Immature integration and segregation of
emotion-related brain circuitry in young children
Shaozheng Qina,1, Christina B. Younga,2, Kaustubh Supekara, Lucina Q. Uddina, and Vinod Menona,b,c,d,1
aDepartment of Psychiatry and Behavioral Sciences,bNeurology and Neurological Sciences,cProgram in Neuroscience, anddStanford Institute for
Neuro-Innovation and Translational Neurosciences, Stanford University School of Medicine, Stanford, CA 94304
Edited by Marcus E. Raichle, Washington University in St. Louis, St. Louis, MO, and approved March 29, 2012 (received for review December 12, 2011)
The human brain undergoes protracted development, with dra-
matic changes in expression and regulation of emotion from child-
hood to adulthood. The amygdala is a brain structure that plays
a pivotal role in emotion-related functions. Investigating develop-
mental characteristics of the amygdala and associated functional
circuits in children is important for understanding how emotion
processing matures in the developing brain. The basolateral amyg-
dala (BLA) and centromedial amygdala (CMA) are two major amyg-
dalar nuclei that contribute to distinct functions via their unique
pattern of interactions with cortical and subcortical regions. Almost
nothing is currently known about the maturation of functional
circuits associated with these amygdala nuclei in the developing
brain. Using intrinsic connectivity analysis of functional magnetic
resonance imaging data, we investigated developmental changes
in functional connectivity of the BLA and CMA in twenty-four 7- to
9-y-old typically developing children compared with twenty-four
19- to 22-y-old healthy adults. Children showed significantly
weaker intrinsic functional connectivity of the amygdala with sub-
cortical, paralimbic, and limbic structures, polymodal association,
and ventromedial prefrontal cortex. Importantly, target networks
associated with the BLA and CMA exhibited greater overlap and
weaker dissociation in children. In line with this finding, children
showed greater intraamygdala connectivity between the BLA and
CMA. Critically, these developmental differences were reproduc-
ibly identified in a second independent cohort of adults and chil-
dren. Taken together, our findings point toward weak integration
and segregation of amygdala circuits in young children. These im-
mature patterns of amygdala connectivity have important impli-
cations for understanding typical and atypical development of
emotion-related brain circuitry.
anced cognitive and affective abilities (1, 2). In particular, human
capabilities for a wide range of affective functions change dra-
matically from childhood to adulthood, suggesting remarkable
core ofbrain’s affective processingsystems (3,4) andmultiplelines
of research in human adults have implicated amygdala-centered
networks in emotion (5–7). The amygdalar complex encompasses
multiple anatomical subregions with distinct connectivity profiles
that support distinct affective functions (3, 8). However, almost
mature with development. Knowledge of how major amygdalar
into typical development of affective functions, but also for un-
derstanding the ontogeny of affective dysfunction in disorders
such as anxiety and depression (9, 10).
The basolateral amygdala (BLA) and centromedial amygdala
(CMA) are two major groups of amygdalar nuclei that form
dedicated networks for distinct functions via their unique pattern
of interactions with other cortical and subcortical structures (3,
4). The CMA is essential for controlling the expression of fear
responses, such as freezing behaviors, through projections to
subcortical structures including thalamus, hypothalamus, stria-
tum, brainstem, and cerebellum. The BLA, in contrast, plays
rom childhood to adulthood, the development of functional
brain networks underlies the maturation of increasingly nu-
a critical role in perception, evaluation, and regulation of emo-
tionally salient stimuli via its abundant projections to widely
distributed cortical regions (3, 4). Most of our knowledge of BLA
and CMA functions and connectivity is based on animal models,
thus the role of subregions of the amygdala in humans is still
poorly understood. In humans, these major amygdala nuclei have
only recently been anatomically delineated using cytoarchitec-
tonic mapping studies on postmortem brains (11). Importantly,
observer-independent cytoarchitectonically defined probabilistic
maps of the amygdala subregions have been established in standard
stereotaxic space (12), allowing delineation of nonoverlapping
amygdala subregions in a quantitatively rigorous manner (13, 14).
Using novel in vivo intrinsic connectivity analysis we recently
demonstrated dissociable patterns of functional connectivity of
BLA and CMA in adults (14), consistent with the wealth of
observations in rodents and monkeys (3, 8). Almost nothing is
currently known about the nature of functional circuits associ-
ated with the BLA and CMA in children and how they mature
from childhood to adulthood.
