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.

complex behaviors are subserved by distributed systems of brain areas (Felleman and Van Essen 1991; Goldman-Rakic 1988; Mesulam 1990). The organization of these systems can be studied in nonhuman animals by using invasive techniques including histology, anatomical tract tracing, electrophysiology, and lesion methods. The organization of brain systems in the human has been inferred by comparing cytoarchitectonically defined homologies between species and by noting similarities in neuropsychological deficits following accidental brain injury to deficits present in animal ablation studies. General agreement has emerged from these comparisons that the basic organization of brain systems is similar across mammalian species. However, there is also evidence that the human cerebral cortex, particularly association cortex, is not simply a scaled version of other species.

The German anatomist Korbinian Brodmann (1909) first emphasized that areas comprising the human inferior parietal lobule do not have clear homologs in the monkey, an observation that continues to motivate contemporary debates (Orban et al. 2004). Gross differences are also observed in the human brain when it is compared to those of our evolutionarily closest relatives. For example, the human brain is triple the size of modern great ape brains, but motor and visual cortices are about the same absolute size (Blinkov and Glezer 1968; Frahm et al. 1984). This observation suggests that expansion of the human cerebrum disproportionately involves areas beyond those subserving basic sensory and motor functions. In a recent analysis of cortical expansion based on 23 homologous areas between the macaque and human, Van Essen and colleagues noted that the greatest growth occurs in regions distributed across frontal, parietal, and temporal association cortices (Van Essen and Dierker 2007; Hill et al. 2010). Preuss (2004) came to a similar conclusion in a detailed review of comparative anatomy. Thus, in addition to expecting the human brain to show broadly similar organizational properties with other well-studied species, expansion and perhaps elaboration of association networks is also expected.

In this article we report results of a comprehensive analysis of networks within the human cerebral cortex using intrinsic functional connectivity MRI (fcMRI). The analysis was based on 1,000 young adults who contributed uniformly collected MRI data. The data were brought into a common surface coordinate system to help preserve the surface topology of the cortical mantle. Analyses were motivated by two goals. First, we sought to provide reference maps that are a current best estimate of the organization of the human cerebral cortex as measured by functional connectivity. Second, we wanted to better understand how patterns of functional connectivity might give rise to the organizational properties that underlie distributed brain systems. Particular focus was placed on parietal and frontal association cortices. The foundations for the present work come from traditional anatomical studies of cortical organization.

Organizational Properties of the Cerebral Cortex in the Nonhuman Primate

Distributed brain systems are organized to facilitate both serial and parallel processing (Felleman and Van Essen 1991; Mesulam 1998). The concept of serial hierarchies is embedded within early ideas about brain organization. For example, William James (1890) proposed that principles governing the reflex arc extend to the cerebral hemispheres. He hypothesized that excitement of sensory systems propagates upwards from lower to higher cerebral centers governing “ideas” and then to centers producing (or inhibiting) movements. Hubel and Wiesel (1962) formally proposed the concept of serial processing across a hierarchy in cat visual cortex based on their observations of increasingly complex receptive field properties from the lateral geniculate nucleus (LGN) to the simple and complex cells of the primary visual cortex (V1). Based on studies of corticocortical connections in the macaque, Pandya and Kuypers (1969) and Jones and Powell (1970) suggested that hierarchical processing across sensory systems converges on transmodal association areas.

The discovery of widespread connections among multiple cortical areas, as well as extensive feedback projections from higher to lower sensory areas, suggested strictly serial processing is not the only organizational scheme in the cerebral cortex. Instead, it was proposed that hierarchical processing exists in a distributed fashion that can be inferred from the laminar distribution of anatomical connectivity (Friedman 1983; Maunsell and Van Essen 1983; Rockland and Pandya 1979). The comprehensive meta-analysis of corticocortical connections in the macaque monkey by Felleman and Van Essen (1991) provided strong evidence that unimodal and heteromodal areas in both the visual and somatomotor systems are organized into separate distributed hierarchies (also see Ungerleider and Desimone 1986; Van Essen et al. 1992). Some projections between areas are organized as feedforward (ascending) projections, others as feedback (descending) projections, and still others as lateral projections. For example, consistent with serial processing, the primary visual area (V1) sends forward connections to and receives feedback connections from V2 in a topographic fashion that connects the corresponding receptive field representation in each area. In contrast to strictly serial processing, these unimodal sensory cortical areas (V1 and V2) both project to higher sensory areas. Lateral projections between areas are also common [e.g., central inferior temporal area (CIT) and posterior superior temporal polysensory area (STPp)].

It becomes considerably more difficult to make inferences about the organization of circuits involving association cortex. Historically, of the four criteria (function, cytoarchitecture, connectivity, and topography) used to define cortical areas and thereby constrain models of organization, topography (e.g., retinotopy) and function are difficult to discern in heteromodal association areas. Cytoarchitecture and connectivity thus become especially valuable for inferring brain circuit organization beyond the sensory and motor systems. However, as noted by Felleman and Van Essen (1991), the number of violated constraints to hierarchical connectivity increases in the progression from early sensory cortex up to association cortex (red lines near the top of the visual hierarchy in Fig. 4 of Felleman and Van Essen 1991).

This raises the interesting possibility that the association areas may not follow as rigid a hierarchical organization as canonical sensory and motor areas. Violations of strict hierarchical arrangements are apparent in the visual system as noted above, but violations and alternative connectivity patterns become common in association areas. For example, paired tracer injections in association areas 7a and 46 lead to interdigitating columnar patterns of terminations in some areas and complementary (feedforward and feedback) patterns in other areas (Selemon and Goldman-Rakic 1988).

While recognizing that convergence and integration of pathways occur in the association cortex, Goldman-Rakic (1988) emphasized that primate association cortex is organized into parallel distributed networks (see also Mesulam 1981). There are two key features to her proposed organization that depart from hierarchical organizational models. First, each distributed network consists of association areas spanning frontal, parietal, temporal, and cingulate cortices. Networks are densely interconnected such that two areas in the parietal and frontal cortices belonging to the same network are not just anatomically connected to each other, but they are also both connected with other components of the same network (Selemon and Goldman-Rakic 1988). Second, multiple distributed networks exist adjacent to each other: adjacent areas in the parietal cortex belonging to separate networks are differentially connected to adjacent areas of corresponding networks in the frontal, temporal, and cingulate cortices (Cavada and Goldman-Rakic 1989a, 1989b).

The possibility of parallel distributed circuits is an important consideration in our analysis of fcMRI networks in the human, particularly within association cortices. An intriguing possibility is that the majority of the human cerebral cortex involves multiple parallel circuits that are interdigitated throughout association cortex such that each cortical lobe contains components of multiple association networks. That is, the expansion of the cerebral association cortex in humans relative to that in the monkey may preferentially involve networks organized in the form outlined by Goldman-Rakic (1988) and anticipated by others (e.g., Mesulam 1981). To explore this possibility, our analyses focus on evidence for hierarchical relations across regions as well as evidence for distributed networks that are interdigitated throughout association cortex.

Insights Into the Organization of the Cerebral Cortex Revealed Through Neuroimaging

Noninvasive neuroimaging methods including positron emission tomography (PET; Raichle 1987) and functional MRI (fMRI; Kwong et al. 1992; Ogawa et al. 1992) allow functional response properties to be measured in the human cerebral cortex. The measures are indirect, reflecting blood flow and oxygenation changes that are coupled to neural activity through incompletely understood mechanisms (Logothetis 2008), and the methods are presently limited to a spatial resolution of a few millimeters (e.g., Engel et al. 1997). Neuroimaging approaches have nonetheless been extremely valuable for providing insights into cortical organization. In some cases it has been possible to directly map the topography within (and borders between) cortical areas (Engel et al. 1994; Sereno et al. 1995). More generally, differential response properties between regions are the source of information about cortical mapping. For example, the increase in the complexity of receptive field properties measured from primary to secondary sensory areas in visual (Wandell et al. 2007), somatosensory (Iwamura 1998), and auditory cortices (Wessinger et al. 2001) suggest that serial hierarchical processing exists in human sensory cortex.

Neuroimaging studies of a wide range of cognitive tasks reveal simultaneous activation in multiple regions in the parietal, frontal, temporal, and cingulate cortices, suggesting distributed systems of brain areas are involved in cognition. However, it is difficult to assess the organization of these distributed systems based solely on task activity because these cognitive tasks likely tap into multiple, overlapping processes, some of which reflect the operation of distributed systems and others which reflect distinct processing demands of the tasks (see Mesulam 1990 and Posner et al. 1988 for relevant discussion). For these reasons, methods that can measure connectivity may provide novel insights into the organization of distributed brain systems.

