Hemispheric asymmetry in cognitive division of anterior cingulate cortex:
A resting-state functional connectivity study
Hao Yana,b,1, Xi-Nian Zuoc,d,1, Deyi Wangc, Jue Wangc, Chaozhe Zhuc, Michael P. Milhamd,
Dai Zhanga,b,⁎, Yufeng Zangc,⁎
aInstitute of Mental Health, Peking University, Beijing 100191, China
bKey Laboratory for Mental Health, Ministry of Health, China
cState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
dPhyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, NYU Child Study Center, New York 10016, USA
a b s t r a c t a r t i c l e i n f o
Received 21 February 2009
Revised 19 May 2009
Accepted 26 May 2009
Available online 6 June 2009
Anterior cingulate cortex
The cognitive division of anterior cingulate cortex (ACC-cd) plays an important role in cognitive control via a
distributed attention network. The structural hemispheric asymmetries of ACC have been revealed by several
neuroimaging studies. However potential functional hemispheric asymmetries of ACC remain less clear.
Investigating the functional hemispheric asymmetries of ACC helps for a better understanding of ACC
function. The aim of this study was to use resting-state functional magnetic resonance imaging (fMRI) to
examine hemispheric differences in the functional networks associated with ACC-cd in the two hemispheres.
ROI-based functional connectivity analysis was performed on a group of 49 right-handed healthy volunteers.
The left and right ACC-cd showed significant differences in their patterns of connectivity with a variety of
brain regions, including the dorsolateral prefrontal cortex, inferior parietal lobule, superior parietal lobule
and dorsal posterior cingulate cortex in their ipsilateral cerebral cortex, as well as cerebellar tonsil and
inferior semilunar lobule in their contralateral cerebellar hemisphere. Specifically, for these areas, we found
significantly greater connectivity strength with ACC-cd in the right hemisphere than the left, regardless of
whether the connection was positive or negative. The current results highlight the presence of clear
asymmetries in functional networks associated with ACC-cd. Future functional imaging studies are needed to
give greater attention to the lateralized ACC functional networks which are observed.
© 2009 Elsevier Inc. All rights reserved.
Centrally located in the medial wall of the brain, the anterior
cingulate cortex (ACC) is consistently implicated in cognitive control
processes, such as conflict monitoring, attentional control, novelty,
and error detection (Bush et al., 2000; Botvinick et al., 2004; Holroyd
et al., 2004; Milham and Banich, 2005; Carter and van Veen, 2007). In
particular, neuroimaging studies have consistently demonstrated
control-related activations within the dorsal and caudal portions of
the ACC, consisting of areas 24b′–c′ and 32′, which have come to be
collectively known as the cognitive division of ACC (ACC-cd; Bush et
al., 2000). Given its critical importance, it is not surprising that
abnormalities in the structure and/or function of ACC-cd are
commonly noted in psychiatric illnesses characterized by symptoms
of comprised cognitive control, such as schizophrenia (Fornito et al.,
2008, 2006; Kerns et al., 2005; Wang et al., 2004), attention-deficit/
hyperactivity disorder (ADHD) (Bush et al., 1999; Dickstein et al.,
2006; Makris et al., 2008) and autism spectrum disorders (ASD) (Di
Martino et al., 2009).
The hemispheric asymmetries in structure and function are
commonly observed in multiple lateral cortical areas including
Broca's speech area, planum temporale and posterior parietal cortex
(PPC), which are thought to reflect the asymmetries of behavioral
traits in language, auditory perception, and sensory acuity, respec-
tively (Toga and Thompson, 2003). Although less commonly cited,
structural studies suggest the presence of hemispheric asymmetries
in various aspects of ACC morphometry. For example, the gray
matter volume (Huster et al., 2007; Paus et al., 1996a) for ACC is
greater in the right hemisphere than the left. Also, the anterior
portion of the cingulum bundle shows a significant greater
fractional anisotropy (FA) in the left hemisphere than the right
(Gong et al., 2005; Kubicki et al., 2003; Park et al., 2004; Wang et
al., 2004). Finally, the paracingulate sulcus (PCS) is found to be
more commonly detected and more pronounced in the left ACC
region compared to the right (Huster et al., 2007; Paus et al., 1996b;
Yucel et al., 2001).
NeuroImage 47 (2009) 1579–1589
⁎ Corresponding authors. Y. Zang is to be contacted at State Key Laboratory of
Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
Fax: +86 10 5880 1023. D. Zhang, Institute of Mental Health, Peking University, Beijing
100191, China. Fax: +86 10 6207 8246.
E-mail addresses: firstname.lastname@example.org (D. Zhang), email@example.com (Y. Zang).
1These authors contributed equally to the current work.
1053-8119/$ – see front matter © 2009 Elsevier Inc. All rights reserved.
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While the majority of studies examine the structural hemispheric
asymmetries of the ACC, a few studies have noted possible
asymmetries in ACC function. For example, Stephan et al. (2003)
found that left ACC-cd showed increased coupling with the left
inferior frontal gyrus for cognitive control during a language-
processing task, whereas the right ACC-cd showed increased coupling
with the right parietal areas during a visuospatial-processing task.
