The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Abstract

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.

Figures

Fig. 1.

Surface coordinate system for functional magnetic resonance imaging (fMRI) analysis. For each subject, the T2* images yielding blood oxygenation level-dependent (BOLD) contrast fMRI data (A) were registered to the T1-weighted structural data (B). The cortical gray-white and pial surfaces were estimated from the structural data. The red lines show the estimated gray-white surface (A and B). Pial surface is shown in C. The gray-white surface was inflated into a sphere (D). The inflated spheres were then aligned across subjects using surface-based registration of the cortical folding pattern, resulting in a common spherical coordinate system (E). BOLD data of individual subjects (A) can then be projected onto the spherical coordinate system (E) in a single transformation step to reduce artifacts due to multiple interpolations.

Fig. 2.

Examples of intrasubject surface extraction and registration of structural-functional images. Examples of extracted cortical gray-white surfaces (red lines) are overlaid on T2* and T1 images of 3 random subjects in their native T1 space. Imperfections are apparent in BOLD data, especially in regions of susceptibility artifact (e.g., orbital frontal cortex).

Fig. 3.

Signal-to-noise ratio (SNR) maps of the functional data from the full sample (N = 1,000). The mean estimate of the BOLD fMRI data SNR is illustrated for multiple views of the left hemisphere in Caret PALS space. A, anterior; P, posterior; D, dorsal; V, ventral.

Fig. 4.

Cortical regions utilized in constructing functional connectivity profiles. A total of 1,175 regions were sampled uniformly on the surface-based representations of the left and right hemispheres within the FreeSurfer surface coordinate system and are shown in Caret PALS space, where each dark patch represents the location of a single regional vertex. Each vertex in the surface coordinate system is characterized by its profile of functional connectivity to the 1,175 regions. The visually nonuniform distribution of the regions in Caret PALS space is due to the nonlinear deformation from FreeSurfer space to Caret PALS space. This image thus also serves to illustrate the subtle differences between the 2 surface coordinate systems.

Fig. 5.

Toy example illustrating clustering. A: hypothetical points are scattered in a structured fashion on a 2-dimensional canvas. Clustering aims to recover the underlying structure. B: example solutions for M = 2, 3, 4, or 5 clusters are shown. The solutions for M = 2 or 5 clusters agree with visual assessment of the underlying structure and are therefore useful representations. On the other hand, seeking 3 or 4 clusters does not lead to satisfying solutions because solutions are ambiguous. For example, the M = 3 solution is not unique in the sense that an “equally good” alternate solution is for one group of points in the red cluster to be grouped with the orange cluster. Seeking M = 3 or 4 clusters is therefore unstable in the sense that different random initializations of the clustering algorithm lead to different “equally good” solutions. In the present study we employed a stability analysis to estimate the numbers of clusters and also examined both a relatively coarse solution (7 networks) and a fine-resolution solution (17 networks) to survey the solution space broadly (see Fig. 6).

Fig. 6.

Seven and 17 networks can be stably estimated. Instability of the clustering algorithm is plotted as a function of the number of estimated networks for the vertex-resampling variant of the stability analysis applied to 1,000 subjects. The clustering algorithm is less stable with increasing number of estimated networks, which is an expected property, since the number of estimated networks enlarges the solution space (and thus complexity) of the clustering problem. The local minima of the graphs (marked with asterisks) indicate the number of networks that can be stably estimated by the clustering algorithm. The stability analysis suggests that 7, 10, 12, or 17 networks can be stably estimated. Resampling the regions of interest yields almost identical results and is not shown. In this study we focus on the 7- and 17-network estimates to provide a broad survey of the solution space.

Fig. 7.

Discovery and replication of a 7-network cortical parcellation. The 7-network estimates are highly consistent across the discovery (n = 500) and replication (n = 500) data sets. A total of 97.4% of the vertices were assigned to the same network.

Fig. 8.

Confidence of the 7-network estimate in the discovery data set. Confidence (silhouette) value for each vertex with respect to its assigned network is shown for the discovery data set. Regions close to the boundaries between networks were less confident of their assignment, although we also observed structured spatial variation within individual components of the estimated networks, such as lateral prefrontal cortex, which foreshadows its division in the 17-network estimate (see Fig. 9).

Fig. 9.

Discovery and replication of a 17-network cortical parcellation. The 17-network estimates are highly consistent across the discovery (n = 500) and replication (n = 500) data sets. A total of 97.0% of the vertices were assigned to the same network.

Fig. 10.