There is now increasing evidence to suggest that brain matu-
ration is characterized by increased integration and differentia-
tion within functional circuits, a process mediated by a complex
interplay of strengthening of long-range wiring, increasing of
regional neuronal specialization, and experience-dependent
plasticity (15, 16). These processes have been validated by other
empirical observations, including widespread increases in mye-
lination of white matter, and the initial increase in thickness and
then slower thinning of gray matter, which occurs from child-
hood through adolescence to adulthood (2, 17). Intrinsic func-
tional connectivity is a powerful tool for investigating how
functional brain circuits mature with age (18, 19). One key
finding that has emerged from this literature is the demonstra-
tion of strengthening links among cortical regions and weakening
connections between subcortical and cortical regions between
childhood and adulthood (20). Very little is known, however,
about the maturation of cortical and subcortical connections
related to the brain’s core emotion processing systems. Our study
addresses this important gap by examining developmental
changes in overall amygdala connectivity as well as the segre-
gation of functional circuits associated with the BLA and CMA,
its two major subdivisions.
We used resting-state functional magnetic resonance imaging
(fMRI) to examine developmental changes in the intrinsic con-
nectivity of amygdalar nuclei in 7- to 9-y-old typically developing
children compared with 19- to 22-y-old healthy adults. This
Author contributions: S.Q. and V.M. designed research; S.Q. and C.B.Y. performed
research; S.Q. and K.S. analyzed data; and S.Q., L.Q.U., and V.M. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1To whom correspondence may be addressed. E-mail: firstname.lastname@example.org or menon@
2Present address: Department of Psychology, Northwestern University, Evanston, IL 60208.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
www.pnas.org/cgi/doi/10.1073/pnas.1120408109 PNAS Early Edition
| 1 of 6
method has been widely used to identify spontaneously coupled
and connected networks of brain regions (21, 22), and provides
a unique way to examine differential patterns of amygdala con-
nectivity over development. Intrinsic functional connectivity of
the amygdala was examined by using observer-independent
cytoarchitectonically determined probabilistic maps of the
BLA and CMA (14). Functional imaging data from a second
independent cohort of young children and adults were used in
replication analyses to confirm the stability and robustness of the
observed developmental effects. We hypothesized that amygdala
connectivity would be weaker and less well differentiated in
children compared with adults.
Weaker Amygdala Connectivity in Children Compared with Adults.
We first examined overall developmental changes in intrinsic
functional connectivity of the amygdala by combining all voxels
in BLA and CMA. In adults and children, we found that intrinsic
activity of the amygdala was positively correlated with activity in
a distributed set of cortical, subcortical, paralimbic, and limbic
structures, as well as the cerebellum (Fig. 1 A and B). A direct
group comparison collapsing across BLA and CMA revealed
weaker intrinsic functional connectivity of the amygdala with
multiple distributed cortical, subcortical, paralimbic, and limbic
structures in children compared with adults (Fig. 1C). Neo-
cortical regions included unimodal and polymodal association
areas, the middle frontal gyrus, and ventromedial prefrontal cor-
tex; limbic and paralimbic structures included the cingulate gyrus,
insula, and parahippocampal gyrus; and subcortical structures in-
cluded the caudate, putamen, midbrain/brainstem, and cerebellum
(SI Appendix, Tables S1 and S2). No brain areas showed greater
connectivity in children, compared with adults. These results
indicate that intrinsic connectivity of the amygdala with a widely
distributed set of cortical and subcortical structures is weaker in
children compared with adults.
Weaker Segregation of BLA and CMA Connectivity in Children
Compared with Adults. We next examined the maturation of
amygdala subregional connectivity segregation, by comparing
functional connectivity of the BLA and CMA in children and
adults in a repeated-measure analysis of variance (ANOVA), with
subregions (BLA vs. CMA) and hemisphere (left vs. right) as
subject factor. We found significant interactions between group
and subregions in several subcortical structures including thala-
mus, hippocampus, caudate, and cerebellum, as well as widely
distributed neocortical areas that included somatosensory cortex,
superior and middle temporal gyri, and posterior visual and in-
ferior temporal cortices (Fig. 2A) (SI Appendix, Table S3).
