European Journal of Neuroscience
European Journal of Neuroscience, Vol. 32, pp. 383–398, 2010
doi:10.1111/j.1460-9568.2010.07279.x
NEUROSYSTEMS
Broca’s region: linking human brain functional
connectivity data and non-human primate
tracing anatomy studies
Clare Kelly,1,* Lucina Q. Uddin,2,* Zarrar Shehzad,1 Daniel S. Margulies,3 F. Xavier Castellanos,1,4
Michael P. Milham1,4 and Michael Petrides5
1
The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York,
NY 10016, USA
2
Department of Psychiatry, Stanford University School of Medicine, Stanford, CA, USA
3
Berlin School of Mind and Brain, Humboldt Universitat, Berlin, Germany
4
The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, United States
5
Montreal Neurological Institute, McGill University, Montreal, QC H3A2B4, Canada
Keywords: clustering, functional magnetic resonance imaging, inferior frontal gyrus, language, resting state
Abstract
Brodmann areas 6, 44 and 45 in the ventrolateral frontal cortex of the left hemisphere of the human brain constitute the anterior
language production zone. The anatomical connectivity of these areas with parietal and temporal cortical regions was recently
examined in an autoradiographic tract-tracing study in the macaque monkey. Studies suggest strong correspondence between
human resting state functional connectivity (RSFC) based on functional magnetic resonance imaging data and experimentally
demonstrated anatomical connections in non-human primates. Accordingly, we hypothesized that areas 6, 44 and 45 of the human
brain would exhibit patterns of RSFC consistent with patterns of anatomical connectivity observed in the macaque. In a primary
analysis, we examined the RSFC associated with regions-of-interest placed in ventrolateral frontal areas 6, 44 and 45, on the basis of
local sulcal and gyral anatomy. We validated the results of the primary hypothesis-driven analysis with a data-driven partitioning of
ventrolateral frontal cortex into regions exhibiting distinct RSFC patterns, using a spectral clustering algorithm. The RSFC of
ventrolateral frontal areas 6, 44 and 45 was consistent with patterns of anatomical connectivity shown in the macaque. We observed
a striking dissociation between RSFC for the ventral part of area 6 that is involved in orofacial motor control and RSFC associated
with Broca’s region (areas 44 and 45). These findings indicate rich and differential RSFC patterns for the ventrolateral frontal areas
controlling language production, consistent with known anatomical connectivity in the macaque brain, and suggest conservation of
connectivity during the evolution of the primate brain.
Introduction
The ventrolateral frontal region, which includes Brodmann areas 6, 44
and 45, in the left hemisphere of the human brain, has been implicated
in language processing since Broca’s (1861) description of the
eponymous speech disorder. Later, Wernicke (1874) suggested that
posterior temporal cortex is important for the receptive aspects of
language, leading to the concept of a fronto-temporal language circuit
linked via the arcuate fasciculus (Geschwind, 1970). Research on the
effects of lesions and electrical stimulation during brain surgery, and
recent functional neuroimaging studies, have shown that the posterior
language zone includes not only posterior temporal cortex, but also the
supramarginal and angular gyri of the inferior parietal lobule (Penfield
& Roberts, 1959; Rasmussen & Milner, 1975; Ojemann et al., 1989;
Correspondence: Dr Michael P. Milham and Dr Michael Petrides, as above.
E-mails: michael.milham@nyumc.org and michael.petrides@mcgill.ca
*C.K. and L.Q.U. contributed equally to this work.
Received 25 November 2009, revised 9 April 2010, accepted 15 April 2010
Binder et al., 1997). Explorations of the structural connectivity of
these regions with diffusion tensor imaging (DTI; Catani et al., 2005;
Croxson et al., 2005; Frey et al., 2008; Saur et al., 2008) suggest that,
in addition to the classical arcuate fasciculus, ventrolateral frontal
cortex interacts with inferior parietal lobule via the superior longitudinal fasciculus and the lateral temporal cortex via the extreme capsule
fasciculus, as originally shown in the macaque monkey (Petrides &
Pandya, 1984, 1988).
Although DTI studies can provide evidence about major white
matter pathways, current methodological limitations do not allow
precise delineation of the origins and terminations of these pathways.
As such, experimental tracer studies in non-human primates remain
the gold standard for uncovering the precise origins and terminations
of cortico-cortical connections. Recently, resting state functional
connectivity (RSFC) analyses, which detect coherent low-frequency
fluctuations in blood oxygen-level-dependent (BOLD) signal, have
emerged as a valuable non-invasive method for mapping the
functional circuitry of the brain that is complementary to DTI.
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
384 C. Kelly et al.
Correspondence between RSFC and anatomical connectivity is not
1 : 1, as RSFC has been observed between regions lacking direct
anatomical connections (Vincent et al., 2007; Di Martino et al., 2008;
Uddin et al., 2008). Nonetheless, patterns of functional and structural
connectivity (measured with diffusion imaging) are convergent
(reviewed by Damoiseaux & Greicius, 2009). Moreover, two recent
studies have demonstrated remarkable consistency between patterns of
RSFC in the human brain and maps of anatomical connectivity
derived from experimental tracer studies in the macaque monkey
(Vincent et al., 2007; Margulies et al., 2009).
Here we examine the hypothesis that the patterns of RSFC between
areas 6, 44 and 45 and posterior parietal and temporal regions in the
human brain are comparable with patterns of anatomical connectivity
between the homologues of these areas in the macaque monkey,
established in a recent autoradiographic study (Petrides & Pandya,
2009). In order to test this overarching hypothesis, we performed a
seed-based RSFC analysis in which the placement of seed regions-ofinterest was determined on an individual basis according to sulcal and
gyral morphology. We thus aimed to adopt a level of rigor similar to
that exemplified by autoradiographic anatomical studies, albeit limited
by the spatial resolution permitted by functional magnetic resonance
imaging (fMRI). We followed this primary examination with a datadriven spectral clustering analysis to verify distinctions emerging from
the seed-based analysis.
(FWHM) 6 mm] and mean-based intensity normalization of all
volumes by the same factor [each subject’s entire four-dimensional
(4-D) dataset was scaled by its global mean].
Registration of high-resolution structural images to the MNI152
template (Montreal Neurological Institute) with 2 mm3 resolution was
carried out using the FSL linear registration tool FLIRT (Jenkinson &
Smith, 2001; Jenkinson et al., 2002). Transformation to MNI152
standard space was then further refined using FNIRT non-linear
registration (Andersson et al., 2007a,b). Linear registration of each
participant’s functional time series to the space of the high-resolution
structural image was also carried out using FLIRT.
Nuisance signal regression
To control for the effects of physiological processes (such as
fluctuations related to cardiac and respiratory cycles) and motion, we
removed signal associated with several nuisance covariates. Specifically, we regressed each subject’s preprocessed 4-D volume on nine
predictors that modeled nuisance signals from white matter, cerebrospinal fluid, the global signal and six motion parameters, as detailed
elsewhere (Kelly et al., 2009).
This nuisance signal regression step produced a 4-D residuals
volume for each participant. As a final preprocessing step, each
participant’s 4-D residuals volume was spatially normalized by
applying the previously computed transformation to MNI152 standard
space, with 1-mm3 resolution.
Materials and methods
Participants
Thirty-six healthy right-handed adult subjects, aged 20–52 years
(19 females, 17 males, mean age = 28.1 ± 7.9), participated in this
study. All subjects were free of psychiatric disorders or history of head
trauma. Participants signed informed consent after the experimental
procedures were explained and received monetary compensation. The
study complied with the Code of Ethics of the World Medical
Association (Declaration of Helsinki) and was approved by the
Institutional Review Boards at New York University and the NYU
School of Medicine. Data from these participants have been included
in previously published studies (e.g. Margulies et al., 2007; Di
Martino et al., 2008; Shehzad et al., 2009).
fMRI data acquisition
Images were acquired on a Siemens Allegra 3-T scanner using an EPI
gradient echo sequence (TR = 2000 ms; TE = 25 ms; Flip angle = 90, 39 slices, matrix 64 · 64; FOV = 192mm; acquisition voxel
size 3 · 3 · 3 mm, 197 volumes, duration = 6 min 38 s) while
subjects rested with eyes open. A T1-weighted anatomical image was
also acquired for registration purposes (MP-RAGE, TR = 2500 ms;
TE = 4.35 ms; TI = 900 ms; Flip angle = 8; 176 slices; FOV =
256 mm, acquisition voxel size 1 · 1 · 1 mm).