Confidence of 17-network estimate in the discovery data set. Confidence (silhouette) value for each vertex with respect to its assigned network is shown for the discovery data set. Again, regions close to the boundaries between networks were less confident of their assignment.

Fig. 11.

A coarse (7-network) parcellation of the human cerebral cortex based on 1,000 subjects. To provide the best estimates of the 7 cortical networks, clustering was performed on the fMRI data of the full 1,000 subjects. A salient feature is the separation of the early sensory and late motor cortices (blue and purple) from the association cortex. The association networks converged and extended on networks previously described in the resting-state literature, including the dorsal attention, ventral attention, frontoparietal control, and default networks.

Fig. 12.

Table of colors assigned to networks in the 7-network estimate. Common names associated with each network in the neuroimaging literature are included in parentheses. This should not be taken to mean that our estimated networks correspond exactly to those in the literature or that the networks code solely for functions associated with their assigned name. As examples of limitations of heuristic reference labels, the violet ventral attention network is likely an aggregate of (or closely adjacent to) multiple networks in the literature variably referred to as the salience (Seeley et al. 2007) and cingulo-opercular networks (Dosenbach et al. 2007), and the red default network can be fractionated (e.g., Andrews-Hanna et al. 2010). Many of these details are reflected in Fig. 13.

Fig. 13.

A fine-resolution (17-network) parcellation of the human cerebral cortex based on 1,000 subjects. To provide the best estimates of the 17 cortical networks, clustering was performed on the fMRI data of the full 1,000 subjects. The 17-network estimate fractionated the 7-network into smaller networks. Some aspects of the fractionations have been previously noted in other studies.

Fig. 14.

Uncertain observations due to limited data resolution and MR susceptibility. When the clustering results are interpreted, potential artifacts and uncertainties must be considered. Because of the close proximity of the somatomotor and auditory cortices (A) and the close proximity of the pre- and postcentral gyri (B), we are unable to resolve whether the clustering of the somatomotor and auditory cortices (A) and the clustering of the primary somatosensory and primary motor cortices (B) are due to the result of fMRI blurring across sulci or a true, coupled network of distributed areas as predicted by macaque tracing studies. C: the orbital frontal-temporopolar network (cream color) consists of temporopolar and orbital frontal regions that are affected by MR susceptibility. Since MR susceptibility spatially distorts the MR signal and reduces SNR, there is uncertainty in the exact boundary of the orbital frontal-temporopolar network, and the true extent of the network is probably underestimated.

Fig. 15.

Eccentricity estimates quantify the division of the early visual cortex into central and peripheral systems. Eccentricity estimates in the early visual areas of 4 subjects were averaged and overlaid on the boundaries (in black) of the 17-network estimate. The boundary between areas 18 and 19 estimated from the histological data set is overlaid in green. The 17-network estimate divides the early visual areas along an isoeccentricity line of ∼4°. Note that the eccentricity estimates are not reliable outside the V1-V3 complex.

Fig. 16.

Evidence that the fractionation of the visual system reflects functional connectivity MRI (fcMRI) topography within the visual cortex. Six left hemisphere seed regions were picked from the discovery dataset: V1c and V1p correspond to central and peripheral visual field representation within V1, respectively; V3cv and V3pv correspond to central and peripheral V3v, respectively; ExC and ExP correspond to 2 seed regions within the extrastriate visual cortex in the estimated locations of the central and peripheral visual fields (purple and bright red at center). The 6 seed regions are illustrated at center, and their coordinate locations are reported in Table 1. Their left hemisphere fcMRI maps were computed using the replication data set and arranged around the center images. Note that the central visual seed regions are selectively correlated with the central visual representation, whereas the peripheral visual seed regions are selectively correlated with the peripheral visual representation.

Fig. 17.

Quantification of fcMRI topography within the visual cortex and independence of the topography from task condition. A: quantification measures of functional connectivity strength are plotted in polar form for V1c (central V1) and V1p (peripheral V1) seed regions for the replication data set. Note that “V1” refers to V1c for the V1p polar plot (blue) and V1p for the V1c polar plot (red). Coordinate locations for all 6 seed regions (V1c, V1p, V3cv, V3pv, ExC, and ExP) are reported in Table 1. B: polar plots from A replicated with the task effects data set (EOR, eyes open rest; ECR, eyes closed rest; FIX, fixation) to ensure that the results obtained using the EOR replication data set were not due to overt eye movements that might shift edges and visual boundaries in and out of the central field. Left: V1p polar plot. Right: V1c polar plot. The polar plots quantify the differential functional coupling of central and peripheral V1 with higher visual areas. The polar scales range from r = −0.1 (center) to r = 0.7 (outer boundary) in 0.2-step increments.