Further analyses revealed that in adults, the BLA had stronger
connectivity with widely distributed unimodal and polymodal
association areas, including perirhinal cortex, temporal pole,
superior and middle temporal gyri, motor and somatosensory
areas, and primary visual cortex, whereas the CMA specifically
showed stronger connectivity with multiple subcortical struc-
tures, including brainstem, striatum, thalamus, and cerebellar
declive/vermis (Fig. 2B). This dissociated pattern of connectivity
between the BLA and CMA is in line with previous findings from
studies conducted in adults (13, 14). Unlike adults, however,
children showed weaker dissociation of functional connectivity
between BLA and CMA, with differences in connectivity only
in perirhinal cortex and temporal pole (Fig. 2C) (SI Appendix,
Table S4). To verify the specificity of our findings with respect
to amygdala subregional connectivity, additional control analyses
were conducted using ventral visual cortical subregions. Although
there was a dissociation of connectivity patterns between these two
visual cortical regions, we found no significant group-by-subregion
interactions (SI Appendix, Figs. S1 and S2). Together, these results
indicate that children, compared with adults, showed generally
weaker and less pronounced differentiation of functional con-
nectivity between BLA and CMA subregions. Notably, we found
similar results in a second cohort of adults and children, including
a main effect of group and group-by-subregion interaction effects
(SI Appendix, Figs. S3 and S4).
Decreased Similarity of BLA and CMA Connectivity with Development.
We then examined the similarity of BLA and CMA functional
connectivity in adults and children. We found that the BLA and
CMA target networks exhibited less overlapping, and distinct
connectivity patterns in adults (Fig. 3A). In sharp contrast to
adults,wefounda morestrongly overlappingpatternoffunctional
connectivitybetween theBLAand CMAtarget networks in young
children (Fig. 3B). Prominent overlap was observed in the brain-
stem, striatum, mid- and dorsal cingulate cortex, ventromedial
prefrontal cortex, and cerebellum. To quantify developmental
differences in the similarity between BLA and CMA target net-
works, we computed a similarity metric between BLA and CMA
connectivity maps in each individual and then compared this
similarity metric between adults and children. We found that
compared with adults, children showed significantly higher simi-
larity between BLA and CMA target networks (t(46) > 2.90,
P < 0.01; Fig. 3C). These patterns of results were replicated in
a second cohort of children and adults (SI Appendix, Fig. S5).
x = 6
Adults > Children
the amygdala in (A) adults and (B) children. (C) Brain regions showing significantly weaker amygdala connectivity in children, compared with adults. Representative
axial and sagittal slices are depicted in panels c1 and c2. These results were replicated in a second cohort of adults and children (SI Appendix, Fig. S3). Connectivity
maps are overlaid on either an inflated brain surface or high-resolution sections in Montreal Neurological Institute (MNI) space. Notes: L, Left; R, Right.
Immature functional connectivity of the amygdala in children compared with adults. Brain regions showing significant intrinsic functional connectivity with
2 of 6
| www.pnas.org/cgi/doi/10.1073/pnas.1120408109Qin et al.
Increased Differentiation Between BLA and CMA Target Networks
with Development. We used a systems neuroscience approach to
further characterize differential patterns of connectivity for BLA
and CMA in adults and children. We examined five major
functional subsystems known to play important roles in affective
information processing (3, 7, 8), as described in SI Appendix,
Text S1, and Fig. S6. These subsystems encompassed subcortical
structures, cerebellum, unimodal and polymodal association
cortex, limbic and paralimbic structures, and prefrontal cortex.
We conducted separate repeated-measures ANOVAs on con-
nectivity measures extracted from each network. ANOVAs on
connectivity β-weights, with amygdala subregion (BLA vs. CMA)
and hemisphere (left vs. right) as within-subject factors and
group (adults vs. children) as a between-subject factor, revealed
a main effect of group for subcortical structures (F(1,46) = 5.72,
P = 0.02), the cerebellum (F(1,46) = 4.23, P = 0.04), and par-
alimbic and limbic structures (F(1,46) = 6.86, P = 0.01), unim-
odal and polymodal association cortex (F(1, 46) = 7.46, P <
0.01), and prefrontal cortex (F(1,46) = 5.96, P = 0.02), with
stronger connectivity in adults in general. These results indicate
that children, compared with adults, show weaker connectivity of
the amygdala with widely distributed subcortical and cortical
structures (Fig. 4A).
