Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease

Abstract

Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset (n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD (n = 10) compared with older controls (n = 29) showed high amyloid-beta deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.

Figures

Figure 1.

Methods for identifying cortical hubs and networks. A, The basis of the present methods is the intrinsic BOLD signal fluctuations that correlate between brain regions reflecting monosynaptic and polysynaptic connections. B, The functional connectivity matrix was computed to represent the strength of correlation between every pair of voxels across the brain; the pattern of these connections is the functional connectivity network (example is of a binary matrix and network of 1000 nodes). C, To determine cortical hubs, the degree of connectivity of each voxel was computed and projected onto the cortical surface of the brain. Candidate hubs are those regions with disproportionately high connectivity and are plotted in yellow and red. D, As a secondary analysis, the networks associated with identified hubs were determined by seeding individual regions located at the peak of the hub and determining the subnetworks that showed correlation.

Figure 2.

Cortical hubs are present and reliable. Heteromodal association regions of cortex reliably showed disproportionately high degree of connectivity in both datasets. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Primary sensory and motor areas were essentially absent hubs. Data from each of the two separate dataset are shown above (dataset 1, n = 24; dataset 2, n = 24). The graph on the bottom shows the voxel-by-voxel correlation between datasets 1 and 2. The two are highly correlated (r = 0.93). The images represent the left hemisphere surface projection on the PALS atlas (Van Essen, 2005).

Figure 3.

Cortical hubs are associated with multiple distinct networks. Examples of networks associated with specific cortical hubs are shown for four hubs from Table 1. Each image shows the functional connectivity map based on a single seed located at the position of the blue circle. The threshold for each map is set at r > 0.25. A, Posterior cingulate location 6 from Table 1. B, Dorsolateral prefrontal cortex location 5 from Table 1. C, Supramarginal gyrus location 7 from Table 1. D, Medial prefrontal cortex location 3 from Table 1. Note that certain hubs (A, D) are linked to a common core network, whereas other hubs (C) are associated with a distinct network.

Figure 4.

Network analysis of cortical hubs. All regions functionally linked to the 10 hubs identified in Table 1 were entered into a formal graph-analytic network analysis. A, The 94 5-mm-radius spherical regions used for analysis are displayed on transverse sections of the MNI152 atlas. Spherical regions are shown in red. B, A graphical representation of the network of regions is displayed using the Kamada–Kawai algorithm such that strongly connected regions appear close to one another and weakly connected regions farther away (see Results). The size of the node reflects the estimate of the betweenness centrality of each region. The five regions with the greatest betweenness centrality are colored in blue and labeled a through e. Note that the majority of hubs link to a single integrated network (I), whereas a subset reflect a distinct network (II). The regions in II reflect the network displayed in Figure 3C. C, The locations of the regions with the five highest estimates of betweenness centrality are illustrated. PCC, Posterior cingulate; MPFC, medial prefrontal cortex.

Figure 5.

The locations of cortical hubs persist across task states. Despite clear differences in degree connectivity, data acquired during rest fixation and continuous task performance show similar locations of the core hubs. A, Cortical hubs are shown for the fixation task from dataset 3. B, A similar plot is shown for the continuous performance task from dataset 3. The core hubs located in posterior cingulate (a), inferior parietal cortex (b), and medial prefrontal cortex (c) are present across task states. There are also differences in the task state, including increased functional connectivity in dorsolateral prefrontal cortex (d). C, The direct contrast of the degree connectivity maps is displayed to illustrate differences between the task states. Yellow shows regions of higher connectivity in the task data, and blue shows regions higher in the fixation data. Note that the difference in functional connectivity parallels differences observed in traditional task-based analyses, including increased functional connectivity in prefrontal, temporal, and midline structure that are commonly observed in semantic classification tasks. These differences are in addition to shared hubs that persist across task states (e.g., b and, to a lesser extent, a).

Figure 6.

Direct comparison of cortical hubs across task states. The voxel-by-voxel correlation between the fixation and continuous task performance data from Figure 5, A and B, are plotted. They are highly correlated (r = 0.78). Thus, despite a measurable effect of task (Fig. 5C), a major portion of the anatomic variation in degree connectivity is preserved across task states, including the continuous heightened activity fluctuations in the core hubs identified in Figure 4.

Figure 7.

A consensus estimate of cortical hubs from 127 participants. To provide our best estimate of the locations of cortical hubs, the data for all available participants were pooled and a map of hubs based on degree connectivity computed. The format is an expanded version of the format used in Figure 2 that shows both the right and left hemispheres as well as the ventral and dorsal surfaces.

Figure 8.

The pattern of Aβ deposition in Alzheimer's disease. Aβ deposition was measured using PiB–PET imaging and is plotted on the cortical surface using the same format as Figure 7. As can be appreciated visually, those regions showing high functional connectivity primarily overlap those regions showing Aβ deposition.

Figure 9.

Direct comparison of cortical hubs and Aβ deposition. The voxel-by-voxel correlation between the cortical hubs from Figure 7 are directly compared with the estimate of Aβ deposition from Figure 8. The two are highly correlated (r = 0.68) with no clear region of discrepancy between the two, consistent with visual inspection of the data.