Thus, Stephan et al. (2003) suggested that cognitive control function
of ACC-cd was localized in the same hemisphere as the other brain
regions implicated in the specific task execution. There was another
study that reported lateralized ACC-cd function (Lutcke and Frahm,
2008). They found that while a bilateral distribution of error-related
processes in ACC, correct inhibitions only seemed to activate ACC in
the right hemisphere. Alternatively, in a study of response-eligibility
in theStrooptask, Milhametal. (2001)foundevidenceofgreater right
hemisphere involvement in cognitive control when conflicts arose at
the response level, as opposed to non-responselevels. Similarly Braver
et al. (2001) suggested that processes such as response inhibition,
target detection and response selection rely primarily on a right-
Of note, the ACC-cd is rarely considered in isolation, as it is part
of a distributed attention network, maintaining strong reciprocal
interconnections with regions such as lateral prefrontal cortex (BA
46/9), parietal cortex (BA 7), and premotor and supplementary
motor areas (Bush et al., 2000). Hence, functional connectivity
approaches, which place greater emphasis on interactions between
regions, may be ideally suited for examining ACC function. In
support of this notion, recent studies examining functional
connectivity during rest have demonstrated the ability of resting-
state functional connectivity to map out ACC circuitries in high
detail (Margulies et al., 2007), and detect ADHD-related differences
in connectivity (Castellanos et al., 2008; Tian et al., 2006). Thus, we
believed resting-state functional connectivity would be an effective
approach to investigate the functional hemispheric asymmetries of
ACC as well.
Here we take a novel approach to investigate the hemispheric
asymmetries in the ACC-cd. Specifically, by using functional MRI, we
employ recently emerging resting-state functional connectivity
approach to map the functional networks associated with ACC-cd in
each of the two hemispheres, and test for the presence of differences.
Based on the evidence of morphological, structural and task activation
asymmetry found by the previous MRI and DTI studies, we hypothe-
sized that ACC-cd should show significant hemispheric asymmetry of
resting-state functional connectivity. In this study, we focus our
interests on the asymmetry of intrahemispheric functional connectiv-
ity of the ACC-cd because the interhemispheric fiber connections are
much less than the intrahemispheric ones. We hypothesized that the
right ACC-cd, which appears to play a more specific role in response-
oriented cognitive processes, may show greater intrahemispheric
Materials and methods
Sixty-four healthy undergraduate or graduate students (20.9±
1.6 years old, 37 females) of Beijing Normal University gave their
written informed consent to participate in this study, which was
approved by the Institutional Review Board of State Key Laboratory of
Cognitive Neuroscience and Learning, Beijing Normal University. All
participants were right-handed assessed with Edinburgh Handedness
Inventory (Oldfield, 1971), and without a history of head injury,
psychiatric or neurological disorder, alcohol or substance abuse based
on their self-report. Fifteen participants (10 females) were excluded
for excessive head movement in the subsequent data analysis (see
fMRI data preprocessing).
Imaging was carried out on a Siemens 3.0 Tesla Trio MR
scanner at the Beijing Normal University Imaging Center for Brain
Research. Head motion was minimized with foam pads. Thirty-
three axial slices with echo planar imaging (EPI) (time repetition
[TR]/time echo [TE]=2000/30 ms, thickness/gap=3/0.6 mm,
matrix=64×64, field of view [FOV]=200×200 mm2, acquisition
voxel size=3.125×3.125×3.6 mm3, flip angle=90°, 240 volumes)
were acquired. Participants were instructed to close their eyes, relax,
and move as little as possible during the resting-state scan. Other
scans included in the imaging sessions were high-resolution T1-
weighted (MP-RAGE) anatomical images and a few cognitive task
scans, which were not used in the current study. Cognitive processing
may have an effect on the resting-state networks (Waites et al.,
2005). Therefore, in the current study the resting-state scan was
acquired after the localizer session and before the other sessions.
Analysis of hemispheric asymmetry of resting-state functional
We propose a novel approach to investigate the hemispheric
asymmetry of functional connectivity of the ACC-cd. Our approach
consists of five fundamental stages: 1) Creating symmetric brain
template and masks for spatial normalization and confounding
timeseries extraction; 2) Defining regions of interest (ROIs) of the
ACC-cd as two symmetric ACC parcellation regions derived from an
anatomical template; 3) Preprocessing resting-state fMRI data; 4)
Generating individual functional connectivity maps of the left ACC-cd
and the right ACC-cd ROIs respectively; 5) Performing a paired t-test
between the left ACC-cd functional connectivity maps and the LR-
flipped right ACC-cd functional connectivity maps. The methods are
fully described in the subsequent paragraphs.
Creating symmetric template and masks
In order to create a symmetric EPI template for spatial normal-
ization, the EPI template available in the SPM2 (Statistical Parametric
Mapping) software package (http://www.fil.ion.ucl.ac.uk/spm/) was
flipped horizontally in the midsagittal plane (X=0), i.e., LR-flipped,
then an average image from the original and the LR-flipped EPI
templates was created, which was in the same way as that used in a
gray matter asymmetry study (Luders et al., 2004). In addition, we
created three sets of symmetric masks, i.e., the entire brain, white
matter (WM) and cerebrospinal fluid (CSF), for the nuisance
covariates extraction (see Functional connectivity analysis). The
processes of creating the masks were as follows. Firstly, the a priori
probability maps of the entire brain, WM and CSF provided by the
SPM2 package were thresholded to ensure 60%, 90% and 50% tissue
type probability, respectively. Then the binary maps of the entire
brain, WM and CSF were LR-flipped. The final symmetric masks of the
entire brain, WM and CSF were created by merging the original binary
maps and their LR-flipped counterparts.