fMRI data analysis
Image preprocessing
Slice timing correction (for interleaved acquisition), motion correction, despiking, temporal band pass filtering (0.009–0.1 Hz) and
quadratic detrending using linear least squares were performed using
AFNI (Cox, 1996). Further image preprocessing steps were completed
using FSL (http://www.fmrib.ox.ac.uk/fsl), and comprised spatial
smoothing [using a Gaussian kernel of full width at half maximum
Method 1 for RSFC analysis: hypothesis-driven seed-based
RSFC
Summary of ventrolateral frontal ROI selection
In order to best delineate the patterns of RSFC associated with ventral
area 6 and areas 44 and 45, the precise placement of the three
ventrolateral frontal regions of interest (ROIs) was determined on an
individual basis. Specifically, to maximize the probability that the
ROIs would lie in architectonic areas 44, 45 and ventral area 6, we
followed a two-step procedure. First, we examined each participant’s
normalized (to MNI152 space) high-resolution structural MR image
and used sulcal landmarks to identify the pars opercularis [Brodmann’s area (BA) 44], pars triangularis (BA 45) and the ventral part of
the anterior precentral region for premotor BA 6 (described in detail
below). Although the depth of the sulci may not always coincide with
architectonic boundaries (Fischl et al., 2008; Lohmann et al., 2008),
all studies that have examined the cytoarchitecture of the inferior
frontal gyrus agree that the bulk of the pars opercularis is occupied by
area 44, while the bulk of the pars triangularis is occupied by area 45
(e.g. Brodmann, 1909; Petrides & Pandya, 1994, 2002; Amunts et al.,
1999).
Subsequent to the initial identification step, we adjusted our
placement of the ROIs according to details of the local morphology of
each particular brain. This second adjustment step was necessary in
order to ensure that the ROIs would not be placed close to the sulci
where there is ambiguity about the exact border between areas, but
rather in a part of the pars opercularis, pars triangularis and rostral
inferior precentral gyrus where all available architectonic studies agree
that areas 44 and 45 and ventral area 6 are located. For instance,
Amunts et al. (1999) have shown that the border of area 44 and
ventral area 6 can vary within the inferior precentral sulcus. Thus, we
made sure that the ROIs for areas 44 and 6 were clearly away from the
inferior precentral sulcus in each individual brain and were located
within the pars opercularis and the inferior precentral gyrus at
positions where all architectonic studies (including those of our own
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
Functional connectivity of Broca’s region 385
laboratory; Petrides & Pandya, 1994, 2002) agree that areas 44 and 6
are located.
The specific criteria used for placement of the three ventrolateral
frontal ROIs are described in detail below. For each participant, once
the desired placement of the three ventrolateral frontal ROIs was
identified, a spherical ROI with a 2-mm radius was created using the
AFNI program 3dUndump.
Brodmann’s area 44
Most of BA 44 lies on the pars opercularis of the inferior frontal
gyrus (e.g. Brodmann, 1909; Petrides & Pandya, 1994, 2002; Amunts
et al., 1999), which is defined caudally by the inferior precentral
sulcus, rostrally by the ascending ramus of the Sylvian fissure and
dorsally by the inferior frontal sulcus. Furthermore, according to the
probabilistic map of BA 44 by Amunts et al. (1999), and the
probabilistic map of the pars opercularis by Tomaiuolo et al. (1999),
BA 44 lies between y = 12 and y = 14 in the left hemisphere, in MNI
standard stereotaxic space. Our first step in ROI placement was
therefore to identify BA 44, using these sulcal landmarks and
coordinates as guidelines. The second step was to examine the local
morphology of the particular brain and to make adjustments to the
ROI placement as necessary. For instance, because the precise
location of the border between area 44 and ventral area 6 can vary,
we made sure that we placed the area 44 ROI clearly in front of the
inferior precentral sulcus. In addition, we know that the pars
opercularis is often divided into an anterior and posterior part by
the diagonal sulcus (Keller et al., 2007) and Amunts et al. (1999)
have reported that in some brains BA 44 stops at the diagonal sulcus.
Thus, if in a particular brain the diagonal sulcus was present, we
placed the ROI posterior to this sulcus to avoid possible overlap with
the anteriorly adjacent BA 45. Finally, we aimed to place the center
of the ROI in the middle of the pars opercularis in the dorsal–ventral
direction, between z = 10 and z = 20, thus avoiding unintended
overlap with cortex lying above the inferior frontal sulcus.
Brodmann’s area 45
Unlike the pars opercularis, which is a clearly delimited part of the
inferior frontal gyrus, the morphology of the pars triangularis, where
BA 45 lies, is more variable. The pars triangularis lies rostral to the
ascending sulcus and dorsal to the horizontal sulcus. Dorsally, it is
delimited partly by the rostral part of the inferior frontal sulcus. Our
first step in ROI placement was therefore to identify BA 45 using these
sulcal landmarks, between y = 24 and y = 26, just above the
horizontal sulcus, at around z = 0. The exact position of the ROI
was then adjusted according to the local sulcal morphology of the
particular brain and knowledge of the relationship of these sulci and
architectonic area 45 obtained from our human architectonic studies
(Petrides & Pandya, 1994, 2002) and other published studies (Amunts
et al., 1999) to maximize the probability that our ROI would be
sampling BA 45 cortex.
Brodmann’s area 6
For the ventral part of BA 6, we placed the center of the ROI in the
rostral part of the ventral precentral gyrus, clearly caudal to the
inferior precentral sulcus (around y = 6), at approximately the same
dorsal–ventral level as the ROI for BA 44 for that particular brain,
i.e. between z = 10 and z = 20. We know that in the ventral half of
the inferior precentral gyrus, the primary motor cortex (area 4) lies
mostly within the anterior bank of the central sulcus and most of the
crown of the ventral precentral gyrus is occupied by BA 6. Thus, by
placing the seed in the anterior part of the ventral precentral gyrus
(but away from the inferior precentral sulcus to avoid overlap with
BA 44), we were maximizing the probability that the ROI would be
sampling BA 6.
RSFC
For each participant, a mean BOLD time series was extracted for
each of the three ventrolateral frontal ROIs (BA 6, BA 44, BA 45)
by averaging across all voxels within the ROI. We then used the
AFNI program 3dfim+ to compute the correlation between each
time series and every other voxel in the brain. Group-level maps of
positive RSFC for each ROI were computed using a one-sample
t-test (against 0), and corrected for multiple comparisons using the
FSL program easythresh (Z > 2.3; cluster significance P < 0.05,
corrected). Direct comparisons between the maps were computed
using paired t-tests, and were also corrected for multiple comparisons using the FSL program easythresh (Z > 2.3; cluster significance P < 0.05, corrected).
Method 2 for RSFC analysis: data-driven spectral clustering
In a second approach, we used data-driven clustering methods to
verify distinctions between ventrolateral frontal areas 6, 44 and 45 on
the basis of their RSFC (i.e. the results of the primary, hypothesisdriven seed-based analysis). Clustering algorithms are used to partition
(classify) data into natural subsets (clusters) such that observations
assigned to the same cluster are more similar to one another than they
are to observations assigned to another cluster. In the context of RSFC,
clustering algorithms have been used to partition the brain into subsets
(clusters) of voxels or regions that are functionally connected with one
another (e.g. van den Heuvel et al., 2008a), or that exhibit similar
patterns of functional connectivity with the rest of the brain (Cohen
et al., 2008). Here, we adopted the latter approach, and used spectral
and hierarchical clustering algorithms to assign voxels within a
ventrolateral frontal ROI (419 voxels in total) to clusters on the basis
of a measure of the similarity between their whole-brain correlation
maps (eta squared - g2).
The starting point for the clustering analysis was to establish the left
ventrolateral frontal ROI, which comprised the inferior precentral
gyrus (inferior to z = 30), the pars opercularis, the pars triangularis
and the remaining inferior frontal gyrus (as defined by the Harvard–
Oxford atlas) posterior to y = 50. The ROI comprised 419 voxels,
each one being 4 · 4 · 4 mm in size.
Measure of similarity: g2
We computed the whole-brain RSFC associated with each of the 419
voxels within the ventrolateral ROI, using the same methods described
above. We then computed the similarity between every possible
pairing of the 419 RSFC maps, using eta squared (g2). The g2 statistic
was recently applied to RSFC data for this purpose by Cohen et al.
(2008), and varies between 0 (no similarity) and 1 (identical). Cohen
et al. suggested that g2 provides a better measure of similarity between
two images than spatial correlation, because it can take into account
differences in scaling and offset between two images, while correlation is unaffected by these factors. We computed a 419 · 419
g2 matrix describing the similarity between each pair of the 419 RSFC
maps for every participant (36 in total).