Fig. 18.

V1 and V3 functional correlations display a smooth transition from the central to peripheral representations. Correlation of 2 series of seed regions spanning the eccentricity axes of V1 and V3v is shown for the full sample of 1,000 subjects. V1 seed regions of low eccentricity are strongly correlated with V3 seed regions of low eccentricity. V1 seed regions of high eccentricity are strongly correlated with V3 seed regions of high eccentricity. There is a gradual transition in functional connectivity strength between the central to peripheral representations.

Fig. 19.

Seven-network boundaries on probabilistic maps of areas 6, 2, and 5L. Boundaries of 7-network estimate based on the full sample of 1,000 subjects are overlaid on the surface-based probabilistic histological maps of areas 6, 2, and 5L. The somatomotor network includes most, if not all, of areas 2 and 5L, but only the caudal half of area 6.

Fig. 20.

Evidence that the fractionation of the somatomotor cortex reflects fcMRI topography within the somatosensory and motor cortex. A: average fMRI activation maps of 24 subjects instructed to move their tongue (blue), right hand (red), or right foot (green) across separate conditions. Black lines correspond to boundaries of the 17-network estimate. The dorsoventral split of the somatomotor network occurs spatially between the tongue and hand activations. B: quantification of correlation strength between the left hemisphere tongue, hand, and foot seed regions selected from the activation maps. Hand coordinates = −41, −20, 62; foot coordinates = −6, −26, 76; and tongue coordinates: −55, −4, 26. Hand-foot correlation is significantly higher than hand-tongue correlation, which is in turn significantly higher than foot-tongue correlation. Tng, tongue.

Fig. 21.

Evidence that the interhemispheric fcMRI of homotopic regions within the primary motor cortex is topographically organized. A: correlation strength of left hemisphere tongue, hand, and foot seed regions with corresponding contralateral seed regions averaged over all 1,000 subjects. Right hemisphere vertices were obtained by reflection across the midline. Hand coordinates = ±41, −20, 62; foot coordinates = ±6, −26, 76; and tongue coordinates = ±55, −4, 26. The tongue representation has the strongest interhemispheric correlation, followed by the foot and then the hand. B: plot of interhemispheric correlation along the ventral (tongue) to dorsal (foot) extent of motor cortex. Maximal interhemispheric correlation is highest near the tongue representation and also peaks between the hand and foot representations, possibly corresponding to the trunk representation.

Fig. 22.

Functional connectivity between MT+ and V1 is topographically organized. A: 2 MT+ seed regions, MT+d and MT+v, were selected in the dorsal and ventral portions of the histological MT+ estimate, respectively. The anterior MT+ (aMT+) seed region was selected anterior to histological MT+. Four V1 seed regions were selected using the histological V1 estimate: V1cd and V1cv were selected in dorsal and ventral central V1; V1pd and V1pd were selected in dorsal and ventral peripheral V1. Coordinate locations of seed regions are reported in Table 2. B: correlation strength of aMT+ and MT+ seed regions with V1 in the replication dataset. There are 4 observations to be noted: 1) V1-aMT+ correlation is weaker than V1-MT+ correlation, 2) MT+ correlation with the lower visual field is stronger than the upper visual field, 3) MT+d correlation with peripheral V1 is stronger than central V1, and 4) MT+v correlation with central V1 is stronger than peripheral V1.

Fig. 23.

Functional connectivity maps of MT+ reveal topographic organization. Functional connectivity maps of aMT+, MT+v, and MT+d were computed using the replication data set and are shown with views focusing on V1. V1 shows little or no correlation to aMT+ but strong correlations with both MT+ seeds. In both MT+ fcMRI maps, there is stronger correlation with dorsal V1 (lower visual field) than ventral V1 (upper visual field). There is also increasing correlation with central V1 as we proceed from MT+d to MT+v. The yellow line denotes the areal boundary of V1.

Fig. 24.

aMT+ and MT+ demonstrate differential functional connectivity with parietal and frontal cortices. Functional connectivity maps of aMT+ and MT+ seed regions were computed using the replication data set. Coordinate locations of the regions are reported in Table 2. MT+ and aMT+ are more strongly correlated with superior parietal lobe (SPL) and intraparietal sulcus (IPS) than with inferior parietal lobe (IPL). Correlation with frontal cortex is mostly limited to precentral sulcus and gyrus. aMT+ demonstrates stronger overall correlation with parietal and frontal cortices, compared with MT+. MT+ and aMT+ are maximally correlated with different parts of parietal and frontal cortices.