Definition of regions of interest
The ROIs of bilateral ACC-cd were defined according to the
Automated Anatomical Labeling (AAL) template (Tzourio-Mazoyer
et al., 2002) which was obtained from software MRIcro (http://www.
psychology.nottingham.ac.uk/staff/cr1/mricro.html). First, the masks
of the bilateral anterior and middle cingulate cortex were extracted
from the AAL template. It should be noted that the masks of the left
and right cingulate cortex are not the same size in the AAL template
(see Fig. 1A). To make the bilateral ROIs more comparative, we first
created symmetric cingulate cortex masks by cutting off any
asymmetric part of the two masks (see Fig. 1B). Then the symmetric
cingulate cortex masks were resampled to 3×3×3 mm3for the
subsequent definition of ACC-cd. The ROIs of bilateral ACC-cd were
H. Yan et al. / NeuroImage 47 (2009) 1579–1589
further defined as regions posterior to the vertical plane at the
anterior boundary of the genu of the corpus callosum, anterior to a
vertical plane through the anterior commissure (Bush et al., 2000),
superior to a horizontal plane through the anterior boundary of the
genu of the corpus callosum (Koski and Paus, 2000; Margulies et al.,
2007), and between X coordinates of [−9, −3] and [3, 9], respectively,
to keep the ROIs mostly in the gray matter (see Fig. 1C). The superior
boundary of the symmetric cingulate cortex masks was used as the
superior border of the ROIs. Each ROI covered 200 voxels (5400 mm3).
fMRI data preprocessing
The fMRI data preprocessing, including slice timing correction,
motion correction, and spatial normalization, was performed using
SPM2 software after the first 10 volumes were discarded. Each
participant's head motion parameters were examined. Datasets with
head motion of more than 1 mm maximum translation or 1°
maximum rotation in any direction were discarded. Excessive move-
ment was found for 15 participants, who were then excluded, with
data for the remaining 49 participants (27 females) being used for
subsequent analysis. During the spatial normalization each dataset
was transformed to the symmetric EPI template described above.
Linear detrending and temporal bandpass filtering (0.01–0.08 Hz)
were carried out using a homemade software package named Resting-
State fMRI Data Analysis Toolkit (REST, by SONG Xiao-Wei et al.,
http://resting-fmri.sourceforge.net). Spatial smoothing was carried
out with a 6-mm full-width half-maximum (FWHM) Gaussian filter
using AFNI (Analysis of Functional NeuroImages) software (http://
Functional connectivity analysis
Functional connectivityanalyses for the bilateral ACC-cd ROIs were
carried out by using the partial correlation as implemented in AFNI
program 3dfim+ (see program 3dfim+ by B.D. Ward, http://afni.
nimh.nih.gov/pub/dist/doc/manual/3dfim+.pdf). For each partici-
pant, the timeseries for each of the two ROIs, left ACC-cd (LACC-cd)
and right ACC-cd (RACC-cd), was obtained by averaging the fMRI
timeseries of all voxels within each ROI. The timeseries of nuisance
covariates were extracted from the symmetric masks of the entire
brain, WM and CSF, respectively, by averaging over all voxels within
the masks. Partial correlation analysis was carried out between the
timeseries of each ACC-cd ROI and that of each voxel of the brain, with
nine nuisance covariates including six head motionparameters, global
signal, WM, and CSF. The partial correlation coefficient between two
timeseries adjusting for the nine nuisance covariates is equivalent to
the correlation coefficient between the two orthogonalized timeseries
with respect to these covariates. To improve normality, these partial
correlation coefficients were then transformed to Z-values using the
Fisher's z-transformation (Lowe et al., 1998).
Individual Z-maps were entered into one-sample t-tests in a
voxel-wise manner to determine the brain regions showing
significantly positive or negative correlation with LACC-cd and
RACC-cd, respectively. The uncorrected pb0.001 (|t|N3.5) and
cluster sizeN17 voxels (459 mm3) were utilized, which correspond
to a corrected pb0.01 determined by Monte Carlo simulation (see
program AlphaSim by B.D. Ward, http://afni.nimh.nih.gov/pub/
In order to examine the hemispheric asymmetry of functional
connectivity, a series of processes was performed on the individual
LACC-cd and RACC-cd Z-maps. In the previous morphological
studies on ACC (Gong et al., 2005; Huster et al., 2007), the
hemispheric asymmetry was examined by directly comparing the
left ACC with the right ACC on some structural attributes, including
gray matter volume, fissurization of the cortex, fractional anisotropy
(FA) of the cingulum, etc. The case for the current study was more
complex than the aforementioned studies. Unilateral ACC-cd has
functional connectivity with brain regions of both the ipsilateral and
contralateral hemispheres. The following example illustrates how
the hemispheric asymmetry of functional connectivity of ACC-cd is
Taking the insula as an example, each of the right ACC-cd (RACC-
cd) and the left ACC-cd (LACC-cd) has functional connectivity with
both the right insula (Rinsula) and the left insula (Linsula). If we want
to investigate the hemispheric asymmetry of functional connectivity
of ACC-cd with insula, the comparison should be performed between
Fig.1. Definition of regions of interest (ROIs). (A) The asymmetric masks of bilateral anterior and middle cingulate cortex extracted from the AAL template. (B) The symmetric masks
of bilateral cingulate cortex. The procedure to make the symmetric masks is as follows. First, the left mask from the AAL template is flipped tothe right side, and then right symmetric
mask is created bygetting the intersection of LR-flipped left mask and right mask from the AAL template. Finally, the right symmetric mask was LR-flipped to get a left symmetric one.