Clustering analyses
We used the spectral clustering toolbox written for Matlab by Verma
and Meila (available at http://www.stat.washington.edu/spectral/) to
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
386 C. Kelly et al.
partition the left ventrolateral frontal ROI into K clusters, where
K ranged from 2 to 10. Specifically, we used the Meila–Shi (multicut)
algorithm (Meila & Shi, 2001), which performs a generalized Eigen
decomposition of the normalized Lagrangian of similarity matrix A
(here, the 419 · 419 matrix of g2 values), then applies the k-means
clustering algorithm to partition the data on the basis of K highest
eigenvectors. The eigenvectors of the similarity matrix provide
information about the data’s structure. By performing partitional
clustering (with k-means) on the basis of these eigenvectors, spectral
clustering makes use of this information (the data’s spectrum) to form
clusters of voxels that maximize intra-cluster similarity (here, g2) and
minimize inter-cluster similarity. For comparison, we also partitioned
the data using standard hierarchical clustering, as implemented in the
Matlab Statistics toolbox. Hierarchical clustering is an agglomerative
method, which starts by treating each data point as a singleton cluster,
then, as K decreases, successively merges previously established
clusters (visualized as a dendrogram or tree). Here, we formed clusters
of voxels on the basis of average linkage, i.e. the unweighted average
of the distances (1)g2) between all pairs of voxels, where one member
of the pair is assigned to one cluster and the other member is assigned
to a different cluster. At each iteration, K clusters are formed by
merging the two clusters (from the K + 1 solution) exhibiting the
smallest average distances.
Clustering the group-average g2 matrix
In order to determine the optimal K for the ventrolateral ROI, we
used a split-half comparison procedure. First, we randomly assigned
each of the 36 participants to one of two groups of 18 participants.
Then, for each group, we: (i) calculated the group-average of the 18
single-subject g2 matrices, and (ii) applied the spectral and
hierarchical clustering algorithms to identify cluster solutions for
the range K = 2 : 12. For each value of K, we compared the cluster
solutions generated for Group 1 and Group 2 using a metric
developed for assessing the similarity of cluster assignments:
variation of information (VI; Meila, 2007). We repeated the entire
process 100 times, each time generating two new groups of 18
participants. We determined the optimal K (or range of K ) by
computing the mean VI across the 100 permuted groups, for each
K, and selecting the non-trivial (i.e. K > 2) solution that showed the
lowest mean VI. The mean VI across solutions also allowed us to
determine which of the two algorithms (spectral or hierarchical)
produced the most consistent solution. The results of the
above-described analysis suggested that the spectral clustering
algorithm produced more consistent clustering solutions (associated
with the lowest mean VI) across the permuted groups, relative to
the hierarchical clustering algorithm (see Results). Accordingly, we
used the spectral clustering algorithm for the remaining analyses.
SðiÞ ¼
ðminð1 gbi ÞÞ ð1 gwi Þ
maxðð1 gwi Þ; minð1 gbi ÞÞ
Instead of estimating a voxel-wise S, we estimated a modified
cluster-wise silhouette value S~ in order to provide a summary measure
of the similarity of points within a cluster, relative to the similarity
between clusters:
ðminð1 gbk ÞÞ ð1 gwk Þ
~
SðkÞ
¼
maxðð1 gwk Þ; minð1 gbk ÞÞ
~ gwk corresponds to the mean g2
In the equation for S,
value describing the similarity between all voxels within cluster
k (gi2k; j2k ),
P
gi2k;j2k
gwk ¼
nk nk
while gbk corresponds to the K)1 mean g2 values describing the
similarity between all pairings of voxels within cluster k (gi2k ) and
voxels within other clusters (gj=2k ):
P
gi2ki ; j=2ki
gbki ¼
nki n2= ki
To compute the mean modified silhouette, we first applied the
spectral clustering algorithm to each participant’s g2 matrix, to identify
cluster solutions for the range K = 2 : 12. We then performed the
calculations described above, to compute the modified silhouette for
each value of K and for each participant. We then plotted the mean and
standard deviation, across participants.
Effect of smoothing
During data preprocessing, we applied a 6-mm FWHM Gaussian
spatial smoothing filter. To assess whether smoothing affects cluster
assignment, we repeated the analyses and g2 matrix generation without
spatial smoothing. We applied the spectral clustering algorithm to the
group-average of all (n = 36) single-subject unsmoothed g2 matrices,
and assessed the similarity between the solutions reached on the basis
of the smoothed and unsmoothed data using the VI metric.
Consensus matrix clustering
An alternative approach to cluster validation is to perform clustering
on an individual subject level and to examine the stability with which
pairs of voxels are assigned to the same cluster, across individuals (e.g.
Steinley, 2008).
We applied the spectral clustering algorithm to each individual
subject’s g2 matrix, to identify cluster solutions for the range K = 2:12
at the single-subject level. For each subject (s), and each K, we
constructed an adjacency matrix,
Modified silhouette
ðsÞ
To further discern the optimal K, we calculated a modified silhouette
value for each value of K, for cluster solutions produced when the
spectral clustering algorithm was applied to each individual’s g2
matrix. The silhouette is a standard metric, which provides, for each
point (in our case, voxel), a measure of how similar it is to other points
within the same cluster, vs. how similar it is to points in other clusters.
In the following equation, S(i) is the silhouette value for a single
voxel, gwi corresponds to the mean of the g2 values describing the
similarity between voxel i and voxels within the same cluster, and
gbi corresponds to the K)1 means of the g2 values describing the
similarity between voxel i and voxels in other clusters:
AðsÞ ¼ ðaij Þ419419
ðsÞ
where aij ¼ 1 if voxels i and j are assigned to the same cluster k, and
0 otherwise.
For each K, we then computed a consensus matrix,
Að s Þ ¼
S
1X
AðsÞ
S s¼1
such that each element of the 419 · 419 consensus matrix corresponds
to the proportion of times a given pair of voxels appeared in the same
cluster, across participants.
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
Functional connectivity of Broca’s region 387
Fig. 1. Patterns of group-level resting state functional connectivity associated with the three manually selected ventrolateral frontal ROIs, and the results of direct
contrasts between them (Z > 2.3; cluster significance P < 0.05, corrected). Images are in MNI152 space and are shown according to neurological convention (right is
right). The numbers (top left panel) indicate the positions of the axial (1) and coronal slices (2 & 3) shown in the lower portion of each panel.
To discern the most stable pattern of cluster assignment across
subjects, we applied the spectral clustering algorithm to the 12
resultant consensus matrices. For each K’s consensus matrix we
y = –32
identified the cluster solution, using the same K, and compared the
quality of the cluster assignments using the modified silhouette
metric (where the silhouette was calculated on the basis of the mean
within- vs. between-cluster consensus values, rather than the
g2 values). Finally, we assessed the similarity between the solutions
reached on the basis of the consensus matrices to those reached on
the basis of the group-average of the single-subject g2 matrices,
using the VI metric.
Seeding the K = 4 spectral clustering solution
Fig. 2. Results of the direct comparison between BA 45 and 44, after
restricting our analysis to the left temporal lobe (Z > 2.3; cluster significance
P < 0.05, corrected for a volume of 22 768 mm3). This small volume-corrected
comparison revealed significantly greater RSFC between BA 45 and the middle
temporal gyrus, relative to BA 44.
The clustering validation methods suggested that the most favorable
clustering solution was that produced by the spectral clustering
algorithm for K = 4 (see Results). To verify the distinctions among the
regions of ventrolateral frontal cortex suggested by this clustering
solution, we created four spherical seed ROIs of diameter 8 mm,
centered on the centers-of-mass of each of the clusters of the groupaverage K = 4 spectral clustering solution. We computed the grouplevel RSFC for each of the seeds, and performed direct comparisons
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
388 C. Kelly et al.
between seeds in the same manner as for the manually selected
ventrolateral prefrontal seeds (Z > 2.3; cluster significance P < 0.05,
corrected).
Results
Seed ROI results
Brodmann’s area 44
The ROI placed in BA 44 exhibited robust positive correlations with
the pars triangularis (BA 45) and pars orbitalis (area 47 ⁄ 12) of the
inferior frontal gyrus, as well as with the inferior premotor region (BA
6). In addition, there were positive correlations with the pre-supplementary motor area, the paracingulate region (BA 32) and the adjacent
medial frontal cortex (BAs 8, 9) (Fig. 1). There were also correlations
with the caudal dorsolateral frontal cortex (BA 8) and the rostral part of
dorsal BA 6. In the parietal cortex, correlations were primarily
restricted to the ventral part of the posterior supramarginal gyrus and
the adjacent angular gyrus. In the temporal lobe, there were significant
correlations with the caudal part of the superior temporal gyrus, the
superior temporal sulcus and the middle temporal gyrus. There were no
correlations with primary somatomotor cortex within the central sulcus
or the somatomotor cortical region around the medial extension of the
central sulcus, i.e. paracentral lobule BA 4. There were also no
significant correlations with the superior parietal lobule, the posterior
cingulate, precuneus and ventromedial prefrontal regions.
