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. 2010 Jun 10;6(6):e1000808.
doi: 10.1371/journal.pcbi.1000808.

The organization of local and distant functional connectivity in the human brain

Jorge Sepulcre  1 , Hesheng Liu, Tanveer Talukdar, Iñigo Martincorena, B T Thomas Yeo, Randy L Buckner
Affiliations

The organization of local and distant functional connectivity in the human brain

Jorge Sepulcre et al. PLoS Comput Biol. .

Abstract

Information processing in the human brain arises from both interactions between adjacent areas and from distant projections that form distributed brain systems. Here we map interactions across different spatial scales by estimating the degree of intrinsic functional connectivity for the local (<or=14 mm) neighborhood directly surrounding brain regions as contrasted with distant (>14 mm) interactions. The balance between local and distant functional interactions measured at rest forms a map that separates sensorimotor cortices from heteromodal association areas and further identifies regions that possess both high local and distant cortical-cortical interactions. Map estimates of network measures demonstrate that high local connectivity is most often associated with a high clustering coefficient, long path length, and low physical cost. Task performance changed the balance between local and distant functional coupling in a subset of regions, particularly, increasing local functional coupling in regions engaged by the task. The observed properties suggest that the brain has evolved a balance that optimizes information-processing efficiency across different classes of specialized areas as well as mechanisms to modulate coupling in support of dynamically changing processing demands. We discuss the implications of these observations and applications of the present method for exploring normal and atypical brain function.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Methods for identifying local and distant functional connectivity.
The basis of the present method is the intrinsic BOLD signal fluctuations that correlate between brain regions. 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. The displayed example represents a matrix and network of 1000 nodes (brain voxels). To estimate local and distant brain connectivity, the normalized degree of intrinsic functional connectivity of every voxel across the brain was computed taking into account physical distance to compute separate estimates of local degree connectivity and distant degree connectivity (see also Figure S1).
Figure 2
Figure 2. Local and distant functional connectivity maps.
Local (left) and distant (right) functional connectivity maps are displayed for the left hemisphere from 100 subjects. Data were acquired during passive (rest) fixation. Notable differences in the topography of the connectivity profiles are present with primary sensory and motor regions showing strong local connectivity and regions of association cortex displaying distant connectivity (see also Figure S3). The surface projection uses the PALS approach of Van Essen (2005; see text). The color bar represents the normalized degree connectivity (Z-score).
Figure 3
Figure 3. Preferential connectivity maps highlight regions with differential functional connectivity profiles.
Data from Figure 2 are contrast to illustrate the relative differences between local and distant degree connectivity. The maps plot the direct subtraction of distant versus local functional connectivity with blue indicating regions of preferential distant connectivity and yellow indicating regions of preferential local connectivity. Note that the primary sensory and motor regions show different profiles as contrast with association cortices in the parietal, temporal and frontal lobes. The insets display the same maps but at an increased threshold to appreciate the high relative local degree connectivity in visual cortex and rostral anterior cingulate (see also Figure S3). ACC = anterior cingulate.
Figure 4
Figure 4. Some regions display both high local and high distant functional connectivity.
Plotted in red, using the same format as Figure 2, are regions that show both high local and distant functional connectivity (normalized degree cutoff of Z-score>1.0). These regions include the posterior cingulate cortex (PCC), a region ventral to the intraparietal sulcus (IPS) extending into the inferior parietal lobule, and the medial prefrontal cortex (MPFC). Note that the region within the MPFC does not extend into the rostral ACC that displays a preferential local connectivity profile (see Figure 3).
Figure 5
Figure 5. Retinotopic visual areas display preferential local functional connectivity.
Preferential functional connectivity data (from Figure 3) are plotted in relation to approximate Brodmann areas (left) and estimated retinotopic boundaries (right) for visual cortex. The display represents a flattened portion of cortex that includes the occipital lobe. Labels in the Brodmann panel represent approximate Brodmann area boundaries (see text). Labels in the Retinotopic panel represent estimated visual areas (see text). The regions of high preferential local connectivity fall within the early retinotopic areas including primary visual cortex.
Figure 6
Figure 6. Somatosensory, motor and auditory areas display preferential local functional connectivity.
Preferential functional connectivity data are plotted in relation to approximate Brodmann areas for somatosensory/motor cortex (left) and auditory (right) cortex. Labels represent Brodmann areas.
Figure 7
Figure 7. Task performance leads to changes in local and distant functional coupling.
Changes in the degree of functional connectivity during performance of a continuous semantic classification task are displayed for local connectivity (left) and distant connectivity (center). An increase in local functional coupling is observed along the inferior frontal gyrus (a), the inferior parietal lobule (b), lateral temporal cortex (c), and the dorsal anterior cingulate (d). More modest (but anatomically similar) increases in functional coupling are noted for distant functional coupling with the exception of visual cortex (e) that shows a more prominent change in distant functional coupling. Despite the task differences, data acquired during rest fixation and continuous task performance show similar locations of preferential local connectivity within motor and sensory regions (right).
Figure 8
Figure 8. Maps of path length, physical cost and clustering coefficient.
Map estimates of path length (left), physical cost (center) and clustering coefficient (right) are displayed. Compared to the local-distant preferential map (Figure 3 and Figure S1), short path lengths and high levels of physical cost are predominant in regions of preferentially distant connectivity. Preferential local connectivity is associated with regions of high path length, low physical cost and high clustering coefficient.
Figure 9
Figure 9. Preferential connectivity maps are similar in three control conditions.
Map estimates of preferential local and distant connectivity do not notably change when subcortical structures are excluded from analysis, although several subtle differences are observed presumably arising from exclusion of distant thalamic, striatal, and cerebellar connections (left). Weighting the connectivity estimates based on the local gray matter volume also does not notably change the results (middle). Alternative normalization using percent normalization ([Distant Degree×100]/[Distant Degree + Local Degree]) shows qualitatively similar results as well (right).
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