(C) The borders of ROIs of bilateral ACC-cd: (1) anterior border, the vertical plane through anterior boundary of the genu of the corpus callosum; (2) posterior border, the vertical
plane through the anterior commissure; (3) superior border, the superior boundary of the symmetric masks of bilateral cingulate cortex; (4) inferior border, the horizontal plane
through the anterior boundary of the genu of the corpus callosum. Restriction of X coordinates between [−9, −3] and [3, 9] is to keep the ROIs mostly in the gray matter.
H. Yan et al. / NeuroImage 47 (2009) 1579–1589
the functional connectivity of RACC-cd-Rinsula and that of LACC-cd-
Linsula, or between RACC-cd-Linsula with LACC-cd-Rinsula.
To implement the comparisons mentioned above, we generated
a new set of Z-maps by LR-flipping the individual RACC-cd Z-maps,
namely LR-flipped RACC-cd Z-maps. Then the LACC-cd and LR-
flipped RACC-cd Z-maps entered into a paired t-test in a voxel-
wise manner to determine the brain showing significant differences
between the LACC-cd and LR-flipped RACC-cd Z-maps. The
resultant t-map represents the hemispheric asymmetry of func-
tional connectivity for ACC-cd. The right side of the t-map is taken
as the representation of the asymmetry of functional connectivity
of ACC-cd with its ipsilateral hemisphere (i.e., differences between
connectivity of LACC-cd-left hemisphere [Lcd-LH] and that of RACC-
cd-right hemisphere [Rcd-RH]), and the left side represents the
asymmetry of functional connectivity of ACC-cd with its contral-
ateral hemisphere (i.e., differences between connectivity of Lcd-RH
and that of Rcd-LH) (see Fig. 2 for an illustrative diagram).
Finally, the aforementioned t-map was corrected for multiple
comparisons by the following procedures to identify the significant
hemispheric asymmetry of functional connectivity of ACC-cd. The
corrected LACC-cd connectivity map and the LR-flipped corrected
RACC-cd connectivity map, which were obtained in the one-sample
t-tests on the individual Z-maps, were combined to produce a mask.
Multiple-comparison correction was performed within this mask by
using Monte Carlo simulation (see program AlphaSim by B.D. Ward,
Fig. 2. The procedures to examine hemispheric asymmetry of functional connectivity of ACC-cd. (A) Individual RACC-cd Z-maps. The schema on the first Z-map represents the
functional connectivityof RACC-cd with the brain regions in the right hemisphere(i.e., Rcd-RH). (B) Individual LACC-cd Z-maps. The schema represents the functional connectivityof
LACC-cd with the brain regions in the left hemisphere (i.e., Lcd-LH). (C) LR-flipped RACC-cd Z-maps. As indicated by the schema, the connectivity of RACC-cd with its ipsilateral brain
regions is flipped to the right side. (D) The t-map of paired t-test on LACC-cd Z-maps and LR-flipped RACC-cd Z-maps. The right side of the t-map represents the asymmetry of
functional connectivity of ACC-cd with its ipsilateral hemisphere (i.e., Lcd-LH vs. Rcd-RH), and the left side represents that with its contralateral hemisphere (i.e., Lcd-RH vs. Rcd-LH).
S1, S2, S3 … are individual Z-maps; Rcd = RACC-cd; Lcd = LACC-cd; cd = ACC-cd; LH = left hemisphere; RH = right hemisphere; IH = ipsilateral hemisphere.
H. Yan et al. / NeuroImage 47 (2009) 1579–1589
a corrected pb0.01 was utilized. Briefly, brain regions were
considered to show significant hemispheric asymmetry of functional
connectivity to ACC-cd if they survived the following two criteria:
(1) uncorrected pb0.001 at voxel level (|t|N3.5); (2) cluster sizeN14
voxels (378 mm3).
Functional connectivity network of ACC-cd
The patterns of functional connectivity for the left and right ACC-
cd regions were generally similar for both positive and negative
connectivity (Fig. 3).
Both the left and right ACC-cd exhibited positive connectivity
bilaterally with the dorsal ACC extending to part of rostral ACC (BA
24/32), dorsal posterior cingulate cortex (dPCC) (BA 23/31),
dorsolateral prefrontal cortex (DLPFC) (BA 9), dorsal medial
prefrontal cortex (dmPFC) (BA 10), supplementary motor area
(SMA) (BA 6), precuneus (BA 31), insula (BA 13), thalamus,
lentiform nucleus, caudate nucleus head and body, claustrum
nucleus, accumbens nucleus, red nucleus, substantia nigra, cerebel-
lum tonsil and culmen (see Tables S1 and S2).
Both the left and right ACC-cd exhibited negative connectivity
with bilateral the subgenual cingulate cortex (BA 25), orbitofrontal
cortex/ventral medial prefrontal cortex (vmPFC) (BA 10/11),
posterior cingulate cortex/precuneus (PCC/PCu), sensorimotor
cortex (BA 1/2/3/4/5), inferior and middle temporal gyrus (BA
20/21), fusiform (BA 37), inferior parietal lobule (IPL) (BA 39/40),
superior parietal lobule (SPL) (BA 7), visual cortex (BA 18/19),
amygdala, hippocampal and parahippocampal gyrus as well as
several cerebellar areas including pyramis, uvula and inferior
semilunar lobule (see Tables S1 and S2).