Brodmann’s area 45
The ROI in BA 45 exhibited a pattern of positive correlations
similar to that of BA 44 (Fig. 1). BA 45 exhibited significant
correlations with BAs 44 and 47 ⁄ 12 in the inferior frontal gyrus, as
well as with the posterior dorsolateral frontal region (BA 8) and
dorsal BA 6. In the parietal cortex, there were positive correlations
with the ventral part of the posterior supramarginal gyrus and the
angular gyrus. In the temporal lobe, there were strong positive
correlations with the caudal superior temporal gyrus, the entire
superior temporal sulcus and middle temporal gyrus. Medially, BA
45 exhibited positive correlations with the pre-supplementary motor
area, the paracingulate region (BA 32) and the medial frontal region
(BAs 8, 9 and 10). In addition, there were robust correlations with
the ventromedial frontal region. There were no correlations with
primary somatomotor cortex within the central sulcus or the
somatomotor cortical region around the medial extension of the
central sulcus, i.e. paracentral lobule BA 4. There were also no
significant correlations with the superior parietal lobule, the posterior
cingulate region or precuneus.
Ventral precentral gyrus (BA 6)
The ventral BA 6 ROI, located in the ventral part of the precentral
gyrus, close to the inferior precentral sulcus, was positively correlated
with BAs 44 and dorsal 45, as well as a region of the middle frontal
gyrus that lies just above the pars triangularis, and which was termed
area 9 ⁄ 46v by Petrides & Pandya (1994). Significant positive
correlations were also observed between BA 6 and the adjacent
motor and somatosensory cortex within the central sulcus, as well as
the medial extension of the somatomotor region on the paracentral
lobule. There were also positive correlations with the secondary
somatosensory region in the frontal and parietal opercula and the
insula. Correlations extended to the superior temporal gyrus and the
posterior-most part of the middle temporal gyrus. Within the posterior
parietal cortex, positive correlations were primarily restricted to the
anterior part of the supramarginal gyrus. On the medial surface of the
brain, the seed in BA 6 was correlated with the supplementary motor
region (medial BA 6) as well as the ventrally adjacent cortex within
the cingulate sulcus and gyrus that correspond to the cingulate motor
areas discovered in the macaque monkey (He et al., 1995). Notably,
the BA 6 seed did not exhibit any correlations with the medial frontal
cortex (i.e. BAs 8, 9 and 10) or the ventromedial prefrontal cortex.
There were also no positive correlations with the posterior cingulate
cortex or precuneus (Fig. 1).
Table 1. Peak coordinates for the direct contrasts between BAs 6, 44 and 45,
as shown in Fig. 1
Peak voxel
x
y
)58
56
8
4
)26
)2
18
16
46
)42
)18
46
36
)12
)52
)34
8
60
54
56
2
BA 6 > BA 45
L Anterior supramarginal gyrus
R Insula
R Anterior supramarginal gyrus
L Superior parietal lobe ⁄ intraparietal sulcus
Medial supplementary motor area
R Superior parietal lobe
L Posterior insula
)62
40
54
)38
0
22
)38
)20
)2
)28
)48
)8
)42
)20
28
10
48
56
54
70
)6
BA 44 > BA 45
L Pars opercularis (BA 44)
L Posterior insula ⁄ postcentral gyrus
R Ventral premotor cortex (BA 6)
R Insula ⁄ superior temporal gyrus
)56
)50
60
40
14
)28
8
)12
10
16
32
)2
BA 44 > BA 6
L Pars opercularis (BA 44)
R Pars orbitalis (BA 47 ⁄ 12)
L Pre-supplementary motor area
L Posterior middle temporal gyrus
L Anterior inferior temporal sulcus
L Cingulate ⁄ paracingulate gyrus
)54
54
)6
)54
)48
)4
14
38
20
)40
0
46
10
)12
52
)8
)32
20
BA 45 > BA 44
L Pars triangularis (BA 45)
L Anterior medial prefrontal cortex
L Angular gyrus
L Ventral medial prefrontal cortex
L Anterior superior frontal gyrus
L Middle temporal gyrus* (shown in Fig. 2)
)50
)2
)42
)4
)12
)64
26
58
)58
18
68
)32
)2
)18
26
)22
20
)14
BA 45 > BA 6
L Pars triangularis (BA 45)
L Middle temporal gyrus
L Superior frontal gyrus
R Pars opercularis (BA 44)
L Anterior medial prefrontal cortex
L Angular gyrus
L Middle frontal gyrus
L Temporal pole
R Temporal pole
L Caudate
R Ventral medial prefrontal cortex
)48
)62
)16
58
)4
)50
)40
)42
52
)14
2
26
)26
36
24
66
)60
4
6
)2
)2
22
2
)14
50
2
14
24
44
)42
)36
12
)30
BA 6 > BA 44
L Ventral premotor cortex (BA 6)
R Anterior supramarginal gyrus
R Paracingulate gyrus ⁄ medial
supplementary motor area
L Precentral gyrus
L Superior parietal lobe
R Postcentral gyrus
R Insula
z
Coordinates are in MNI152 space. *Significant at small volume correction
threshold (Z > 2.3; cluster significance P < 0.05, corrected for a volume of
22 768 mm3).
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
Functional connectivity of Broca’s region 389
D
A
B
E
F
G
H
C
Fig. 3. Clustering indices. (A) The group-average (mean) of the individual g2 matrices computed on the basis of the smoothed resting state data. (B) The groupaverage of the individual g2 matrices computed on the basis of the unsmoothed resting state data. (C) The consensus matrix for K = 4. This matrix represents, when
K = 4, the stability with which pairs of voxels were assigned to the same cluster across individuals. (D) For each value of K, we assessed the similarity of the cluster
solutions generated for Group 1 (n = 18) and Group 2 (n = 18) in the permuted-groups split-half comparison procedure using the variation of information (VI) metric
(Meila, 2007). The graph plots the mean VI across 100 permuted groups, for each K, for the hierarchical and spectral clustering algorithms. Lower VI scores indicate
better similarity (consistency) between the solutions computed for each group. (E) Mean (and standard deviation) of the modified silhouette for each value of K, for
cluster solutions produced when the spectral clustering algorithm was applied to each individual’s g2 matrix. (F) Modified silhouette values computed for clustering
solutions computed on the basis of the consensus matrices, for each value of K. (G) Similarity between the cluster assignments for the group-average (mean) of the
individual g2 matrices (i.e. shown in A) and those for the consensus matrices (example shown in C). (H) Similarity between the cluster assignments for the groupaverage (mean) of the individual g2 matrices computed on the basis of the smoothed resting state data (i.e. shown in A) and those computed on the basis of the
unsmoothed resting state data (example shown in B).
Direct contrasts: BA 45 > BA 44 and BA 44 > BA 45
To examine the differences between BA 44 and BA 45, direct contrasts
were carried out between these two ROIs. Relative to BA 44, BA 45
exhibited greater positive correlations with the pars orbitalis region of
the inferior frontal gyrus where area 47 ⁄ 12 is located (see Petrides &
Pandya, 1994), with the ventromedial prefrontal cortex and with the
angular gyrus. Note that on the surface of the brain, this stronger RSFC
appears to be restricted to the dorsal part of the angular gyrus, but this is
simply the result of the fact that much of the correlated activity lies just
below the cortex and within the parietal extension of the superior
temporal sulcus, which will not show on the surface of the brain, as can
be seen in the appropriate coronal section in Fig. 2 (BA 45 > BA 44).
BA 44 exhibited greater RSFC (relative to BA 45) with the premotor BA
6, the secondary somatosensory cortex within the upper bank of the
Sylvian fissure and the caudal superior temporal gyrus (Fig. 1, Table 1).
The above RSFC results were in excellent agreement with the
predictions of connectivity from parietal and temporal cortex to the
homologous ventrolateral regions in the macaque monkey based on
the experimental anatomical study of these connections (Petrides &
Pandya, 2009). However, there was also an apparent contradiction. In
the study with the macaque monkey, the connections of area 45 with
lateral temporal cortex appeared to be more widespread than those of
area 44 and to include a more ventral component of the lateral
temporal cortex. Comparison of the surface of the brain in Fig. 2
(compare panels BA 45 and BA 44) appears to confirm this greater
activity in the lateral temporal cortex for BA 45 than for BA 44.