Moreover, we found a group-by-amygdala subregion in-
teraction for subcortical structures (F(1, 46) = 10.71, P < 0.01),
unimodal and polymodal association cortex (F(1, 46) = 19.34;
P < 0.001), and the cerebellum (F(1, 46) = 4.30, P = 0.04), but
not for limbic and paralimbic structures (F < 1) and prefrontal
cortex (F < 1). Additional analyses revealed that in adults, the
BLA showed stronger intrinsic connectivity with unimodal and
polymodal association cortex (both left and right hemispheres of
seed-based connectivity analyses or “bilateral” herewith: t (1, 23)
> 4.23, P < 0.001), whereas the CMA showed stronger connec-
tivity to subcortical structures (bilateral: t (1, 23) > 4.15, P <
0.001) and in the cerebellum (bilateral t (23) > 2.55, P < 0.02). In
children, we observed weaker yet significant (bilateral t (1, 46) >
3.20, P < 0.01) dissociation of CMA versus BLA connectivity
only with subcortical structures (bilateral: t (1, 23) > 3.05, P <
0.01). There was no dissociation between BLA and CMA con-
nectivity in other networks in children (t (1, 23) < 1.80, P > 0.05).
Together, these results indicate that compared with adults,
children show significantly less pronounced dissociation of
functional connectivity between the BLA and CMA subregions
(Fig. 4 A and B). Crucially, similar patterns of results were found
in the second cohort of children (SI Appendix, Fig. S6), indicating
high reliability of our findings.
Increased Differentiation of Intraamygdala Coupling with Development.
To directly examine differentiation of intrinsic activity within the
amygdala subregions, we computed interregional correlations
between time series representing mean BLA and CMA sponta-
neous activity in each hemisphere. These analyses revealed that
adults, compared with children, showed weaker correlation be-
tween BLA and CMA activity (SI Appendix, Fig. S7), reflecting
increased differentiation of these intraamygdala nuclei with de-
velopment. Differences were observed both in ipsilateral con-
nectivity within the left and right hemispheres (both: t (1, 46) >
2.42, P < 0.01) as well as across-hemisphere contralateral con-
nectivity (BLA and CMA: t (1, 46) > 2.50, P < 0.01). Interestingly,
the degree of intraamygdala coupling averaged across two
hemispheres was positively correlated with the similarity of BLA
and CMA target networks in both adults (r = 0.50, P = 0.014) and
children (r = 0.68, P < 0.001). These results indicate that com-
pared with adults, children show stronger intraamygdala coupling
and greater similarity or weaker functional differentiation be-
tween the BLA and CMA target networks.
In this study, we investigated developmental changes in functional
connectivity of the BLA and CMA, two major, cytoarchitectoni-
cally distinct, nuclei of the amygdala. Compared with adults, the
multiple distributed cortical and subcortical regions. Critically,
networks associated with each of the two individual amygdala
nuclei showed stronger overlap and weaker dissociation of con-
nectivitypatternsin children than in adults. Furthermore,children
also showed greater intraamygdala connectivity between the BLA
and CMA. Because of potential developmental differences in
y = -22
z = 10
Adults: BLA vs CMA
z = 10
Interaction: Group x Subregion
y = -18
y = 10
x = 6
x = 6
z = 10
y = -22
Children: BLA vs CMA
connectivity in children compared with adults. Brain regions exhibiting
significant interaction (A), and stronger functional connectivity with BLA, com-
(B) adults, and (C) children. This pattern of results was replicated in a second
cohort ofadultsand children(SI Appendix, Fig. S4).Other details are as in Fig. 1.
2.Immaturedifferentiation between BLA andCMA functional
children. Brain regions showing CMA target network (shown in red) and BLA
target network (shown in blue) in (A) adults and (B) children. Overlap be-
tween CMA and BLA target networks is shown in pink. (C) Similarity be-
tween BLA and CMA target networks in adults and children. Target
networks for the BLA and CMA showed greater overlap in children, com-
pared with adults, in both the Left (Upper) and Right (Lower) hemispheres.
Other details are as in Fig. 1. Notes: L, Left; R, Right; **P < 0.01.
Overlap between BLA and CMA target networks in adults and
Qin et al. PNAS Early Edition
| 3 of 6
amygdala structure, wesoughtto replicate our findings in a second
large-scale amygdalar connectivity were identified in an inde-
pendent cohort of children and adults. Our findings demonstrate
that amygdala circuits in children are characterized by weaker
overall integration and less segregated functional networks. Below
maturation of emotion-related circuits in the developing brain.