Hemispheric asymmetry of functional connectivity of ACC-cd
The hemispheric asymmetry of functional connectivity of ACC-cd
was evaluated through comparison of LACC-cd with LR-flipped RACC-
cd Z-maps. As described in Statistical analysis (see Fig. 2D), the
survived foci in the right side of the t-map were the brain regions that
cd; and foci in the left side of the t-map were the ones that showed
asymmetric functional connectivity with their contralateral ACC-cd.
The ACC-cd has fiber connections with its ipsilateral cerebral hemi-
spheric motor cortex (Morecraft and Van Hoesen 1992), frontal and
parietal cortex (Morecraft et al. 1993) and limbic system (Morecraft
and Van Hoesen 1998). And the ACC-cd also has fiber projections to its
contralateral cerebellar hemisphere via pontocerebellar projection
from the contralateral pontine nucleus (Brodal 1978; Schmahmann
and Pandya 1997; Timmann and Daum 2007). The connections of
ACC-cd with its contralateral cerebral hemisphere and ipsilateral
cerebellar hemisphere are more complex and indirect, and therefore
the current study mainly discussed on the functional connectivity of
ACC-cd with its ipsilateral cerebral hemisphere and contralateral
cerebellar hemisphere. However, all results are presented in the
Asymmetric connectivity with ipsilateral cerebral hemisphere
The ACC-cd showed significant asymmetric connectivity with its
ipsilateral cerebral hemisphere on several brain regions, which
included IPL (BA 40) extending to SPL (BA 7), superior frontal gyrus
(BA 10) extending to DLPFC (BA 9/46), superior, middle and inferior
frontal gyrus (BA 8/47), medial frontal gyrus (BA 11), paracentral
lobule/dorsal PCC (dPCC) (BA 5/31), inferior and middle temporal
gyrus (BA 20/21/37), etc. (see Fig. 4 and Table 1i).
Asymmetric connectivity with contralateral cerebellar hemisphere
foci in its contralateral cerebellar hemisphere, including declive,
Fig. 3. Functional connectivity networks of the left ACC-cd (A) and the right ACC-cd (B). The significantly positive (warmcolor) and negative(cool color) connectivity for the bilateral
ACC-cd seeds are illustrated in MNI space. Color bar indicates the t-value. LH = left hemisphere; RH = right hemisphere.
H. Yan et al. / NeuroImage 47 (2009) 1579–1589
Asymmetric connectivity with contralateral cerebral hemisphere and
ipsilateral cerebellar hemisphere
The ACC-cd also showed significant asymmetric connectivity with
several brain regions in its contralateral cerebral hemisphere and
ipsilateral cerebellar hemisphere. These brain regions were almost
mirror regions of those reported in the asymmetric connectivity with
ipsilateral cerebral and contralateral cerebellar hemisphere (see
details in Fig. 4, Table 1iii and iv).
Patterns of hemispheric asymmetry
In order to further evaluate the functional connectivity strength of
LACC-cd and RACC-cd, the maximum t-values were picked out from
the one-sample t-test maps of LACC-cd and LR-flipped RACC-cd
according to the coordinates of the peak voxels within each cluster
generated by paired t-test as shown in Table 1i and ii (Fig. 5A), as well
as Table 1iii and iv (Fig. 5C). Compared with the connectivity of LACC-
cd, the connectivity of RACC-cd with all the peak voxels within each
cluster in Fig. 5A was significantly larger in magnitude except one in
middle temporal gyrus, regardless of whether the connection was
positive or negative (i.e., positive and negative t-value in one-sample
t-test). As aforementioned, the foci in Table 1iii and iv were almost
mirror regions of those in Table 1i and ii, and they showed almost
reversed trend for the connectivityof LACC-cd and RACC-cd compared
with that in Fig. 5A (see Fig. 5C). As shown in Figs. 5B and D, LACC-cd
and RACC-cd showed asymmetric patterns of functional connectivity
strength with several brain areas in Figs. 5A and C. The mean t-values
were calculated from all voxels within each cluster, and the mean t-
value showed the similar pattern to that observed in the maximum t-
values in Fig. 5 (see Fig. S1).
Overall and major findings
Building upon the previous utility of resting-state approach in
mapping ACC circuitries, the current work used this approach to
understand the functional hemispheric asymmetries in the ACC-cd. In
line with a previous study (Margulies et al., 2007), we found that both
of the left and right ACC-cd had widespread positive and negative
functional connectivity with brain regions in the two hemispheres,
and the connectivity patterns for the left and right ACC-cd werehighly
similar. Despite the high degree of similarity, the functional networks
associated with LACC-cd and RACC-cd showed significantly asym-
metric patterns of connectivity strength in several brain regions, such
as DLPFC (BA 9/46), IPL (BA 40), SPL (BA 7), dPCC (BA 31), and the
cerebellar tonsil. Specifically, for these areas, we found significantly
greater intrahemispheric connectivity strength with ACC-cd in the
right hemisphere than the left, regardless of whether the connection
was positive or negative. Thus, our data highlights the presence of
clear asymmetries of connectivity strength in functional networks
associated withACC-cd,andthispatternis verylikelytobe a reflection
of the functional lateralization of ACC-cd function.
Short summary on positive and negative connectivity
The resting-state functional connectivity technique delineates
functional networks typically observed in task activation-based
studies (Fox and Raichle, 2007). Recently, a hypothesis was proposed
that the correlated spontaneous activities between different brain
Fig. 4. Brain regions showing significant hemispheric asymmetry of functional connectivity with ACC-cd. The foci in the right side show the significant asymmetric functional
connectivity with their ipsilateral ACC-cd, and the foci in the left side show the significant asymmetric functional connectivity with their contralateral ACC-cd. Color bar indicates the
t-value. IH = ipsilateral hemisphere; CH = contralateral hemisphere.