Functional Connectivity and Diffusion MRI Provide Tools to Explore Cortical Organization

Diffusion MRI (dMRI) and fcMRI have recently emerged as promising tools for mapping the connectivity of the human brain, each with distinct strengths and weaknesses. dMRI measures the diffusion of water, thus allowing direct noninvasive mapping of white matter pathways (Basser et al. 1994). However, dMRI is presently limited to resolving major fiber tracts. By contrast, fcMRI measures intrinsic functional correlations between brain regions (Biswal et al. 1995) and is sensitive to coupling between distributed as well as adjacent brain areas (e.g., see Sepulcre et al. 2010 for discussion). Although not a direct measure of anatomical connectivity, the functional couplings detected by fcMRI are sufficiently constrained by anatomy to provide insights into properties of circuit organization (for reviews, see Fox and Raichle 2007; Van Dijk et al. 2010). When describing these correlations, we use the term functional connectivity as coined by Karl Friston (1994) to denote “temporal correlations between remote neurophysiological events” for which the causal relation is undetermined.

There are important limitations of fcMRI, including sensitivity to indirect anatomical connectivity and functional coupling that changes in response to recent experience and the current task being engaged (Buckner 2010). For these reasons, some discussions of fcMRI have emphasized that intrinsic activity measured by fcMRI reflects the prior history of activity through brain systems and not simply static anatomical connectivity (Power et al. 2010). fcMRI also does not presently provide information about whether connections are feedforward (ascending) or feedback (descending). These limitations constrain how analyses are conducted and results can be interpreted.

Directly relevant to the present study, prior investigations using fcMRI provide estimates of large-scale cortical networks that have generally (but not in all details) converged across a variety of analytic approaches, including seed-based fcMRI (Biswal et al. 1995), independent component analysis (Beckmann and Smith 2004; Smith et al. 2009), clustering (Bellec et al. 2010; Golland et al. 2007), and graph theory (Dosenbach et al. 2007). Because of uncertainties regarding their relation to underlying anatomical brain systems, networks identified using fcMRI have often been labeled on the basis of their relations to task-based functional networks. Some of these networks, such as the default network (Greicius et al. 2003) and dorsal attention system (Fox et al. 2006), have been proposed to be related to anatomical tracing and task-based fMRI in the macaque (Buckner et al. 2008; Saleem et al. 2008; Vincent et al. 2007).

Motivated by the usefulness of connectivity in establishing the organization of the cerebral cortex in nonhuman primates, this study analyzed fcMRI data from 1,000 subjects with two main goals. First, the analyses sought to provide reference maps that are a current best estimate of the organization of human cortical networks as measured by functional connectivity. Second, by using the power of a large data sample to quantitatively measure functional connectivity strength among many regions, the study explored the patterns of corticocortical functional coupling that give rise to these networks.

METHODS

Overview

The present study explored the organization of large-scale distributed networks in the human cerebral cortex using resting-state fcMRI. The main analyses were based on a core dataset of 1,000 healthy, young adults whose fMRI data were acquired using the same MRI sequence on the same hardware (3-Tesla field strength, 12-channel receive coil array). For several analyses the data were divided into discovery (n = 500) and replication (n = 500) data samples to test for reliability and for unbiased quantification of functional connectivity patterns. Additional supplementary datasets were used to address specific questions that arose during analysis. A first supplementary fMRI data set (n = 16) contrasted different passive tasks engaged during resting-state functional data acquisition. A second supplementary fMRI data set (n = 4) consisted of data acquired during visual stimulation optimized to define retinotopic boundaries of early visual areas (Hinds et al. 2009; Polimeni et al. 2005). A final supplementary data set used human histological data to define a range of cytoarchitectonic areas including human V1 (Amunts et al. 2000, Fischl et al. 2008) and the putative homolog to macaque middle temporal area complex (MT+; Malikovic et al. 2007, Yeo et al. 2010b). All data (fMRI and histological) were brought into a common surface coordinate system based on the cortical surface as reconstructed from each participant's structural anatomy. Data analyses began by examining broad properties of cortical network organization and progressed to quantify the detailed patterns of functional connectivity within and between networks.

In the first set of analyses, a clustering algorithm was used to parcellate the cerebral cortex into networks of functionally coupled regions. Parcellations were examined for a coarse solution that organized the cortex into 7 networks as well as a finer solution that identified 17 networks. As the results reveal, the estimated networks were consistent across the discovery and replication data samples and were confirmed by region-based fcMRI analyses. The full data set was used to construct a best-estimate parcellation of the human cerebral cortex to serve as a reference for future studies.

The second set of analyses explored the coupling of regions that fell within sensory and motor pathways. Since these areas are relatively well understood in both humans and macaques, they provide the opportunity to evaluate the utility and limitations of functional connectivity methods. Analyses examined quantitative coupling properties between individual regions that were within the same network as well as coupling properties between networks focusing on a sensory-motor pathway that is the putative homologue of the well-studied system in the monkey involving MT+, parietal regions at or near lateral intraparietal area (LIP), and premotor regions at or near frontal eye field (FEF).

The final set of analyses characterized the organization of distributed networks in higher order association cortex. The connectivity patterns of regions within frontal and parietal association cortices were quantified. These analyses involved constructing a series of small seed regions across frontal and parietal cortices and examining functional connectivity strength to multiple regions distributed throughout the cerebral cortex, allowing the “fingerprint” of functional coupling to be identified for each region. For these analyses, regions were always defined in the discovery data sample or some other source, such as histology, and functional connectivity was quantified in the independent replication data sample.

Participants

Paid participants were clinically healthy, native English-speaking young adults with normal or corrected-to-normal vision (ages 18–35 yr). Subjects were excluded if their fMRI signal-to-noise ratio (SNR) was low (<100; see below), artifacts were detected in the MR data, their self-reported health information indicated the presence of any prior neurological or psychiatric condition, or they were taking any psychoactive medications. The core data set consisted of 1,000 individuals imaged during eyes open rest (EOR) and was divided into two independent samples (each n = 500; labeled the discovery and replication samples). Age and sex were matched for the discovery (mean age = 21.3 yr, 42.6% male) and replication (mean age = 21.3 yr, 42.8% male) data sets. These data are new data presented for the first time in this study and were acquired as part of a collaborative effort across multiple local laboratories all acquiring data on matched MRI scanners (at Harvard and at the Massachusetts General Hospital). Participants provided written informed consent in accordance with guidelines set by institutional review boards of Harvard University or Partners Healthcare.

Two smaller supplementary data sets were also analyzed. The task effect data set (n = 16, mean age = 21.1 yr, 25.0% male) consisted of fMRI data collected under different passive conditions (eyes closed rest, ECR; EOR; and fixation, FIX) and was analyzed previously (Van Dijk et al. 2010). The visuotopic dataset (n = 4; mean age = 34.5 yr, 100% male) consisted of previously published visuotopic data (Hinds et al. 2009; Polimeni et al. 2005).

MRI Data Acquisition

All data were collected on matched 3T Tim Trio scanners (Siemens, Erlangen, Germany) using a 12-channel phased-array head coil, except for the visuotopic data set, which was acquired on a custom-built 4-channel phased-array surface coil. A software upgrade (VB15 to VB17) occurred on all scanners during the study. Validation studies that acquired structural and functional data on the same individuals before and after the upgrade could not detect an effect of the upgrade. The functional imaging data were acquired using a gradient-echo echo-planar imaging (EPI) sequence sensitive to blood oxygenation level-dependent (BOLD) contrast. Whole brain coverage including the entire cerebellum was achieved with slices aligned to the anterior commissure-posterior commissure plane using an automated alignment procedure, ensuring consistency among subjects (van der Kouwe et al. 2005). Structural data included a high-resolution multiecho T1-weighted magnetization-prepared gradient-echo image (multiecho MP-RAGE; van der Kouwe et al. 2008).

For the core data set, subjects were instructed to remain still, stay awake, and keep their eyes open. EPI parameters were as follows: repetition time (TR) = 3,000 ms, echo time (TE) = 30 ms, flip angle (FA) = 85°, 3 × 3 × 3-mm voxels, field of view (FOV) = 216, and 47 axial slices collected with interleaved acquisition and no gap between slices. Each functional run lasted 6.2 min (124 time points). One or two runs were acquired per subject (average of 1.7 runs). Parameters for the structural scan (multiecho MP-RAGE; van der Kouwe et al. 2008) were as follows: TR = 2,200 ms, inversion time (TI) = 1,100 ms, TE = 1.54 ms for image 1 to 7.01 ms for image 4, FA = 7°, 1.2 × 1.2 × 1.2-mm voxels, and FOV = 230. The multiecho MP-RAGE allows increased contrast through weighted averaging of the four derived images.