We found that amygdala connectivity with multiple widely
distributed cortical and subcortical regions is weaker in children
than in adults. Cortical regions included the insula, cingulate
gyrus, and parahippocampal gyrus, as well as unimodal and
polymodal association cortex and ventromedial prefrontal cor-
tex. The insula and cingulate gyrus are considered key nodes of
the “salience network,” involved in detection and processing of
novel and salient events (23). The ventromedial prefrontal cortex
is known to play a critical role in emotion appraisal, as well as
regulation (24, 25). The weaker connectivity of the amygdala
with these regions in children may account for the poorer per-
formance in tasks requiring rapid and accurate recognition and
discrimination of different types of emotions (26, 27). Our
findings are also consistent with previous accounts of maturation
at the whole-brain level, reporting overall increases in long-range
connectivity from childhood through adolescence to early
adulthood (1, 19, 20). The observed increase in amygdala con-
nectivity in adults compared with children stands in contrast to
weakening connectivity, with age, of subcortical structures in-
cluding the basal ganglia and thalamus. These subcortical areas
have been found to be more strongly connected with primary
sensory, association, and paralimbic areas in children (20),
pointing to heterogonous patterns of functional maturation
within different subcortical systems (28). Our findings in amyg-
dala connectivity are more consistent with patterns of stronger
corticocortical connectivity between paralimbic, limbic, and as-
sociation areas observed in adults, supporting the notion that
strengthening of specific long-range connections with age pro-
motes faster communication and connection efficiency (1, 29).
A second major finding of our study is that 7- to 9 –y-old chil-
dren show stronger similarity and less distinct BLA and CMA
connectivity networks. In contrast, adults showed a clearly disso-
ciable pattern of connectivity between BLA and CMA networks.
The distinct connectivity profile observed in adults is reminiscent
of established animal models of amygdala circuits (3, 8) and
replicates previous findings from two neuroimaging studies of
amygdala connectivity in adults (13, 14). Specifically, the BLA
showed stronger connectivity with regions in widely distributed
unimodal and polymodal association cortex whereas the CMA
showed stronger connectivity with several subcortical structures
including brainstem, thalamus, striatum, and cerebellum. These
findings are in line with animal models in which the BLA, co-
ordinating with sensory and polymodal association areas, plays
critical roles in detecting andperceiving a stimulus associated with
fear. On the other hand, the CMA and its interactions with sub-
cortical regions are essential for regulating reflexive and defensive
responses to fear (3, 8). To our knowledge no previous study has
characterized the emergence of such a pattern of differentiated
functional circuits from childhood to adulthood. We suggest that
this development-related reconfiguration of intrinsic functional
networks likely underlies the maturation of increasingly complex
affective functions that typically occur during adolescence.
Distinct BLA and CMA connectivity in adults
Limbic & paralimbic structures
Connectivity, β weights (a.u.)
Uni- and polymodal
Immature BLA and CMA connectivity in children
Limbic & paralimbic structures
Uni- and polymodal asscoation cortex
Uni- and polymodal asscoation cortex
connectivity between the BLA (shown in blue) and CMA (shown in red) with five target networks of interest – subcortical structures, cerebellum, uni-
and polymodal association cortex, limbic and paralimbic structures, and prefrontal cortex. (B and C) Schematic polar plots illustrating weaker integration and
differentiation of BLA and CMA connectivity in children compared to adults. (B) In adults, the BLA has stronger functional connectivity with unimodal and
polymodal association cortex, whereas the CMA showed stronger functional connectivity with subcortical structures and cerebellum. (C) In children, these
differential patterns are significantly less pronounced. See SI Appendix, Fig. S6 for abbreviations and additional details on anatomically-defined target
networks and replication in the second cohort of children. n.s., not significant; *P < 0.05; **P < 0.01; ***P < 0.001.
Differential patterns of BLA and CMA functional connectivity in adults and children. (A) Parameter estimates represent the strength of functional
4 of 6
| www.pnas.org/cgi/doi/10.1073/pnas.1120408109Qin et al.