H. Yan et al. / NeuroImage 47 (2009) 1579–1589
regions could be a result of longstanding history of coactivation, e.g.,
the coactivation during everyday activity or across many cognitive
tasks (Dosenbach et al., 2007; Fair et al., 2008, 2007). Therefore, based
on the reasonable speculation, it could be helpful to combine the
findings of the previous studies of ACC's activation to discuss the
possible mechanism of our resting-state functional connectivity
results. In addition, we reported negative correlations in the LACC-
cd and RACC-cd resting-state networks, and the asymmetric ACC-cd
functional network. In the current study, we used a global signal
regression in the preprocessing step to remove potential physiological
noise as several previous studies did (Fox et al., 2005; Fransson, 2005;
Greicius et al., 2003). However, in a recent study, Murphy et al. (2009)
suggested that global signal regression could potentially lead the
voxels with an extreme lack of a relationship with a seed region (i.e.,
r=0) to be detected as having negative relationship instead.
Considering the precise mechanism underlying negative correlations
remains unknown and the negative connectivity was the minority in
the asymmetric ACC-cd functional network, we mainly discussed in
the following on the positive connectivity associated with LACC-cd or
RACC-cd in the asymmetric ACC-cd resting-state network.
Our findings revealed a greater positive correlation between the
right ACC-cd and right DLPFC than that between the left ACC-cd and
left DLPFC. Functional coupling of ACC with lateral prefrontal cortex
for implementing cognitive control was supported by a broad array of
studies (e.g., Badre and Wagner, 2004; Botvinick et al., 2004; Bunge
et al., 2004; Carter and van Veen, 2007; Kondo et al., 2004a, 2004b;
MacDonald et al., 2000; Milham et al., 2001; Stephan et al., 2003). For
example, a coupling between the ACC and the left prefrontal cortex
was reported in a language-processing task (Stephan et al., 2003);
ACC also appears to couple with right DLPFC during certain forms of
declarative memory retrieval (Bunge et al., 2004). However, the
characteristic of the hemispheric lateralization for the connectivity of
ACC and DLPFC is not well understood. Of note, hemispheric
lateralization of regional activation in DLPFC has been revealed by
several studies, e.g., the left DLPFC was associated with strategic
control/attentional biasing processes (Carter and van Veen, 2007;
MacDonald et al., 2000; Milham et al., 2001), whereas the right
DLPFC was related to response inhibition and conflict monitoring
(Garavan et al., 2002; Kerns et al., 2004; Milham et al., 2001). The
ACC-cd is also thought to be involved in the cognitive control
processes of response inhibition and conflict monitoring (e.g., Bush et
al., 2000; Carter et al., 2000; Milham and Banich, 2005). Moreover,
activation in the right DLPFC is directly related to the right ACC
activation, that is to say, the right ACC detects response conflict and
signals to the right DLPFC to resolve it (Bunge et al., 2004; Carter and
van Veen, 2007; Garavan et al., 2002; Kerns et al., 2004). Milham et
al. (2001) found that the left DLPFC activation was observed under
attentionally demanding conditions in a Stroop task, even when no
response conflict was present. Further, Garavan et al. (2002) also
found that the activation in the left prefrontal region was present for
the entire block of trials with or without response conflict. Therefore,
based on the findings of this resting-state functional connectivity
study and previous studies involving cognitive tasks, we speculate
that the involvement of the right DLPFC in execution of cognitive
control may be closely linked to the ACC function in response-level
control processes, while in contrast, the left DLPFC may act more
independently in strategic attentional control. This hypothesis needs
to be tested by further studies.
The posterior parietal cortex (PPC), including IPL (BA 40) and
SPL (BA 7), was an important brain area showing asymmetric
connectivity with ipsilateral ACC-cd in our study. The function of
PPC has been characterized in terms of space-based attention and
motor intention (Andersen and Buneo, 2002), as well as successful
memory retrieval (Konishi et al., 2000; Leube et al., 2003; Shannon
and Buckner, 2004). The lateralized function of the parietal cortex
has been reported by several studies, e.g., the right parietal cortex
has been proposed to play a specific role in visual-spatial attention
(Sturm et al., 2006; Weiss et al., 2006), and the left parietal cortex
has more often been thought to participate in memory retrieval
(Hofer et al., 2007; Konishi et al., 2000; Leube et al., 2003). A
recent study by Liston et al. (2006) observed a strong association
between the ACC and the PPC, and indicated a function of the PPC
in conflict detection. They found that the PPC activity could predict
subsequent DLPFC activity and behavioral adjustments, as the ACC
did (Kerns et al., 2004). The bilateral ACC and PCC were engaged in
the cognitive control associated with task switching, only the right
ACC and the right PPC were sensitive to conflict processing. In the
current study, greater positive ACC-cd/PCC and ACC-cd/DLPFC
connectivity was both in the right hemisphere. Thus we may
speculate that the conflict-monitoring network is right-hemisphere
Brain regions showing significantly hemispheric asymmetry of functional connectivity
Regions BA Coordinatesa
i. Asymmetric connectivity with ipsilateral cerebral hemisphere
Inferior parietal lobule extending
Supplementary motor area
Superior frontal gyrus extending
Inferior frontal gyrus
Inferior temporal gyrus
Superior frontal gyrus
Middle temporal gyrus
ii. Asymmetric connectivity with contralateral cerebellar hemisphere
Inferior semilunar lobule
iii. Asymmetric connectivity with contralateral cerebral hemisphere
Inferior frontal gyrus
Supplementary motor area
Superior frontal gyrus
extending to DLPFC
Inferior parietal lobule
Superior temporal gyrus
Superior temporal gyrus
Orbital prefrontal cortex
Middle temporal gyrus
Superior frontal gyrus
iv. Asymmetric connectivity with ipsilateral cerebellar hemisphere
BA, Brodmann area; SPL, superior parietal lobule; DLPFC, dorsolateral prefrontal cortex;
dPCC, dorsal posterior cingulate cortex.
