Conceived and designed the experiments: JS RLB. Performed the experiments: JS. Analyzed the data: JS HL BTY. Contributed reagents/materials/analysis tools: JS HL TT IM. Wrote the paper: JS RLB.
The authors have declared that no competing interests exist.
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 (≤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.
Information processing in the human brain arises from both interactions between adjacent brain areas and from distant projections that form distributed systems. Here we estimated functional connectivity profiles in the human brain using a novel approach to map the regional balance between local and distant functional connectivity. We discovered that the human brain exhibits distinct connectivity profiles across regions with primary sensory and motor areas displaying preferential local connectivity and heteromodal association areas displaying preferential distant connectivity. These findings expand our knowledge of how the human brain has specialized its architecture to optimize processing efficiency and provides an approach to measure, in individuals, the degree to which the typical balance of local and distant connectivity is present.
The human brain is a complex biological structure with specializations for local, modular processing that are distinct from anatomical properties that facilitate integrative processing. Specifically, anatomic projection patterns suggest a division between areas that form domain-specific hierarchical connections
Studies of comparative anatomy suggest that the ratio of local to distributed areal projections may be critical to the evolution of higher-order cognitive functions including language, reasoning, and foresight. The hominin brain has tripled in absolute size over the past 2–3 million years including a disproportionate enlargement of cortical surface area
The relative proportion of association cortex differs further in the human
The modern human brain also possesses a high proportion of cerebral white matter relative to contemporary primates including the great apes
All of these findings converge to suggest that the balance between long-range projections and local areal interactions is important for efficient cortical processing. While this balance has been recognized for some time (e.g., see
Moreover, the extent to which an individual area is central to maximizing communication between multiple areas can be quantified and cortical regions possessing hub-like properties can be mapped. Applying this analysis strategy to structural
Although previous studies have focused their attention in network topological modularity
There are two likely reasons for this omission. First, human studies using diffusion techniques to measure anatomic connectivity (diffusion tensor imaging; DTI) provide poor information about connectivity between areas that are supported by local association fibers (u-fibers) and neighborhood association fibers that connect immediately adjacent and nearby areas
Second, functional connectivity approaches that measure cortical-cortical interactions indirectly via correlated blood oxygenation level-dependent contrast (BOLD)
Motivated by this possibility, we developed and applied a novel approach to map the regional balance between local and distant functional connectivity in the human brain. We first extended a computationally efficient approach based on network graph theory
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
Local and distant functional connectivity are plotted separately (
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
Data from
Plotted in red, using the same format as
A striking feature of the maps is that regions near primary sensory and motor cortex show strong preferential local connectivity. Examining the topography of the regions in more detail revealed that they track estimated boundaries of primary sensory and motor areas (
Preferential functional connectivity data (from
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.
Although functional connectivity patterns measured at rest provide valuable information about the intrinsic architecture of the brain, they are not synonymous with anatomic connectivity and are influenced by the task state (see
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).
First, engaging the task influenced both local and distant functional connectivity in regions typically active during performance of the abstract/concrete classification task. The changes were particularly prominent in the local connectivity estimates and included prefrontal cortex along the inferior frontal gyrus, lateral temporal cortex, dorsal anterior cingulate and a posterior parietal region linked to the frontal-parietal control system (e.g.,
Second, the regions of preferential local functional connectivity, as revealed by the direct contrast of the local to distant connectivity maps obtained from the task data, included the primary sensory and motor cortices (
To situate our findings in the context of other well-known network measures, we computed the average path length, physical cost and clustering coefficient in our data set (
Map estimates of path length (left), physical cost (center) and clustering coefficient (right) are displayed. Compared to the local-distant preferential map (
As shown in
The primary results of our analyses are the map estimates of local and distant functional connectivity. Several parameters were set to complete the analyses (e.g., the distance threshold) and therefore processing decisions may have affected the results. A series of control analyses were conducted to boost confidence in the approach and to establish that the reported results are robust. First, test-retest reliability was assessed for the local and distant connectivity maps by comparing maps derived from two independent datasets each comprising 50 participants (