For the task effect data set, subjects were instructed to remain still with their eyes open (EOR; 2 runs) or closed (ECR; 2 runs) or to passively fixate a centrally presented crosshair (FIX; 2 runs). For details, see Van Dijk et al. (2010). The order of rest conditions was counterbalanced across subjects. EPI parameters were as follows: TR = 3,000 ms; TE = 30 ms; FA = 90°; 3 × 3 × 3-mm voxels; FOV = 288, and 43 slices collected with interleaved acquisition and no gap between slices. Each functional run lasted 5.20 min (104 time points). Parameters for structural scans (MP-RAGE) were as follows: TR = 2,530 ms, TI = 1,100 ms, TE = 3.44 ms, FA = 7°, 1 × 1 × 1-mm voxels, and FOV = 256.

The visuotopic data set was collected using a custom-built four-channel phased-array surface coil placed at the back of the head. During each functional run, subjects were presented with one of four visual stimuli: a clockwise rotating wedge, a counterclockwise rotating wedge, an expanding ring, or a contracting ring (DeYoe et al. 1996; Engel et al. 1994; Sereno et al. 1995). Because the four-channel surface coil provided only partial brain coverage, structural data for these four subjects were collected separately on a 1.5T Allegra scanner (Siemens). Further details of the acquisition and data processing protocol can be found elsewhere (Hinds et al. 2009; Polimeni et al. 2005).

Except where noted, the description of data processing and analysis below applies to the whole brain data (the core data set of 1,000 subjects and the task effect data set) and not the visuotopic data.

Functional MRI Data Preprocessing

The fMRI data were preprocessed with a series of steps common to fMRI analyses. Preprocessing involved 1) discarding the first four volumes of each run to allow for T1-equilibration effects, 2) compensating for slice acquisition-dependent time shifts per volume with SPM2 (Wellcome Department of Cognitive Neurology, London, UK), and 3) correcting for head motion using rigid body translation and rotation with the FSL package (Jenkinson et al. 2002; Smith et al. 2004).

The data underwent further processing using procedures adapted from Biswal et al. (1995) and optimized for fcMRI analysis (Fox et al. 2005; Van Dijk et al. 2010; Vincent et al. 2006). Briefly, constant offset and linear trend over each run were removed and a temporal filter was applied to retain frequencies below 0.08 Hz. Sources of spurious variance, along with their temporal derivatives, were removed through linear regression, including 1) six parameters obtained by correction for rigid body head motion, 2) the signal averaged over the whole brain, 3) the signal averaged over the ventricles, and 4) the signal averaged over the deep cerebral white matter. This regression procedure minimized signal contributions of nonneuronal origin, including respiration-induced signal fluctuations (Van Dijk et al. 2010). Unlike previously established fcMRI preprocessing procedures, no spatial smoothing of the resting-state data occurred up to this point of the preprocessing stream.

Structural MRI Data Preprocessing and Functional-Structural Data Alignment

The structural data were processed using the FreeSurfer (http://surfer.nmr.mgh.harvard.edu) version 4.5.0 software package. FreeSurfer constitutes a suite of automated algorithms for reconstructing accurate surface mesh representations of the cortex from individual subjects' T1 images (Fig. 1, B and C) and the overlay of fMRI on the surfaces for group analysis (Fig. 1E). Briefly, the cortical surface extraction process (Fig. 1, B and C) involved 1) correcting for intensity variations due to MR inhomogeneities (Dale et al. 1999), 2) removing extracerebral voxels through “skull-stripping” (Ségonne et al. 2004), 3) segmenting cortical gray and white matter voxels based on the intensity difference and geometric structure of the gray-white interface (Dale et al. 1999), 4) computing cutting planes to disconnect the two hemispheres and subcortical structures (Dale et al. 1999), 5) filling the interior holes of the segmentation using a connected-component analysis (Dale et al. 1999), 6) tessellating a triangular mesh over the gray-white boundary of each hemispheric volume and deforming the mesh to produce a smooth representation of the gray-white interface and pial surface (Dale et al. 1999), and 7) correcting topological defects in the surface so that the mesh achieves a spherical topology (Fischl et al. 2001; Ségonne et al. 2007).

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.

After segmentation of the cortical surface, spatial correspondences among the subjects' cortical folding patterns were established via the use of a spherical coordinate system (Fig. 1, D and E). Briefly, the process involved 1) inflating each subject's surface mesh into a sphere while minimizing geometric distortion of the original cortical surface as measured by geodesic distances among surface vertices and ensuring the inflation constituted a one-to-one mapping, and 2) computing a smooth, invertible deformation of the resulting spherical mesh to a common spherical coordinate system that aligned the cortical folding patterns across subjects (Fischl et al. 1999a, 1999b).

Once the common spherical coordinate system was established, the structural and functional images were aligned (Fig. 1, A and B) using boundary-based registration (Greve and Fischl 2009) that is provided as part of FreeSurfer's companion package, FsFast (http://surfer.nmr.mgh.harvard.edu/fswiki/FsFast). The preprocessed resting-state fMRI data were then propagated to the common spherical coordinate system via sampling from the middle of the cortical ribbon in a single interpolation step (Fig. 1, A–E). The choice of sampling fMRI data from the middle of the cortical ribbon was motivated by the desire to reduce the blurring of fMRI signal across sulci or gyri and also by a recent study on the point-spread function of fMRI (Polimeni et al. 2010). The study showed that large draining vessels on the pial surface increased BOLD signal close to the pial surface but reduced spatial specificity of the hemodynamic response. Sampling fMRI data from the middle of the cortical ribbon therefore represented a trade-off between spatial specificity and signal sensitivity. Since our fMRI voxels were relatively large (3 mm), we were not as concerned about laminar bias in the functional connectivity analysis.

The cerebral cortex is a thin sheet, with common organizational features along its radial axis. Along the dimensions parallel to this sheet is a mosaic of cortical areas that differ in function, cytoarchitecture, connectivity, and topography (Felleman and Van Essen 1991; Kaas 1987). The spherical representation of the cortex therefore affords a more accurate alignment of the cortical folding pattern and has the consequence of improving cytoarchitectonic (Fischl et al. 2008; Hinds et al. 2008; Yeo et al. 2010a) and functional (Fischl et al. 1999b; Van Essen 2005) correspondences across subjects compared with three-dimensional volumetric registration, even though cortical folds do not completely predict cytoarchitecture or function (Rajkowska and Goldman-Rakic 1995; Thirion et al. 2007; Yeo et al. 2010b). The acquisition resolution and inherent limitations of the BOLD signal also provided restrictions on achievable resolution.

A 6-mm full-width half-maximum (FWHM) smoothing kernel was applied to the fMRI data in the surface space, and the data were downsampled to a 4-mm mesh.1 Smoothing after the fMRI data were projected onto the surface helped to minimize the blurring of fMRI signal across sulci or gyri. Since our algorithms are not perfectly accurate, any registration or segmentation errors will likely cause blurring of fMRI signal across sulci or gyri. Consequently, we did not expect to eliminate the blurring issues completely, which is important to keep in mind when interpreting the results. The steps taken could only minimize the problem.

The processing of the visuotopic data set was broadly similar except that older versions of FreeSurfer and FsFast were used for the processing, so manual interventions were required to correct the T2* to T1 registration. Details of the processing can be found elsewhere (Hinds et al. 2009; Polimeni et al. 2005).

Quality Control

Visual inspection of the registered data suggested that accurate representation of the cortical surface was extracted for each subject and that structural and functional image registration was successful. Figure 2 shows the results of cortical surface extraction from the T1 images and T2* to T1 registration of three randomly chosen subjects. These examples represent typical subjects. Note that functional data distortion remains in areas prone to susceptibility artifacts, including anterior prefrontal regions, regions near lateral temporal cortex, and orbital frontal cortex.

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).

Visualization

Although all subsequent analyses were performed in FreeSurfer surface space, for the purpose of visualization, all maps were transformed and displayed on the inflated PALS cortical surfaces using Caret software (Van Essen 2004, 2005; Van Essen and Dierker 2007). In addition, this study also transformed and visualized the estimated networks in FMRIB Software Library (FSL) MNI152 space (Smith et al. 2004). The mapping between FSL MNI152 volumetric space and FreeSurfer surface space is detailed in our companion study (Buckner et al., in press).

