The Organization of the Human Cerebral Cortex
Estimated by Functional Connectivity
B.T. Thomas Yeo1,2,*, Fenna M. Krienen1,2,*, Jorge Sepulcre1,2,3, Mert R. Sabuncu2,4,
Danial Lashkari4, Marisa Hollinshead1,2,3, Joshua L. Roffman5, Jordan W. Smoller5,6,
Lilla Zöllei2, Jonathan R. Polimeni2, Bruce Fischl2,4,7, Hesheng Liu2,
and Randy L. Buckner1,2,3,5
1Harvard University Department of Psychology, Center for Brain Science, Cambridge,
MA; 2Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology,
Massachusetts General Hospital, Charlestown, MA; 3Howard Hughes Medical Institute,
Cambridge, MA; 4Computer Science and Artificial Intelligence Lab, Massachusetts
Institute of Technology; 5Department of Psychiatry, Massachusetts General Hospital,
Boston, MA; 6Center for Human Genetics Research, MGH, Boston, MA; 7Massachusetts
Institute of Technology-Harvard Division of Health Sciences and Technology,
Cambridge, MA; *BTTY and FMK contributed equally to this work
RUNNING HEAD: The Human Cerebral Cortex
Keywords: prefrontal, parietal, association cortex, fMRI, functional connectivity, default
Address correspondence to:
Dr. Randy L. Buckner 24
Harvard University 25
52 Oxford Street, Northwest Building, 280.06 26
Cambridge, MA 02138 27
Articles in PresS. J Neurophysiol (June 8, 2011). doi:10.1152/jn.00338.2011 Articles in PresS. J Neurophysiol (June 8, 2011). doi:10.1152/jn.00338.2011
Copyright © 2011 by the American Physiological Society.Copyright © 2011 by the American Physiological Society.
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. Here the organization of networks in the human cerebrum
was explored using resting-state functional connectivity MRI (fcMRI). Data from 1000
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 V1, putative MT+, LIP and FEF 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 non-human animals using invasive techniques including
histology, anatomic 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 homologues 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 our
evolutionarily closest relatives. For example, the human brain is triple the size of modern
great apes 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 paper 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 1000 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 non-human 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”, 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 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
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., CIT and 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 occurs 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 will be 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 the macaque 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, analyses will
focus both 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 mm (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 (Wessinger et al.
2001) cortices 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 non-invasive
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). While 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 anatomic
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 anatomic brain
systems, networks identified using fcMRI have often been labeled based on 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 non-human primates, this study analyzed fcMRI data from 1000
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
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 1000 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 dataset (n = 16)
contrasted different passive tasks engaged during resting-state functional data acquisition.
A second supplementary fMRI dataset (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 dataset 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 homologue to macaque 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 will reveal, the estimated networks
were consistent across the Discovery and Replication data samples and confirmed by
region-based fcMRI analyses. The full dataset 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 LIP, and premotor regions at or near 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 quantified in the independent
Replication data sample.
Paid participants were clinically healthy, native English speaking young adults
with normal or corrected-to-normal vision (ages 18 to 35). 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 dataset consisted of 1000 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 gender were matched for the Discovery (mean age = 21.3
yr, 42.6% male) and Replication (mean age = 21.3 yr, 42.8% male) datasets. 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 datasets were also analyzed. The Task Effect dataset
(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 dataset,
which was acquired on a custom-built four-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 (AC-PC) plane using an automated alignment procedure ensuring
consistency between subjects (van der Kouwe et al. 2005). Structural data included a
high-resolution multi-echo T1-weighted magnetization-prepared gradient-echo image
(multi-echo MP-RAGE; van der Kouwe et al. 2008).
For the core dataset, subjects were instructed to remain still, stay awake and to
keep their eyes open. EPI parameters were as follows: TR = 3000 ms, TE = 30 ms, FA =
85º, 3 x 3 x 3 mm voxels, 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 (multi-echo MP-RAGE; van der Kouwe et al. 2008) were as follows:
TR = 2200 ms, TI = 1100 ms, TE=1.54 ms for image 1 to 7.01 ms for image 4, FA = 7º,
1.2 x 1.2 x 1.2 mm voxels and FOV = 230. The multi-echo MP-RAGE allows increased
contrast through weighted-averaging of the four derived images.
For the Task Effect dataset, subjects were instructed to remain still with their eyes
open (EOR; two runs), eyes closed (ECR; two runs) or to passively fixate a centrally
presented crosshair (FIX; two runs). For details see Van Dijk et al (2010). The order of
rest conditions was counterbalanced across subjects. EPI parameters were as follows: TR
= 3000 ms; TE = 30 ms; FA = 90º; 3 x 3 x 3 mm voxels; FOV = 288 and 43 slices
collected with interleaved acquisition and no gap between slices. Each functional run
lasted 7.15 min (143 time points). Parameters for structural scans (MP-RAGE) were as
follows: TR = 2530 ms, TI = 1100 ms, TE = 3.44 ms, FA = 7º, 1 x 1 x 1 mm voxels and
FOV = 256.
The Visuotopic dataset 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, Erlangen, Germany). 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 dataset of 1000 subjects and the Task Effect dataset) 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, a low-pass temporal filter removed constant offsets and
linear trends over each run while retaining 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 non-neuronal 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. The FreeSurfer
software package constitutes a suite of automated algorithms for reconstructing accurate
surface mesh representations of the cortex from individual subjects’ T1 images (Fig. 1B-
C) and the overlay of fMRI on the surfaces for group analysis (Fig. 1D-E). Briefly, the
cortical surface extraction process (Fig. 1B-C) involved (1) correcting for intensity
variations due to MR inhomogeneities (Dale et al. 1999), (2) removing extra-cerebral
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).
Insert Figure 1 About here
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. 1D-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; Fischl et al. 1999b).
Once the common spherical coordinate system was established, the structural and
functional images were aligned (Fig. 1A-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. 1A-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 (3mm), 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 architecture, connectivity, function, and/or topographic organization (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 cytoarchitectural (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 3D 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.
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A 6mm FWHM (full width at half maximum) smoothing kernel was applied to
the fMRI data in the surface space and the data were downsampled to a 4mm mesh1.
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 dataset was broadly similar except that older
versions of FreeSurfer and FsFast were used for the processing and 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).
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 3 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.
Insert Figure 2 About here
1 It is not possible to generate a high resolution uniform mesh on the sphere. However,
one can work with approximately uniform spherical meshes at different spatial
resolutions by starting with a regular icosahedron mesh consisting of 20 equal faces and
12 vertices and iteratively subdividing each mesh triangle into four smaller triangles.
Here each cortical hemisphere is represented by a subdivided icosahedron mesh with
20480 faces and 10242 vertices, where neighboring pairs of vertices are on average 3.8
mm apart (max = 4.1 mm, min = 3.4 mm).