We further examined whether correlations between the hemispheres across the midline contributed to the observed results. Bilateral contributions might cause a bias in overestimating local connectivity values along midline structures. Maps that included degree connectivity for only one hemisphere were highly similar to those that included both hemispheres (
The present study applied a novel approach to analyze regional functional connectivity profiles taking into account the distance between correlated regions. We found that the human brain exhibits cortical functional connectivity profiles at rest that fall into three major categories: one for sensory and motor cortical regions (preferential local connectivity), one for many regions near heteromodal association areas (preferential distant connectivity), and one related to a subset of heteromodal association and paralimbic regions that fall along the midline, in regions that are core components of the default network (high local and high distant connectivity). Specifically, preferential local and distant connectivity profiles revealed that regions within or near primary sensory and motor areas display high local connectivity consistent with a modular organization. By contrast, distant connectivity is prominent across association areas in parietal, lateral temporal, and frontal cortices as well as paralimbic cortex including posterior cingulate. These regions have been previously described as important for higher-order cognitive functions such as attentional, memory and language processes. Among the set of regions with a high degree of distant connectivity is a subset that simultaneously possess both high local and distant connectivity. The presence of densely interconnected local (modular) systems linked by connectional hubs is indicative of complex systems that gain efficiency through “small world” properties
A strong expectation that human primary sensory cortex will possess properties consistent with a high local distance connectivity organization is provided by prior analyses of macaque anatomic connectivity (e.g.,
Regions at or near primary somatosensory, auditory cortex, and motor cortex also display high levels of local connectivity. Some of the details of the mapping are not fully aligned with expectations, particularly the dense local connectivity observed in area 43 but not area 41 in auditory cortex. It is presently unclear whether this is a differential property of the area, an inaccuracy of the present method, or a consequence of inaccurate estimates of the areal boundaries. Of the regions studied, the best alignment between expectations from macaque anatomy and estimated human areal boundaries was for the retinotopically mapped visual areas
We also found that certain regions along the midline, including portions of the anterior cingulate, have preferential local connectivity and less distant connectivity similar to somatosensory/motor, auditory or visual cortices (see inset detail in
Certain regions were estimated to simultaneously possess both high levels of local and distant connectivity (
The regions displaying the hybrid connectivity profile overlap regions that belong to the default network
These observations raise two questions: What are the functional consequences of a hybrid functional connectivity profile and why is it so prominently represented in the default network? While there are too few constraints to offer more than broad speculations, it is worthwhile to generate hypotheses to encourage further exploration. Given that the regions possessing hybrid connectivity profiles are active during internal modes of thought, including during passive fixation, it is intriguing to hypothesize that information processing that persists independent of strong sensory constraints might require a set of modular, tightly coupled areas to maintain efficient local processing. That is, the default network may possess features so that information processing is able to maintain stable in situ information (high local network connectivity) and simultaneously to associate distributed information from key limbic, parietal and prefrontal regions of the brain (high distant network connectivity). This idea extends what has been previously articulated. For example, in Mesulam's foundational work on transmodal areas
An interesting unexpected result was the detection of clear local functional coupling changes when a task was engaged (
There are two separate implications of this observation. First, the result is a reminder that low-frequency functional correlations as measured by BOLD contrast fMRI are not an exact proxy for anatomical connectivity. Coupling among regions changes as a function of task state. Second, measures of local functional coupling may provide a means to investigate regional activity levels during tasks. It is possible, although not explicitly tested here, that local functional coupling may provide a powerful approach for identifying task-activated regions including for brief epochs of task performance. It will be interesting in future studies to explore this possibility and to generally examine what can be learned from task-induced changes in local functional coupling. A radical possibility is that task contrasts will not be required and local functional coupling by itself, or referenced to other aspects of the data or normative data, will be sufficient to estimate properties of brain activity.