SNR Maps

Signal loss and distortion (susceptibility artifacts) occur as a result of magnetic field inhomogeneities. Field inhomogeneities are particularly pronounced in regions where the brain is adjacent to air, causing signal loss and distortion in T2*-dependent (BOLD) images (Ojemann et al. 1997). To estimate the effects of susceptibility artifacts in the present data, we computed the SNR of the motion-corrected fMRI time series for each voxel in subjects' native volumetric space by averaging the signal intensity across the whole run and dividing it by the standard deviation over time. SNR was also used as exclusionary criteria. If the SNR for the whole brain (mean SNR over all voxels within the brain mask) was <100 for an fMRI run, the subject was excluded. Thus all 1,000 subjects contributed data with SNR > 100 for each fMRI run. For subjects with two runs, the SNR was averaged across the runs. The SNR was then projected to FreeSurfer surface space, averaged across the 1,000 subjects from the core data set, and displayed in Caret PALS space (Fig. 3). As expected, low SNR is present in the anterior portion of the inferior and medial temporal lobe, as well as in the orbital frontal cortex. There is also clear spatial variation in the SNR across the cortical mantle, which is important to keep in mind when interpreting the results, such as the absence of a cortical region of low SNR from a network.

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.

Clustering

We applied a clustering approach to define the boundaries of functionally distinct cortical regions and their relations to regions distributed throughout the cerebral cortex (forming networks). Distinguishing neighboring cortical regions by their pattern of connectivity has a long history in both nonhuman primate (e.g., Cavada and Goldman-Rakic 1989a; Goldman-Rakic 1988; Passingham et al. 2002) and human research (e.g., Cohen et al. 2008; Johansen-Berg et al. 2004; Nelson et al. 2010). We began our analyses by defining cortical networks to be sets of cortical regions with similar profiles of corticocortical functional connectivity. The idea follows the empirical finding that in primates, regions of association cortex that are anatomically connected tend to have similar patterns of anatomical connectivity to other cortical and subcortical regions, thus forming a densely connected distributed network (Goldman-Rakic 1988). Note that this assumption about the organizational properties of corticocortical connectivity is probably neither a characteristic of all cortical regions nor a full characterization of the connectivity pattern of any cortical region. As will be shown, the procedure identified functionally coupled networks that could be verified with seed-based regional analyses that made no assumptions about the connectivity patterns.

For this initial analysis, we defined the connectivity profile of a cortical region to be its functional coupling to 1,175 region of interest (ROI) vertices. The 1,175 ROI vertices were uniformly sampled in FreeSurfer surface space (shown in Caret PALS space in Fig. 4) and consisted of single vertices spaced about 16 mm apart. For each subject, we computed the Pearson's product moment correlation between the fMRI time series at each spatial location (18,715 vertices) and the 1,175 ROI vertices. Each spatial location is therefore characterized by its functional coupling to the 1,175 ROI vertices. We binarized the 18,715 × 1,175 matrix of correlations for each subject by keeping the top 10% of the correlations and averaged the binarized matrices independently across each group of 500 subjects in the discovery and replication samples. If a subject had two runs, we averaged the correlation matrices across the two runs before binarization. Binarization of the correlation matrix leads to significantly better clustering results, although the algorithm appears robust to the particular choice of threshold. Visual inspection of the connectivity profiles (not shown) suggested that the 1,175 ROI vertices were sufficiently dense to capture spatial variation in corticocortical connectivity given the limits of our acquisition procedures.

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.

A clustering algorithm was then applied separately to the discovery and replication samples to estimate networks of cortical regions with similar connectivity profiles. The two independent data sets thus allowed exploration of the reliability of estimated networks. The idea behind clustering can be illustrated with a toy example. Figure 5A shows hypothetical points scattered in a structured fashion on a two-dimensional canvas. Clustering aims to recover this structure by dividing the points into different groups so that points within a group are physically close, as shown in Fig. 5B.

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).

The clustering algorithm employed in this study modeled the data with a von Mises-Fisher distribution (Lashkari et al. 2010). More specifically, the data were modeled as 18,715 points on an 1,174-dimensional unit hypersphere embedded in an 1,175-dimensional Euclidean space, where distances between points were measured by their geodesic distance on the hypersphere. Like the toy example, clustering aims to group vertices that are close together in this non-Euclidean canvas (i.e., have similar connectivity profiles) into the same cluster or network. Measuring distances between points by their geodesic distance is equivalent to defining the similarity between two correlation profiles to be the correlation between the correlation profiles. By using correlation as a measure of similarity, differences in correlation strength were normalized among points so that regions are clustered together based on their connectivity profiles (rather than their strengths of connectivity). In theory, this should mitigate some of the effects of spatial variation in SNR (Fig. 3).

The algorithm operated by randomly assigning the 18,715 points to different groups and then iteratively reassigning the group memberships of points to maximize the agreement of connectivity profiles among points of the same group. More details of the clustering algorithm can be found elsewhere (Lashkari et al. 2010).

Stability Analysis

A drawback of most clustering approaches is that one must choose the number of clusters a priori. In this instance the question is, how many clusters (cerebral networks) are needed to correctly parcellate the cortex? We do not have an answer to this question or know if there is a single correct answer given that the cerebral cortex possesses complex patterns of diverging and converging connections among areas. As such, none of our conclusions will depend on a strong assumption that there is a single correct solution to parcellating the cortex. Nonetheless, we sought a principled approach to identify parcellation solutions that captured significant portions of the correlation structure among cortical regions.

One popular method for estimating the number of clusters is by analyzing the stability of the clustering algorithm (Ben-Hur et al. 2002; Lange et al. 2004; also see Fig. 5). We employed two variations of the stability analysis on the full set of 1,000 subjects. Both variations estimated the same numbers of clusters. The first variation involved (repeatedly and randomly) dividing the ROIs into two groups and measuring the reproducibility of the clustering algorithm's results when applied separately to the two groups of ROIs. The second variation involved (repeatedly and randomly) dividing the 18,715 vertices into two groups and applying the clustering algorithm separately to the two groups of vertices. The model parameters learned from clustering one group of vertices were then used to predict the clustering results of the second group of vertices. The agreement between the prediction and clustering results of the second group measured the generalization power of the clustering results (Fig. 6). Further details of the stability analysis can be found elsewhere (Lange et al. 2004).

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.

The stability analyses (Fig. 6) suggested 7 and 17 networks were appropriate starting points for parcellating the cortex. As the results reveal, these parcellation solutions were excellent for capturing significant components of the regional variation that could be replicated across data sets and independently revealed by seed-based analyses. However, the focus on 7- and 17-network solutions should not be taken to imply that meaningful properties are absent in alternative parcellation schemes. By focusing on both a relatively coarse solution (7 networks) and a fine-resolution solution (17 networks), we were able to survey the solution space broadly.

Parcellation Maps

Parcellation maps of the cerebral cortex were generated for both 7-network and 17-network solutions for the discovery sample and replicated in the replication sample. The reliability analysis was conducted to illustrate the stability of the topographic boundaries on which the solutions converged. In this regard, a powerful feature of analyzing large data samples is that the analyses are able to detect the presence of stable cerebral networks and also to establish the boundaries of regions with a high degree of confidence, including contiguous regions that may be part of distinct networks. As a final step, the full sample (N = 1,000) was used to compute parcellations that represent our best estimates of the networks (see Figs. 11 and 13).

Confidence Maps

A useful visualization of the cortical parcellation is to look at the confidence of each spatial location belonging to its assigned network. We used the silhouette measure (Rousseeuw 1987) from the clustering literature for this purpose (see Figs. 8 and 10). The silhouette of a data point (spatial location in our case) measures the similarity (correlation in our case) of the data point to other data points of the same cluster (network in our case) compared with data points belonging to the next closest cluster. The resulting silhouette at each spatial location lies between −1 and 1 so that a larger value indicates higher confidence of the spatial location in belonging to its assigned network. A negative value indicates that the connectivity profile at the spatial location is on average closer to the next closest cluster than to its assigned cluster. A negative value is therefore unlikely but is still possible because the clustering cost function is not equivalent to the silhouette measure.

Correlation Maps and Correlations Between Regions

Large-scale cortical networks can be reliably estimated. To better understand the meaning of the networks resolved by the clustering technique, we followed up all salient results with focused analyses using seed-based regional analysis. The coordinates for all seed regions used in these analyses can be found in Tables 1–5. For these analyses, group-averaged functional connectivity maps were used to inspect the validity of clustering results and to visualize differences in connectivity patterns of regions in sensory and association cortices.

