indicates the extent to which a node has ties to other subgraphs. Here, low (cool color) values indicate that nodes are connected almost exclusively to members of their own subgraph. One-factor ANOVAs indicate a significant effect of subgraph at all thresholds for both indices (all with p −6), and post-hoc t-tests indicate that the cyan, blue, and red subgraphs have significantly higher local efficiencies and lower participation coefficients at most or all thresholds than the yellow subgraph. Node assignments for a single threshold (4% tie density) are shown on a brain and in a spring embedded layout, and the local efficiencies and participation coefficients of relevant subgraphs at this threshold are shown. Note that local efficiency is independent of subgraph assignment, whereas participation coefficients depend upon subgraph assignment.

Figure 7. Functional systems are arranged into topological motifs across the cortex

In charts, particular subgraphs at a single threshold are selected, the spatial boundaries of that subgraph are found, and the distribution of spatial interfaces (en face voxels) to other subgraphs are calculated. The most frequent interfaces are plotted as percents of the total subgraph interface volume. Motifs are inferred by finding instances where subgraphs interfacing with a subgraph are themselves very unlikely to interface. For instance, in the top chart, the light blue subgraph interfaces most frequently with the yellow and red subgraphs, but red is only 3.6% of yellow’s interface, and yellow is only 2.6% of red’s interface. Below each chart, plots of relevant subgraphs on brain surfaces visually demonstrate the repeated spatial patterns of subgraphs. Data from the modified voxelwise analysis at 1% tie density in the replication cohort are presented.