A third important finding of our study is that children showed
greater intraamygdala connectivity. Specifically, intrinsic functional
hemispheres. This pattern is consistent with our finding of over-
lapping target networks associated with the BLA and CMA and
suggests a possible mechanism by which distal target networks be-
come more differentiated with development. Previous studies in
nonhuman primates have suggested that differentiation of multiple
cortical regions and networks is related to local synaptic pruning,
deletion of unnecessary synapses, and an increase in processing
efficiency and regional specialization (15, 16). Human data in sup-
port of regional anatomical changes comes from developmental
neuroimaging studies showing aninitial increase in thickness and
then slower thinning of gray matter from childhood to adulthood
(2, 17, 28). We suggest that similar neurobiological processes
during childhood may lead to increased functional differentiation
between the BLA and CMA and consequently to more distinct
functional circuits involving these nuclei. Converging evidence
from studies in rodents and humans suggests that experience-
dependent development also plays a critical role in modulating
development of neural wiring and regional specialization pro-
cesses (1, 2, 16). For instance, studies have shown that behavioral
training shapes intrinsic characteristics, such as volume, mor-
phometry, myelination, and local circuits, in task-relevant brain
structures (30, 31). Similar principles are likely to operate in the
BLA and CMA, leading to increased segregation of function
within these regions. This in turn contributes to the development
ofdistinct connectivity withdistributed brain regions thatmediate
more mature affective functions involving perception, detection,
regulation, and appraisal of salient emotional stimuli.
To our knowledge, no studies to date have attempted to dis-
entangle developmental changes in functional contributions of
the BLA and CMA as they develop from childhood to adult-
hood. Previous structural neurodevelopmental studies of the
amygdala have focused on changes in its volume and mor-
phometry (28, 32), whereas functional studies have examined its
overall functional reactivity and connectivity during emotion
processing in children (27, 33) and adolescents (34). Notably,
even in adults, only a limited set of studies has focused on dis-
sociating the contributions of these distinct subregions. Recent
studies in patients with anxiety disorders, however, have pro-
vided strong evidence that distinct connectivity patterns can be
reliably identified for the BLA and CMA of the human amygdala
(14). Furthermore, in patients with generalized anxiety disorder,
BLA and CMA connectivity patterns were significantly less dis-
tinct (14). These findings raise important questions as to whether
protracted immaturity in these circuits might be a risk factor for
affective disorders for some children as they mature.
Further research is needed to clarify and extend our findings.
adults, as similar maps are not currently available in children.
However,findings fromonerecentpostmortemcasestudyofa 10-
y-old boy suggests that the relative subregional positions and
proportions of anatomical boundaries are similar in children and
adults (35). Crucially, these cytoarchitectonic probabilistic maps
have been shown to have high reliability and accuracy for guiding
anatomical segmentation of amygdala subregions in children as
young as 6- to 7-y old (35). In addition, evidence from our control
analyses in visual cortical subregions also confirmed the feasibility
and validity of our approach. Second, future studies, using high-
resolution fMRI techniques, are required to better determine
structural and functional amygdala connectivity, and how their
interrelations evolve with development. Finally, analysis of
a wider age group is needed to delineate changes during adoles-
cence, a period important for development of affective and social
In conclusion, the present study demonstrates that amygdalar
nuclei in children show generally weaker large-scale connectivity
with distributed cortical and subcortical regions. Importantly, in-
trinsic functional circuits associated with individual amygdalar nu-
connectivity between the BLA and CMA in children. Taken to-
gether, our findings point to weak integration and segregation of
amygdala subregional networks in young children and provide
unique insights into the nature of immature emotion-related cir-
cuitry in young children. The current work represents an important
step toward characterizing the developmental maturation of func-
tional brain systems underlying affective processing. Examining
potentially aberrant maturation of amygdala functional circuits
identified in our study may be important for understanding de-
velopment psychopathology and affective disorders in children
Materials and Methods
Participants. Two cohorts of children and adults (n = 87 in total) participated
in this study after giving written, informed consent. Details regarding par-
ticipant demographics are described in SI Appendix, Text S1.
Data Acquisition and Preprocessing. For the resting-state fMRI scan, partic-
ipants were instructed to keep their eyes closed and remain still for the
duration of an 8-min scan. Whole brain functional images were acquired on
a 3T GE Signa scanner. Details are provided in SI Appendix, Text S1.
Regions of Interest (ROIs) Definition. Two ROIs encompassing the BLA and
CMA were created using cytoarchitectonically defined probabilistic maps
of the amygdala. Maximum probability maps were used to create non-
overlapping amygdala subregions using the Anatomy Toolbox (12). Voxels
were included in the maximum probability maps only if the probability of
their assignment to the BLA or CMA was higher than any other nearby
structures with greater than 40% likelihood. Each voxel was exclusively
assigned to only one region. Overlapping voxels were assigned to the region
that had the greatest probability, resulting in four nonoverlapping ROIs
representing CMA and BLA subregions in left and right hemispheres. To
mitigate potential confounds related to differences in the size of the
amygdala, we also analyzed the data using the first eigenvalue, rather than
mean, of the ROI time series. The results were nearly identical to our original
analyses (SI Appendix, Fig. S8).