Each region consisted of single surface vertex (∼4 × 4 mm) but should be considered spatially more extensive because of the spatial smoothing and intersubject averaging. Correlation maps were obtained by computing the Pearson's product moment correlation between the region's preprocessed resting fMRI time course and the time courses of all other vertices across the cortical mantle. To obtain a group-averaged correlation z-map, the correlation map of each subject in the group was converted to individual subject z-map using Fisher's r-to-z transformation and then averaged across all subjects in the group. The Fisher's r-to-z transformation increases normality of the distribution of correlations in the sample. For subjects with multiple runs, the individual subject z-maps were first averaged across the runs before submitting to the group average. An inverse Fisher's r-to-z transformation was then applied to the group-averaged correlation z-map, yielding a group-averaged correlation map.

To quantify functional connectivity among regions, Fisher's r-to-z-transformed correlations were computed among the regions for each subject within a group. For several targeted, a priori analyses, classical statistical tests, including t-tests (e.g., see Figs. 20 and 21) and ANOVA (e.g., see Figs. 22 and 27), were performed on the z-transformed correlations using Matlab 7.4 (The Mathworks, Natick, MA) or SPSS 18.0 (IBM, Armonk, NY). All tests survive Bonferroni correction for multiple comparisons.

Selecting Regions for Functional Connectivity Analysis

Throughout the analyses, seed regions for functional connectivity were selected using different criteria depending on the purpose of the analysis. In all cases, if a particular data set was used for selecting the region (e.g., discovery sample), functional connectivity was always computed with a different data set (e.g., replication sample), thus providing an unbiased measurement of correlation strength. We have detailed the method used for region selection in the results as implemented for each particular analysis in results. The following procedures describe the general strategies adopted.

First, when testing for seed-based confirmation of resolved networks, the estimated network boundaries and confidence maps of the discovery sample were used to derive regional vertices to be tested in the replication sample (e.g., see Fig. 16). Regions were chosen for 1) maximal spatial coverage of estimated networks, 2) avoiding network boundaries, and/or 3) their confidence in network assignments. We also defined new regions based on the correlation maps from the discovery sample. For example, new regions might be chosen to be at or near the peaks of the correlation maps.

Second, for some analyses we utilized task-based fMRI to select regions. For example, visuotopic and functional characteristics revealed using fMRI can be used to estimate visual areas in the human (Hadjikhani et al. 1998; Sereno et al. 1995). The Caret software database provides estimated locations of multiple visual areas that were mapped into Caret PALS space using surface-based registration of an individual case in Hadjikhani et al. (1998), although the foveal and peripheral extents of these areas are likely to be underestimated for technical reasons (Van Essen 2004). Landmark-based surface registration between FreeSurfer and Caret PALS allowed us to utilize these fMRI-defined visuotopic regions for guiding our selection of regions in FreeSurfer surface space (e.g., see V3A in Fig. 25). In addition, we also considered peak activation coordinates reported in fMRI literature (e.g., see Fig. 27). When the peak coordinates were reported in MNI space, we projected the coordinates to FreeSurfer surface space. The mapping between MNI152 volumetric space and FreeSurfer surface space is detailed in our companion study (Buckner et al., in press). In cases where the peak activation coordinates were reported in the atlas space of Talairach and Tournoux (1988), the coordinates were first mapped to FSL MNI152 space (Lancaster et al. 2007) before being projected to FreeSurfer surface space.

Third, probabilistic histological maps in FreeSurfer surface space allowed for the selection of regions within histologically defined areas (e.g., see Fig. 22). Postmortem human brains of 15 subjects with no history of neurologic or psychiatric diseases were processed and analyzed (Amunts et al. 1999; Schleicher et al. 1999; Schormann and Zilles 1998). The histological sections were aligned to postmortem MR volume of the same brain using nonlinear warping (Schormann and Zilles 1998) to build an undistorted three-dimensional histological volume. Cytoarchitectonic areas, including V1 (Amunts et al. 2000) and hOc5/MT+ (Malikovic et al. 2007), were segmented using observer-independent criteria (Schleicher et al. 1999). The MR volumes were segmented to separate white matter from other tissue classes, and the segmentation was used to generate topologically correct and geometrically accurate surface representations of the cerebral cortex using FreeSurfer (Fischl et al. 2008). The cortical surfaces of the 15 subjects were registered to FreeSurfer surface space, and the histological areas were sampled onto the surface space. Whereas there were 15 subjects, each cytoarchitectonic area was only analyzed in at most 10 subjects.

Prior work has demonstrated good across-subject alignment of lower order cortical areas in the surface coordinate system, with average misregistration errors as small as 2–3 mm for V1 (Fischl et al. 2008; Hinds et al. 2008; Yeo et al. 2010a), which is around the spatial resolution of the present fMRI data. For higher order regions such as BA44, BA45, and hOc5/MT+, intersubject agreement is worse, with average misalignment errors in the order of 6–12 mm (Fischl et al. 2008; Yeo et al. 2010a; 2010b), but still an improvement from standard volumetric alignment (Amunts et al. 1999). In the case of histologically defined hOc5, considered to be putative MT+ (Malikovic et al. 2007), we were able to verify (not shown) that the probabilistic map of hOc5 in FreeSurfer surface space (Yeo et al. 2010b) was consistent with that of MT+ defined in Caret PALS space (Van Essen 2004) and peak MT+ coordinates reported in Shulman et al. (1999). Certain anatomical landmarks were also useful in the selection of regional vertices. For example, the calcarine fissure was used as a guide to select regions in the lower and upper visual field representations as well as in the central and peripheral visual field representations within V1 (e.g., see Fig. 22).

Comparison of Network Boundaries With Cytoarchitectonic Areas

In addition to their utility for selecting regions, the probabilistic histological maps were useful in relating the estimated network boundaries to human cytoarchitectonic areas. Because the Statistical Parametric Mapping (SPM) Anatomy toolbox contained a more complete set of probabilistic histological maps of the same subjects in MNI Colin27 volumetric space (Eickhoff et al. 2005), we projected these probability maps to FreeSurfer surface space by establishing spatial correspondence between Colin27 and FreeSurfer surface space using the same procedure as that used for mapping between MNI152 volumetric space and FreeSurfer surface space (Buckner et al., in press). Cytoarchitectonic areas common to both data sets were those of the primary motor cortex (areas 4a and 4p; Geyer et al. 1996), premotor cortex (area 6; Geyer 2004), primary somatosensory cortex (areas 3, 2, and 1; Geyer et al. 1999), early visual cortex (areas 17 and 18; Amunts et al. 2000), hOc5/MT+ (Malikovic et al. 2007), and BA44/45 (Amunts et al. 1999). Consistent with previous discussion about surface-based vs. volume-based registration, we found Eickhoff's probabilistic maps in FreeSurfer space to be more diffuse than the maps obtained from the purely surface-based approach, possibly a result of differences in intersubject alignment. Consequently, for cytoarchitectonic areas common to both data sets, the surface-based probabilistic maps were used (Fischl et al. 2008; Yeo et al. 2010b) (e.g., see Fig. 22). We were also able to verify reasonable overlap (not shown) between the projected Eickhoff's maps and the purely surface-based probabilistic maps for areas common to both data sets, substantiating the validity of the mapping between the Colin27 space and FreeSurfer surface space.

Effect of Resting Condition on Functional Connectivity

For certain analyses, it was important to check that findings were not the result of overt eye movements that might shift edges and visual boundaries in and out of the central field. The core dataset (N = 1,000) employed an EOR condition, because it is comparable to visual fixation in terms of signal strength (Van Dijk et al. 2010) but can be acquired in studies that do not employ a setup for visual display. To examine the effects of the task employed during the resting state, the effect of condition was analyzed for certain key analyses (e.g., for analyses that quantified the functional connectivity strengths among visual regions). As the results show, the type of resting condition (EOR, ECR, or FIX) is not a significant factor influencing our results (e.g., see Fig. 17).

Visuotopic fMRI Data

The analyses of the visual cortex involved the visuotopic organization of the V1-V3 complex. The fMRI data were analyzed in the native subjects' volumetric space, and the results were sampled onto FreeSurfer surface space and averaged across subjects. The details of the analysis, which provided eccentricity estimates of the visual representation in the V1-V3 complex, are described elsewhere (Hinds et al. 2009). A 1-mm smoothing kernel was applied to the averaged eccentricity estimate in FreeSurfer surface space. Because of the limited range of visual angle that could be stimulated in the MRI scanner, and because fixational eye movements that occur during visual stimulation prevent stable stimulation of the fovea, the eccentricity estimates did not cover the representation of the periphery or of the center of visual field within the V1-V3 complex (Hinds et al. 2009; Polimeni et al. 2005) but were sufficient for our analyses.