Fig. 25.

aMT+ and MT+ functional connectivity patterns generalize across task conditions. A: 4 visual, 4 parietal, and 2 frontal seed regions were used to quantify the functional coupling of aMT+ and MT+ to distributed cortical regions. Coordinate locations of the seed regions are reported in Table 2 and were chosen using either the discovery data set or meta-analysis of fMRI studies (Table 3). B: polar plots of MT+ (blue) and aMT+ (red) connectivity with the visual, parietal, and frontal seed regions were computed using the replication data set. MT+ is more strongly correlated with visual cortex compared with parietal and frontal cortices. The converse is true for aMT+. C and D: polar plots of MT+ (blue) and aMT+ (red) connectivity replicated in the task effects data set demonstrate that the functional coupling differences generalize across multiple data acquisition conditions. The polar scales range from r = −0.1 (center) to r = 0.6 (outer boundary) in 0.35-step increments.

Fig. 26.

Differential connectivity of dorsal and ventral caudal frontal cortex with SPL and IPS. Functional connectivity maps of frontal eye field (FEF) and precentral ventral frontal region (PrCv) were computed using the replication data set and are shown with view focusing on parietal cortex. Both FEF and PrCv demonstrate strong correlation with SPL and IPS. PrCv is more strongly correlated with ventral portions of rostral SPL and IPS, whereas FEF is more strongly correlated with caudal SPL and IPS.

Fig. 27.

Evidence for segregated pathways linking caudal frontal cortex with SPL and IPS. A: 5 parietal seed regions were selected along the rostrocaudal extent of SPL and IPS. Two frontal seed regions, FEF and PrCv, were selected in dorsal and ventral precentral sulcus. All seed regions were selected using the discovery data set or meta-analysis of fMRI studies (Table 3). The coordinate locations are reported in Table 2. B: functional connectivity of FEF and PrCv with the 5 parietal seed regions, arranged in rostral (lateral) to caudal (medial) order, from the replication data set. Rostrolateral IPS seed regions (IPS1, IPS2, and IPS3m) are more strongly correlated with PrCv than FEF, whereas the mediocaudal SPL seed regions (SPL7A and SPL7P) are more strongly correlated with FEF than PrCv.

Fig. 28.

Examples of satisfied and violated constraints in estimating the functional hierarchy of cortical regions based on fcMRI. A functional hierarchy is estimated based on the assumption that regions closer in a hierarchy have stronger correlation. A: 5 cortical regions are arranged in a 4-level hierarchy whose functional connectivity strengths satisfy both hierarchical and lateral constraints. B: identical arrangement of 5 cortical regions in a 4-level hierarchy with different functional connectivity strengths that violate both hierarchical and lateral constraints. Thick lines correspond to strong correlations. Thin lines correspond to weak correlations. i: regions a and c are farther apart than regions a and b. In the example in A, correlation of regions a and c is weaker than correlation of regions a and b, so a hierarchical constraint is satisfied. In the example in B, correlation of regions a and c is stronger than correlation of regions a and b, so a hierarchical constraint is violated. ii: regions c and d are on the same hierarchical level. In the example in A, correlation of regions c and e is approximately the same as the correlation of regions d and e, so a lateral constraint is satisfied. In the example in B, correlation of regions c and e is stronger than the correlation of regions d and e, so a lateral constraint is violated. In the context of hierarchy estimation in this article, we consider 2 correlations within 0.2 of each other to be approximately the same when assessing lateral constraints. Given the pairwise correlations of a set of seed regions and a known number of levels in the hierarchy, we can seek the best hierarchical arrangement of the seed regions with the following local optimization procedure: 1) randomly arrange the seed regions into a hierarchy, 2) consider swapping a pair of seed regions or shifting a single seed region to a different hierarchical level without changing the number of levels in the hierarchy such that the proportion of violated constraints is maximally decreased, 3) terminate when no further improvement in the proportion of violated constraints can be achieved, and 4) repeat the preceding steps 20 times, picking the solution with the least proportion of violated constraints. The best solution obtained using this optimization procedure is (in practice) the same as a brute-force search over all possible hierarchical arrangements of the seed regions. We note that we are generally unable to infer the number of levels in the functional hierarchy, since the number of possible constraints can be drastically different for hierarchies with a different number of levels, and so the proportion of violated or satisfied constraints is not comparable across hierarchies with different levels. In practice, however, the solution space is robust; for example, the best solution for a 5-level hierarchy typically differs from the best solution for a 4-level hierarchy by the collapsing of regions from 2 adjacent levels into 1 level. Uncovering the true hierarchical structure in the macaque visual hierarchy based on anatomical connectivity has also proved to be problematic (Hilgetag et al. 1996).