However, this did not reach the accepted level of significance in the
direct comparison BA 45 > BA 44. Given our prediction that
differential RSFC would be observed, we repeated the direct
comparison between BA 44 and BA 45 RSFC, restricting our analysis
to the left temporal lobe (Z > 2.3; cluster significance P < 0.05,
corrected for a volume of 22 768 mm3). This restricted comparison
did reveal significantly greater RSFC between BA 45 and the middle
temporal gyrus, relative to BA 44 (Fig. 2).
Direct contrasts: BA 6 > BA 44 and BA 6 > BA 45
To examine the differences between BA 6 and BAs 44 and 45, direct
contrasts were carried out between these ROIs. Relative to both BAs
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
390 C. Kelly et al.
Fig. 4. Surface maps (MNI152 brain) for the spectral clustering solutions for K = 2 : 6 for (1) the group-average of individual participants’ g2 matrices computed
on the basis of the smoothed resting state data (g2 Smoothed – first column); (2) the group-average of the individual g2 matrices computed on the basis of the
unsmoothed resting state data (g2 Unsmoothed – second column); and (3) the consensus matrix (Consensus – third column).
44 and 45, BA 6 exhibited stronger RSFC with primary somatic and
motor areas around the central sulcus, and the secondary somatosensory areas within the frontal and parietal opercula, and the insula.
There were also stronger correlations between BA 6 and the superior
parietal lobule and the anterior part of the supramarginal gyrus,
relative to both BAs 44 and 45. There were stronger correlations
between BA 6 and the supplementary motor region and the motor
region in the central cingulate gyrus and sulcus, which probably
correspond to the cingulate motor areas discovered in the macaque
monkey (He et al., 1995) (Fig. 1, Table 1).
Direct contrasts: BA 44 > BA 6 and BA 45 > BA 6
Relative to BA 6, BAs 44 and 45 exhibited stronger correlations with
the pars orbitalis of the inferior frontal gyrus (area 47 ⁄ 12) and the
caudal dorsolateral frontal cortex corresponding to BA 8 (Fig. 1,
Table 1). In the temporal lobe, there were significantly stronger
correlations with the cortex within the superior temporal sulcus and
the middle temporal gyrus. On the medial surface, BAs 44 and 45
showed stronger correlations than BA 6 with medial frontal cortex
anterior to the supplementary motor area involving BAs 8, 9 and 10,
as well as the paracingulate BA 32. Additionally, BA 45 exhibited
stronger RSFC with the medial part of the frontal pole (BA 10), the
ventromedial frontal cortex and the angular gyrus, relative to BA 6,
while BA 44 did not show these differences.
Clustering results
Clustering the group-average g2 matrix
Using a permuted-groups split-half comparison procedure, we
applied spectral and hierarchical clustering algorithms to identify
cluster solutions for the range K = 2 : 12, where K is the number of
clusters. For each value of K, we assessed the similarity of the
cluster solutions generated for Group 1 (n = 18) and Group 2
(n = 18) using the VI metric (Meila, 2007). Figure 3D plots the
mean VI across 100 permuted groups, for each K, and each
clustering algorithm. The results indicate that the most similar
(consistent) solutions (associated with the lowest mean VI) were
generated by the spectral clustering algorithm. The most consistent
non-trivial solution (i.e. K > 2) appears to be K = 4, although there
is good mean similarity for the range K = 2:6.
We subsequently applied the spectral clustering algorithm to the
group-average of all (n = 36) single-subject g2 matrices. Figure 4
displays the surface maps for the spectral clustering solutions for
K = 2 : 6 (for comparison, the surface maps of the hierarchical
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
Functional connectivity of Broca’s region 391
Fig. 5. Patterns of group-level resting state functional connectivity and the results of direct contrasts for the four seed ROIs located at the centers-of-mass of the
group-average K = 4 spectral clustering solution. The clusters are marked in Fig. 4 and correspond to: the superior part of the inferior frontal gyrus, bordering the
inferior frontal sulcus (Cluster 1); the lateral pars opercularis and pars triangularis (Cluster 2); inferior precentral cortex (Cluster 3); and a fourth region extending
medially within the Sylvian fissure from the inferior-most tip of ventral premotor cortex and the pars opercularis towards the anterior insula (Cluster 4). Z > 2.3;
cluster significance P < 0.05, corrected. Images are in MNI152 space and are shown according to neurological convention (right is right). The numbers (top left
panel) indicate the positions of the axial slices shown in the lower portion of each panel.
clustering solutions for K = 2 : 6 are presented in supplementary
Fig. S1).
Modified silhouette
To further discern the optimal K, we calculated a modified silhouette
value for each value of K, for cluster solutions produced when the
spectral clustering algorithm was applied to each individual’s g2
matrix. As shown in Fig. 3E, the modified silhouette criterion
suggested that K = 4 represents the most favorable solution.
Effect of smoothing
To assess the impact of smoothing on cluster assignment, we repeated
the analyses and g2 matrix generation without spatial smoothing.
Figure 4 shows the surface maps for the spectral clustering solutions
for K = 2 : 6, computed on the basis of group-average unsmoothed
g2 matrices (Fig. 3B). Qualitatively, the maps are highly similar,
a conclusion which is supported quantitatively by the VI metric
(Fig. 3H), which indicates good similarity between the smoothed and
unsmoothed solutions for K £ 7.
Consensus matrix clustering
In a second approach to cluster validation, we performed spectral
clustering on an individual subject level, then examined the stability
with which pairs of voxels were assigned to the same cluster across
individuals, by computing a consensus matrix (Steinley, 2008), an
example of which is shown in Fig. 3C. To discern the most stable
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
392 C. Kelly et al.
Fig. 6. Schematic diagram integrating the observed patterns of functional connectivity between BAs 6, 44 and 45 and perisylvian parietal and temporal regions (i.e.
the results of the present study) with information concerning the white matter tracts that join these regions, derived from experimental anatomical tracer studies in the
macaque monkey that can demonstrate the precise origin, trajectory and termination of axonal fiber systems (Petrides & Pandya, 2009). Lines are dashed to indicate
that the white matter pathways underlying the observed functional connectivity are hypothesized, but not measured directly in the present study. SLF, superior
longitudinal fasciculus; AF, arcuate fasciculus; ECF, extreme capsule fasciculus.
pattern of cluster assignment across subjects, we applied the spectral
clustering algorithm to the consensus matrices and computed the
modified silhouette. Figure 3F plots the modified silhouette values,
and suggests that, across subjects, the most stable pattern of cluster
assignment is for K = 4. Qualitatively, the surface maps for the
solutions computed on the basis of the consensus matrix are highly
similar to those computed on the basis of the group-average g2 matrix
(Fig. 4), and the VI metric demonstrates that the best similarity
between the clustering solutions is for K = 2 : 4 (Fig. 3G).
Seeding the K = 4 cluster solution
On the basis of the clustering analyses, we concluded that K = 4
represented the most favorable solution (see Fig. 4). Qualitatively,
the four clusters were located in the superior part of the inferior
frontal gyrus, bordering the inferior frontal sulcus (Cluster 1), the
lateral pars opercularis and pars triangularis (Cluster 2), inferior
precentral cortex (Cluster 3) and a fourth region extending medially
within the Sylvian fissure from the inferior-most tip of the ventral
premotor cortex and the pars opercularis towards the anterior insula
(Cluster 4).
To verify these clusters as functionally distinct regions of ventrolateral frontal cortex, we examined the RSFC associated with four
spherical seed ROIs of 4-mm radius, centered on the centers-of-mass
of each of the clusters of the group-average K = 4 spectral clustering
solution. Figure 5 shows the group-level (Z > 2.3; cluster significance
P < 0.05, corrected) RSFC for each of the four clusters, as well as
direct comparisons between clusters.