aThe peak voxel in MNI coordinates.
bThe positive or negative sign of x coordinate has been omitted to avoid confusion,
because it does not indicate right or left hemisphere in the current study.
cMinimum cluster size: 14 voxels (378 mm3).
H. Yan et al. / NeuroImage 47 (2009) 1579–1589
Additional hemispheric asymmetries were noted in the functional
connectivity of the ACC-cd with the ipsilateral dPCC (BA 31).
Specifically, the positive connectivity between RACC-cd and the
right dPCC was stronger than that between LACC-cd and the left
dPCC. Based on the neuronal architecture, the PCC can be divided into
a dorsal and a ventral division, of which the dPCC comprises BA 23d,
dorsal BA 23a/b/c and adjacent BA 31; the vPCC comprises ventral
23a/b and caudal BA 31 (Vogt et al., 2006). Based on the evidence
from neuroimaging studies (Blakemore et al.,1998; Inoue et al.,1998;
Maguire et al., 1997, 1998), dPCC has been implicated in self-
orientation in visual space, and is a part of the dorsal visual pathways
involved in spatial attention network (Vogt et al., 2006). It has been
proposed that the spatial attention network is a predominantly right-
lateralized cortical network, and the ACC is involved in it (Sturm et al.,
2006). Vogt et al. (2006) reported a highly correlated activity in the
dPCC and the ACC-cd. Thus, our finding of stronger functional
connectivity between RACC-cd and the right dPCC than that between
LACC-cd and the left dPCC could be taken as further evidence of a
right-lateralized spatial attention network.
In the aforementioned discussion, we have speculated that the
right ACC-cd should be implicated in a right-lateralized cognitive
control network. For the cerebellum, the right ACC-cd had stronger
positive connectivity with several contralateral cerebellar regions
than the left ACC-cd did. It is well known that the cerebellarcortex has
anatomical connections with its contralateral cerebral cortex (Brodal
1978; Schmahmann and Pandya 1997; Timmann and Daum 2007).
The stronger functional connectivity between the right ACC-cd and
Fig. 5. (A) Connectivity strength of LACC-cd and RACC-cd with the regions in its ipsilateral cerebral hemisphere and contralateral cerebellar hemisphere. (B) Pattern of the asymmetric
connectivitystrengthof theLACC-cdandRACC-cdwiththe regionsinits ipsilateralcerebralhemisphere andcontralateralcerebellarhemisphere.(C)Connectivitystrengthof LACC-cdand
RACC-cd with theregionsinits contralateralcerebralhemisphere and ipsilateralcerebellarhemisphere.(D) Patternof asymmetric connectivitystrength ofLACC-cd andRACC-cd with the
regionsinits contralateralcerebralhemisphere and ipsilateral cerebellarhemisphere. Theconnectivitystrengthin(A)and(C) isindicatedbythet-value of the peak voxelof thefoci(peak
voxelt-value). Several brain regions are selected to illustrate the asymmetric patterns of ACC-cd networks in(B) and(D). The solid lines andbroken lines indicate the significantand non-
significantconnectivity,respectively(one-samplet-test,pb0.001.seedetailsinStatisticalanalysis).Thethickerlinesindicate largert-valuecomparedwiththehomologousconnectivity in
contralateral hemisphere. Rcd = RACC-cd; Lcd = LACC-cd; IPL = inferior parietal lobule; SMA = supplementary motor area; DLPFC = dorsolateral prefrontal cortex; dPCC = dorsal
posterior cingulate cortex; ITG = inferior temporal gyrus; SFG = superior frontal gyrus; PCu = precuneus; MTG = middle temporal gyrus; RG = rectal gyrus; PPG = parahippocampal
gyrus; ISL = inferior semilunar lobule; IFG = inferior frontal gyrus; PM = premotor area; STG = superior temporal gyrus; OPFC = orbital prefrontal cortex.
H. Yan et al. / NeuroImage 47 (2009) 1579–1589
the left cerebellar hemisphere may indicate that the left cerebellar
hemisphere may be a part of less motor-oriented cognitive networks
(Haarmeier and Thier, 2007). Our findings may supply the functional
connectivity evidence for the lateralized cerebellar function in
cognitive processing, e.g., the left cerebellar hemisphere is thought
to be associated with visuo-spatial function and the right cerebellar
hemisphere with language processing (Timmann and Daum, 2007).
Such speculations clearly merit further investigation.
Possible relationship between the functional and structural asymmetry
We have interpreted our findings of the hemispheric asymmetryof
functional connectivity of the ACC-cd from a view of task activations.
However, it should be noted that the gray matter volume has been
found greater in the right ACC than in the left one (Huster et al., 2007;
Paus et al.,1996a). Larger gray matter in the right ACC-cd might have
more fiber projections, which might be one source of the right-
lateralizedfunctionalconnectivitystrengthfoundin thecurrent study.