Fig. 29.

Functional connectivity estimates of the hierarchical organization of a canonical sensory-motor pathway. A: 6 seed regions arranged into a 5-level functional hierarchy using the replication data set. B and C: 2 best hierarchical arrangements of the seed regions as measured by the proportion of violated hierarchical and lateral constraints. A violation occurred when the ordering placed more strongly correlated regions farther apart in the hierarchy than more weakly correlated regions (see Fig. 28). D and E: 2 poor hierarchical arrangements of the seed regions as measured by the proportion of violated hierarchical and lateral constraints. Relative ordering of the seed regions (A and B) within the functional hierarchy agrees well with the proposed macaque visual hierarchy (see text).

Fig. 30.

Adjacent parietal regions exhibit distinct functional connectivity fingerprints. Correlations of 8 parietal seed regions (center) with 22 cortical target regions (top) from the replication data set, displayed as polar plots. Colors represent the 7-network segmentation (from Fig. 11). The coordinate locations are reported in Table 4. Parietal seed regions that belong to the same network (e.g., TPJ, PGpv, and PGpd) have generally similar functional connectivity fingerprints that are distinct from other parietal seed regions. Close inspection of the polar plots reveals distinct connectivity fingerprints even for parietal regions within the same network, some of which anticipate the further fractionation of the parietal cortex in the 17-network estimate (Fig. 13). Note that the cortical targets from anterior cingulate cortex to pCun on the left side of the polar plots are the same as that of the frontal polar plots (see Fig. 31) to allow for comparison across the 2 sets of polar plots. The remaining cortical targets are different across the 2 sets of polar plots but are arranged so that cortical targets at the same location in the polar plots belong to the same network in the 7-network estimate. The polar scales range from r = −0.4 (center) to r = 0.5 (outer boundary) in 0.3-step increments.

Fig. 31.

Adjacent frontal regions exhibit distinct functional connectivity fingerprints. The format and plotting are the same as for Fig. 30 with regions tailored for exploration of frontal cortex. The coordinate locations are reported in Table 4. The polar scales range from r = −0.4 (center) to r = 0.5 (outer boundary) in 0.3-step increments.

Fig. 32.

Functional connectivity for regions within the canonical distributed cortical network. This network is often called the dorsal attention network. The 6 seed regions are displayed in the center overlaid on top of the 7-network parcellation from Fig. 11. The coordinate locations are reported in Table 5. Each panel A–F displays the functional connectivity map for 1 of the 6 seed regions for the replication data set overlaid on a surface that shows the 7-network boundaries (in light black lines) as reference. Each seed region displays functional coupling with all of the regions of the distributed network. However, there are important differences between regions. In particular, the regions near aMT+ (D) and SPL7A (C) show strong functional coupling with earlier visual areas. The region at or near the putative homolog of FEF (A) shows minimal functional coupling with earlier visual areas but does show strong coupling with midline motor regions (see midline section of A). The color scale (bottom) shows the plotted correlation range for the maps.

Fig. 33.

Functional connectivity for distributed regions within a second large-scale association network. This network is often called the ventral attention network (but see Fig. 12 legend). The format and plotting are the same as for Fig. 32, and coordinate locations are reported in Table 5. Each seed region is functionally coupled mostly with regions within the same network, revealing that each component of the network recapitulates the others. There is a general absence of cross talk between networks except for local correlation around the seed regions.

Fig. 34.

Functional connectivity for distributed regions within a third large-scale association network. This network is often called the frontoparietal control network. The format and plotting are the same as for Fig. 32, and coordinate locations are reported in Table 5. In addition to a general absence of cross talk between networks, this network also shows no functional coupling to sensory and motor regions. Rather, its topography reveals a distributed network that is interdigitated with the networks illustrated in Figs. 32, 33 and 35.

Fig. 35.

Functional connectivity for distributed regions within a fourth large-scale association network. This network makes up the prominent components of the network often called the default network. The format and plotting are the same as for Fig. 32, and coordinate locations are reported in Table 5. Each seed region is functionally coupled mostly with regions within the same network, again revealing that each component of the network recapitulates components the remaining network, with some exceptions. For example, the seed region in the parahippocampal cortex (E) shows functional connectivity with the retrosplenial cortex, ventral medial prefrontal cortex, and a specific region with the caudal IPL. These patterns of functional connectivity lead to the fractionation into subnetworks as illustrated in Fig. 13. The fractionation of this particular network is largely to subdivide the broader network, rather than to display functional coupling with regions in distinct networks.