The pattern of RSFC observed for Cluster 2, which includes the
central parts of the pars opercularis and pars triangularis, is very
similar to those observed for ROIs based in BAs 44 and 45 (compare
Cluster 2 in Fig. 5 with BA 44 and 45 in Fig. 1). Similarly, the pattern
of RSFC for Cluster 3, which includes the inferior part of the
precentral gyrus, is consistent with that for the ROI based in BA 6
(compare Cluster 3 in Fig. 5 with BA 6 in Fig. 1). The voxels in
Cluster 1 probably separate from the rest of the large ventrolateral
frontal region of interest that was defined for the clustering analysis by
virtue of the fact that they are located along the inferior frontal sulcus
on the border with the middle frontal gyrus, which would include
voxels of areas 8 and 9 ⁄ 46v in the upper bank of the inferior frontal
sulcus and adjacent middle frontal gyrus. Specifically, Cluster 1
exhibited RSFC with almost all of the inferior frontal gyrus, anterior to
and including the inferior precentral sulcus, dorsal BA 6 and BA 8 in
the middle frontal gyrus, the intraparietal sulcus, and the caudal
middle and inferior temporal cortex. The comparison Cluster
1 > Cluster 2 (Fig. 5) isolates this border region in the frontal lobe
and highlights the stronger RSFC with the intraparietal sulcus for
voxels that may belong to area 8 and 9 ⁄ 46 in comparison with voxels
that are more likely to lie in areas 45 and 44. Finally, Cluster 4
exhibited a pattern of RSFC similar to that of Cluster 2, but with less
extensive RSFC with the lateral temporal lobe and the medial frontal
cortex, and more extensive RSFC with the dorsal cingulate gyrus and
supplementary motor areas, as well as anterior frontal cortex. It may
represent a region that would include voxels in the anterior insula
region and the frontal opercular region.
Overall, the patterns of RSFC associated with the K = 4 spectral
clustering solution were consistent with those of the primary seedbased analysis of the ventrolateral frontal regions, and confirmed a
significant distinction between premotor BA 6 and BAs 44 and 45, but
greater similarity than difference between BAs 44 and 45 in terms of
their RSFC.
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
Functional connectivity of Broca’s region 393
Discussion
The traditional view of the cortical language circuit has been of a
ventrolateral frontal speech zone (Broca’s area) in the left hemisphere of
the human brain that is associated with a language comprehension zone
in the posterior superior temporal region via the arcuate fasciculus
(Geschwind, 1970). However, several lines of evidence suggest that
cortical language circuits must be much more complex than the classical
scheme. Electrical stimulation studies during brain surgery and functional neuroimaging studies have shown that the posterior language zone
is very wide and includes not only posterior superior temporal cortex,
but also the superior temporal sulcus and the adjacent middle temporal
gyrus, as well as the supramarginal and angular gyri of the inferior
parietal lobule (e.g. Penfield & Roberts, 1959; Rasmussen & Milner,
1975; Ojemann et al., 1989; Binder et al., 1997). Furthermore, the
ventrolateral frontal language production zone includes three distinct
parts: the ventral part of the premotor zone (BA 6) that is involved in the
control of the orofacial musculature, as well as area 44 and area 45 that
together comprise Broca’s region. Electrical stimulation of ventral
premotor area 6 results in vocalization, while stimulation of area 44 and
the caudal part of area 45 results in speech arrest (e.g. Penfield &
Roberts, 1959; Rasmussen & Milner, 1975; Ojemann et al., 1989).
Establishing the similarities and differences in connectivity of these
three ventrolateral frontal areas involved in language production with
the perisylvian posterior parietal and temporal regions that constitute the
posterior language zone is critical to our understanding of the neural
networks underlying language processing.
Experimental anatomical tracing studies in the macaque monkey
have shown that a major branch of the superior longitudinal fasciculus
links the inferior parietal region with the ventrolateral frontal region
(Petrides & Pandya, 1984) and a major pathway running in the
extreme capsule links the lateral temporal region with the ventrolateral
frontal region (Petrides & Pandya, 1988). Recent DTI studies have
also succeeded in demonstrating these pathways in the human brain
(e.g. Catani et al., 2005; Croxson et al., 2005; Makris et al., 2005;
Anwander et al., 2007; Frey et al., 2008; Makris & Pandya, 2009) and
evidence is beginning to emerge that they are involved in languagerelated processing (e.g. Saur et al., 2008). However, DTI analyses do
not currently permit delineation of the precise origins and terminations
of pathways from specific cortical areas and thus limit the extent to
which the similarities and differences in connectivity of areas 6, 44
and 45 can be revealed using that method alone. RSFC analyses offer
complementary information concerning patterns of inter-regional
connectivity, and there is increasing evidence to suggest that patterns
of RSFC track (to a large extent, although not in a 1 : 1 manner)
underlying anatomical connectivity (Vincent et al., 2007; van den
Heuvel et al., 2008b, 2009; Skudlarski et al., 2008; Honey et al.,
2009; Margulies et al., 2009). Here, we used RSFC to test hypotheses
about the connectivity of the ventrolateral frontal areas with parietal
and temporal cortex in the human brain derived from experimental
anatomical studies of the macaque monkey.
Predictions from experimental studies of cortico-cortical
connections in the monkey
The recent demonstration of the homologues of Broca’s area in the
macaque monkey ventrolateral frontal cortex (Petrides et al., 2005) has
permitted the utilization of experimental anatomical tracing to explore
the details of the connectivity of these areas with the posterior
perisylvian parietal and temporal regions using the autoradiographic
method (Petrides & Pandya, 2009). Tract tracing studies in the
macaque have shown that ventral premotor region BA 6 (which is
critical for orofacial motor control) is strongly connected with the most
anterior part of the inferior parietal lobule, which exhibits a distinct
architecture and is known as area PF in the monkey. By contrast, areas
44 and 45 are strongly connected with more posterior inferior parietal
lobule areas which, in the monkey, are referred to as areas PFG and PG
(Petrides, 2006; Petrides & Pandya, 2009). Based on comparative
architectonic studies, area PF of the macaque monkey corresponds to
the anterior part of the supramarginal gyrus in the human, whereas area
PFG corresponds to the human posterior supramarginal gyrus and area
PG to the human angular gyrus (M. Petrides and D. N. Pandya,
unpublished observations).
The macaque studies have also shown that areas 44 and 45 are
strongly linked with the cortex in the superior temporal sulcus and the
ventrally adjacent temporal cortex, which in the human brain
corresponds to the middle temporal gyrus. Petrides & Pandya
(2009) showed that, in the macaque, although areas 44 and 45 have
similar anatomical connectivity with posterior parietal and temporal
areas, there are differences in emphasis. Specifically, in the macaque,
area 45 has stronger connections than area 44 with area PG
(corresponding to the human angular gyrus) and stronger connections
than area 44 with the temporal cortex that lies within and below the
superior temporal sulcus (which corresponds to the middle temporal
gyrus of the human brain).
These findings in the macaque monkey provide strong predictions
of differential functional connectivity in the human brain that are
testable using RSFC data. We hypothesized that the patterns of
functional connectivity between areas 6, 44 and 45 and posterior
temporal and parietal regions in the human brain would exhibit a
degree of specificity similar to that established for connections
between the homologues of these areas in the macaque monkey, using
the autoradiographic method. To test this hypothesis, we performed an
a-priori seed-based functional connectivity analysis of human resting
state data, in which the precise placement of seed regions of interest in
areas 6, 44 and 45 was determined on an individual basis according to
sulcal and gyral morphology. We then verified the observed distinctions between the patterns of RSFC exhibited by these regions by
performing a data-driven spectral clustering analysis, in which we
partitioned the inferior frontal ROI into groups of voxels exhibiting
similar patterns of RSFC.
The results of these two analyses were consistent with one another,
and with the predictions from the experimental anatomical tracing
studies in the macaque monkey. These findings indicate that the
perisylvian parietal and temporal functional connectivity with left
ventrolateral frontal cortex in the human brain maintains the same
basic patterns observed in non-human primates. These patterns of
connectivity are schematically summarized in Fig. 6.
Robust separation of ventral area 6 from areas 44 and 45
The present RSFC analyses demonstrated a striking dissociation
between the pattern of RSFC associated with the ventral part of area 6
that is involved in orofacial control and the patterns of RSFC
associated with the two areas that comprise Broca’s region (areas 44
and 45). The RSFC profile of BA 6 was that of a motor zone – it
exhibited functional connectivity with dorsal premotor cortex, the
primary motor and somatosensory cortex within and around the
central sulcus, the secondary somatosensory areas in the upper bank of
the Sylvian fissure and, on the medial surface of the brain, the
supplementary motor area and the cingulate motor areas. This pattern
of RSFC (which is consistent with the known anatomical connectivity
of ventral premotor area 6 established in monkey anatomical tracing
studies) was not shared with areas 44 and 45.