Validity of using these cytoarchitectonic probabilistic maps in children and
additional control analyses were summarized in SI Appendix, Text S2.
Functional Connectivity Analysis. Regional time series within each seed ROI
were extractedfrom data filtered with abandpass temporal filter (0.008–0.10
Hz). Subsequently, each time series was submitted into an individual level
fixed-effects analysis under the framework of the general linear model. A
global signal regressor and six motion parameters for each participant were
included as covariates of no interest in the model to account for physio-
logical noise and movement-related artifacts. For each participant, four
separate functional connectivity analyses were performed for both BLA and
CMA in left and right hemispheres.
The contrast parameter images for each of the four seed regions from the
individual level analyses were submitted to a second-level group analysis
that treated participants as a random variable in a 2-by-2-by-2 ANOVA as
described in Results. Significant clusters were estimated using a height
threshold of P < 0.001 uncorrected, and familywise error corrections for
multiple spatial comparisons using an extent threshold of P < 0.05 in terms
of nonstationary suprathreshold cluster-size distributions based on Monte
Carlo simulations (39).
To characterize differential patterns of BLA and CMA target networks in
children and adults, complementary ROI analyses were conducted for five
target networks of interest. Details are provided in SI Appendix, Text S1 and
Fig. S6. Mean parameter estimates, representing the strength of functional
connectivity of each seed with corresponding target masks, were extracted
from BLA- and CMA-seeded functional connectivity analyses.
Spatial correlations, reflecting similarity of BLA and CMA connectivity,
were computed between each participant’s BLA- and CMA-seeded func-
tional connectivity maps. Moreover, temporal correlations between BLA
and CMA seeds were calculated on the basis of their bandpass-filtered time
series in ipsilateral and contralateral hemispheres to specifically investigate
Qin et al.PNAS Early Edition
| 5 of 6
developmental differences in interregional coupling of BLA and CMA. Corre- Download full-text
sponding correlation coefficients were then Fisher’s Z transformed for fur-
ther statistical testing. More details are described in SI Appendix, Text S1.
ACKNOWLEDGMENTS. This work was supported by grants from National
Institutes of Health HD047520, HD059205, and K01MH092288 and Netherlands
Organization for Scientific Research NWO 446.10.010.
1. Toga AW, Thompson PM, Sowell ER (2006) Mapping brain maturation. Trends Neu-
2. Paus T (2005) Mapping brain maturation and cognitive development during adoles-
cence. Trends Cogn Sci 9:60–68.
3. LeDoux JE (2000) Emotion circuits in the brain. Annu Rev Neurosci 23:155–184.
4. Davis M, Whalen PJ (2001) The amygdala: Vigilance and emotion. Mol Psychiatry 6:
5. Adolphs R, Tranel D, Damasio H, Damasio A (1994) Impaired recognition of emotion
in facial expressions following bilateral damage to the human amygdala. Nature 372:
6. Dolan RJ, Vuilleumier P (2003) Amygdala automaticity in emotional processing. Ann N
Y Acad Sci 985:348–355.
7. Pessoa L, Adolphs R (2010) Emotion processing and the amygdala: From a ’low road’
to ’many roads’ of evaluating biological significance. Nat Rev Neurosci 11:773–783.
8. LeDoux J (2007) The amygdala. Curr Biol 17:R868–R874.
9. Thomas KM, et al. (2001) Amygdala response to fearful faces in anxious and de-
pressed children. Arch Gen Psychiatry 58:1057–1063.
10. Menon V (2011) Functional Connectivity, Neurocognitive Networks and Brain Dy-
namics (MIT Press, Cambridge, MA).
11. Amunts K, et al. (2005) Cytoarchitectonic mapping of the human amygdala, hippo-
campal region and entorhinal cortex: Intersubject variability and probability maps.
Anat Embryol (Berl) 210:343–352.
12. Eickhoff SB, et al. (2005) A new SPM toolbox for combining probabilistic cytoarchi-
tectonic maps and functional imaging data. Neuroimage 25:1325–1335.