Distribution of Parcellations and Raw Data

A primary result of this study is the parcellation of cortical networks and the estimation of boundaries of regions within the networks. The parcellations of parietal and prefrontal cortices, in particular, represent demarcations of complex topographical regions that have been perplexing to understand in relation to task-based functional neuroimaging studies. We have uploaded the parcellations in Caret PALS surface space into the Surface Management System Database (SumsDB) for open use (Dickson et al. 2001) (http://sumsdb.wustl.edu:8081/sums/directory.do?id=8286317). The parcellations in FreeSurfer surface space are also available (http://www.freesurfer.net/fswiki/CorticalParcellation_Yeo2011). Movies of the region-based functional connectivity estimates can be downloaded from http://www.youtube.com/yeokrienen. The raw fMRI data from the 1,000 subjects will be made openly available to researchers using the procedures established by the OASIS data releases (Marcus et al. 2007, 2010) and the 1,000 Functional Connectomes Project (Biswal et al. 2010).

RESULTS

Estimates of Cerebral Networks Are Reliable

The cerebral cortex was parcellated into multiple networks using clustering. The parcellations resulted in networks that involved primarily adjacent areas (e.g., visual cortex) and networks that involved areas widely distributed throughout the cortex (e.g., heteromodal association cortex). Figure 7 shows the 7-network estimates for the discovery and replication samples. A total of 97.4% of the vertices were assigned to the same networks across both data sets. Varying the particular choice of binarization threshold (ranging from 5% to 15%) and smoothing (ranging from no smoothing to 6-mm FWHM) minimally affected the results (not shown). Figure 8 shows the confidence (silhouette) value for each vertex with respect to its assigned network for the 7-network estimate. Regions close to the boundaries between networks were less confident in their assignment. Spatial variation within individual components of the estimated networks was also observed beyond the boundary regions. Often these low-confidence regions anticipated further fractionation of the networks into smaller subnetworks that emerged when larger numbers of networks were allowed (e.g., compare the lateral prefrontal extent of the orange network in Fig. 7 with its confidence map in Fig. 8, and then note subsequent fractionation of this region in Fig. 9). Figure 9 shows the 17-network estimates for the discovery and replication data samples, and Fig. 10 shows the confidence map for the discovery data set. For the 17-network estimate, 97.0% of the vertices were assigned to the same networks across both data sets.

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.

Estimates of Cerebral Networks From 1,000 Subjects

To provide the best estimates of the cerebral cortical networks, clustering was performed on the full sample of 1,000 subjects. Figures 11 and 13 show the 7- and 17-network parcellation estimates, respectively. Several results are notable. A salient feature of the estimated networks is the separation of the early sensory and late motor cortices (blue and purple) from association cortex, consistent with the observation that early sensory and late motor regions exhibit dense local anatomical connectivity in primates (Felleman and Van Essen 1991; Jones et al. 1978; Markov et al. 2010) and preferential local functional coupling in humans (Sepulcre et al. 2010). Sensory and motor cortices, whose functional connectivity networks were preferentially local, comprised only 35% of the cerebral mantle and were the exception in terms of network structure.

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.

The majority of the human cerebral cortex is made up of multiple, distinct networks of association areas. The association networks in the 7-network estimate converged and extended on networks previously described in the resting-state literature, including those referred to as the dorsal and ventral attention (green and violet, respectively; Fox et al. 2006), the frontoparietal control (orange; Dosenbach et al. 2007; Vincent et al. 2008), and the default (red; Buckner et al. 2008; Greicius et al. 2003) networks (Fig. 12). We also note that the 7-network parcellation of the parietal cortex is similar to those proposed using seed-based approaches (Vincent et al. 2008) and using the areal boundary detection method (Cohen et al. 2008; Nelson et al. 2010). The convergence of multiple different analysis approaches suggests that the parcellation is intrinsic to the resting-state data rather than an artifact of the algorithm used.

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.

Generally, the 17-network estimate (Fig. 13) fractionated the 7-network estimate into smaller subnetworks. Some aspects of the fractionation, such as the emergence of a parahippocampal-retrosplenial-lateral parietal network, are anticipated by other studies using hierarchical clustering techniques (e.g., Andrews-Hanna et al. 2010). Other aspects of the fractionation were unexpected, such as the emergence of subnetworks within the visual and motor cortices that did not respect areal boundaries but rather appear to align with topographic organization. In the following sections, we quantify and further explore the patterns of functional connectivity that give rise to these networks.

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.

A Cautionary Note About Potential Artifacts

Before exploring the estimated networks in more detail, it is important to point out aspects of the data that are difficult to interpret because of potential fMRI signal blurring across gyri and signal loss associated with susceptibility (Ojemann et al. 1997). Figure 14A illustrates one example. Somatomotor, auditory, and posterior insular cortices are correlated within a single network (also see Fig. 11). The ventral portion of somatomotor cortex is clustered with the auditory cortex in the 17-network parcellation (Fig. 13). Although nonhuman primate tracing studies suggest auditory and somatomotor cortices are connected via multiple areas within the insular cortex (Disbrow et al. 2003; Mesulam and Mufson 1982), an equally likely explanation for the observed correlation is blurring of the BOLD signal across the Sylvian fissure (Fig. 14A). We could not find a way, in these data, to resolve whether the coupling was an artifact of limited resolution or a true, coupled network.

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.

Figure 14B illustrates a second example of how fMRI signal blurring might affect the interpretation of the results. In this case, the primary somatosensory cortex (S1) and primary motor cortex (M1) are clustered within the same network (Fig. 11). Although there is anatomical evidence of direct connectivity between S1 and M1 in the macaque (Jones et al. 1978; Pons and Kaas 1986), we are unable to resolve whether the coupling was an artifact of limited resolution due to the close proximity of M1 and S1 in volumetric space.

As an example of uncertainty occurring near regions of MR susceptibility, Fig. 14C illustrates a cream-colored network of regions in the temporal pole and orbital frontal cortex (also see Fig. 11). Although there is anatomical evidence from the primate tracing literature supporting the existence of this network (Carmichael and Price 1995; Kondo et al. 2003; Moran et al. 1987), the spatial distortion and signal loss caused by MR susceptibility creates uncertainty in the location of the network boundaries.

Sensory and Motor Cortices Exhibit Topographically Specific Functional Connectivity

The sensory and motor cortices were clustered separately from the association cortex in the 7-network estimate (Fig. 11). This result by itself suggests that sensory and motor cortices distinguish themselves from distributed networks of association areas. Within the sensory and motor networks, there were a number of further observations. Of most interest, the 17-network parcellation fractionated the sensory and motor cortices into subnetworks (Fig. 13). Specifically, early visual regions formed two distinct subnetworks that did not respect areal boundaries. Somatomotor cortex was similarly fractionated along its lateral extent. Our hypothesis is that these fractionations reflect topographic organization: topographic representation of visual space in the visual regions and topographic representation of body space in the somatomotor regions.

Visual topography.

The visual network in the 7-network estimate (Fig. 11) was fractionated into two separate subnetworks (purple and bright red) in the 17-network estimate (Fig. 13). The boundary between the two visual subnetworks cut perpendicularly across the calcarine fissure, suggesting the division of the early visual areas into central and peripheral components. To evaluate this possibility, Fig. 15 overlays the eccentricity estimates of the visuotopic data set over the boundaries of the two separate visual subnetworks. The early visual areas were divided into two subnetworks along an isoeccentricity line of ∼4°. We refer to these two subnetworks as “central” and “peripheral,” although an immediate question arises as to whether the division of lower visual areas into central and peripheral components extends to higher visual areas. The particular traveling wave paradigm used in the visuotopic data set was designed to estimate visual eccentricity within the V1-V3 complex and is therefore unreliable outside the complex. To gain further clarification on visual areas outside the V1-V3 complex, we inspected the boundaries of the 17-network parcellation overlaid on the map of approximate human visual areas provided by Van Essen (2004). The boundary between the central and peripheral representations continues through the extrastriate visual areas consistent with the possibility that the division of lower visual areas into central and peripheral components generally applies to the extrastriate cortex, with certain caveats that will be taken up in the discussion.