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
394 C. Kelly et al.
Of particular interest was the RSFC of ventral area 6 with the
supramarginal gyrus. In the macaque monkey, ventral area 6 exhibits
strong cortico-cortical connections only with the most anterior part of
the inferior parietal lobule (referred to as area PF) (Petrides & Pandya,
1984, 2009; Matelli et al., 1986; Cavada & Goldman-Rakic, 1989),
whereas areas 44 and 45 of the monkey brain are linked more strongly
with area PFG and PG, which lie more caudally in the inferior parietal
region (Petrides & Pandya, 2009) and correspond to the caudal
supramarginal and angular gyri. This pattern of anatomical connectivity was confirmed with RSFC in the human brain and clearly sets
ventral area 6 apart from areas 44 and 45. As can be seen in Fig. 1, the
functional connectivity of area 6 was restricted to the anterior part of
the supramarginal gyrus that is delimited by the posterior ascending
ramus of the Sylvian fissure. The pattern of RSFC associated with
Cluster 3 (Fig. 5) supports this conclusion, which was also confirmed
by the direct contrasts between BA 6, and BAs 44 and 45 (as shown in
Fig. 1 and Table 1).
The strong RSFC of BA 6 with the most anterior part of the inferior
parietal lobule and the absence of correlations with the posterior part
of the supramarginal gyrus and the angular gyrus define a unique
profile of parietal RSFC for ventral BA 6. By contrast, areas 44 and 45
exhibited a functional connectivity pattern with the posterior supramarginal gyrus and the angular gyrus (Fig. 1), consistent with
predictions from the macaque monkey studies (Petrides & Pandya,
2009). Furthermore, areas 44 and 45 had strong correlations with the
cortex in the superior temporal sulcus and the temporal cortex just
below it, namely the middle temporal gyrus (Figs 1 and 2). The strong
distinction between the connectivity patterns associated with ventral
area 6 relative to areas 44 and 45 is most evident in the results of the
clustering analysis. The simplest and most robust partitioning of the
data (K = 2, see Fig. 3) was one that separated ventral area 6 into one
cluster, and areas 44, 45 and the rest of the inferior frontal gyrus into
another (see top row of Fig. 4). The clear separation between ventral
area 6 and area 44 anteriorly was also present for the optimal solution
(K = 4, see Fig. 4).
In both monkey and human brains, ventral area 6 is a typical
premotor cortex that lacks layer IV, whereas area 45 is a typical
prefrontal cortex with a well-developed layer IV (Brodmann, 1909;
Amunts et al., 1999; Petrides & Pandya, 2002). Area 44, which lies
between areas 6 and 45, does possess a layer IV, but it is interrupted
and not well developed. Consequently, there has long been confusion
as to whether BA 44 should be considered a premotor zone that is
functionally similar to premotor cortex or whether BA 44 is
functionally more similar to prefrontal BA 45. For instance, some
investigators have considered Broca’s region to include both BAs 44
and 45 (Amunts et al., 1999) while others have restricted it to BA 44
(Mohr et al., 1978). The present results address this issue. The
functional connectivity patterns of BAs 44 and 45, which together
comprise Broca’s area, were more similar to one another than to the
RSFC of ventral BA 6. This conclusion is also consistent with a study
by Amunts & Zilles (2006), who examined the architectonic and
neurochemical profiles of BA 44 and concluded that it shares more
features with BA 45 than with BA 6. Furthermore, anatomical
connectivity studies of the homologues of areas 44 and 45 in the
macaque also show that these two areas have similar connection
patterns and clearly differ in connectivity from ventral area 6 (Petrides
& Pandya, 2009).
Functional connectivity of areas 44 and 45
Although areas 44 and 45 share a similar pattern of cortico-cortical
connectivity that sets them apart from the caudally adjacent
premotor area 6, they have some subtle but important differences
in connectivity. The recent experimental anatomical tracer study
(Petrides & Pandya, 2009) examining perisylvian parietal and
temporal connections with the ventrolateral frontal region noted that
connections from area PG (especially its dorsal part close to the
intraparietal sulcus) were stronger with area 45. The same anatomical tracing study also noted that, although both areas 44 and 45
receive inputs from the cortex in the superior temporal sulcus, they
differ in that area 45 (but not area 44) had strong connections with
the ventrally adjacent temporal cortex. Although the RSFC of areas
44 and 45 were very similar (see Fig. 2, BA 44 and BA 45, and the
results of the clustering analyses, Fig. 4), the direct comparison
between areas 44 and 45 demonstrated greater RSFC of BA 45 in
the dorsal part of the angular gyrus close to the intraparietal sulcus
(see Fig. 2, BA 45 > BA 44, 3-D brain surface and coronal section).
However, these whole-brain comparisons did not reveal significantly
greater RSFC in any part of the temporal lobe for BA 45 relative to
BA 44. Given our a priori hypotheses concerning such a difference,
we restricted our comparison to the superolateral temporal cortex
(i.e. the cortex on the superior temporal gyrus, the superior temporal
sulcus and the middle temporal gyrus), which is the zone known to
connect to the ventrolateral frontal region. This directed analysis
did indeed demonstrate stronger RSFC between BA 45 and the
middle portion of the middle temporal gyrus, relative to BA 44
(Fig. 2).
The present results provide a more complete picture of languagerelated cortico-cortical connections than the traditional view of a
posterior superior temporal language zone that interacts with an
anterior frontal speech zone via the arcuate fasciculus (Geschwind,
1970). Consistent with results from macaque tracer studies, the present
findings show that the inferior part of the parietal lobe also interacts
with the anterior language zone. Specifically, we demonstrated linkage
between rostral supramarginal gyrus and ventral BA 6, and between
the caudal supramarginal and angular gyri and BAs 44 and 45 in the
human brain. Furthermore, we demonstrated greater linkage of the
middle section of the middle temporal gyrus with BA 45 than BA 44,
consistent with experimental findings in the macaque monkey
(Petrides & Pandya, 1988).
Relevance for theories for language processing and production
The richer view of the cortico-cortical pathways linking languagerelated regions demonstrated here agrees with recent diffusion tensor
imaging studies of the complexity of the white matter connectivity
between these regions (Saur et al., 2008; Makris & Pandya, 2009). It is
also in agreement with theoretical views that suggest a dual stream
model for auditory language processing (e.g. Hickok & Poeppel, 2004;
Warren et al., 2005). According to the dual stream model, initial
auditory processing in the superior temporal gyrus proceeds via a dorsal
stream to the inferior parietal lobule and then to the ventrolateral frontal
region for auditory-motor integration, which is necessary for mapping
the acoustic speech sounds to articulatory acts. At the same time, a
ventral stream is hypothesized to map sounds to meaning in lateral
temporal areas. A recent study (Saur et al., 2008) combined fMRI
during two prototypical tasks tapping dorsal (speech repetition) and
ventral (language comprehension) streams with diffusion tensor imaging. The authors showed that fibers of the arcuate fasciculus and the
superior longitudinal fasciculus are indeed linked to speech repetition
and those of the extreme capsule to language comprehension. A clearer
understanding of how different zones of language-related cortex are
linked together, using both DTI and RSFC approaches, will have a major
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
Functional connectivity of Broca’s region 395
impact on our understanding of the neural circuits underlying various
aspects of linguistic processing.
The primary purpose of the present study was to examine the
correspondence between the RSFC of the anterior language production zone, comprising left ventrolateral frontal areas 6, 44 and 45,
and the findings of a recent autoradiographic tract tracing study that
established the anatomical connections between the homologues of
these areas and perisylvian parietal and temporal cortex in the
macaque (Petrides & Pandya, 2009). As such, we limited our
primary analyses to the ventrolateral frontal areas 6, 44 and 45.
However, area 47 ⁄ 12 (Petrides, 2005), located on the pars orbitalis,
also plays a role in human language processing, particularly in
higher level aspects of semantic processing that rely on memory
retrieval (Petrides & Pandya, 2002). Although beyond the scope of
this study, the RSFC related to area 47 ⁄ 12 and its consonance with
or differentiation from the RSFC exhibited by surrounding cortical
areas, such as area 45, is an issue that should be explored in future
studies.
The human mirror neuron system
‘Mirror neurons’ were initially described by Rizzolatti et al., (1996) in
monkey ventral premotor cortex (Gallese et al., 1996) and later in
inferior parietal cortex (Fogassi et al., 2005). The defining characteristic of these neurons is that they discharge during the execution of
certain actions, but also during the observation of a similar action
performed by another agent (Rizzolatti & Craighero, 2004). For
example, if a mirror neuron discharges when the monkey grasps an
object, it will also fire when the monkey observes another agent
(human experimenter) grasping the same object.