Therefore, it is possible that the structural (both gray matter volume
and fiber connections) hemispheric asymmetry of ACC-cd underlies
the hemispheric asymmetry of functional connectivity. It will be an
intriguing topic to combine the functional connectivity, gray matter
volume and DTI fiber tractography together to further investigate the
hemispheric asymmetry of the ACC-cd.
Signal contamination between hemispheres
The left and right ACC, separated by the interhemispheric fissure,
are close to each other. One concern is the cross-hemispheric
contamination of the fMRI signals of the ROIs due to the partial
volume effect and the spatial smoothing. To reduce potential
contamination, the medial boundaries of bilateral ROIs of ACC-cd in
the current study were 6 mm apart (see Fig. 1C). Of note, the spatial
smoothing with a 6-mm Gaussian kernel may still cause the signal
contamination of the bilateral ACC-cd. We repeated our analyses with
the mean timeseries of the ROIs being extracted before spatial
smoothing to avoid possible confounds from the spatial smoothing.
The results were nearly identical to the original ones (i.e., extracting
the timeseries of ROIs after spatial smoothing), with some subtle
differences being noted (see Fig. S2).
Gender effect on the ACC asymmetry has been inconsistent in
previous studies. While the significant gender effect has been found to
influence the fissurization variations of the ACC (Huster et al., 2007;
Yucel et al., 2001), no gender effect was observed on the volume and
the glucose metabolism asymmetries of the ACC (Good et al., 2001;
Willis et al., 2002). As the gender effect was not the main purpose of
the current study, we did not take the gender into account in our main
procedures of data analysis. Afterwards, we examined the gender
effect on functional connectivity of bilateral ACC-cd and asymmetry of
the ACC-cd networks. While there were some gender effects on
functional connectivity of the left and right ACC-cd, respectively, we
found no significant gender effect on hemispheric asymmetry of the
ACC-cd networks even a more liberal p-value (pb0.05, corrected) was
used (see Fig. S3 and Table S3).
Innovation and potential application in mental disorders
In neuroimaging studies, hemispheric asymmetry has been
observed in human brain in terms of structure and function (Toga
and Thompson, 2003). The most frequently used method is to
calculate the laterality or asymmetry index, i.e., directly comparing
quantitative parameters of structure or activations of bilateral
homologous regions (e.g., volume or number of activated voxels)
(Luders et al., 2004; Lutcke and Frahm, 2008; Steinmetz, 1996).
Functional connectivity analysis has been widely used in the resting-
state fMRI data to study the task-independent networks in physiolo-
gical (Biswal et al., 1995; Cordes et al., 2001; Lowe et al., 1998) and
pathological states (Lowe et al., 2002; Wang et al., 2006; Zhou et al.,
2008). However, fewstudies have focused on hemispheric asymmetry
of the brain networks. In the current study, using resting-state
functional connectivity, we proposed a novel strategy to explore the
hemispheric asymmetry of network. We focused our interest on the
ACC-cd, an important division of the ACC, and revealed significant
asymmetrycorrelation of ACC-cdwith multiple brain regions. The ACC
is notonlyan important region in cognitivecontrol but also a common
affected region in mental disorders. The lesions studies suggested that
the combination of lesions in the ACC and other areas could cause
severe mental disorders such as schizophrenia (Devinsky et al.,1995).
Schizophrenia has been found to show abnormalities in structural
hemispheric asymmetries of the ACC (Albanese et al.,1995; Le Provost
et al., 2003; Park et al., 2004; Wanget al., 2004; Yucel et al., 2002), and
the abnormal hemispheric dominant of the ACC in schizophrenia was
suggested to be related with their impaired executive function
(Fornito et al., 2006). Therefore, it could be a promising approach to
use the functional connectivityanalysis to investigate the hemispheric
asymmetry of the brain networks in physiological and pathological
states in the future.
Limitations and future works
First, the participants in the present work are primarily college
students. As such, our results may not generalize to older populations.
Future work may be merited to examine the impact of age on ACC
circuitry. Second, we did not investigated the hemispheric asymmetry
of functional connectivity of other divisions of the ACC which were
beyond the scope of the present work, such as the affective division of
ACC. The affective division of ACC has been implicated in some normal
emotional processing and a few affective disorders. Therefore, there is
no doubt that an investigation of the affective division in the future
studies is necessaryand important.Third, wedid not makeany further
discussion on the findings of the contralateral cerebral and ipsilateral
cerebellar areas because there are less direct fiber connections
between the ACC-cd and those areas. Although the relationship
between the ACC-cd and those areas is more complex, the findings
should have underlying significance and need to be further studied in
the future. Finally, the interpretation of negative correlation is still an
the current study. Therefore, we did not further discuss the negative
Concluding and remarks
In summary, the present study provided further evidence that the
resting-state functional connectivity network of the ACC-cd consisted
of positive network and negative network, including widespread
cortical and subcortical areas. More importantly, our findings
demonstrated that left and right ACC-cd showed significant asymme-
tries in the strength of functional connectivity with multiple cortical
brain areas during rest that appear to be reflective of patterns of
coactivation observed in task-based studies. The analysis approach of
network asymmetry could be a promising candidate for investigating
various mental disorders.
This work was supported by the National Natural Science
Foundation of China (30530290, 30770594, 30621130074); the
National Basic Research Program of China (2007CB512301); and the
Capital Foundation of Medical Developments (2007-1001).
H. Yan et al. / NeuroImage 47 (2009) 1579–1589
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
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