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.

To assess the validity of the clustering analysis of visual cortex, six seed regions were selected from the discovery sample (Table 1), and their fcMRI maps were computed using the replication sample. The regions were selected to include V1 and V3 regions that fell within the central and peripheral representations. Using the calcarine fissure, histological V1 estimates (Amunts et al. 2000; Fischl et al. 2008), and the network boundaries as landmarks, two regions labeled V1 c and V1 p were selected to correspond to central and peripheral V1, respectively. Using the probabilistic histological maps of the SPM Anatomy toolbox (Eickhoff et al. 2005), we selected two regions, labeled V3 cv and V3 pv , at or near visual area V3v (Rottschy et al. 2007; Wilms et al. 2010), corresponding to the central and peripheral representations, respectively. The two remaining regions were selected from the extrastriate regions of the central and peripheral visual subnetworks. In all cases, the confidence (silhouette) map of the 17-network estimate from the discovery sample (Fig. 10) was used as a guide.

The fcMRI maps of the six seed regions confirm the network demarcations (Fig. 16). Compared with the other seed regions of the central visual system, V1 c demonstrated weaker correlation to the extrastriate regions of the central visual system. This is reflected by the lower confidence of V1 c in its network assignment as shown in Table 1. Consistent with this observation, as the number of networks in the clustering analysis was increased (not shown), the central V1 region separated from the extrastriate component of the central visual system.

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: V1 c and V1 p correspond to central and peripheral visual field representation within V1, respectively; V3 cv and V3 pv 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.

To quantify the dissociation between the central and peripheral representations within the visual subnetworks, Fig. 17A shows polar plots of the correlation of V1 p and V1 c with five regions in the replication sample. Since the discovery and replication samples consist of resting data collected under the EOR condition, the patterns of functional connectivity were also quantified for the task effect data set (Fig. 17B). The results revealed that the central and peripheral V1 seed regions displayed distinct patterns of functional connectivity that generalized across multiple data acquisition conditions, including ECR and FIX.

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 V1 c (central V1) and V1 p (peripheral V1) seed regions for the replication data set. Note that “V1” refers to V1 c for the V1 p polar plot (blue) and V1 p for the V1 c polar plot (red). Coordinate locations for all 6 seed regions (V1 c , V1 p , V3 cv , V3 pv , 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: V1 p polar plot. Right: V1 c 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.

Although there are differences in visual field properties, such as magnification factors and receptive field sizes, between the central and peripheral regions of the V1-V3 complex, these differences vary smoothly from central to peripheral vision within an area (Balasubramanian et al. 2002; Dow et al. 1981; Rovamo and Virsu 1979). To explore this further, we examined functional connectivity among seed regions spanning the eccentricity axes of V1 and V3v (Fig. 18). Results did not suggest a sharp transition in functional connectivity between V1 and V3v seed regions moving from central to peripheral representations. The resulting division of the V1-V3 complex along the isoeccentricity line of 4° was therefore likely driven by the functional connectivity of visual regions outside the V1-V3 complex or may be an artifact of the small number of networks being mandated by the analyses.

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.

Somatomotor topography.

The 7-network parcellation estimate clustered the somatomotor cortex into a single network (the blue network in Fig. 11). Figure 19 shows the boundaries of the 7-network estimate overlaid on the probabilistic histological maps of areas 6 (Geyer 2004), 2 (Grefkes et al. 2001), and 5L (Scheperjans et al. 2008a, 2008b). The histological estimates of areas 1, 3, and 4 (Geyer et al. 1996, 1999) are sandwiched between areas 2 and 6 and are not shown. Recent work (Amunts et al. 2010) has delineated three additional premotor areas anterior to the ventral half of area 6 shown in Fig. 19; the ventral half of area 6 in Fig. 19 is therefore an underestimation. On the basis of these areal references, the somatomotor network likely includes MI (area 4) and caudal premotor area 6, SI (areas 3, 1, and 2), and most, if not all, of early somatosensory area 5L. The somatomotor network also includes a small portion of the midcingulate sulcus and possibly area 5M (not shown; Scheperjans et al. 2008a; 2008b).

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.

The 17-network parcellation divided the somatomotor strip into dorsal and ventral subnetworks across the axis that represents body space (Fig. 13). To investigate this division, the parcellations were compared with activation maps of 24 subjects who were instructed to move their tongue, hand, or foot in response to a visual cue (for a detailed explanation of this data set, see Buckner et al., in press). As shown in Fig. 20A, the boundary between the dorsal and ventral somatomotor subnetworks was roughly positioned between the hand and tongue representations. To quantify this observation, seed regions were selected from the left hemisphere hand, foot, and tongue activation maps and verified to fall within the probabilistic histological map of area 4 (Fischl et al. 2008; Geyer et al. 1996). Figure 20B shows pairwise correlations computed among the hand, foot, and tongue regions averaged over 1,000 subjects (hand coordinates: −41, −20, 62; foot coordinates: −6, −26, 76; tongue coordinates: −55, −4, 26). The hand-foot correlation was significantly higher than the hand-tongue correlation (P < 0.001) and foot-tongue correlation (P < 0.001). Thus, like the visual system, functional coupling forms networks within the somatomotor system that reflect topographic organization.

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.

There are two further results that must be interpreted cautiously because of volumetric signal blurring. The 7- and 17-network parcellations of somatomotor cortex included both the precentral and postcentral representations of body space, thus forming networks that spanned areas (M1 and S1). This observation is difficult to interpret because precentral and postcentral gyri abut each other in volumetric space (see Fig. 14B). Similarly, the ventral somatomotor network in the 17-network parcellation included parts of the insula and auditory cortex, perhaps reflecting a polysynaptic circuit of functional coupling linked to speech movements and hearing one's own voice. We do not interpret this observation further because of their volumetric proximity (see Fig. 14A).

Asymmetry of Functional Coupling Varies Across Somatomotor Topography

Analysis of asymmetries in functional coupling is beyond the scope of this article. However, we observed an interesting variation in the asymmetry of functional coupling that reinforces the observation of functional differences along the somatomotor body representation. Specifically, pairwise correlations between homotopic pairs of hand, foot, and tongue representations (hand coordinates: ±41, −20, 62; foot coordinates: ±6, −26, 76; tongue coordinates: ±55, −4, 26) were measured between the hemispheres (Fig. 21A). The homotopic hand correlation was significantly weaker than that of the homotopic foot (P < 0.001) and tongue (P < 0.001) correlations. We are unable to rule out the possibility that the higher correlation between homotopic tongue regions is an artifact of subjects moving their tongues during scanning.

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.

To explore the somatotopy of interhemispheric fcMRI beyond the hand, foot, and tongue representations, we estimated the sequence of vertices lying in the shortest path connecting the left tongue region with the left hand region and those lying in the shortest path connecting the left hand region with the left foot region. For each left hemisphere motor vertex, we found the corresponding right hemisphere vertex that was maximally correlated with it in the discovery sample. Figure 21B plots the correlation between the left hemisphere vertices, arranged ventral to dorsal, and the corresponding right hemisphere vertices in the replication data sample. Defining the maximally correlated right hemisphere vertices in the discovery sample avoided bias in the correlation values and the issue of picking truly homotopic representations in either hemisphere. Consistent with Fig. 21A, the maximal tongue correlation was higher than that of the foot, which was in turn higher than that of the hand. The region in between the hand and foot representations, possibly corresponding to the trunk representation, also displayed higher maximal interhemispheric correlation than those of the hand or foot representations.

The observed somatotopy of interhemispheric fcMRI is consistent with nonhuman primate studies that have shown that the representations of midline structures in S1 and M1, such as the face and trunk, have denser callosal connections than those of distal limbs, such as the hand and foot (Gould et al. 1986; Jones and Wise 1977; Killackey et al. 1983; Pandya and Vignolo 1971).

Hierarchical Processing Within a Canonical Sensory-Motor Pathway

Parcellation of the cerebral cortex into distinct networks will not capture information about interactions between regions that fall across separate networks. This is particularly problematic because the canonical system-level description of cortical processing involves interactions across hierarchical pathways of sensory and motor areas. The above analyses leave open the question of how the distinct networks interact as is expected for sensory-motor pathways.