Mirror neurons were originally observed in area F5 of the monkey
(Gallese et al., 1996; Rizzolatti et al., 1996), which is the homologue
of ventral area 6, extending into area 44 in the human, as well as in
area PF in the rostral part of the inferior parietal lobule (Fogassi et al.,
2005), corresponding to human anterior supramarginal gyrus. We have
shown that, in the human, ventral area 6 exhibits a specific pattern of
RSFC with anterior supramarginal gyrus that is distinct from the
pattern of RSFC exhibited by area 44 (and area 45). This network may
thus constitute the human analog of the mirror neuron system. Area
44, which is linked to ventral area 6, may provide (in the languagedominant hemisphere) the means by which semantic information
retrieved from memory controls action intended to convey a linguistic
message (Petrides, 2002, 2006).
Previous studies of ventrolateral–perisylvian RSFC
Previous studies have demonstrated significant RSFC between
ventrolateral and perisylvian areas (e.g. Dosenbach et al., 2007; Fair
et al., 2007; Vincent et al., 2008), and two previous studies specifically examined the functional connectivity of Broca’s area (Hampson
et al., 2002; Xiang et al., 2009). Xiang et al. used a seed ROI-based
RSFC analysis in a small sample of 12 participants to demonstrate
topographical functional organization in Broca’s area. They reported
substantial overlap in functional connectivity patterns of pars opercularis and pars triangularis, consistent with the results of our study.
Similarly, Hampson et al. demonstrated significant functional connectivity between Broca’s area, defined as all voxels within BAs 44
and 45 activated when listening to continuous speech, and Wernicke’s
area, defined as those activated voxels in the superior temporal gyrus
and angular gyrus. However, neither of these previous studies aimed to
examine differential functional connectivity of the ventrolateral frontal
areas, and therefore did not show the striking dissociation that we
observed between ventral area 6 and the two areas that are traditionally
considered to constitute Broca’s region, namely areas 44 and 45.
Furthermore, the present study was able to confirm the subtle differences in RSFC between areas 44 and 45, based on predictions of
patterns of anatomical connectivity obtained from experimental tracer
studies in the macaque (Petrides & Pandya, 2009) that were not noted
in those previous studies.
Clustering approaches to RSFC
Here we demonstrated the potential utility of voxel-wise RSFC-based
clustering as an objective, data-driven approach for characterizing
functional differentiation in structurally and functionally complex
brain regions, such as ventrolateral frontal cortex. The partitions
emerging from our examination of the ventrolateral frontal region
(Fig. 4), as well as the subsequent ROI-based RSFC analysis (Fig. 5),
provided additional support for the distinctions between areas 6, 44
and 45 that were demonstrated using the a priori ROIs (Fig. 1).
Importantly, we demonstrated that the clustering solutions were not
dependent on spatial smoothing.
A similar confirmatory clustering analysis was performed by
Margulies et al. (2009) in their comparison of posteromedial cortex
RSFC in the human and the macaque monkey. Our approach
represents an advance on that of Margulies et al., however. Specifically, whereas Margulies et al. partitioned posteromedial cortex by
clustering their a priori seed regions, we performed clustering of the
ventrolateral region on a voxel-wise basis. We thereby allowed
distinctions between ventrolateral subregions to emerge directly from
the data, without the imposition of any a priori restrictions on the
partitioning, beyond the selection of the ventrolateral ROI itself. There
is considerable potential for the application of this approach to other
functionally heterogeneous regions of the brain, such as anterior
cingulate cortex, in order to elucidate their complex functional
architecture in an objective, data-driven manner.
Along with others (van den Heuvel et al., 2008a; Bellec et al.,
2010), the present work demonstrates the utility of performing cluster
analyses at the individual participant level, computing a consensus
matrix representing the consistency of cluster assignment across the
group, then deriving the group-level clustering solutions on the basis
of that consensus matrix. Focusing on the consensus matrix in this
way may be particularly important for areas characterized by relatively
high morphometric interindividual variability, such as ventrolateral
frontal cortex (Amunts et al., 1999; Tomaiuolo et al., 1999; Keller
et al., 2007).
Despite their utility, clustering analyses are subject to the same core
limitation as other model-free approaches, namely parameter estimation. Because of the lack of a priori knowledge concerning the ‘true’
number of clusters (i.e. the true K), a range of cluster solutions must be
tested and reported. This is very similar to the requirement to examine
varying threshold levels in network analyses, and varying levels of
dimensionality in independent components analysis. Future work
focusing on methods for optimizing estimates for the clustering
parameters would be beneficial.
Limitations
The anatomical basis of RSFC extends beyond direct, monosynaptic
neuronal connectivity, to include polysynaptic connections (Vincent
et al., 2007; O’Reilly et al., 2009). It has been observed that
ª The Authors (2010). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd
European Journal of Neuroscience, 32, 383–398
396 C. Kelly et al.
functional connections can exist where no direct structural connections
are present (Uddin et al., 2008; Vincent et al., 2008; Honey et al.,
2009; Roy et al., 2009). Although the patterns of RSFC observed in
the present study were consistent with predictions from monosynaptic
pathways in the macaque monkey, we observed some correlations that
were not consistent with known anatomical connectivity in the
monkey. Such ‘additional’ connectivity may, at least in part, be due to
the spatial resolution of our data (acquisition voxel size was
3 · 3 · 3 mm, which is typical of whole-brain functional MRI
studies), and the application of spatial smoothing (also standard,
FWHM = 6 mm). On the other hand, the physiological bases of RSFC
remain incompletely understood, and a more complete understanding
may shed light on the bases of these patterns of RSFC.
Likewise, it is likely that our analyses may have missed even more
subtle differences in functional connectivity that may exist between
BAs 44 and 45, due to factors such as spatial smoothing, or the limits
of our image resolution. However, the data-driven clustering analyses
also did not distinguish between BAs 44 and 45 on the basis of their
RSFC, even when the analyses were repeated using data that had not
been spatially smoothed. Thus, it does not appear that the failure to
distinguish between these two areas is due to the smoothness of the
data. Future studies may consider acquiring data with greater spatial
resolution than the 3 · 3 · 3-mm voxel size employed in the present
study. Another possibility is that the considerable interindividual
variability in morphometry of the ventrolateral frontal region (i.e. the
presence or absence of particular sulci and gyri, and their arrangement;
Amunts et al., 1999; Tomaiuolo et al., 1999; Keller et al., 2007) may
have contributed to these findings. However, we took several steps to
minimize the impact of such variability, including manual determination of seed placement on the basis of local sulcal and gyral anatomy,
and use of non-linear registration to the template (MNI) brain. Finally,
as methods for integrating information about both structural (e.g. DTI)
and functional connectivity are developed, we may be better able to
elucidate the subtle distinctions between adjacent, functionally similar
regions such as BAs 44 and 45.
Concluding remarks
To summarize, we observed a striking dissociation between the
orofacial component of the ventral premotor cortex (BA 6) and
Broca’s region (BAs 44 and 45) in terms of their patterns of RSFC that
was consistent with predictions from experimental anatomical studies
of the monosynaptic connectivity of homologous areas in the macaque
monkey. We were also able to uncover some of the differences in
functional connectivity between areas 44 and 45. These observations
add to a growing list of studies, anatomical and functional, that are
changing the traditional conceptualization of how the different
components of Broca’s region interact with parietal and temporal
cortical areas that are involved in different aspects of language
processing. These results also provide further support for the utility of
resting state functional connectivity in delineating complex neural
circuits in the human brain in vivo.
Supporting Information
Additional supporting information may be found in the online version
of this article:
Fig. S1. Surface maps (MNI152 brain) for the hierarchical clustering
solutions for K = 2 : 6, for the group-average of individual participants’ g2 matrices computed on the basis of the smoothed resting state
data.
Please note: As a service to our authors and readers, this journal
provides supporting information supplied by the authors. Such
materials are peer-reviewed and may be re-organized for online
delivery, but are not copy-edited or typeset by Wiley-Blackwell.
Technical support issues arising from supporting information (other
than missing files) should be addressed to the authors.
Acknowledgements
We would like to thank Pierre Bellec, Alexander Cohen and Cameron
Craddock for helpful discussions and suggestions. This study was partially
supported by grants from the Stavros Niarchos Foundation, National Institute of
Mental Health (R21MH066393, T32MH067763) to F.X.C. and by the Leon
Levy Foundation (F.X.C. and M.P.M.), a CIHR grant (MOP-14620) to M.P. and
a National Institute on Drug Abuse grant (R03DA024775-01) awarded to C.K.
L.Q.U. is supported by a Mosbacher Postdoctoral Fellowship and NIMH award
number K01MH092288.
Abbreviations
BA, Brodmann’s area; DTI, diffusion tensor imaging; fMRI, functional
magnetic resonance imaging; FWHM, full width at half maximum; ROI, region
of interest; RSFC, resting state functional connectivity; VI, variation of
information.
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