The present work maps relative connectivity profiles that distinguish local and distant functional connectivity. As such it moves into an arena that is at the resolution boundary of present functional imaging approaches. Further, the connectivity method applied was based on intrinsic activity correlations measured using BOLD contrast fMRI. As we have discussed previously
One potential limitation of our study is that we are approximating real cortical distance with Euclidean distance. In the case of nodes in adjacent gyri, the Euclidean distance is underestimating the true cortical distance, since the white matter tracts often bend around the intervening sulcus. Future work will be needed in order to include additional information regarding surface distance and ideally white matter tract path lengths (to approximate this approach see an example in
A further limitation of the present paper is the focus on group data. An important avenue for future work will be to explore these properties, perhaps even at higher spatial resolution, in the context of task-based estimates of functional areas. For example, it will be useful to explore whether individual differences in estimated boundaries of retinotopic visual areas track estimates of local distance connectivity. We predict they will. Similarly, the regions of preferentially distinct connectivity overlap with regions that are active during tasks of remembering, foresight, and other forms of high-level cognition
Exploration of individual differences has potentially important implications for study of genetics and neuropsychiatric illness (e.g. Alzheimer's disease, epilepsy, schizophrenia, bipolar disorder, and autism). A particularly interesting area of future exploration concerns the development of local and distinct connectivity and the relation of our metrics to atypical development. Certain neuropsychiatric disorders are suspected to result from molecular disruptions that give rise to aberrant connectivity patterns. For example, autism is associated with overgrowth of the brain early in development and adult white-matter abnormalities
Relevant to this possibility, Fransson and colleagues
Functional connectivity MRI was used to analyze network properties across the human brain introducing spatial distance information. We discovered that mapping regions based on whether they exhibit preferentially local versus preferentially distant functional connectivity at rest easily separates early sensorimotor, heteromodal association cortices and core regions of the default mode network. This observation reveals a parsimonious property of cortical network architecture that divides processing between many parallel systems characterized by extensive local processing and transmodal regions that serve as hubs connecting these local systems. As a practical application of our approach, metrics of connectivity profiles that reflect local and distributed connectivity can be made rapidly in individual participants. These metrics may thus have value for exploring individual differences both in relation to genetics and also in developmental neuropsychiatric disorders where atypical connectivity profiles may be present. More broadly, the observation that connectivity hubs fall within regions of estimated cortical expansion between monkey and humans
112 healthy young adults participated in MRI for payment.
Data Set 1 | Date Set 2 | Composite Set | Task Data Set | |
50 (21 Male) | 50 (25 male) | 100 (46 male) | 12 (3 male) | |
22.1 (3.1) | 22.3 (2.9) | 22.2 (3.0) | 22.1 (2.3) |
Notes: SD = standard deviation. Data Sets 1 and 2 included data acquired during passive (rest) fixation. The Task Data Set included separate runs of fixation and continuous task performance (see text).
Written informed consent was obtained in accordance with guidelines set forth by the institutional review board of Partners Healthcare Inc, and this research has been conducted according to the Declaration of Helsinki.
Scanning was performed on a 3 Tesla TimTrio system (Siemens, Erlangen, Germany) using the 12-channel phased-array head coil supplied by the vendor. High-resolution 3D T1-weighted magnetization prepared rapid acquisition gradient echo (MP-RAGE) images were acquired for anatomic reference (TR = 2530 ms, TE = 3.44 ms, FA = 7°, 1.0 mm isotropic voxels). Functional data were acquired using a gradient-echo echo-planar pulse sequence sensitive to BOLD contrast (TR = 2500 ms, TE = 30 ms, FA = 90°, 36–43 axial slices parallel to plane of the anterior commissure-posterior commissure, 3.0 mm isotropic voxels, 0.5 mm gap between slices). Head motion was restricted using a pillow and foam, and earplugs were used to attenuate scanner noise. During the functional runs, the participants fixated on a visual cross-hair (plus sign, black on white) centered on a screen for each of two runs (each run 7 min 24 sec; 148 time points). Participants were asked to stay awake and remain as still as possible. For the task condition, we used a data set previously reported in Buckner et al.
MRI analysis procedures were optimized for functional connectivity MRI (fcMRI) analysis
Several sources of spurious or regionally nonspecific variance then were removed by regression of nuisance variables including: six parameter rigid body head motion (obtained from motion correction), the signal averaged over the whole-brain, the signal averaged over the lateral ventricles, and the signal averaged over a region centered in the deep cerebral white matter. Temporally-shifted versions of these waveforms also were removed by inclusion of the first temporal derivatives (computed by backward differences) in the linear model. This regression procedure removes variance unlikely to represent regionally specific correlations of neuronal origin. Of note, the global (whole-brain) signal correlates with respiration-induced fMRI signal fluctuations
The present study used fcMRI to map the local and distant degree of functional connectivity in the human brain. fcMRI measures intrinsic activity correlations between brain regions
Degree centrality (or degree) is a network measure that quantifies the number of links or edges connected to a node
Thus, we computed a variation of the classic degree centrality measure in graph theory (e.g.,
For these analyses, the time course of each voxel from the participant's brain defined within a whole-brain mask was correlated to every other voxel time course. As a result an
Finally, undirected and unweighted local and distant degree values were estimated for each voxel of the brain. The measure of degree connectivity for a voxel is given by:
To situate our findings in the context of other well-known network measures, we computed in the same sample of subjects the following measures per node: path length (
The clustering coefficient of a node is the proportion of all pairs of its neighbors that are directly connected to each other in the graph or, in other words, the number of links between the neighborhood vertices divided by the number of links that could possibly exist between them
Several control analyses were performed to explore the influences of processing decisions on the degree connectivity maps, to measure test-retest reliability, and to estimate the effects of local anatomy (
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).