To explore this question, we selected the canonical sensory-motor pathway that extends from primary visual cortex to the precentral motor regions (including the putative homolog of FEF) via the motion-sensitive MT+ complex and posterior parietal cortex at or near putative human LIP. In the macaque literature, this pathway has been extensively studied in relation to sensory-guided decisions resulting in eye movements and associated processes linked to spatial attention (e.g., Andersen and Buneo 2002; Colby and Goldberg 1999; Gold and Shadlen 2007; Shadlen and Newsome 2001). In the human literature, this pathway has been studied both in relation to spatially directed movements and also in relation to spatial attention, with components of the pathway sometimes referred to as the dorsal attention system or network (Corbetta and Shulman 2002). Most critically, the anatomical relations between the areas within the pathway have been extensively explored, beginning with the seminal work of Maunsell and Van Essen (1983).

Early visual cortex.

Analysis focused initially on the bottom of the sensory-motor pathway by investigating functional connectivity between V1 and putative human MT+.2 For this analysis, two V1 regions were selected in dorsal (V1 cd ) and ventral (V1 cv ) central V1 corresponding to the lower and upper visual field representations. Two additional V1 regions were selected in dorsal (V1 pd ) and ventral (V1 pv ) peripheral V1 corresponding to the lower and upper visual field representations. Two MT+ seed regions (MT+ d and MT+ v ) were selected following the dorsoventral extent of the surface-based probabilistic histological map of MT+ (Malikovic et al. 2007; Yeo et al. 2010b). The MT+ d and MT+ v seed regions likely correspond to peripheral and central visual field representations, respectively (Huk et al. 2002; Maunsell and Van Essen 1987), and were analyzed to illustrate differential connectivity within MT+. Because of the overrepresentation of the lower visual field within macaque MT (Maunsell and Van Essen 1987), the distinct MT+ seed regions might also represent the lower visual field, although human fMRI studies have so far failed to yield strong evidence of this representational bias within MT+ (Amano et al. 2009; Kolster et al. 2010; Tootell et al. 1995). In the following analyses, MT+ was either analyzed as two regions using the extreme seed regions (MT+ d and MT+ v ) or as a single region using the center seed region (MT+) for analyses that did not require the complexities of topographic distinctions. A single anterior MT+ (aMT+) region was chosen anterior to and outside the histological MT+.3 The regions are shown in Figs. 22A and 24; their coordinates are reported in Table 2.

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: V1 cd and V1 cv were selected in dorsal and ventral central V1; V1 pd and V1 pd 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.

Table 2. Locations of seed regions utilized in the sensory-motor pathway analysis Seed Region Coordinates MT+ d −44, −72, 8 MT+ −45, −72, 3 MT+ v −45, −79, −1 aMT+ −51, −64, −2 V1 pd −18, −70, 8 V1 pv −8, −63, 6 V1 cd −8, −95, 3 V1 cv −8, −92, −5 V3A −17, −92, 20 V4 −22, −65, −9 FEF −26, −6, 48 PrC v −50, 6, 30 IPS2 −40, −37, 42 IPS3 m −31, −48, 46 SPL7A −28, −61, 60 SPL7P −14, −68,64 IPS1 −46, −49, 51

Figure 22B quantifies the differential functional coupling of the four V1 regions with MT+ and aMT+ within the replication sample. V1 showed strong functional coupling to MT+ and significantly weaker coupling to aMT+ (P < 0.001 for both MT+ v and MT+ d ). The dorsal and ventral MT+ regions also demonstrated differential functional coupling with the four V1 regions, confirmed by a 2 × 2 × 2 ANOVA including eccentricity (central or peripheral), polar angle (upper or lower visual field), and MT+ region (dorsal or ventral). Both MT+ regions were more strongly coupled with the lower visual field V1 seed regions than the upper visual field V1 seed regions (P < 0.001). Furthermore, dorsal MT+ was more strongly coupled with peripheral V1 than central V1 (P < 0.001), whereas ventral MT+ was more strongly coupled with central V1 than peripheral V1 (P < 0.05). These results suggest the topographic pathways between V1 and the MT+ complex are largely consistent with the visual field representations of the two areas.

Since V1 is a relatively large structure compared with MT+, to ensure the results were robust to the particular choice of the V1 seed regions, fcMRI maps were produced of the aMT+, MT+ d , and MT+ v regions computed using the replication data set (Fig. 23). By all accounts, V1 is functionally coupled to MT+, whereas aMT+ shows minimal coupling, leading to their separation into distinct networks in the cortical parcellation.

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.

Visual association and parietal association cortices.

Moving up the sensory-motor pathway, the functional connectivity of MT+ and aMT+ with parietal and frontal cortices was next examined. Figure 24 shows the fcMRI maps of MT+ and aMT+ seed regions computed using the replication sample with views focusing on the parietal and lateral frontal cortices. In the parietal lobe, both MT+ and aMT+ demonstrated correlation with the superior parietal lobule (SPL) and intraparietal sulcus (IPS) but little or no correlation with the inferior parietal lobule (IPL). In the frontal cortex, correlations were mostly limited to the precentral sulcus and gyrus. Compared with the MT+ seed region, the aMT+ seed region demonstrated stronger correlation with the parietal and frontal cortices. The MT+ and aMT+ seed regions were also maximally correlated with different parts of the parietal and frontal cortices, suggesting differential influence on nearby regions of sensory-association cortex.

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.

To quantify the pattern of differential functional coupling, we computed the correlation of the MT+ and aMT+ seed regions with four visual, four parietal, and two frontal cortical regions (Fig. 25A). Two of the visual cortex regions, V1 cd and V1 pd , were previously used in the V1-MT+ connectivity analysis. V3A and V4 were selected on the basis of their high correlation with MT+ in the discovery sample and named using the approximate map of human visual areas (Van Essen 2004) as reference. The four parietal regions were chosen at or near the IPS. Three of the parietal regions (IPS2, SPL7A, and SPL7P) were selected on the basis of a meta-analysis of fMRI literature of tasks that reportedly activate the human homologs of macaque areas AIP (anterior intraparietal), LIP (lateral intraparietal), and PIP (posterior intraparietal), respectively. The final parietal region, IPS3 m , was selected using the discovery sample to be physically between IPS2 and SPL7A. The parietal regions were labeled on the basis of their proximity to the probabilistic histological maps. In particular, IPS2 is at or near area hIP2 at the anterior most part of IPS (Choi et al. 2006), whereas SPL7A and SPL7P are at or near areas 7A and 7P of the SPL (Scheperjans et al. 2008a; 2008b). Finally, IPS3 m is on the medial wall of IPS at or near area hIP3 (Scheperjans et al. 2008a; 2008b). The dorsal frontal region, putatively FEF, was selected on the basis of the meta-analysis of fMRI literature of saccade tasks. The ventral frontal region PrC v (precentral ventral) was selected on the basis of its high correlation with aMT+ in the discovery sample. The locations of the visual, parietal, and frontal regions are reported in Table 2. Table 3 summarizes the set of fMRI studies used to derive the coordinates of IPS2, SPL7A, SPL7P, and FEF.

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.

Figure 25B shows the polar plots of the regional correlations using the replication sample. To ensure that the results were not the result of overt eye movements, the polar plots were also replicated using the task effect data set in Fig. 25, C and D. In all cases, MT+ had significantly stronger correlation with early visual cortex compared with aMT+. In contrast, aMT+ had significantly stronger correlation with the parietal and frontal regions compared with MT+. MT+ and aMT+ are strongly coupled to one another (r = 0.30 in the replication sample). Thus consideration of functional coupling between these regions in detail suggests that early visual areas are coupled to regions at or near MT+, which are in turn directly (or indirectly through intermediate regions, such as aMT+) coupled to parietal and frontal regions associated with sensory-motor integration. The differential coupling of MT+ and aMT+ resulted in their inclusion into distinct cortical networks.

Frontoparietal interactions.

We next considered the differential functional coupling of FEF and PrC v with the parietal cortex. Figure 26 shows the fcMRI maps of FEF and PrC v computed using the replication sample. Although both FEF and PrC v demonstrated strong correlation with the SPL and IPS, differences in correlation patterns also emerged. Specifically, PrC v was strongly correlated with more ventral portions of rostral SPL and IPS, whereas FEF was strongly correlated with caudal SPL and IPS. To quantify this phenomenon, we computed the correlation of FEF and PrC v with five parietal regions at or near SPL and IPS. Four of the parietal regions (IPS2, IPS3 m , SPL7A, and SPL7P) were the same as those used in the previous analysis. With the use of the discovery sample, a fifth parietal region was selected on the lateral wall of rostral IPS, within what is often termed the frontoparietal control network (orange; Fig. 11). Since the region was located at or near the histological map of hIP1 (Choi et al. 2006) projected to FreeSurfer space, we labeled the seed IPS1. Th