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 
and distinct heteromodal association areas that receive widespread projections from distributed brain systems 
. The dichotomy is not absolute. Sensory systems contain divergent projections and display multimodal convergence at advanced processing stages. Nonetheless, dominance for one connectivity profile over the other is present for many areas and suggests a fundamental organizing principle of cortical-cortical connectivity. Early sensory cortical areas are examples of areas with predominantly local hierarchical connections (e.g., see 
) while prefrontal, lateral temporal, limbic and paralimbic areas form hubs linking widely distributed connections – neural epicenters of large-scale distributed networks 
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 
. However, expansion comes with a cost to information processing efficiency 
. Proliferation of long-distance connections and increasing brain volume could lead to untenable wiring lengths if they evolved unchecked 
. Thus, there is a compensatory pressure to modularize information flow within parallel processing pathways and to maximize efficient communication among areas of similar function. Van Essen 
proposed that there is a specific selection pressure to optimize wiring length between adjacent functionally-similar areas within the same hemisphere. Consistent with this possibility, cortical folding patterns in the macaque brain minimize between-area wiring lengths for sensory (e.g., Broadmann's area [BA] 17 to BA 18) and motor (e.g., BA 4 to BA 6) pathways.
The relative proportion of association cortex differs further in the human 
. The human brain is three times larger than that of modern great apes yet primary motor (BA 4) and visual (BA 17) cortices are about the same absolute size 
. Preuss 
, in a detailed analysis of cortical growth, concluded that widely distributed associated areas exhibited an increase in absolute surface area during hominin evolution including higher-order parietal and temporal areas as well as prefrontal cortex. Thus, the long-held belief that the prefrontal cortex is preferentially expanded in humans is only partially correct; heteromodal association areas are likely expanded throughout cortex including those areas falling within prefrontal cortex. Bolstering these observations, surface-based analysis of cortical differences between macaque and human based on 23 estimated homologous areas reveals a high degree of expansion in parietal, lateral temporal, and dorsolateral prefrontal regions and a relative compression of sensorimotor and visual areas 
The modern human brain also possesses a high proportion of cerebral white matter relative to contemporary primates including the great apes 
(see also 
for a broad analysis of primates). Comparative study of the arcuate fasciculus, the major fiber bundle connecting anterior and posterior heteromodal language zones, shows that it is enlarged in humans as compared to chimpanzees or macaques 
. Thus long-distance association projections have expanded as well and may have done so in relation to specific functional adaptations. One can presume that there has been considerable pressure to maintain efficient wiring and network properties as the complexity of cortical connectivity and association cortex has increased, especially considering long-distance projections are well represented in the human brain (see 
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 
), recent computational explorations of connectional patterns have brought the issue into sharp focus 
. Graph theory, in particular, provides informative metrics to analyze properties of complex networks 
. When applied to the study of connectional anatomy, analyses consistently reveal that cortical networks exhibit “small world” properties 
. Connections are not randomly dispersed among cortical areas but rather show strong clustering patterns and hubs that allow for relatively short path lengths to propagate information through the networks 
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 
and functional 
human connectivity data reveals a core set of regions along the cortex including paralimbic areas and parietal association areas that behave as hubs. The resulting map of these regions in humans includes the many known heteromodal association areas spread throughout prefrontal, parietal, and lateral temporal cortex and bares a strong resemblance to the estimated regions of cortical expansion in human as compared to macaque (e.g., contrast 
Although previous studies have focused their attention in network topological modularity 
and in some aspects of the relationship between physical distance and connectivity 
, connectivity profiles that differentiate local and distant projection patterns have not been fully characterized. Physical distance and network path length, as discussed above, are among of the most central properties to efficient information propagation.
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 
. Commonly used diffusion imaging techniques capture long association fibers that travel in discrete fascicles within the hemisphere and commissural fibers that pass between the hemispheres (but see 
for a recent exception), and usually discard fibers or fail to adequately measure information from close or adjacent regions.
Second, functional connectivity approaches that measure cortical-cortical interactions indirectly via correlated blood oxygenation level-dependent contrast (BOLD) 
have not focused on local anatomic correlations because of the relatively poor spatial resolution of the approach. While the blood flow response is locally regulated (under certain conditions at the level of the cortical column; e.g., 
), the current practical resolution for exploring large cortical regions is about 3–4 mm 
. This makes exploring within-area lateral connections challenging. However, the achievable resolution of functional MRI (fMRI) is well within the expected resolution needed to provide information about adjacent and nearby areas that are distinct from interactions carried by long association fibers and other long-range connections. Measurements at this intermediate resolution should be rich in information about the connectional architecture of the human brain including information about whether cortical areas possess local modularity.
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 
to map the degree of intrinsic functional connectivity between regions throughout the brain, taking into account the local neighborhood connections as well as the remote or distant connections (within and outside 14 mm of a neighborhood area) (). Control analyses showed that the method successfully and reliably identified distinct local degree values across the brain. Estimates of these values were then used to explore the properties of regions across the brain and to compare these estimates to those derived from well-known network measures including path length, physical cost, and clustering coefficient. Finally, we examined functional connectivity during an active task (as contrast to rest) to examine how functional coupling dynamically changes in response to task demands.
Methods for identifying local and distant functional connectivity.