Data were visualized on the cortical surface using the population-average, landmark- and surface-based (PALS) surface and plotted using Caret software
Projections illustrate different visualization thresholds for preferential connectivity map. For comparison purpose, we display the preferential connectivity map at three distinct thresholds and include both left and right hemisphere projections to illustrate that its topography is qualitatively consistent across all variations.
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Projections illustrate different visualization thresholds for regions that display both high local and high distant connectivity. Plotted in red are regions that show both high local and distant connectivity for three different thresholds: threshold 1 using normalized degree cutoff of Z-score>1.0, threshold 2 using normalized degree cutoff of Z-score>0.9 and threshold 3 using normalized degree cutoff of Z-score>0.8. As shown in the maps using threshold 1, the regions in both hemispheres that have high local and high distant connectivity at the same time are the regions that fall within the default network, such as the posterior cingulate, a region within the inferior parietal lobule, and the medial prefrontal cortex. Other regions, especially the superior parietal cortex, increase overlap while relaxing the threshold level. Left lateral and left medial view in threshold 1 are the same as
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Volume display of local, distant and preferential map. The results were projected on cortical surface in the main paper to aid visualization of the cortical surface. For reference we plot here brain volume maps of local degree (A), distant degree (B) and the preferential connectivity map (C) that include subcortical and cerebellar regions.
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Test-retest reliability for local and distant connectivity measures. The overall test-retest reliability of our approach was assessed with two different datasets of 50 participants each (dataset 1 and dataset 2). Degree maps for local (A) and distant (B) connectivity are highly correlated (r>0.90 in both cases). The figures on the left show the cortical projection in both samples and the graphs on the right the voxel-by-voxel correlation between Data Sets 1 and 2 for both analyses.
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The effect of neighborhood distance threshold. In order to explore the influence of the neighborhood distance threshold for the analysis, we tested different sized spheres. The image shows maps for distance thresholds ranging from 6 to 18 mm in an example participant. A small radius such as 6 mm (approximately only one voxel around target voxel) yields a map that did not notably distinguish areal topography - the image is largely flat. This is likely because local correlations between very adjacent voxels dominate the computation. That is, there is little information about differential functional connectivity. Neighborhood sizes greater than 10 mm show clear topological differences along the cortex. However, neighborhood radii more than 10–14 mm are largely similar. As one extends the distant threshold further, the map eventually becomes equivalent to the distant-connectivity maps (data not shown). Therefore, we conservatively used a distance threshold of 14 mm for all analyses.
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The effect of Gaussian smoothing. The influence of Gaussian smoothing was examined by comparing maps without spatial smoothing to the chosen 4 mm FWHM smoothing kernel. Comparison between degree measures with and without Gaussian smooth for local (A) and distant (B) degree maps reveals qualitatively similar results with both approaches. Some differences were noted with the predominant effect being the lower degree connectivity estimates obtained when no smoothing was applied (consistent with a reduction in signal-to-noise ratio).
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Local connectivity along the midline. Estimates of local connectivity along the midline may be inflated because they include homologous right and left hemisphere regions. For this reason, it was important to evaluate the potential bias in overestimating local connectivity values along the midline. Here we show that maps that include local degree connectivity for only one hemisphere (right) are highly similar to the results in the main paper that involve both hemispheres (left). Thus contralateral correlations do not account for our observations in local connectivity.
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We thank Marsel M. Mesulam and Dante Chialvo for insightful comments and discussion. David Van Essen generously provided the use of CARET software. Avi Snyder assisted in developing the fcMRI procedures. Larry Wald, Mary Foley, and the Athinoula A. Martinos Center MRI Core provided assistance with MRI imaging.