13. Roy AK, et al. (2009) Functional connectivity of the human amygdala using resting
state fMRI. Neuroimage 45:614–626.
14. Etkin A, Prater KE, Schatzberg AF, Menon V, Greicius MD (2009) Disrupted amygdalar
subregion functional connectivity and evidence of a compensatory network in gen-
eralized anxiety disorder. Arch Gen Psychiatry 66:1361–1372.
15. Bourgeois JP, Rakic P (1993) Changes of synaptic density in the primary visual cortex
of the macaque monkey from fetal to adult stage. J Neurosci 13:2801–2820.
16. Rakic P, Bourgeois JP, Goldman-Rakic PS (1994) Synaptic development of the cerebral
cortex: Implications for learning, memory, and mental illness. Prog Brain Res 102:
17. Shaw P, et al. (2008) Neurodevelopmental trajectories of the human cerebral cortex.
J Neurosci 28:3586–3594.
18. Uddin LQ, Supekar K, Menon V (2010) Typical and atypical development of functional
human brain networks: Insights from resting-state FMRI. Front Syst Neurosci 4:21.
19. Dosenbach NU, et al. (2010) Prediction of individual brain maturity using fMRI. Sci-
20. Supekar K, Musen M, Menon V (2009) Development of large-scale functional brain
networks in children. PLoS Biol 7:e1000157.
21. Greicius MD, Krasnow B, Reiss AL, Menon V (2003) Functional connectivity in the
resting brain: A network analysis of the default mode hypothesis. Proc Natl Acad Sci
22. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with
functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711.
23. Seeley WW, et al. (2007) Dissociable intrinsic connectivity networks for salience pro-
cessing and executive control. J Neurosci 27:2349–2356.
24. Dougherty DD, et al. (2004) Ventromedial prefrontal cortex and amygdala dysfunc-
tion during an anger induction positron emission tomography study in patients with
major depressive disorder with anger attacks. Arch Gen Psychiatry 61:795–804.
25. Goldin PR, McRae K, Ramel W, Gross JJ (2008) The neural bases of emotion regulation:
Reappraisal and suppression of negative emotion. Biol Psychiatry 63:577–586.
26. Gross AL, Ballif B (1991) Children’s understanding of emotion from facial expressions
and situations: A review. Dev Rev 11:368–398.
27. Thomas KM, et al. (2001) Amygdala response to facial expressions in children and
adults. Biol Psychiatry 49:309–316.
28. Ostby Y, et al. (2009) Heterogeneity in subcortical brain development: A structural
magnetic resonance imaging study of brain maturation from 8 to 30 years. J Neurosci
29. Hagmann P, et al. (2010) White matter maturation reshapes structural connectivity in
the late developing human brain. Proc Natl Acad Sci USA 107:19067–19072.
30. Woollett K, Spiers HJ, Maguire EA (2009) Talent in the taxi: A model system for ex-
ploring expertise. Philos Trans R Soc Lond B Biol Sci 364:1407–1416.
31. Gaser C, Schlaug G (2003) Brain structures differ between musicians and non-musi-
cians. J Neurosci 23:9240–9245.
32. Giedd JN, et al. (1996) Quantitative MRI of the temporal lobe, amygdala, and hip-
pocampus in normal human development: ages 4-18 years. J Comp Neurol 366:
33. Perlman SB, Pelphrey KA (2011) Developing connections for affective regulation:
Age-related changes in emotional brain connectivity. J Exp Child Psychol 108:607–620.
34. Guyer AE, et al. (2008) A developmental examination of amygdala response to facial
expressions. J Cogn Neurosci 20:1565–1582.
35. Kim JE, et al. (2010) Laterobasal amygdalar enlargement in 6- to 7-year-old children
with autism spectrum disorder. Arch Gen Psychiatry 67:1187–1197.
36. Blakemore SJ (2008) The social brain in adolescence. Nat Rev Neurosci 9:267–277.
37. Crone EA, Güro? glu TE, eds (2012) Development of Emotion and Social Reasoning in
Adolescence (Oxford Press, New York).
38. Steinberg L (2005) Cognitive and affective development in adolescence. Trends Cogn
39. Nichols T, Hayasaka S (2003) Controlling the familywise error rate in functional
neuroimaging: A comparative review. Stat Methods Med Res 12:419–446.
6 of 6
| www.pnas.org/cgi/doi/10.1073/pnas.1120408109 Qin et al.